From f57ac5d90f28c0fa8102cad5e8c1b94caef27aaa Mon Sep 17 00:00:00 2001 From: Thapelo Date: Sat, 26 Mar 2022 13:40:16 +0200 Subject: [PATCH 1/7] Committing changes made from IBM Cloud Watson Studio --- 02_data_wrangling.ipynb | 5131 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 5131 insertions(+) create mode 100644 02_data_wrangling.ipynb diff --git a/02_data_wrangling.ipynb b/02_data_wrangling.ipynb new file mode 100644 index 000000000..aaf17406d --- /dev/null +++ b/02_data_wrangling.ipynb @@ -0,0 +1,5131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 Data wrangling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Contents\n", + "* [2 Data wrangling](#2_Data_wrangling)\n", + " * [2.1 Contents](#2.1_Contents)\n", + " * [2.2 Introduction](#2.2_Introduction)\n", + " * [2.2.1 Recap Of Data Science Problem](#2.2.1_Recap_Of_Data_Science_Problem)\n", + " * [2.2.2 Introduction To Notebook](#2.2.2_Introduction_To_Notebook)\n", + " * [2.3 Imports](#2.3_Imports)\n", + " * [2.4 Objectives](#2.4_Objectives)\n", + " * [2.5 Load The Ski Resort Data](#2.5_Load_The_Ski_Resort_Data)\n", + " * [2.6 Explore The Data](#2.6_Explore_The_Data)\n", + " * [2.6.1 Find Your Resort Of Interest](#2.6.1_Find_Your_Resort_Of_Interest)\n", + " * [2.6.2 Number Of Missing Values By Column](#2.6.2_Number_Of_Missing_Values_By_Column)\n", + " * [2.6.3 Categorical Features](#2.6.3_Categorical_Features)\n", + " * [2.6.3.1 Unique Resort Names](#2.6.3.1_Unique_Resort_Names)\n", + " * [2.6.3.2 Region And State](#2.6.3.2_Region_And_State)\n", + " * [2.6.3.3 Number of distinct regions and states](#2.6.3.3_Number_of_distinct_regions_and_states)\n", + " * [2.6.3.4 Distribution Of Resorts By Region And State](#2.6.3.4_Distribution_Of_Resorts_By_Region_And_State)\n", + " * [2.6.3.5 Distribution Of Ticket Price By State](#2.6.3.5_Distribution_Of_Ticket_Price_By_State)\n", + " * [2.6.3.5.1 Average weekend and weekday price by state](#2.6.3.5.1_Average_weekend_and_weekday_price_by_state)\n", + " * [2.6.3.5.2 Distribution of weekday and weekend price by state](#2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state)\n", + " * [2.6.4 Numeric Features](#2.6.4_Numeric_Features)\n", + " * [2.6.4.1 Numeric data summary](#2.6.4.1_Numeric_data_summary)\n", + " * [2.6.4.2 Distributions Of Feature Values](#2.6.4.2_Distributions_Of_Feature_Values)\n", + " * [2.6.4.2.1 SkiableTerrain_ac](#2.6.4.2.1_SkiableTerrain_ac)\n", + " * [2.6.4.2.2 Snow Making_ac](#2.6.4.2.2_Snow_Making_ac)\n", + " * [2.6.4.2.3 fastEight](#2.6.4.2.3_fastEight)\n", + " * [2.6.4.2.4 fastSixes and Trams](#2.6.4.2.4_fastSixes_and_Trams)\n", + " * [2.7 Derive State-wide Summary Statistics For Our Market Segment](#2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment)\n", + " * [2.8 Drop Rows With No Price Data](#2.8_Drop_Rows_With_No_Price_Data)\n", + " * [2.9 Review distributions](#2.9_Review_distributions)\n", + " * [2.10 Population data](#2.10_Population_data)\n", + " * [2.11 Target Feature](#2.11_Target_Feature)\n", + " * [2.11.1 Number Of Missing Values By Row - Resort](#2.11.1_Number_Of_Missing_Values_By_Row_-_Resort)\n", + " * [2.12 Save data](#2.12_Save_data)\n", + " * [2.13 Summary](#2.13_Summary)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This step focuses on collecting your data, organizing it, and making sure it's well defined. Paying attention to these tasks will pay off greatly later on. Some data cleaning can be done at this stage, but it's important not to be overzealous in your cleaning before you've explored the data to better understand it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2.1 Recap Of Data Science Problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The purpose of this data science project is to come up with a pricing model for ski resort tickets in our market segment. Big Mountain suspects it may not be maximizing its returns, relative to its position in the market. It also does not have a strong sense of what facilities matter most to visitors, particularly which ones they're most likely to pay more for. This project aims to build a predictive model for ticket price based on a number of facilities, or properties, boasted by resorts (*at the resorts).* \n", + "This model will be used to provide guidance for Big Mountain's pricing and future facility investment plans." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2.2 Introduction To Notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notebooks grow organically as we explore our data. If you used paper notebooks, you could discover a mistake and cross out or revise some earlier work. Later work may give you a reason to revisit earlier work and explore it further. The great thing about Jupyter notebooks is that you can edit, add, and move cells around without needing to cross out figures or scrawl in the margin. However, this means you can lose track of your changes easily. If you worked in a regulated environment, the company may have a a policy of always dating entries and clearly crossing out any mistakes, with your initials and the date.\n", + "\n", + "**Best practice here is to commit your changes using a version control system such as Git.** Try to get into the habit of adding and committing your files to the Git repository you're working in after you save them. You're are working in a Git repository, right? If you make a significant change, save the notebook and commit it to Git. In fact, if you're about to make a significant change, it's a good idea to commit before as well. Then if the change is a mess, you've got the previous version to go back to.\n", + "\n", + "**Another best practice with notebooks is to try to keep them organized with helpful headings and comments.** Not only can a good structure, but associated headings help you keep track of what you've done and your current focus. Anyone reading your notebook will have a much easier time following the flow of work. Remember, that 'anyone' will most likely be you. Be kind to future you!\n", + "\n", + "In this notebook, note how we try to use well structured, helpful headings that frequently are self-explanatory, and we make a brief note after any results to highlight key takeaways. This is an immense help to anyone reading your notebook and it will greatly help you when you come to summarise your findings. **Top tip: jot down key findings in a final summary at the end of the notebook as they arise. You can tidy this up later.** This is a great way to ensure important results don't get lost in the middle of your notebooks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this, and subsequent notebooks, there are coding tasks marked with `#Code task n#` with code to complete. The `___` will guide you to where you need to insert code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Imports" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Placing your imports all together at the start of your notebook means you only need to consult one place to check your notebook's dependencies. By all means import something 'in situ' later on when you're experimenting, but if the imported dependency ends up being kept, you should subsequently move the import statement here with the rest." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'library'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msb_utils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msave_file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'library'" + ] + } + ], + "source": [ + "#Code task 1#\n", + "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "\n", + "from library.sb_utils import save_file\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.4 Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are some fundamental questions to resolve in this notebook before you move on.\n", + "\n", + "* Do you think you may have the data you need to tackle the desired question?\n", + " * Have you identified the required target value?\n", + " * Do you have potentially useful features?\n", + "* Do you have any fundamental issues with the data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.5 Load The Ski Resort Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
0Alyeska ResortAlaskaAlaska3939250025010.002...1.01610.0113.0150.060.0669.065.085.0150.0550.0
1Eaglecrest Ski AreaAlaskaAlaska26001540120000.000...2.0640.060.045.044.0350.047.053.090.0NaN
2Hilltop Ski AreaAlaskaAlaska2090294179600.000...1.030.030.0150.036.069.030.034.0152.030.0
3Arizona SnowbowlArizonaArizona115002300920000.010...2.0777.0104.0122.081.0260.089.089.0122.0NaN
4Sunrise Park ResortArizonaArizona11100180092000NaN01...1.2800.080.0115.049.0250.074.078.0104.080.0
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5 rows × 27 columns

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" + ], + "text/plain": [ + " Name Region state summit_elev vertical_drop \\\n", + "0 Alyeska Resort Alaska Alaska 3939 2500 \n", + "1 Eaglecrest Ski Area Alaska Alaska 2600 1540 \n", + "2 Hilltop Ski Area Alaska Alaska 2090 294 \n", + "3 Arizona Snowbowl Arizona Arizona 11500 2300 \n", + "4 Sunrise Park Resort Arizona Arizona 11100 1800 \n", + "\n", + " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", + "0 250 1 0.0 0 2 ... 1.0 \n", + "1 1200 0 0.0 0 0 ... 2.0 \n", + "2 1796 0 0.0 0 0 ... 1.0 \n", + "3 9200 0 0.0 1 0 ... 2.0 \n", + "4 9200 0 NaN 0 1 ... 1.2 \n", + "\n", + " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", + "0 1610.0 113.0 150.0 60.0 \n", + "1 640.0 60.0 45.0 44.0 \n", + "2 30.0 30.0 150.0 36.0 \n", + "3 777.0 104.0 122.0 81.0 \n", + "4 800.0 80.0 115.0 49.0 \n", + "\n", + " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", + "0 669.0 65.0 85.0 150.0 \n", + "1 350.0 47.0 53.0 90.0 \n", + "2 69.0 30.0 34.0 152.0 \n", + "3 260.0 89.0 89.0 122.0 \n", + "4 250.0 74.0 78.0 104.0 \n", + "\n", + " NightSkiing_ac \n", + "0 550.0 \n", + "1 NaN \n", + "2 30.0 \n", + "3 NaN \n", + "4 80.0 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "import os, types\n", + "import pandas as pd\n", + "from botocore.client import Config\n", + "import ibm_boto3\n", + "\n", + "def __iter__(self): return 0\n", + "\n", + "# @hidden_cell\n", + "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n", + "# You might want to remove those credentials before you share the notebook.\n", + "client_3220f9abb4b84a9f8e470dd042b0c83c = ibm_boto3.client(service_name='s3',\n", + " ibm_api_key_id='juEZkC4PKYA_ajZxXwx_DAnVUDpGiSZD9FnRHEBXHM3Y',\n", + " ibm_auth_endpoint=\"https://iam.cloud.ibm.com/oidc/token\",\n", + " config=Config(signature_version='oauth'),\n", + " endpoint_url='https://s3.private.eu.cloud-object-storage.appdomain.cloud')\n", + "\n", + "body = client_3220f9abb4b84a9f8e470dd042b0c83c.get_object(Bucket='sbproject-donotdelete-pr-lzajghiksaeuv7',Key='ski_resort_data.csv')['Body']\n", + "# add missing __iter__ method, so pandas accepts body as file-like object\n", + "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n", + "\n", + "df_data_1 = pd.read_csv(body)\n", + "df_data_1.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "ski_data = df_data_1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './ski_resort_data.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/wsuser/ipykernel_155/1422985096.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# the supplied CSV data file is the raw_data directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mski_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./ski_resort_data.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './ski_resort_data.csv'" + ] + } + ], + "source": [ + "# the supplied CSV data file is the raw_data directory\n", + "ski_data = pd.read_csv('./ski_resort_data.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good first steps in auditing the data are the info method and displaying the first few records with head." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 330 entries, 0 to 329\n", + "Data columns (total 27 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 330 non-null object \n", + " 1 Region 330 non-null object \n", + " 2 state 330 non-null object \n", + " 3 summit_elev 330 non-null int64 \n", + " 4 vertical_drop 330 non-null int64 \n", + " 5 base_elev 330 non-null int64 \n", + " 6 trams 330 non-null int64 \n", + " 7 fastEight 164 non-null float64\n", + " 8 fastSixes 330 non-null int64 \n", + " 9 fastQuads 330 non-null int64 \n", + " 10 quad 330 non-null int64 \n", + " 11 triple 330 non-null int64 \n", + " 12 double 330 non-null int64 \n", + " 13 surface 330 non-null int64 \n", + " 14 total_chairs 330 non-null int64 \n", + " 15 Runs 326 non-null float64\n", + " 16 TerrainParks 279 non-null float64\n", + " 17 LongestRun_mi 325 non-null float64\n", + " 18 SkiableTerrain_ac 327 non-null float64\n", + " 19 Snow Making_ac 284 non-null float64\n", + " 20 daysOpenLastYear 279 non-null float64\n", + " 21 yearsOpen 329 non-null float64\n", + " 22 averageSnowfall 316 non-null float64\n", + " 23 AdultWeekday 276 non-null float64\n", + " 24 AdultWeekend 279 non-null float64\n", + " 25 projectedDaysOpen 283 non-null float64\n", + " 26 NightSkiing_ac 187 non-null float64\n", + "dtypes: float64(13), int64(11), object(3)\n", + "memory usage: 69.7+ KB\n" + ] + } + ], + "source": [ + "#Code task 2#\n", + "#Call the info method on ski_data to see a summary of the data\n", + "ski_data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`AdultWeekday` is the price of an adult weekday ticket. `AdultWeekend` is the price of an adult weekend ticket. The other columns are potential features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This immediately raises the question of what quantity will you want to model? You know you want to model the ticket price, but you realise there are two kinds of ticket price!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
0Alyeska ResortAlaskaAlaska3939250025010.002...1.01610.0113.0150.060.0669.065.085.0150.0550.0
1Eaglecrest Ski AreaAlaskaAlaska26001540120000.000...2.0640.060.045.044.0350.047.053.090.0NaN
2Hilltop Ski AreaAlaskaAlaska2090294179600.000...1.030.030.0150.036.069.030.034.0152.030.0
3Arizona SnowbowlArizonaArizona115002300920000.010...2.0777.0104.0122.081.0260.089.089.0122.0NaN
4Sunrise Park ResortArizonaArizona11100180092000NaN01...1.2800.080.0115.049.0250.074.078.0104.080.0
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5 rows × 27 columns

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" + ], + "text/plain": [ + " Name Region state summit_elev vertical_drop \\\n", + "0 Alyeska Resort Alaska Alaska 3939 2500 \n", + "1 Eaglecrest Ski Area Alaska Alaska 2600 1540 \n", + "2 Hilltop Ski Area Alaska Alaska 2090 294 \n", + "3 Arizona Snowbowl Arizona Arizona 11500 2300 \n", + "4 Sunrise Park Resort Arizona Arizona 11100 1800 \n", + "\n", + " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", + "0 250 1 0.0 0 2 ... 1.0 \n", + "1 1200 0 0.0 0 0 ... 2.0 \n", + "2 1796 0 0.0 0 0 ... 1.0 \n", + "3 9200 0 0.0 1 0 ... 2.0 \n", + "4 9200 0 NaN 0 1 ... 1.2 \n", + "\n", + " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", + "0 1610.0 113.0 150.0 60.0 \n", + "1 640.0 60.0 45.0 44.0 \n", + "2 30.0 30.0 150.0 36.0 \n", + "3 777.0 104.0 122.0 81.0 \n", + "4 800.0 80.0 115.0 49.0 \n", + "\n", + " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", + "0 669.0 65.0 85.0 150.0 \n", + "1 350.0 47.0 53.0 90.0 \n", + "2 69.0 30.0 34.0 152.0 \n", + "3 260.0 89.0 89.0 122.0 \n", + "4 250.0 74.0 78.0 104.0 \n", + "\n", + " NightSkiing_ac \n", + "0 550.0 \n", + "1 NaN \n", + "2 30.0 \n", + "3 NaN \n", + "4 80.0 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 3#\n", + "#Call the head method on ski_data to print the first several rows of the data\n", + "ski_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.6 Explore The Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.1 Find Your Resort Of Interest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your resort of interest is called Big Mountain Resort. Check it's in the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameBig Mountain Resort
RegionMontana
stateMontana
summit_elev6817
vertical_drop2353
base_elev4464
trams0
fastEight0.0
fastSixes0
fastQuads3
quad2
triple6
double0
surface3
total_chairs14
Runs105.0
TerrainParks4.0
LongestRun_mi3.3
SkiableTerrain_ac3000.0
Snow Making_ac600.0
daysOpenLastYear123.0
yearsOpen72.0
averageSnowfall333.0
AdultWeekday81.0
AdultWeekend81.0
projectedDaysOpen123.0
NightSkiing_ac600.0
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" + ], + "text/plain": [ + " 151\n", + "Name Big Mountain Resort\n", + "Region Montana\n", + "state Montana\n", + "summit_elev 6817\n", + "vertical_drop 2353\n", + "base_elev 4464\n", + "trams 0\n", + "fastEight 0.0\n", + "fastSixes 0\n", + "fastQuads 3\n", + "quad 2\n", + "triple 6\n", + "double 0\n", + "surface 3\n", + "total_chairs 14\n", + "Runs 105.0\n", + "TerrainParks 4.0\n", + "LongestRun_mi 3.3\n", + "SkiableTerrain_ac 3000.0\n", + "Snow Making_ac 600.0\n", + "daysOpenLastYear 123.0\n", + "yearsOpen 72.0\n", + "averageSnowfall 333.0\n", + "AdultWeekday 81.0\n", + "AdultWeekend 81.0\n", + "projectedDaysOpen 123.0\n", + "NightSkiing_ac 600.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 4#\n", + "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", + "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", + "#transpose method, but you can access this conveniently with the `T` property.\n", + "ski_data[ski_data.Name == 'Big Mountain Resort'].T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's good that your resort doesn't appear to have any missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.2 Number Of Missing Values By Column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count the number of missing values in each column and sort them." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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fastEight16650.303030
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" + ], + "text/plain": [ + " count %\n", + "Name 0 0.000000\n", + "total_chairs 0 0.000000\n", + "double 0 0.000000\n", + "triple 0 0.000000\n", + "quad 0 0.000000\n", + "fastQuads 0 0.000000\n", + "fastSixes 0 0.000000\n", + "surface 0 0.000000\n", + "trams 0 0.000000\n", + "base_elev 0 0.000000\n", + "vertical_drop 0 0.000000\n", + "summit_elev 0 0.000000\n", + "state 0 0.000000\n", + "Region 0 0.000000\n", + "yearsOpen 1 0.303030\n", + "SkiableTerrain_ac 3 0.909091\n", + "Runs 4 1.212121\n", + "LongestRun_mi 5 1.515152\n", + "averageSnowfall 14 4.242424\n", + "Snow Making_ac 46 13.939394\n", + "projectedDaysOpen 47 14.242424\n", + "TerrainParks 51 15.454545\n", + "daysOpenLastYear 51 15.454545\n", + "AdultWeekend 51 15.454545\n", + "AdultWeekday 54 16.363636\n", + "NightSkiing_ac 143 43.333333\n", + "fastEight 166 50.303030" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 5#\n", + "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", + "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", + "#Order them (increasing or decreasing) using sort_values\n", + "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", + "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n", + "missing.columns=['count', '%']\n", + "missing.sort_values(by='count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.3 Categorical Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameRegionstate
0Alyeska ResortAlaskaAlaska
1Eaglecrest Ski AreaAlaskaAlaska
2Hilltop Ski AreaAlaskaAlaska
3Arizona SnowbowlArizonaArizona
4Sunrise Park ResortArizonaArizona
............
325Meadowlark Ski LodgeWyomingWyoming
326Sleeping Giant Ski ResortWyomingWyoming
327Snow King ResortWyomingWyoming
328Snowy Range Ski & Recreation AreaWyomingWyoming
329White Pine Ski AreaWyomingWyoming
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330 rows × 3 columns

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" + ], + "text/plain": [ + " Name Region state\n", + "0 Alyeska Resort Alaska Alaska\n", + "1 Eaglecrest Ski Area Alaska Alaska\n", + "2 Hilltop Ski Area Alaska Alaska\n", + "3 Arizona Snowbowl Arizona Arizona\n", + "4 Sunrise Park Resort Arizona Arizona\n", + ".. ... ... ...\n", + "325 Meadowlark Ski Lodge Wyoming Wyoming\n", + "326 Sleeping Giant Ski Resort Wyoming Wyoming\n", + "327 Snow King Resort Wyoming Wyoming\n", + "328 Snowy Range Ski & Recreation Area Wyoming Wyoming\n", + "329 White Pine Ski Area Wyoming Wyoming\n", + "\n", + "[330 rows x 3 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 6#\n", + "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", + "ski_data.select_dtypes('object')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", + "\n", + "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", + "* Is `Region` always the same as `state`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.1 Unique Resort Names" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Crystal Mountain 2\n", + "Alyeska Resort 1\n", + "Brandywine 1\n", + "Boston Mills 1\n", + "Alpine Valley 1\n", + "Name: Name, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 7#\n", + "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", + "ski_data['Name'].value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have a duplicated resort name: Crystal Mountain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 8#\n", + "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 9#\n", + "#Concatenate 'Name' and 'state' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A: 1** Your answer here" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
104Crystal MountainMichiganMichigan113237575700.001...0.3102.096.0120.063.0132.054.064.0135.056.0
295Crystal MountainWashingtonWashington7012310044001NaN22...2.52600.010.0NaN57.0486.099.099.0NaNNaN
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2 rows × 27 columns

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" + ], + "text/plain": [ + " Name Region state summit_elev vertical_drop \\\n", + "104 Crystal Mountain Michigan Michigan 1132 375 \n", + "295 Crystal Mountain Washington Washington 7012 3100 \n", + "\n", + " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", + "104 757 0 0.0 0 1 ... 0.3 \n", + "295 4400 1 NaN 2 2 ... 2.5 \n", + "\n", + " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", + "104 102.0 96.0 120.0 63.0 \n", + "295 2600.0 10.0 NaN 57.0 \n", + "\n", + " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", + "104 132.0 54.0 64.0 135.0 \n", + "295 486.0 99.0 99.0 NaN \n", + "\n", + " NightSkiing_ac \n", + "104 56.0 \n", + "295 NaN \n", + "\n", + "[2 rows x 27 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data[ski_data['Name'] == 'Crystal Mountain']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.2 Region And State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What's the relationship between region and state?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 10#\n", + "#Calculate the number of times Region does not equal state\n", + "(ski_data.Region != ski_data.state).sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "New York 33\n", + "Michigan 29\n", + "Sierra Nevada 22\n", + "Colorado 22\n", + "Pennsylvania 19\n", + "Wisconsin 16\n", + "New Hampshire 16\n", + "Vermont 15\n", + "Minnesota 14\n", + "Idaho 12\n", + "Montana 12\n", + "Massachusetts 11\n", + "Washington 10\n", + "New Mexico 9\n", + "Maine 9\n", + "Wyoming 8\n", + "Utah 7\n", + "Salt Lake City 6\n", + "North Carolina 6\n", + "Oregon 6\n", + "Connecticut 5\n", + "Ohio 5\n", + "Virginia 4\n", + "West Virginia 4\n", + "Illinois 4\n", + "Mt. Hood 4\n", + "Alaska 3\n", + "Iowa 3\n", + "South Dakota 2\n", + "Arizona 2\n", + "Nevada 2\n", + "Missouri 2\n", + "Indiana 2\n", + "New Jersey 2\n", + "Rhode Island 1\n", + "Tennessee 1\n", + "Maryland 1\n", + "Northern California 1\n", + "Name: Region, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data['Region'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "state Region \n", + "California Sierra Nevada 20\n", + " Northern California 1\n", + "Nevada Sierra Nevada 2\n", + "Oregon Mt. Hood 4\n", + "Utah Salt Lake City 6\n", + "Name: Region, dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 11#\n", + "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n", + "#group that by 'state' and perform `value_counts` on the 'Region'\n", + "(ski_data[ski_data.Region != ski_data.state]\n", + " .groupby('state')['Region']\n", + " .value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.3 Number of distinct regions and states" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Region 38\n", + "state 35\n", + "dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 12#\n", + "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n", + "#the number of unique values in each\n", + "ski_data[['Region', 'state']].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because a few states are split across multiple named regions, there are slightly more unique regions than states." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.4 Distribution Of Resorts By Region And State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 13#\n", + "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n", + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,8))\n", + "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", + "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n", + "#Give the plot a helpful title of 'Region'\n", + "ax[0].set_title('Region')\n", + "#Label the xaxis 'Count'\n", + "ax[0].set_xlabel('Count')\n", + "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", + "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n", + "#Give the plot a helpful title of 'state'\n", + "ax[1].set_title('State')\n", + "#Label the xaxis 'Count'\n", + "ax[1].set_xlabel('Count')\n", + "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n", + "plt.subplots_adjust(wspace=0.5);\n", + "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n", + "# as the importance of easy-to-read and informative figures is frequently understated\n", + "# and you will find the ability to tweak figures invaluable later on" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.5 Distribution Of Ticket Price By State" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.3.5.1 Average weekend and weekday price by state" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AdultWeekdayAdultWeekend
state
Illinois35.00000043.333333
Iowa35.66666741.666667
Tennessee36.00000065.000000
Massachusetts40.90000057.200000
North Carolina41.83333364.166667
\n", + "
" + ], + "text/plain": [ + " AdultWeekday AdultWeekend\n", + "state \n", + "Illinois 35.000000 43.333333\n", + "Iowa 35.666667 41.666667\n", + "Tennessee 36.000000 65.000000\n", + "Massachusetts 40.900000 57.200000\n", + "North Carolina 41.833333 64.166667" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 14#\n", + "# Calculate average weekday and weekend price by state and sort by the average of the two\n", + "# Hint: use the pattern dataframe.groupby()[].mean()\n", + "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean().sort_values(by='AdultWeekday')\n", + "state_price_means.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n", + "# Compare the index order you get from\n", + "# state_price_means.index\n", + "# with\n", + "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n", + "# See how this expression simply sits within the reindex()\n", + "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n", + " .sort_values(ascending=False)\n", + " .index)\n", + " .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n", + "plt.xlabel('Price ($)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.3.5.2 Distribution of weekday and weekend price by state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 15#\n", + "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n", + "#specify 'state' for `id_vars`\n", + "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", + "#call the resultant price column 'Price' via the `value_name` argument,\n", + "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", + "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], \n", + " id_vars='state', \n", + " var_name='Ticket', \n", + " value_vars=['AdultWeekday', 'AdultWeekend'], \n", + " value_name='Price')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateTicketPrice
0AlaskaAdultWeekday65.0
1AlaskaAdultWeekday47.0
2AlaskaAdultWeekday30.0
3ArizonaAdultWeekday89.0
4ArizonaAdultWeekday74.0
\n", + "
" + ], + "text/plain": [ + " state Ticket Price\n", + "0 Alaska AdultWeekday 65.0\n", + "1 Alaska AdultWeekday 47.0\n", + "2 Alaska AdultWeekday 30.0\n", + "3 Arizona AdultWeekday 89.0\n", + "4 Arizona AdultWeekday 74.0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ticket_prices.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 16#\n", + "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", + "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", + "#This will use boxplot's x, y, hue, and data arguments.\n", + "plt.subplots(figsize=(12, 8))\n", + "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n", + "plt.xticks(rotation='vertical')\n", + "plt.ylabel('Price ($)')\n", + "plt.xlabel('State');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", + "\n", + "* disregard State completely\n", + "* retain all State information\n", + "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", + "\n", + "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus we currently have two main questions you want to resolve:\n", + "\n", + "* What do you do about the two types of ticket price?\n", + "* What do you do about the state information?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.4 Numeric Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.4.1 Numeric data summary" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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summit_elev330.04591.8181823735.535934315.01403.753127.57806.0013487.0
vertical_drop330.01215.427273947.86455760.0461.25964.51800.004425.0
base_elev330.03374.0000003117.12162170.0869.001561.56325.2510800.0
trams330.00.1727270.5599460.00.000.00.004.0
fastEight164.00.0060980.0780870.00.000.00.001.0
fastSixes330.00.1848480.6516850.00.000.00.006.0
fastQuads330.01.0181822.1982940.00.000.01.0015.0
quad330.00.9333331.3122450.00.000.01.008.0
triple330.01.5000001.6191300.00.001.02.008.0
double330.01.8333331.8150280.01.001.03.0014.0
surface330.02.6212122.0596360.01.002.03.0015.0
total_chairs330.08.2666675.7986830.05.007.010.0041.0
Runs326.048.21472446.3640773.019.0033.060.00341.0
TerrainParks279.02.8207892.0081131.01.002.04.0014.0
LongestRun_mi325.01.4332311.1561710.00.501.02.006.0
SkiableTerrain_ac327.0739.8012231816.1674418.085.00200.0690.0026819.0
Snow Making_ac284.0174.873239261.3361252.050.00100.0200.503379.0
daysOpenLastYear279.0115.10394335.0632513.097.00114.0135.00305.0
yearsOpen329.063.656535109.4299286.050.0058.069.002019.0
averageSnowfall316.0185.316456136.35684218.069.00150.0300.00669.0
AdultWeekday276.057.91695726.14012615.040.0050.071.00179.0
AdultWeekend279.064.16681024.55458417.047.0060.077.50179.0
projectedDaysOpen283.0120.05300431.04596330.0100.00120.0139.50305.0
NightSkiing_ac187.0100.395722105.1696202.040.0072.0114.00650.0
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% 50% \\\n", + "summit_elev 330.0 4591.818182 3735.535934 315.0 1403.75 3127.5 \n", + "vertical_drop 330.0 1215.427273 947.864557 60.0 461.25 964.5 \n", + "base_elev 330.0 3374.000000 3117.121621 70.0 869.00 1561.5 \n", + "trams 330.0 0.172727 0.559946 0.0 0.00 0.0 \n", + "fastEight 164.0 0.006098 0.078087 0.0 0.00 0.0 \n", + "fastSixes 330.0 0.184848 0.651685 0.0 0.00 0.0 \n", + "fastQuads 330.0 1.018182 2.198294 0.0 0.00 0.0 \n", + "quad 330.0 0.933333 1.312245 0.0 0.00 0.0 \n", + "triple 330.0 1.500000 1.619130 0.0 0.00 1.0 \n", + "double 330.0 1.833333 1.815028 0.0 1.00 1.0 \n", + "surface 330.0 2.621212 2.059636 0.0 1.00 2.0 \n", + "total_chairs 330.0 8.266667 5.798683 0.0 5.00 7.0 \n", + "Runs 326.0 48.214724 46.364077 3.0 19.00 33.0 \n", + "TerrainParks 279.0 2.820789 2.008113 1.0 1.00 2.0 \n", + "LongestRun_mi 325.0 1.433231 1.156171 0.0 0.50 1.0 \n", + "SkiableTerrain_ac 327.0 739.801223 1816.167441 8.0 85.00 200.0 \n", + "Snow Making_ac 284.0 174.873239 261.336125 2.0 50.00 100.0 \n", + "daysOpenLastYear 279.0 115.103943 35.063251 3.0 97.00 114.0 \n", + "yearsOpen 329.0 63.656535 109.429928 6.0 50.00 58.0 \n", + "averageSnowfall 316.0 185.316456 136.356842 18.0 69.00 150.0 \n", + "AdultWeekday 276.0 57.916957 26.140126 15.0 40.00 50.0 \n", + "AdultWeekend 279.0 64.166810 24.554584 17.0 47.00 60.0 \n", + "projectedDaysOpen 283.0 120.053004 31.045963 30.0 100.00 120.0 \n", + "NightSkiing_ac 187.0 100.395722 105.169620 2.0 40.00 72.0 \n", + "\n", + " 75% max \n", + "summit_elev 7806.00 13487.0 \n", + "vertical_drop 1800.00 4425.0 \n", + "base_elev 6325.25 10800.0 \n", + "trams 0.00 4.0 \n", + "fastEight 0.00 1.0 \n", + "fastSixes 0.00 6.0 \n", + "fastQuads 1.00 15.0 \n", + "quad 1.00 8.0 \n", + "triple 2.00 8.0 \n", + "double 3.00 14.0 \n", + "surface 3.00 15.0 \n", + "total_chairs 10.00 41.0 \n", + "Runs 60.00 341.0 \n", + "TerrainParks 4.00 14.0 \n", + "LongestRun_mi 2.00 6.0 \n", + "SkiableTerrain_ac 690.00 26819.0 \n", + "Snow Making_ac 200.50 3379.0 \n", + "daysOpenLastYear 135.00 305.0 \n", + "yearsOpen 69.00 2019.0 \n", + "averageSnowfall 300.00 669.0 \n", + "AdultWeekday 71.00 179.0 \n", + "AdultWeekend 77.50 179.0 \n", + "projectedDaysOpen 139.50 305.0 \n", + "NightSkiing_ac 114.00 650.0 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 17#\n", + "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", + "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", + "#will be useful again\n", + "ski_data.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 82.424242\n", + "2 14.242424\n", + "1 3.333333\n", + "dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", + "missing_price.value_counts()/len(missing_price) * 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.4.2 Distributions Of Feature Values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 18#\n", + "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", + "#Try passing it an argument figsize=(15,10)\n", + "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", + "#It's important you create legible and easy-to-read plots\n", + "ski_data.hist(figsize=(15,10))\n", + "plt.subplots_adjust(hspace=0.5);\n", + "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What features do we have possible cause for concern about and why?\n", + "\n", + "* SkiableTerrain_ac because values are clustered down the low end,\n", + "* Snow Making_ac for the same reason,\n", + "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", + "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", + "* trams also may get an amber flag for the same reason,\n", + "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.1 SkiableTerrain_ac" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "39 26819.0\n", + "Name: SkiableTerrain_ac, dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 19#\n", + "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", + "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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39
NameSilverton Mountain
RegionColorado
stateColorado
summit_elev13487
vertical_drop3087
base_elev10400
trams0
fastEight0.0
fastSixes0
fastQuads0
quad0
triple0
double1
surface0
total_chairs1
RunsNaN
TerrainParksNaN
LongestRun_mi1.5
SkiableTerrain_ac26819.0
Snow Making_acNaN
daysOpenLastYear175.0
yearsOpen17.0
averageSnowfall400.0
AdultWeekday79.0
AdultWeekend79.0
projectedDaysOpen181.0
NightSkiing_acNaN
\n", + "
" + ], + "text/plain": [ + " 39\n", + "Name Silverton Mountain\n", + "Region Colorado\n", + "state Colorado\n", + "summit_elev 13487\n", + "vertical_drop 3087\n", + "base_elev 10400\n", + "trams 0\n", + "fastEight 0.0\n", + "fastSixes 0\n", + "fastQuads 0\n", + "quad 0\n", + "triple 0\n", + "double 1\n", + "surface 0\n", + "total_chairs 1\n", + "Runs NaN\n", + "TerrainParks NaN\n", + "LongestRun_mi 1.5\n", + "SkiableTerrain_ac 26819.0\n", + "Snow Making_ac NaN\n", + "daysOpenLastYear 175.0\n", + "yearsOpen 17.0\n", + "averageSnowfall 400.0\n", + "AdultWeekday 79.0\n", + "AdultWeekend 79.0\n", + "projectedDaysOpen 181.0\n", + "NightSkiing_ac NaN" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 20#\n", + "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n", + "#Hint: don't forget the transpose will be helpful here\n", + "ski_data[ski_data.SkiableTerrain_ac > 10000].T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A: 2** Your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what can you do when you have one record that seems highly suspicious?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Silverton Mountain information](images/silverton_mountain_info.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can spot check data. You see your top and base elevation values agree, but the skiable area is very different. Your suspect value is 26819, but the value you've just looked up is 1819. The last three digits agree. This sort of error could have occured in transmission or some editing or transcription stage. You could plausibly replace the suspect value with the one you've just obtained. Another cautionary note to make here is that although you're doing this in order to progress with your analysis, this is most definitely an issue that should have been raised and fed back to the client or data originator as a query. You should view this \"data correction\" step as a means to continue (documenting it carefully as you do in this notebook) rather than an ultimate decision as to what is correct." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26819.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 21#\n", + "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n", + "ski_data.loc[39, 'SkiableTerrain_ac']" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 22#\n", + "#Use the .loc accessor again to modify this value with the correct value of 1819\n", + "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1819.0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 23#\n", + "#Use the .loc accessor a final time to verify that the value has been modified\n", + "ski_data.loc[39, 'SkiableTerrain_ac']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NB whilst you may become suspicious about your data quality, and you know you have missing values, you will not here dive down the rabbit hole of checking all values or web scraping to replace missing values.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does the distribution of skiable area look like now?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ski_data.SkiableTerrain_ac.hist(bins=30)\n", + "plt.xlabel('SkiableTerrain_ac')\n", + "plt.ylabel('Count')\n", + "plt.title('Distribution of skiable area (acres) after replacing erroneous value');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now see a rather long tailed distribution. You may wonder about the now most extreme value that is above 8000, but similarly you may also wonder about the value around 7000. If you wanted to spend more time manually checking values you could, but leave this for now. The above distribution is plausible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.2 Snow Making_ac" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11 3379.0\n", + "18 1500.0\n", + "Name: Snow Making_ac, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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11
NameHeavenly Mountain Resort
RegionSierra Nevada
stateCalifornia
summit_elev10067
vertical_drop3500
base_elev7170
trams2
fastEight0.0
fastSixes2
fastQuads7
quad1
triple5
double3
surface8
total_chairs28
Runs97.0
TerrainParks3.0
LongestRun_mi5.5
SkiableTerrain_ac4800.0
Snow Making_ac3379.0
daysOpenLastYear155.0
yearsOpen64.0
averageSnowfall360.0
AdultWeekdayNaN
AdultWeekendNaN
projectedDaysOpen157.0
NightSkiing_acNaN
\n", + "
" + ], + "text/plain": [ + " 11\n", + "Name Heavenly Mountain Resort\n", + "Region Sierra Nevada\n", + "state California\n", + "summit_elev 10067\n", + "vertical_drop 3500\n", + "base_elev 7170\n", + "trams 2\n", + "fastEight 0.0\n", + "fastSixes 2\n", + "fastQuads 7\n", + "quad 1\n", + "triple 5\n", + "double 3\n", + "surface 8\n", + "total_chairs 28\n", + "Runs 97.0\n", + "TerrainParks 3.0\n", + "LongestRun_mi 5.5\n", + "SkiableTerrain_ac 4800.0\n", + "Snow Making_ac 3379.0\n", + "daysOpenLastYear 155.0\n", + "yearsOpen 64.0\n", + "averageSnowfall 360.0\n", + "AdultWeekday NaN\n", + "AdultWeekend NaN\n", + "projectedDaysOpen 157.0\n", + "NightSkiing_ac NaN" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data[ski_data['Snow Making_ac'] > 3000].T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What, then, is your rough guess for the area covered by snowmaking?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2880.0" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + ".6 * 4800" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.3 fastEight" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look at the different fastEight values more closely:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 163\n", + "1.0 1\n", + "Name: fastEight, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data.fastEight.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 24#\n", + "#Drop the 'fastEight' column from ski_data. Use inplace=True\n", + "ski_data.drop(columns='fastEight', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34 104.0\n", + "115 2019.0\n", + "Name: yearsOpen, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 25#\n", + "#Filter the 'yearsOpen' column for values greater than 100\n", + "ski_data.yearsOpen[ski_data.yearsOpen > 100]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 26#\n", + "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n", + "#Pass the argument bins=30 to hist(), but feel free to explore other values\n", + "ski_data.yearsOpen[ski_data.yearsOpen < 2000].hist(bins=30)\n", + "plt.xlabel('Years open')\n", + "plt.ylabel('Count')\n", + "plt.title('Distribution of years open excluding 2019');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above distribution of years seems entirely plausible, including the 104 year value. You can certainly state that no resort will have been open for 2019 years! It likely means the resort opened in 2019. It could also mean the resort is due to open in 2019. You don't know when these data were gathered!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's review the summary statistics for the years under 1000." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 328.000000\n", + "mean 57.695122\n", + "std 16.841182\n", + "min 6.000000\n", + "25% 50.000000\n", + "50% 58.000000\n", + "75% 68.250000\n", + "max 104.000000\n", + "Name: yearsOpen, dtype: float64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The smallest number of years open otherwise is 6. You can't be sure whether this resort in question has been open zero years or one year and even whether the numbers are projections or actual. In any case, you would be adding a new youngest resort so it feels best to simply drop this row." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "ski_data = ski_data[ski_data.yearsOpen < 1000]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.4 fastSixes and Trams" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The other features you had mild concern over, you will not investigate further. Perhaps take some care when using these features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.7 Derive State-wide Summary Statistics For Our Market Segment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have, by this point removed one row, but it was for a resort that may not have opened yet, or perhaps in its first season. Using your business knowledge, you know that state-wide supply and demand of certain skiing resources may well factor into pricing strategies. Does a resort dominate the available night skiing in a state? Or does it account for a large proportion of the total skiable terrain or days open?\n", + "\n", + "If you want to add any features to your data that captures the state-wide market size, you should do this now, before dropping any more rows. In the next section, you'll drop rows with missing price information. Although you don't know what those resorts charge for their tickets, you do know the resorts exists and have been open for at least six years. Thus, you'll now calculate some state-wide summary statistics for later use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many features in your data pertain to chairlifts, that is for getting people around each resort. These aren't relevant, nor are the features relating to altitudes. Features that you may be interested in are:\n", + "\n", + "* TerrainParks\n", + "* SkiableTerrain_ac\n", + "* daysOpenLastYear\n", + "* NightSkiing_ac\n", + "\n", + "When you think about it, these are features it makes sense to sum: the total number of terrain parks, the total skiable area, the total number of days open, and the total area available for night skiing. You might consider the total number of ski runs, but understand that the skiable area is more informative than just a number of runs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A fairly new groupby behaviour is [named aggregation](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html). This allows us to clearly perform the aggregations you want whilst also creating informative output column names." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_ac
0Alaska32280.0345.04.0580.0
1Arizona21577.0237.06.080.0
2California2125948.02738.081.0587.0
3Colorado2243682.03258.074.0428.0
4Connecticut5358.0353.010.0256.0
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" + ], + "text/plain": [ + " state resorts_per_state state_total_skiable_area_ac \\\n", + "0 Alaska 3 2280.0 \n", + "1 Arizona 2 1577.0 \n", + "2 California 21 25948.0 \n", + "3 Colorado 22 43682.0 \n", + "4 Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "0 345.0 4.0 \n", + "1 237.0 6.0 \n", + "2 2738.0 81.0 \n", + "3 3258.0 74.0 \n", + "4 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac \n", + "0 580.0 \n", + "1 80.0 \n", + "2 587.0 \n", + "3 428.0 \n", + "4 256.0 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 27#\n", + "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n", + "#call them 'state_total_days_open', 'state_total_terrain_parks', and 'state_total_nightskiing_ac',\n", + "#respectively\n", + "#Finally, add a call to the reset_index() method (we recommend you experiment with and without this to see\n", + "#what it does)\n", + "state_summary = ski_data.groupby('state').agg(\n", + " resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n", + " state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n", + " state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n", + " state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n", + " state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n", + ").reset_index()\n", + "state_summary.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.8 Drop Rows With No Price Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 82.317073\n", + "2 14.329268\n", + "1 3.353659\n", + "dtype: float64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", + "missing_price.value_counts()/len(missing_price) * 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 28#\n", + "#Use `missing_price` to remove rows from ski_data where both price values are missing\n", + "ski_data = ski_data[missing_price != 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.9 Review distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ski_data.hist(figsize=(15, 10))\n", + "plt.subplots_adjust(hspace=0.5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values. Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.10 Population data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 29#\n", + "#Use pandas' `read_html` method to read the table from the URL below\n", + "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n", + "usa_states = pd.read_html(states_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(usa_states)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(usa_states)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Name &postal abbs. [1]CitiesEstablished[A]Population[B][3]Total area[4]Land area[4]Water area[4]Numberof Reps.
Name &postal abbs. [1]Name &postal abbs. [1].1CapitalLargest[5]Established[A]Population[B][3]mi2km2mi2km2mi2km2Numberof Reps.
0AlabamaALMontgomeryBirminghamDec 14, 181949031855242013576750645131171177545977
1AlaskaAKJuneauAnchorageJan 3, 195973154566538417233375706411477953947432453841
2ArizonaAZPhoenixPhoenixFeb 14, 1912727871711399029523411359429420739610269
3ArkansasARLittle RockLittle RockJun 15, 183630178045317913773252035134771114329614
4CaliforniaCASacramentoLos AngelesSep 9, 18503951222316369542396715577940346679162050153
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" + ], + "text/plain": [ + " Name &postal abbs. [1] Cities \\\n", + " Name &postal abbs. [1] Name &postal abbs. [1].1 Capital Largest[5] \n", + "0 Alabama AL Montgomery Birmingham \n", + "1 Alaska AK Juneau Anchorage \n", + "2 Arizona AZ Phoenix Phoenix \n", + "3 Arkansas AR Little Rock Little Rock \n", + "4 California CA Sacramento Los Angeles \n", + "\n", + " Established[A] Population[B][3] Total area[4] Land area[4] \\\n", + " Established[A] Population[B][3] mi2 km2 mi2 \n", + "0 Dec 14, 1819 4903185 52420 135767 50645 \n", + "1 Jan 3, 1959 731545 665384 1723337 570641 \n", + "2 Feb 14, 1912 7278717 113990 295234 113594 \n", + "3 Jun 15, 1836 3017804 53179 137732 52035 \n", + "4 Sep 9, 1850 39512223 163695 423967 155779 \n", + "\n", + " Water area[4] Numberof Reps. \n", + " km2 mi2 km2 Numberof Reps. \n", + "0 131171 1775 4597 7 \n", + "1 1477953 94743 245384 1 \n", + "2 294207 396 1026 9 \n", + "3 134771 1143 2961 4 \n", + "4 403466 7916 20501 53 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "usa_states = usa_states[0]\n", + "usa_states.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "#Code task 30#\n", + "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n", + "#It should be a column of dates\n", + "established = usa_states.iloc[:, 4]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Dec 14, 1819\n", + "1 Jan 3, 1959\n", + "2 Feb 14, 1912\n", + "3 Jun 15, 1836\n", + "4 Sep 9, 1850\n", + "5 Aug 1, 1876\n", + "6 Jan 9, 1788\n", + "7 Dec 7, 1787\n", + "8 Mar 3, 1845\n", + "9 Jan 2, 1788\n", + "10 Aug 21, 1959\n", + "11 Jul 3, 1890\n", + "12 Dec 3, 1818\n", + "13 Dec 11, 1816\n", + "14 Dec 28, 1846\n", + "15 Jan 29, 1861\n", + "16 Jun 1, 1792\n", + "17 Apr 30, 1812\n", + "18 Mar 15, 1820\n", + "19 Apr 28, 1788\n", + "20 Feb 6, 1788\n", + "21 Jan 26, 1837\n", + "22 May 11, 1858\n", + "23 Dec 10, 1817\n", + "24 Aug 10, 1821\n", + "25 Nov 8, 1889\n", + "26 Mar 1, 1867\n", + "27 Oct 31, 1864\n", + "28 Jun 21, 1788\n", + "29 Dec 18, 1787\n", + "30 Jan 6, 1912\n", + "31 Jul 26, 1788\n", + "32 Nov 21, 1789\n", + "33 Nov 2, 1889\n", + "34 Mar 1, 1803\n", + "35 Nov 16, 1907\n", + "36 Feb 14, 1859\n", + "37 Dec 12, 1787\n", + "38 May 29, 1790\n", + "39 May 23, 1788\n", + "40 Nov 2, 1889\n", + "41 Jun 1, 1796\n", + "42 Dec 29, 1845\n", + "43 Jan 4, 1896\n", + "44 Mar 4, 1791\n", + "45 Jun 25, 1788\n", + "46 Nov 11, 1889\n", + "47 Jun 20, 1863\n", + "48 May 29, 1848\n", + "49 Jul 10, 1890\n", + "Name: (Established[A], Established[A]), dtype: object" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "established" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the state name, population, and total area (square miles) columns." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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statestate_populationstate_area_sq_miles
0Alabama490318552420
1Alaska731545665384
2Arizona7278717113990
3Arkansas301780453179
4California39512223163695
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" + ], + "text/plain": [ + " state state_population state_area_sq_miles\n", + "0 Alabama 4903185 52420\n", + "1 Alaska 731545 665384\n", + "2 Arizona 7278717 113990\n", + "3 Arkansas 3017804 53179\n", + "4 California 39512223 163695" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 31#\n", + "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n", + "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n", + "#respectively.\n", + "usa_states_sub = usa_states.iloc[:, [0, 5, 6]].copy()\n", + "usa_states_sub.columns = ['state', 'state_population', 'state_area_sq_miles']\n", + "usa_states_sub.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do you have all the ski data states accounted for?" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 32#\n", + "#Find the states in `state_summary` that are not in `usa_states_sub`\n", + "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n", + "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", + "missing_states" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No?? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20 Massachusetts[C]\n", + "37 Pennsylvania[C]\n", + "38 Rhode Island[D]\n", + "45 Virginia[C]\n", + "47 West Virginia\n", + "Name: state, dtype: object" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete square brackets and their contents and try again:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20 Massachusetts\n", + "37 Pennsylvania\n", + "38 Rhode Island\n", + "45 Virginia\n", + "47 West Virginia\n", + "Name: state, dtype: object" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 33#\n", + "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n", + "#with the empty string. Do this inplace, so you need to specify the arguments:\n", + "#to_replace='\\[.*\\]' #literal square bracket followed by anything or nothing followed by literal closing bracket\n", + "#value='' #empty string as replacement\n", + "#regex=True #we used a regex in our `to_replace` argument\n", + "#inplace=True #Do this \"in place\"\n", + "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n", + "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 34#\n", + "#And now verify none of our states are missing by checking that there are no states in\n", + "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n", + "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", + "missing_states" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acstate_populationstate_area_sq_miles
0Alaska32280.0345.04.0580.0731545665384
1Arizona21577.0237.06.080.07278717113990
2California2125948.02738.081.0587.039512223163695
3Colorado2243682.03258.074.0428.05758736104094
4Connecticut5358.0353.010.0256.035652785543
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" + ], + "text/plain": [ + " state resorts_per_state state_total_skiable_area_ac \\\n", + "0 Alaska 3 2280.0 \n", + "1 Arizona 2 1577.0 \n", + "2 California 21 25948.0 \n", + "3 Colorado 22 43682.0 \n", + "4 Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "0 345.0 4.0 \n", + "1 237.0 6.0 \n", + "2 2738.0 81.0 \n", + "3 3258.0 74.0 \n", + "4 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac state_population state_area_sq_miles \n", + "0 580.0 731545 665384 \n", + "1 80.0 7278717 113990 \n", + "2 587.0 39512223 163695 \n", + "3 428.0 5758736 104094 \n", + "4 256.0 3565278 5543 " + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 35#\n", + "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n", + "#specify the arguments how='left' and on='state'\n", + "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n", + "state_summary.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.11 Target Feature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " AdultWeekend AdultWeekday\n", + "141 42.0 42.0\n", + "142 63.0 63.0\n", + "143 49.0 49.0\n", + "144 48.0 48.0\n", + "145 46.0 46.0\n", + "146 39.0 39.0\n", + "147 50.0 50.0\n", + "148 67.0 67.0\n", + "149 47.0 47.0\n", + "150 39.0 39.0\n", + "151 81.0 81.0" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 37#\n", + "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n", + "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is there any reason to prefer weekend or weekday prices? Which is missing the least?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AdultWeekend 4\n", + "AdultWeekday 7\n", + "dtype: int64" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "ski_data.drop(columns='AdultWeekday', inplace=True)\n", + "ski_data.dropna(subset=['AdultWeekend'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(277, 25)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perform a final quick check on the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.11.1 Number Of Missing Values By Row - Resort" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " count %\n", + "329 5 20.0\n", + "62 5 20.0\n", + "141 5 20.0\n", + "86 5 20.0\n", + "74 5 20.0\n", + "146 5 20.0\n", + "184 4 16.0\n", + "108 4 16.0\n", + "198 4 16.0\n", + "39 4 16.0" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n", + "missing.columns=['count', '%']\n", + "missing.sort_values(by='count', ascending=False).head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These seem possibly curiously quantized..." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 4., 8., 12., 16., 20.])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing['%'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes, the percentage of missing values per row appear in multiples of 4." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 107\n", + "4.0 94\n", + "8.0 45\n", + "12.0 15\n", + "16.0 10\n", + "20.0 6\n", + "Name: %, dtype: int64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing['%'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 277 entries, 0 to 329\n", + "Data columns (total 25 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 277 non-null object \n", + " 1 Region 277 non-null object \n", + " 2 state 277 non-null object \n", + " 3 summit_elev 277 non-null int64 \n", + " 4 vertical_drop 277 non-null int64 \n", + " 5 base_elev 277 non-null int64 \n", + " 6 trams 277 non-null int64 \n", + " 7 fastSixes 277 non-null int64 \n", + " 8 fastQuads 277 non-null int64 \n", + " 9 quad 277 non-null int64 \n", + " 10 triple 277 non-null int64 \n", + " 11 double 277 non-null int64 \n", + " 12 surface 277 non-null int64 \n", + " 13 total_chairs 277 non-null int64 \n", + " 14 Runs 274 non-null float64\n", + " 15 TerrainParks 233 non-null float64\n", + " 16 LongestRun_mi 272 non-null float64\n", + " 17 SkiableTerrain_ac 275 non-null float64\n", + " 18 Snow Making_ac 240 non-null float64\n", + " 19 daysOpenLastYear 233 non-null float64\n", + " 20 yearsOpen 277 non-null float64\n", + " 21 averageSnowfall 268 non-null float64\n", + " 22 AdultWeekend 277 non-null float64\n", + " 23 projectedDaysOpen 236 non-null float64\n", + " 24 NightSkiing_ac 163 non-null float64\n", + "dtypes: float64(11), int64(11), object(3)\n", + "memory usage: 56.3+ KB\n" + ] + } + ], + "source": [ + "ski_data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are still some missing values, and it's good to be aware of this, but leave them as is for now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.12 Save data" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(277, 25)" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ski_data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'save_file' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/wsuser/ipykernel_154/3665361625.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# save the data to a new csv file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdatapath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msave_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mski_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ski_data_cleaned.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatapath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'save_file' is not defined" + ] + } + ], + "source": [ + "# save the data to a new csv file\n", + "datapath = '../data'\n", + "save_file(ski_data, 'ski_data_cleaned.csv', datapath)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# save the state_summary separately.\n", + "datapath = '../data'\n", + "save_file(state_summary, 'state_summary.csv', datapath)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.13 Summary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A: 3** Your answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, a dataset for multiple ski resorts accross the United States was taken from a github respository and cleaned/transformed to make it more suitable for analysis and possibly feeding it into a model. The dataset had 330 records with 27 features including the record for 'Big Mountain Resort' which is located in Montana and is the resort we are looking to develop a pricing model for. The dataset appeared to have quite a few missing values, but fortunatedly, the record for Big Mountain Resort had no missing values. With that said, some records were missing values for the features 'AdultWeekday' and 'AdultWeekend' which represent the prices for each resort and were decidedly the target variables for the study. The feature with the most missing values was 'fastEight' feature with about 50% of records missing a value for this feature. This feature would eventually be dropped along with the only resort to have been open for over 100 years\n", + "\n", + "The records mostly consisted of numerical variables with the only categorical variables being 'name', 'state', and 'region'. Sometimes, state and region can have the same name on the same record and regions can have the same names in different states. But there are no records that have the exact same name, state, and region meaning there are no duplicate records. \n", + "\n", + "The states Colorado, Michigan, and New York had the highest number of resorts " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 2d80a78de4228a635fcde3ce5c453b6a0a19827c Mon Sep 17 00:00:00 2001 From: Thapelo Date: Sat, 26 Mar 2022 14:29:13 +0200 Subject: [PATCH 2/7] Moved the file to Notebooks directory --- 02_data_wrangling.ipynb | 5131 --------------------------------------- 1 file changed, 5131 deletions(-) delete mode 100644 02_data_wrangling.ipynb diff --git a/02_data_wrangling.ipynb b/02_data_wrangling.ipynb deleted file mode 100644 index aaf17406d..000000000 --- a/02_data_wrangling.ipynb +++ /dev/null @@ -1,5131 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 Data wrangling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.1 Contents\n", - "* [2 Data wrangling](#2_Data_wrangling)\n", - " * [2.1 Contents](#2.1_Contents)\n", - " * [2.2 Introduction](#2.2_Introduction)\n", - " * [2.2.1 Recap Of Data Science Problem](#2.2.1_Recap_Of_Data_Science_Problem)\n", - " * [2.2.2 Introduction To Notebook](#2.2.2_Introduction_To_Notebook)\n", - " * [2.3 Imports](#2.3_Imports)\n", - " * [2.4 Objectives](#2.4_Objectives)\n", - " * [2.5 Load The Ski Resort Data](#2.5_Load_The_Ski_Resort_Data)\n", - " * [2.6 Explore The Data](#2.6_Explore_The_Data)\n", - " * [2.6.1 Find Your Resort Of Interest](#2.6.1_Find_Your_Resort_Of_Interest)\n", - " * [2.6.2 Number Of Missing Values By Column](#2.6.2_Number_Of_Missing_Values_By_Column)\n", - " * [2.6.3 Categorical Features](#2.6.3_Categorical_Features)\n", - " * [2.6.3.1 Unique Resort Names](#2.6.3.1_Unique_Resort_Names)\n", - " * [2.6.3.2 Region And State](#2.6.3.2_Region_And_State)\n", - " * [2.6.3.3 Number of distinct regions and states](#2.6.3.3_Number_of_distinct_regions_and_states)\n", - " * [2.6.3.4 Distribution Of Resorts By Region And State](#2.6.3.4_Distribution_Of_Resorts_By_Region_And_State)\n", - " * [2.6.3.5 Distribution Of Ticket Price By State](#2.6.3.5_Distribution_Of_Ticket_Price_By_State)\n", - " * [2.6.3.5.1 Average weekend and weekday price by state](#2.6.3.5.1_Average_weekend_and_weekday_price_by_state)\n", - " * [2.6.3.5.2 Distribution of weekday and weekend price by state](#2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state)\n", - " * [2.6.4 Numeric Features](#2.6.4_Numeric_Features)\n", - " * [2.6.4.1 Numeric data summary](#2.6.4.1_Numeric_data_summary)\n", - " * [2.6.4.2 Distributions Of Feature Values](#2.6.4.2_Distributions_Of_Feature_Values)\n", - " * [2.6.4.2.1 SkiableTerrain_ac](#2.6.4.2.1_SkiableTerrain_ac)\n", - " * [2.6.4.2.2 Snow Making_ac](#2.6.4.2.2_Snow_Making_ac)\n", - " * [2.6.4.2.3 fastEight](#2.6.4.2.3_fastEight)\n", - " * [2.6.4.2.4 fastSixes and Trams](#2.6.4.2.4_fastSixes_and_Trams)\n", - " * [2.7 Derive State-wide Summary Statistics For Our Market Segment](#2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment)\n", - " * [2.8 Drop Rows With No Price Data](#2.8_Drop_Rows_With_No_Price_Data)\n", - " * [2.9 Review distributions](#2.9_Review_distributions)\n", - " * [2.10 Population data](#2.10_Population_data)\n", - " * [2.11 Target Feature](#2.11_Target_Feature)\n", - " * [2.11.1 Number Of Missing Values By Row - Resort](#2.11.1_Number_Of_Missing_Values_By_Row_-_Resort)\n", - " * [2.12 Save data](#2.12_Save_data)\n", - " * [2.13 Summary](#2.13_Summary)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.2 Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This step focuses on collecting your data, organizing it, and making sure it's well defined. Paying attention to these tasks will pay off greatly later on. Some data cleaning can be done at this stage, but it's important not to be overzealous in your cleaning before you've explored the data to better understand it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2.1 Recap Of Data Science Problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The purpose of this data science project is to come up with a pricing model for ski resort tickets in our market segment. Big Mountain suspects it may not be maximizing its returns, relative to its position in the market. It also does not have a strong sense of what facilities matter most to visitors, particularly which ones they're most likely to pay more for. This project aims to build a predictive model for ticket price based on a number of facilities, or properties, boasted by resorts (*at the resorts).* \n", - "This model will be used to provide guidance for Big Mountain's pricing and future facility investment plans." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2.2 Introduction To Notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notebooks grow organically as we explore our data. If you used paper notebooks, you could discover a mistake and cross out or revise some earlier work. Later work may give you a reason to revisit earlier work and explore it further. The great thing about Jupyter notebooks is that you can edit, add, and move cells around without needing to cross out figures or scrawl in the margin. However, this means you can lose track of your changes easily. If you worked in a regulated environment, the company may have a a policy of always dating entries and clearly crossing out any mistakes, with your initials and the date.\n", - "\n", - "**Best practice here is to commit your changes using a version control system such as Git.** Try to get into the habit of adding and committing your files to the Git repository you're working in after you save them. You're are working in a Git repository, right? If you make a significant change, save the notebook and commit it to Git. In fact, if you're about to make a significant change, it's a good idea to commit before as well. Then if the change is a mess, you've got the previous version to go back to.\n", - "\n", - "**Another best practice with notebooks is to try to keep them organized with helpful headings and comments.** Not only can a good structure, but associated headings help you keep track of what you've done and your current focus. Anyone reading your notebook will have a much easier time following the flow of work. Remember, that 'anyone' will most likely be you. Be kind to future you!\n", - "\n", - "In this notebook, note how we try to use well structured, helpful headings that frequently are self-explanatory, and we make a brief note after any results to highlight key takeaways. This is an immense help to anyone reading your notebook and it will greatly help you when you come to summarise your findings. **Top tip: jot down key findings in a final summary at the end of the notebook as they arise. You can tidy this up later.** This is a great way to ensure important results don't get lost in the middle of your notebooks." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this, and subsequent notebooks, there are coding tasks marked with `#Code task n#` with code to complete. The `___` will guide you to where you need to insert code." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.3 Imports" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Placing your imports all together at the start of your notebook means you only need to consult one place to check your notebook's dependencies. By all means import something 'in situ' later on when you're experimenting, but if the imported dependency ends up being kept, you should subsequently move the import statement here with the rest." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'library'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msb_utils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msave_file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'library'" - ] - } - ], - "source": [ - "#Code task 1#\n", - "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import os\n", - "\n", - "from library.sb_utils import save_file\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.4 Objectives" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are some fundamental questions to resolve in this notebook before you move on.\n", - "\n", - "* Do you think you may have the data you need to tackle the desired question?\n", - " * Have you identified the required target value?\n", - " * Do you have potentially useful features?\n", - "* Do you have any fundamental issues with the data?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.5 Load The Ski Resort Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
0Alyeska ResortAlaskaAlaska3939250025010.002...1.01610.0113.0150.060.0669.065.085.0150.0550.0
1Eaglecrest Ski AreaAlaskaAlaska26001540120000.000...2.0640.060.045.044.0350.047.053.090.0NaN
2Hilltop Ski AreaAlaskaAlaska2090294179600.000...1.030.030.0150.036.069.030.034.0152.030.0
3Arizona SnowbowlArizonaArizona115002300920000.010...2.0777.0104.0122.081.0260.089.089.0122.0NaN
4Sunrise Park ResortArizonaArizona11100180092000NaN01...1.2800.080.0115.049.0250.074.078.0104.080.0
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" - ], - "text/plain": [ - " Name Region state summit_elev vertical_drop \\\n", - "0 Alyeska Resort Alaska Alaska 3939 2500 \n", - "1 Eaglecrest Ski Area Alaska Alaska 2600 1540 \n", - "2 Hilltop Ski Area Alaska Alaska 2090 294 \n", - "3 Arizona Snowbowl Arizona Arizona 11500 2300 \n", - "4 Sunrise Park Resort Arizona Arizona 11100 1800 \n", - "\n", - " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", - "0 250 1 0.0 0 2 ... 1.0 \n", - "1 1200 0 0.0 0 0 ... 2.0 \n", - "2 1796 0 0.0 0 0 ... 1.0 \n", - "3 9200 0 0.0 1 0 ... 2.0 \n", - "4 9200 0 NaN 0 1 ... 1.2 \n", - "\n", - " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", - "0 1610.0 113.0 150.0 60.0 \n", - "1 640.0 60.0 45.0 44.0 \n", - "2 30.0 30.0 150.0 36.0 \n", - "3 777.0 104.0 122.0 81.0 \n", - "4 800.0 80.0 115.0 49.0 \n", - "\n", - " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", - "0 669.0 65.0 85.0 150.0 \n", - "1 350.0 47.0 53.0 90.0 \n", - "2 69.0 30.0 34.0 152.0 \n", - "3 260.0 89.0 89.0 122.0 \n", - "4 250.0 74.0 78.0 104.0 \n", - "\n", - " NightSkiing_ac \n", - "0 550.0 \n", - "1 NaN \n", - "2 30.0 \n", - "3 NaN \n", - "4 80.0 \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "import os, types\n", - "import pandas as pd\n", - "from botocore.client import Config\n", - "import ibm_boto3\n", - "\n", - "def __iter__(self): return 0\n", - "\n", - "# @hidden_cell\n", - "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n", - "# You might want to remove those credentials before you share the notebook.\n", - "client_3220f9abb4b84a9f8e470dd042b0c83c = ibm_boto3.client(service_name='s3',\n", - " ibm_api_key_id='juEZkC4PKYA_ajZxXwx_DAnVUDpGiSZD9FnRHEBXHM3Y',\n", - " ibm_auth_endpoint=\"https://iam.cloud.ibm.com/oidc/token\",\n", - " config=Config(signature_version='oauth'),\n", - " endpoint_url='https://s3.private.eu.cloud-object-storage.appdomain.cloud')\n", - "\n", - "body = client_3220f9abb4b84a9f8e470dd042b0c83c.get_object(Bucket='sbproject-donotdelete-pr-lzajghiksaeuv7',Key='ski_resort_data.csv')['Body']\n", - "# add missing __iter__ method, so pandas accepts body as file-like object\n", - "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n", - "\n", - "df_data_1 = pd.read_csv(body)\n", - "df_data_1.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "ski_data = df_data_1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: './ski_resort_data.csv'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/wsuser/ipykernel_155/1422985096.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# the supplied CSV data file is the raw_data directory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mski_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./ski_resort_data.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/envs/Python-3.9/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './ski_resort_data.csv'" - ] - } - ], - "source": [ - "# the supplied CSV data file is the raw_data directory\n", - "ski_data = pd.read_csv('./ski_resort_data.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Good first steps in auditing the data are the info method and displaying the first few records with head." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 330 entries, 0 to 329\n", - "Data columns (total 27 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Name 330 non-null object \n", - " 1 Region 330 non-null object \n", - " 2 state 330 non-null object \n", - " 3 summit_elev 330 non-null int64 \n", - " 4 vertical_drop 330 non-null int64 \n", - " 5 base_elev 330 non-null int64 \n", - " 6 trams 330 non-null int64 \n", - " 7 fastEight 164 non-null float64\n", - " 8 fastSixes 330 non-null int64 \n", - " 9 fastQuads 330 non-null int64 \n", - " 10 quad 330 non-null int64 \n", - " 11 triple 330 non-null int64 \n", - " 12 double 330 non-null int64 \n", - " 13 surface 330 non-null int64 \n", - " 14 total_chairs 330 non-null int64 \n", - " 15 Runs 326 non-null float64\n", - " 16 TerrainParks 279 non-null float64\n", - " 17 LongestRun_mi 325 non-null float64\n", - " 18 SkiableTerrain_ac 327 non-null float64\n", - " 19 Snow Making_ac 284 non-null float64\n", - " 20 daysOpenLastYear 279 non-null float64\n", - " 21 yearsOpen 329 non-null float64\n", - " 22 averageSnowfall 316 non-null float64\n", - " 23 AdultWeekday 276 non-null float64\n", - " 24 AdultWeekend 279 non-null float64\n", - " 25 projectedDaysOpen 283 non-null float64\n", - " 26 NightSkiing_ac 187 non-null float64\n", - "dtypes: float64(13), int64(11), object(3)\n", - "memory usage: 69.7+ KB\n" - ] - } - ], - "source": [ - "#Code task 2#\n", - "#Call the info method on ski_data to see a summary of the data\n", - "ski_data.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`AdultWeekday` is the price of an adult weekday ticket. `AdultWeekend` is the price of an adult weekend ticket. The other columns are potential features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This immediately raises the question of what quantity will you want to model? You know you want to model the ticket price, but you realise there are two kinds of ticket price!" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
0Alyeska ResortAlaskaAlaska3939250025010.002...1.01610.0113.0150.060.0669.065.085.0150.0550.0
1Eaglecrest Ski AreaAlaskaAlaska26001540120000.000...2.0640.060.045.044.0350.047.053.090.0NaN
2Hilltop Ski AreaAlaskaAlaska2090294179600.000...1.030.030.0150.036.069.030.034.0152.030.0
3Arizona SnowbowlArizonaArizona115002300920000.010...2.0777.0104.0122.081.0260.089.089.0122.0NaN
4Sunrise Park ResortArizonaArizona11100180092000NaN01...1.2800.080.0115.049.0250.074.078.0104.080.0
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5 rows × 27 columns

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" - ], - "text/plain": [ - " Name Region state summit_elev vertical_drop \\\n", - "0 Alyeska Resort Alaska Alaska 3939 2500 \n", - "1 Eaglecrest Ski Area Alaska Alaska 2600 1540 \n", - "2 Hilltop Ski Area Alaska Alaska 2090 294 \n", - "3 Arizona Snowbowl Arizona Arizona 11500 2300 \n", - "4 Sunrise Park Resort Arizona Arizona 11100 1800 \n", - "\n", - " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", - "0 250 1 0.0 0 2 ... 1.0 \n", - "1 1200 0 0.0 0 0 ... 2.0 \n", - "2 1796 0 0.0 0 0 ... 1.0 \n", - "3 9200 0 0.0 1 0 ... 2.0 \n", - "4 9200 0 NaN 0 1 ... 1.2 \n", - "\n", - " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", - "0 1610.0 113.0 150.0 60.0 \n", - "1 640.0 60.0 45.0 44.0 \n", - "2 30.0 30.0 150.0 36.0 \n", - "3 777.0 104.0 122.0 81.0 \n", - "4 800.0 80.0 115.0 49.0 \n", - "\n", - " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", - "0 669.0 65.0 85.0 150.0 \n", - "1 350.0 47.0 53.0 90.0 \n", - "2 69.0 30.0 34.0 152.0 \n", - "3 260.0 89.0 89.0 122.0 \n", - "4 250.0 74.0 78.0 104.0 \n", - "\n", - " NightSkiing_ac \n", - "0 550.0 \n", - "1 NaN \n", - "2 30.0 \n", - "3 NaN \n", - "4 80.0 \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 3#\n", - "#Call the head method on ski_data to print the first several rows of the data\n", - "ski_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.6 Explore The Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.1 Find Your Resort Of Interest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your resort of interest is called Big Mountain Resort. Check it's in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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151
NameBig Mountain Resort
RegionMontana
stateMontana
summit_elev6817
vertical_drop2353
base_elev4464
trams0
fastEight0.0
fastSixes0
fastQuads3
quad2
triple6
double0
surface3
total_chairs14
Runs105.0
TerrainParks4.0
LongestRun_mi3.3
SkiableTerrain_ac3000.0
Snow Making_ac600.0
daysOpenLastYear123.0
yearsOpen72.0
averageSnowfall333.0
AdultWeekday81.0
AdultWeekend81.0
projectedDaysOpen123.0
NightSkiing_ac600.0
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" - ], - "text/plain": [ - " 151\n", - "Name Big Mountain Resort\n", - "Region Montana\n", - "state Montana\n", - "summit_elev 6817\n", - "vertical_drop 2353\n", - "base_elev 4464\n", - "trams 0\n", - "fastEight 0.0\n", - "fastSixes 0\n", - "fastQuads 3\n", - "quad 2\n", - "triple 6\n", - "double 0\n", - "surface 3\n", - "total_chairs 14\n", - "Runs 105.0\n", - "TerrainParks 4.0\n", - "LongestRun_mi 3.3\n", - "SkiableTerrain_ac 3000.0\n", - "Snow Making_ac 600.0\n", - "daysOpenLastYear 123.0\n", - "yearsOpen 72.0\n", - "averageSnowfall 333.0\n", - "AdultWeekday 81.0\n", - "AdultWeekend 81.0\n", - "projectedDaysOpen 123.0\n", - "NightSkiing_ac 600.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 4#\n", - "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", - "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", - "#transpose method, but you can access this conveniently with the `T` property.\n", - "ski_data[ski_data.Name == 'Big Mountain Resort'].T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's good that your resort doesn't appear to have any missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.2 Number Of Missing Values By Column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Count the number of missing values in each column and sort them." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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count%
Name00.000000
total_chairs00.000000
double00.000000
triple00.000000
quad00.000000
fastQuads00.000000
fastSixes00.000000
surface00.000000
trams00.000000
base_elev00.000000
vertical_drop00.000000
summit_elev00.000000
state00.000000
Region00.000000
yearsOpen10.303030
SkiableTerrain_ac30.909091
Runs41.212121
LongestRun_mi51.515152
averageSnowfall144.242424
Snow Making_ac4613.939394
projectedDaysOpen4714.242424
TerrainParks5115.454545
daysOpenLastYear5115.454545
AdultWeekend5115.454545
AdultWeekday5416.363636
NightSkiing_ac14343.333333
fastEight16650.303030
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" - ], - "text/plain": [ - " count %\n", - "Name 0 0.000000\n", - "total_chairs 0 0.000000\n", - "double 0 0.000000\n", - "triple 0 0.000000\n", - "quad 0 0.000000\n", - "fastQuads 0 0.000000\n", - "fastSixes 0 0.000000\n", - "surface 0 0.000000\n", - "trams 0 0.000000\n", - "base_elev 0 0.000000\n", - "vertical_drop 0 0.000000\n", - "summit_elev 0 0.000000\n", - "state 0 0.000000\n", - "Region 0 0.000000\n", - "yearsOpen 1 0.303030\n", - "SkiableTerrain_ac 3 0.909091\n", - "Runs 4 1.212121\n", - "LongestRun_mi 5 1.515152\n", - "averageSnowfall 14 4.242424\n", - "Snow Making_ac 46 13.939394\n", - "projectedDaysOpen 47 14.242424\n", - "TerrainParks 51 15.454545\n", - "daysOpenLastYear 51 15.454545\n", - "AdultWeekend 51 15.454545\n", - "AdultWeekday 54 16.363636\n", - "NightSkiing_ac 143 43.333333\n", - "fastEight 166 50.303030" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 5#\n", - "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", - "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", - "#Order them (increasing or decreasing) using sort_values\n", - "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", - "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n", - "missing.columns=['count', '%']\n", - "missing.sort_values(by='count')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.3 Categorical Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameRegionstate
0Alyeska ResortAlaskaAlaska
1Eaglecrest Ski AreaAlaskaAlaska
2Hilltop Ski AreaAlaskaAlaska
3Arizona SnowbowlArizonaArizona
4Sunrise Park ResortArizonaArizona
............
325Meadowlark Ski LodgeWyomingWyoming
326Sleeping Giant Ski ResortWyomingWyoming
327Snow King ResortWyomingWyoming
328Snowy Range Ski & Recreation AreaWyomingWyoming
329White Pine Ski AreaWyomingWyoming
\n", - "

330 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " Name Region state\n", - "0 Alyeska Resort Alaska Alaska\n", - "1 Eaglecrest Ski Area Alaska Alaska\n", - "2 Hilltop Ski Area Alaska Alaska\n", - "3 Arizona Snowbowl Arizona Arizona\n", - "4 Sunrise Park Resort Arizona Arizona\n", - ".. ... ... ...\n", - "325 Meadowlark Ski Lodge Wyoming Wyoming\n", - "326 Sleeping Giant Ski Resort Wyoming Wyoming\n", - "327 Snow King Resort Wyoming Wyoming\n", - "328 Snowy Range Ski & Recreation Area Wyoming Wyoming\n", - "329 White Pine Ski Area Wyoming Wyoming\n", - "\n", - "[330 rows x 3 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 6#\n", - "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", - "ski_data.select_dtypes('object')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", - "\n", - "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", - "* Is `Region` always the same as `state`?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.1 Unique Resort Names" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Crystal Mountain 2\n", - "Alyeska Resort 1\n", - "Brandywine 1\n", - "Boston Mills 1\n", - "Alpine Valley 1\n", - "Name: Name, dtype: int64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 7#\n", - "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", - "ski_data['Name'].value_counts().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have a duplicated resort name: Crystal Mountain." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Alyeska Resort, Alaska 1\n", - "Snow Trails, Ohio 1\n", - "Brandywine, Ohio 1\n", - "Boston Mills, Ohio 1\n", - "Alpine Valley, Ohio 1\n", - "dtype: int64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 8#\n", - "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", - "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Alyeska Resort, Alaska 1\n", - "Snow Trails, Ohio 1\n", - "Brandywine, Ohio 1\n", - "Boston Mills, Ohio 1\n", - "Alpine Valley, Ohio 1\n", - "dtype: int64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 9#\n", - "#Concatenate 'Name' and 'state' and count the values again (as above)\n", - "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A: 1** Your answer here" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
104Crystal MountainMichiganMichigan113237575700.001...0.3102.096.0120.063.0132.054.064.0135.056.0
295Crystal MountainWashingtonWashington7012310044001NaN22...2.52600.010.0NaN57.0486.099.099.0NaNNaN
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2 rows × 27 columns

\n", - "
" - ], - "text/plain": [ - " Name Region state summit_elev vertical_drop \\\n", - "104 Crystal Mountain Michigan Michigan 1132 375 \n", - "295 Crystal Mountain Washington Washington 7012 3100 \n", - "\n", - " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", - "104 757 0 0.0 0 1 ... 0.3 \n", - "295 4400 1 NaN 2 2 ... 2.5 \n", - "\n", - " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", - "104 102.0 96.0 120.0 63.0 \n", - "295 2600.0 10.0 NaN 57.0 \n", - "\n", - " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", - "104 132.0 54.0 64.0 135.0 \n", - "295 486.0 99.0 99.0 NaN \n", - "\n", - " NightSkiing_ac \n", - "104 56.0 \n", - "295 NaN \n", - "\n", - "[2 rows x 27 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data[ski_data['Name'] == 'Crystal Mountain']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.2 Region And State" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What's the relationship between region and state?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "33" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 10#\n", - "#Calculate the number of times Region does not equal state\n", - "(ski_data.Region != ski_data.state).sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "New York 33\n", - "Michigan 29\n", - "Sierra Nevada 22\n", - "Colorado 22\n", - "Pennsylvania 19\n", - "Wisconsin 16\n", - "New Hampshire 16\n", - "Vermont 15\n", - "Minnesota 14\n", - "Idaho 12\n", - "Montana 12\n", - "Massachusetts 11\n", - "Washington 10\n", - "New Mexico 9\n", - "Maine 9\n", - "Wyoming 8\n", - "Utah 7\n", - "Salt Lake City 6\n", - "North Carolina 6\n", - "Oregon 6\n", - "Connecticut 5\n", - "Ohio 5\n", - "Virginia 4\n", - "West Virginia 4\n", - "Illinois 4\n", - "Mt. Hood 4\n", - "Alaska 3\n", - "Iowa 3\n", - "South Dakota 2\n", - "Arizona 2\n", - "Nevada 2\n", - "Missouri 2\n", - "Indiana 2\n", - "New Jersey 2\n", - "Rhode Island 1\n", - "Tennessee 1\n", - "Maryland 1\n", - "Northern California 1\n", - "Name: Region, dtype: int64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data['Region'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "state Region \n", - "California Sierra Nevada 20\n", - " Northern California 1\n", - "Nevada Sierra Nevada 2\n", - "Oregon Mt. Hood 4\n", - "Utah Salt Lake City 6\n", - "Name: Region, dtype: int64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 11#\n", - "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n", - "#group that by 'state' and perform `value_counts` on the 'Region'\n", - "(ski_data[ski_data.Region != ski_data.state]\n", - " .groupby('state')['Region']\n", - " .value_counts())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.3 Number of distinct regions and states" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Region 38\n", - "state 35\n", - "dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 12#\n", - "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n", - "#the number of unique values in each\n", - "ski_data[['Region', 'state']].nunique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because a few states are split across multiple named regions, there are slightly more unique regions than states." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.4 Distribution Of Resorts By Region And State" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Code task 13#\n", - "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n", - "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,8))\n", - "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n", - "#Give the plot a helpful title of 'Region'\n", - "ax[0].set_title('Region')\n", - "#Label the xaxis 'Count'\n", - "ax[0].set_xlabel('Count')\n", - "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n", - "#Give the plot a helpful title of 'state'\n", - "ax[1].set_title('State')\n", - "#Label the xaxis 'Count'\n", - "ax[1].set_xlabel('Count')\n", - "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n", - "plt.subplots_adjust(wspace=0.5);\n", - "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n", - "# as the importance of easy-to-read and informative figures is frequently understated\n", - "# and you will find the ability to tweak figures invaluable later on" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.5 Distribution Of Ticket Price By State" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.3.5.1 Average weekend and weekday price by state" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AdultWeekdayAdultWeekend
state
Illinois35.00000043.333333
Iowa35.66666741.666667
Tennessee36.00000065.000000
Massachusetts40.90000057.200000
North Carolina41.83333364.166667
\n", - "
" - ], - "text/plain": [ - " AdultWeekday AdultWeekend\n", - "state \n", - "Illinois 35.000000 43.333333\n", - "Iowa 35.666667 41.666667\n", - "Tennessee 36.000000 65.000000\n", - "Massachusetts 40.900000 57.200000\n", - "North Carolina 41.833333 64.166667" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 14#\n", - "# Calculate average weekday and weekend price by state and sort by the average of the two\n", - "# Hint: use the pattern dataframe.groupby()[].mean()\n", - "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean().sort_values(by='AdultWeekday')\n", - "state_price_means.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n", - "# Compare the index order you get from\n", - "# state_price_means.index\n", - "# with\n", - "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n", - "# See how this expression simply sits within the reindex()\n", - "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n", - " .sort_values(ascending=False)\n", - " .index)\n", - " .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n", - "plt.xlabel('Price ($)');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.3.5.2 Distribution of weekday and weekend price by state" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 15#\n", - "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n", - "#specify 'state' for `id_vars`\n", - "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", - "#call the resultant price column 'Price' via the `value_name` argument,\n", - "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", - "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], \n", - " id_vars='state', \n", - " var_name='Ticket', \n", - " value_vars=['AdultWeekday', 'AdultWeekend'], \n", - " value_name='Price')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stateTicketPrice
0AlaskaAdultWeekday65.0
1AlaskaAdultWeekday47.0
2AlaskaAdultWeekday30.0
3ArizonaAdultWeekday89.0
4ArizonaAdultWeekday74.0
\n", - "
" - ], - "text/plain": [ - " state Ticket Price\n", - "0 Alaska AdultWeekday 65.0\n", - "1 Alaska AdultWeekday 47.0\n", - "2 Alaska AdultWeekday 30.0\n", - "3 Arizona AdultWeekday 89.0\n", - "4 Arizona AdultWeekday 74.0" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ticket_prices.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Code task 16#\n", - "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", - "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", - "#This will use boxplot's x, y, hue, and data arguments.\n", - "plt.subplots(figsize=(12, 8))\n", - "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n", - "plt.xticks(rotation='vertical')\n", - "plt.ylabel('Price ($)')\n", - "plt.xlabel('State');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", - "\n", - "* disregard State completely\n", - "* retain all State information\n", - "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", - "\n", - "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus we currently have two main questions you want to resolve:\n", - "\n", - "* What do you do about the two types of ticket price?\n", - "* What do you do about the state information?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.4 Numeric Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.4.1 Numeric data summary" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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summit_elev330.04591.8181823735.535934315.01403.753127.57806.0013487.0
vertical_drop330.01215.427273947.86455760.0461.25964.51800.004425.0
base_elev330.03374.0000003117.12162170.0869.001561.56325.2510800.0
trams330.00.1727270.5599460.00.000.00.004.0
fastEight164.00.0060980.0780870.00.000.00.001.0
fastSixes330.00.1848480.6516850.00.000.00.006.0
fastQuads330.01.0181822.1982940.00.000.01.0015.0
quad330.00.9333331.3122450.00.000.01.008.0
triple330.01.5000001.6191300.00.001.02.008.0
double330.01.8333331.8150280.01.001.03.0014.0
surface330.02.6212122.0596360.01.002.03.0015.0
total_chairs330.08.2666675.7986830.05.007.010.0041.0
Runs326.048.21472446.3640773.019.0033.060.00341.0
TerrainParks279.02.8207892.0081131.01.002.04.0014.0
LongestRun_mi325.01.4332311.1561710.00.501.02.006.0
SkiableTerrain_ac327.0739.8012231816.1674418.085.00200.0690.0026819.0
Snow Making_ac284.0174.873239261.3361252.050.00100.0200.503379.0
daysOpenLastYear279.0115.10394335.0632513.097.00114.0135.00305.0
yearsOpen329.063.656535109.4299286.050.0058.069.002019.0
averageSnowfall316.0185.316456136.35684218.069.00150.0300.00669.0
AdultWeekday276.057.91695726.14012615.040.0050.071.00179.0
AdultWeekend279.064.16681024.55458417.047.0060.077.50179.0
projectedDaysOpen283.0120.05300431.04596330.0100.00120.0139.50305.0
NightSkiing_ac187.0100.395722105.1696202.040.0072.0114.00650.0
\n", - "
" - ], - "text/plain": [ - " count mean std min 25% 50% \\\n", - "summit_elev 330.0 4591.818182 3735.535934 315.0 1403.75 3127.5 \n", - "vertical_drop 330.0 1215.427273 947.864557 60.0 461.25 964.5 \n", - "base_elev 330.0 3374.000000 3117.121621 70.0 869.00 1561.5 \n", - "trams 330.0 0.172727 0.559946 0.0 0.00 0.0 \n", - "fastEight 164.0 0.006098 0.078087 0.0 0.00 0.0 \n", - "fastSixes 330.0 0.184848 0.651685 0.0 0.00 0.0 \n", - "fastQuads 330.0 1.018182 2.198294 0.0 0.00 0.0 \n", - "quad 330.0 0.933333 1.312245 0.0 0.00 0.0 \n", - "triple 330.0 1.500000 1.619130 0.0 0.00 1.0 \n", - "double 330.0 1.833333 1.815028 0.0 1.00 1.0 \n", - "surface 330.0 2.621212 2.059636 0.0 1.00 2.0 \n", - "total_chairs 330.0 8.266667 5.798683 0.0 5.00 7.0 \n", - "Runs 326.0 48.214724 46.364077 3.0 19.00 33.0 \n", - "TerrainParks 279.0 2.820789 2.008113 1.0 1.00 2.0 \n", - "LongestRun_mi 325.0 1.433231 1.156171 0.0 0.50 1.0 \n", - "SkiableTerrain_ac 327.0 739.801223 1816.167441 8.0 85.00 200.0 \n", - "Snow Making_ac 284.0 174.873239 261.336125 2.0 50.00 100.0 \n", - "daysOpenLastYear 279.0 115.103943 35.063251 3.0 97.00 114.0 \n", - "yearsOpen 329.0 63.656535 109.429928 6.0 50.00 58.0 \n", - "averageSnowfall 316.0 185.316456 136.356842 18.0 69.00 150.0 \n", - "AdultWeekday 276.0 57.916957 26.140126 15.0 40.00 50.0 \n", - "AdultWeekend 279.0 64.166810 24.554584 17.0 47.00 60.0 \n", - "projectedDaysOpen 283.0 120.053004 31.045963 30.0 100.00 120.0 \n", - "NightSkiing_ac 187.0 100.395722 105.169620 2.0 40.00 72.0 \n", - "\n", - " 75% max \n", - "summit_elev 7806.00 13487.0 \n", - "vertical_drop 1800.00 4425.0 \n", - "base_elev 6325.25 10800.0 \n", - "trams 0.00 4.0 \n", - "fastEight 0.00 1.0 \n", - "fastSixes 0.00 6.0 \n", - "fastQuads 1.00 15.0 \n", - "quad 1.00 8.0 \n", - "triple 2.00 8.0 \n", - "double 3.00 14.0 \n", - "surface 3.00 15.0 \n", - "total_chairs 10.00 41.0 \n", - "Runs 60.00 341.0 \n", - "TerrainParks 4.00 14.0 \n", - "LongestRun_mi 2.00 6.0 \n", - "SkiableTerrain_ac 690.00 26819.0 \n", - "Snow Making_ac 200.50 3379.0 \n", - "daysOpenLastYear 135.00 305.0 \n", - "yearsOpen 69.00 2019.0 \n", - "averageSnowfall 300.00 669.0 \n", - "AdultWeekday 71.00 179.0 \n", - "AdultWeekend 77.50 179.0 \n", - "projectedDaysOpen 139.50 305.0 \n", - "NightSkiing_ac 114.00 650.0 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 17#\n", - "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", - "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", - "#will be useful again\n", - "ski_data.describe().T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 82.424242\n", - "2 14.242424\n", - "1 3.333333\n", - "dtype: float64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", - "missing_price.value_counts()/len(missing_price) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.4.2 Distributions Of Feature Values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Code task 18#\n", - "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", - "#Try passing it an argument figsize=(15,10)\n", - "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", - "#It's important you create legible and easy-to-read plots\n", - "ski_data.hist(figsize=(15,10))\n", - "plt.subplots_adjust(hspace=0.5);\n", - "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What features do we have possible cause for concern about and why?\n", - "\n", - "* SkiableTerrain_ac because values are clustered down the low end,\n", - "* Snow Making_ac for the same reason,\n", - "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", - "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", - "* trams also may get an amber flag for the same reason,\n", - "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.1 SkiableTerrain_ac" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "39 26819.0\n", - "Name: SkiableTerrain_ac, dtype: float64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 19#\n", - "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", - "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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39
NameSilverton Mountain
RegionColorado
stateColorado
summit_elev13487
vertical_drop3087
base_elev10400
trams0
fastEight0.0
fastSixes0
fastQuads0
quad0
triple0
double1
surface0
total_chairs1
RunsNaN
TerrainParksNaN
LongestRun_mi1.5
SkiableTerrain_ac26819.0
Snow Making_acNaN
daysOpenLastYear175.0
yearsOpen17.0
averageSnowfall400.0
AdultWeekday79.0
AdultWeekend79.0
projectedDaysOpen181.0
NightSkiing_acNaN
\n", - "
" - ], - "text/plain": [ - " 39\n", - "Name Silverton Mountain\n", - "Region Colorado\n", - "state Colorado\n", - "summit_elev 13487\n", - "vertical_drop 3087\n", - "base_elev 10400\n", - "trams 0\n", - "fastEight 0.0\n", - "fastSixes 0\n", - "fastQuads 0\n", - "quad 0\n", - "triple 0\n", - "double 1\n", - "surface 0\n", - "total_chairs 1\n", - "Runs NaN\n", - "TerrainParks NaN\n", - "LongestRun_mi 1.5\n", - "SkiableTerrain_ac 26819.0\n", - "Snow Making_ac NaN\n", - "daysOpenLastYear 175.0\n", - "yearsOpen 17.0\n", - "averageSnowfall 400.0\n", - "AdultWeekday 79.0\n", - "AdultWeekend 79.0\n", - "projectedDaysOpen 181.0\n", - "NightSkiing_ac NaN" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 20#\n", - "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n", - "#Hint: don't forget the transpose will be helpful here\n", - "ski_data[ski_data.SkiableTerrain_ac > 10000].T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A: 2** Your answer here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But what can you do when you have one record that seems highly suspicious?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Silverton Mountain information](images/silverton_mountain_info.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can spot check data. You see your top and base elevation values agree, but the skiable area is very different. Your suspect value is 26819, but the value you've just looked up is 1819. The last three digits agree. This sort of error could have occured in transmission or some editing or transcription stage. You could plausibly replace the suspect value with the one you've just obtained. Another cautionary note to make here is that although you're doing this in order to progress with your analysis, this is most definitely an issue that should have been raised and fed back to the client or data originator as a query. You should view this \"data correction\" step as a means to continue (documenting it carefully as you do in this notebook) rather than an ultimate decision as to what is correct." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "26819.0" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 21#\n", - "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n", - "ski_data.loc[39, 'SkiableTerrain_ac']" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 22#\n", - "#Use the .loc accessor again to modify this value with the correct value of 1819\n", - "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1819.0" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 23#\n", - "#Use the .loc accessor a final time to verify that the value has been modified\n", - "ski_data.loc[39, 'SkiableTerrain_ac']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**NB whilst you may become suspicious about your data quality, and you know you have missing values, you will not here dive down the rabbit hole of checking all values or web scraping to replace missing values.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What does the distribution of skiable area look like now?" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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gD5tOurbTXsBp8k3KzMxGnLYVpohYHBG35+cvkq4UPZF0aYpz8mjnkO4NQ26fFREvR8R80uU5dmlXfGZmVk5D8huTpCnAm0kXJuyKdItp8t/X5dEmsvLl5BfR/K0hzMxsNdH2s/LylbkvJd2p8oV0kezao9Zoe81//+brn80A6OrqolKptBRXb29vy9OOBM5PY85PY85PY85PY20tTPmy65cC50fEZbl5iaQJEbE4X9fqydy+iJXvnVLzni75+mczAbq7u6PVUy47fbpm2Tk/jTk/jTk/jTk/jbXzrDyRLss/LyJOKQy6inRve/LfKwvt0/O9WLYgXYDx1nbFZ2Zm5dTOPabdSPdmuUvS3Nx2LHAScLGkQ4FHgY8CRMQ9ki4m3YdmGfC5fC8YMzMbQdpWmCLiJurfhnf3Wo0RcSJwYrtiqjbl6J82Nd6Ck/ZtcyRmZtbHV34wM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NScWEyM7NSaVthknSWpCcl3V1ou0jS3PxYIGlubp8i6U+FYWe0Ky4zMyu30W3s+2zgB8C5fQ0R8fG+55JOBpYWxn8oInZsYzxmZjYMtK0wRcSNkqbUGiZJwMeA97Rr/mZmNjwpItrXeSpM10TE9lXt7wJOiYjuwnj3AA8ALwDHR8Sv6/Q5A5gB0NXVtdOsWbNaiq23t5f5S5c3Ne4OEzdoaR7DWW9vL2PHju10GKXl/DTm/DTW6fxMmzZtTt/nbxm181BeIwcAFxZeLwY2j4hnJO0EXCFpu4h4oXrCiJgJzATo7u6Onp6elgKoVCqcfNNLTY274MDW5jGcVSoVWs3tSOD8NOb8NOb8NDbkZ+VJGg18GLiory0iXo6IZ/LzOcBDwFZDHZuZmXVeJ04X3wO4LyIW9TVI2kTSqPx8S2Aq8HAHYjMzsw5r5+niFwI3A1tLWiTp0DxoOisfxgN4F3CnpDuAS4DDI+LZdsVmZmbl1c6z8g6o035IjbZLgUvbFYuZmQ0fvvKDmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmViguTmZmVSjtvrX6WpCcl3V1oO0HSY5Lm5sc+hWHHSHpQ0v2S9mxXXGZmVm7t3GM6G9irRvupEbFjfvwMQNK2wHRguzzNaZJGtTE2MzMrqbYVpoi4EXi2ydH3B2ZFxMsRMR94ENilXbGZmVl5je7APD8v6ZPAbODIiHgOmAjcUhhnUW57DUkzgBkAXV1dVCqVloLo7e3lyB2WNzVuq/MYznp7e0fkcjfL+WnM+WnM+WlsqAvT6cC3gch/TwY+BajGuFGrg4iYCcwE6O7ujp6enpYCqVQqnHzTS02Nu+DA1uYxnFUqFVrN7Ujg/DTm/DTm/DQ2pGflRcSSiFgeEX8FfsiKw3WLgEmFUTcDHh/K2MzMrByGtDBJmlB4+SGg74y9q4DpktaWtAUwFbh1KGMzM7NyaNuhPEkXAj3AeEmLgG8APZJ2JB2mWwAcBhAR90i6GLgXWAZ8LiKa+wHIzMxWK20rTBFxQI3mMxuMfyJwYrviMTOz4cFXfjAzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JxYTIzs1JpW2GSdJakJyXdXWj7rqT7JN0p6XJJG+b2KZL+JGlufpzRrrjMzKzc2rnHdDawV1XbdcD2EfFG4AHgmMKwhyJix/w4vI1xmZlZibWtMEXEjcCzVW3XRsSy/PIWYLN2zd/MzIYnRUT7OpemANdExPY1hl0NXBQR5+Xx7iHtRb0AHB8Rv67T5wxgBkBXV9dOs2bNaim23t5e5i9d3tS4O0zcoKV5DGe9vb2MHTu202GUlvPTmPPTWKfzM23atDkR0d2xAPoxuhMzlXQcsAw4PzctBjaPiGck7QRcIWm7iHihetqImAnMBOju7o6enp6WYqhUKpx800tNjbvgwNbmMZxVKhVaze1I4Pw05vw05vw0NuRn5Uk6GNgPODDy7lpEvBwRz+Tnc4CHgK2GOjYzM+u8IS1MkvYCvgZ8ICL+WGjfRNKo/HxLYCrw8FDGZmZm5dC2Q3mSLgR6gPGSFgHfIJ2FtzZwnSSAW/IZeO8CviVpGbAcODwinq3ZsZmZrdbaVpgi4oAazWfWGfdS4NJ2xWJmZsOHr/xgZmal4sJkZmal4sJkZmal4sJkZmal4sJkZmal0lRhkrRbM21mZmarqtk9pv9sss3MzGyVNPw/JklvA94ObCLpy4VB6wOj2hmYmZmNTP39g+1awNg83rhC+wvAR9oVlJmZjVwNC1NE/Ar4laSzI+KRIYrJzMxGsGYvSbS2pJnAlOI0EfGedgRlZmYjV7OF6SfAGcCPSBdZNTMza4tmC9OyiDi9rZGYmZnR/OniV0v6rKQJkjbue7Q1MjMzG5Ga3WM6OP/9SqEtgC0HNxwzMxvpmipMEbFFuwMxMzODJguTpE/Wao+Icwc3HDMzG+ma/Y1p58LjncAJwAcaTSDpLElPSrq70LaxpOsk/SH/3agw7BhJD0q6X9KeA14SMzNbLTRVmCLiHwuPzwBvJl0VopGzgb2q2o4Gro+IqcD1+TWStgWmA9vlaU6T5EsemZmNQK3e9uKPwNRGI0TEjcCzVc37A+fk5+cAHyy0z4qIlyNiPvAgsEuLsZmZ2TDW7G9MV5POwoN08dZtgItbmF9XRCwGiIjFkl6X2ycCtxTGW5TbasUyA5gB0NXVRaVSaSEM6O3t5cgdmvtf4VbnMZz19vaOyOVulvPTmPPTmPPTWLOni3+v8HwZ8EhELBrEOFSjLWq0EREzgZkA3d3d0dPT09IMK5UKJ9/0UlPjLjiwtXkMZ5VKhVZzOxI4P405P405P401+xvTr4D7SFcY3wh4pcX5LZE0ASD/fTK3LwImFcbbDHi8xXmYmdkw1uwdbD8G3Ap8FPgY8DtJrdz24ipW/LPuwcCVhfbpktaWtAXp96tbW+jfzMyGuWYP5R0H7BwRTwJI2gT4JXBJvQkkXQj0AOMlLQK+AZwEXCzpUOBRUqEjIu6RdDFwL+lQ4eciwheLNTMbgZotTGv0FaXsGfrZ24qIA+oM2r3O+CcCJzYZj5mZraaaLUw/l/QL4ML8+uPAz9oTkpmZjWQNC5Ok15NO8f6KpA8D7yCdQXczcP4QxGdmZiNMfyc/fB94ESAiLouIL0fEl0h7S99vb2hmZjYS9VeYpkTEndWNETGbdJt1MzOzQdVfYVqnwbB1BzMQMzMz6L8w3SbpM9WN+XTvOe0JyczMRrL+zso7Arhc0oGsKETdpCuLf6iNcZmZ2QjVsDBFxBLg7ZKmAdvn5p9GxP+1PTIzMxuRmr21+g3ADW2OxczMrOX7MZmZmbWFC5OZmZWKC5OZmZWKC5OZmZWKC5OZmZWKC5OZmZWKC5OZmZWKC5OZmZVKszcKHDSStgYuKjRtCXwd2BD4DPBUbj82InwzQjOzEWbIC1NE3A/sCCBpFPAYcDnw98CpEfG9oY7JzMzKo9OH8nYHHoqIRzoch5mZlUSnC9N04MLC689LulPSWZI26lRQZmbWOYqIzsxYWgt4HNguIpZI6gKeBgL4NjAhIj5VY7oZwAyArq6unWbNmtXS/Ht7e5m/dHlT4+4wcYOW5jGc9fb2Mnbs2E6HUVrOT2POT2Odzs+0adPmRER3xwLoRycL0/7A5yLifTWGTQGuiYjtXzNhQXd3d8yePbul+VcqFQ75+UtNjbvgpH1bmsdwVqlU6Onp6XQYpeX8NOb8NNbp/EgqdWHq5KG8AygcxpM0oTDsQ8DdQx6RmZl13JCflQcgaQzwXuCwQvN3JO1IOpS3oGqYmZmNEB0pTBHxR+BvqtoO6kQsZmZWLp0+K8/MzGwlLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqLkxmZlYqHbm1uqQFwIvAcmBZRHRL2hi4CJgCLAA+FhHPdSI+MzPrnE7uMU2LiB0joju/Phq4PiKmAtfn12ZmNsKU6VDe/sA5+fk5wAc7F4qZmXWKImLoZyrNB54DAvjviJgp6fmI2LAwznMRsVGNaWcAMwC6urp2mjVrVksx9Pb2Mn/p8qbG3WHiBi3NYzjr7e1l7NixnQ6jtJyfxpyfxjqdn2nTps0pHK0qnY78xgTsFhGPS3odcJ2k+5qdMCJmAjMBuru7o6enp6UAKpUKJ9/0UlPjLjiwtXkMZ5VKhVZzOxI4P405P405P4115FBeRDye/z4JXA7sAiyRNAEg/32yE7GZmVlnDXlhkrSepHF9z4H3AXcDVwEH59EOBq4c6tjMzKzzOnEorwu4XFLf/C+IiJ9Lug24WNKhwKPARzsQm5mZddiQF6aIeBh4U432Z4DdhzoeMzMrlzKdLm5mZubCZGZm5eLCZGZmpeLCZGZmpeLCZGZmpeLCZGZmpeLCZGZmpeLCZGZmpeLCZGZmpeLCZGZmpeLCZGZmpdKp+zENK1OO/mlT4y04ad82R2JmtvrzHpOZmZWKC5OZmZWKC5OZmZWKC5OZmZWKC5OZmZXKkBcmSZMk3SBpnqR7JH0xt58g6TFJc/Njn6GOzczMOq8Tp4svA46MiNsljQPmSLouDzs1Ir7XgZjMzKwkhrwwRcRiYHF+/qKkecDEoY7DzMzKSRHRuZlLU4Abge2BLwO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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ski_data.SkiableTerrain_ac.hist(bins=30)\n", - "plt.xlabel('SkiableTerrain_ac')\n", - "plt.ylabel('Count')\n", - "plt.title('Distribution of skiable area (acres) after replacing erroneous value');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You now see a rather long tailed distribution. You may wonder about the now most extreme value that is above 8000, but similarly you may also wonder about the value around 7000. If you wanted to spend more time manually checking values you could, but leave this for now. The above distribution is plausible." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.2 Snow Making_ac" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11 3379.0\n", - "18 1500.0\n", - "Name: Snow Making_ac, dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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11
NameHeavenly Mountain Resort
RegionSierra Nevada
stateCalifornia
summit_elev10067
vertical_drop3500
base_elev7170
trams2
fastEight0.0
fastSixes2
fastQuads7
quad1
triple5
double3
surface8
total_chairs28
Runs97.0
TerrainParks3.0
LongestRun_mi5.5
SkiableTerrain_ac4800.0
Snow Making_ac3379.0
daysOpenLastYear155.0
yearsOpen64.0
averageSnowfall360.0
AdultWeekdayNaN
AdultWeekendNaN
projectedDaysOpen157.0
NightSkiing_acNaN
\n", - "
" - ], - "text/plain": [ - " 11\n", - "Name Heavenly Mountain Resort\n", - "Region Sierra Nevada\n", - "state California\n", - "summit_elev 10067\n", - "vertical_drop 3500\n", - "base_elev 7170\n", - "trams 2\n", - "fastEight 0.0\n", - "fastSixes 2\n", - "fastQuads 7\n", - "quad 1\n", - "triple 5\n", - "double 3\n", - "surface 8\n", - "total_chairs 28\n", - "Runs 97.0\n", - "TerrainParks 3.0\n", - "LongestRun_mi 5.5\n", - "SkiableTerrain_ac 4800.0\n", - "Snow Making_ac 3379.0\n", - "daysOpenLastYear 155.0\n", - "yearsOpen 64.0\n", - "averageSnowfall 360.0\n", - "AdultWeekday NaN\n", - "AdultWeekend NaN\n", - "projectedDaysOpen 157.0\n", - "NightSkiing_ac NaN" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data[ski_data['Snow Making_ac'] > 3000].T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What, then, is your rough guess for the area covered by snowmaking?" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2880.0" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - ".6 * 4800" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.3 fastEight" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Look at the different fastEight values more closely:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0 163\n", - "1.0 1\n", - "Name: fastEight, dtype: int64" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data.fastEight.value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 24#\n", - "#Drop the 'fastEight' column from ski_data. Use inplace=True\n", - "ski_data.drop(columns='fastEight', inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34 104.0\n", - "115 2019.0\n", - "Name: yearsOpen, dtype: float64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 25#\n", - "#Filter the 'yearsOpen' column for values greater than 100\n", - "ski_data.yearsOpen[ski_data.yearsOpen > 100]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Code task 26#\n", - "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n", - "#Pass the argument bins=30 to hist(), but feel free to explore other values\n", - "ski_data.yearsOpen[ski_data.yearsOpen < 2000].hist(bins=30)\n", - "plt.xlabel('Years open')\n", - "plt.ylabel('Count')\n", - "plt.title('Distribution of years open excluding 2019');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above distribution of years seems entirely plausible, including the 104 year value. You can certainly state that no resort will have been open for 2019 years! It likely means the resort opened in 2019. It could also mean the resort is due to open in 2019. You don't know when these data were gathered!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's review the summary statistics for the years under 1000." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 328.000000\n", - "mean 57.695122\n", - "std 16.841182\n", - "min 6.000000\n", - "25% 50.000000\n", - "50% 58.000000\n", - "75% 68.250000\n", - "max 104.000000\n", - "Name: yearsOpen, dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The smallest number of years open otherwise is 6. You can't be sure whether this resort in question has been open zero years or one year and even whether the numbers are projections or actual. In any case, you would be adding a new youngest resort so it feels best to simply drop this row." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "ski_data = ski_data[ski_data.yearsOpen < 1000]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.4 fastSixes and Trams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The other features you had mild concern over, you will not investigate further. Perhaps take some care when using these features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.7 Derive State-wide Summary Statistics For Our Market Segment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have, by this point removed one row, but it was for a resort that may not have opened yet, or perhaps in its first season. Using your business knowledge, you know that state-wide supply and demand of certain skiing resources may well factor into pricing strategies. Does a resort dominate the available night skiing in a state? Or does it account for a large proportion of the total skiable terrain or days open?\n", - "\n", - "If you want to add any features to your data that captures the state-wide market size, you should do this now, before dropping any more rows. In the next section, you'll drop rows with missing price information. Although you don't know what those resorts charge for their tickets, you do know the resorts exists and have been open for at least six years. Thus, you'll now calculate some state-wide summary statistics for later use." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many features in your data pertain to chairlifts, that is for getting people around each resort. These aren't relevant, nor are the features relating to altitudes. Features that you may be interested in are:\n", - "\n", - "* TerrainParks\n", - "* SkiableTerrain_ac\n", - "* daysOpenLastYear\n", - "* NightSkiing_ac\n", - "\n", - "When you think about it, these are features it makes sense to sum: the total number of terrain parks, the total skiable area, the total number of days open, and the total area available for night skiing. You might consider the total number of ski runs, but understand that the skiable area is more informative than just a number of runs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A fairly new groupby behaviour is [named aggregation](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html). This allows us to clearly perform the aggregations you want whilst also creating informative output column names." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_ac
0Alaska32280.0345.04.0580.0
1Arizona21577.0237.06.080.0
2California2125948.02738.081.0587.0
3Colorado2243682.03258.074.0428.0
4Connecticut5358.0353.010.0256.0
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" - ], - "text/plain": [ - " state resorts_per_state state_total_skiable_area_ac \\\n", - "0 Alaska 3 2280.0 \n", - "1 Arizona 2 1577.0 \n", - "2 California 21 25948.0 \n", - "3 Colorado 22 43682.0 \n", - "4 Connecticut 5 358.0 \n", - "\n", - " state_total_days_open state_total_terrain_parks \\\n", - "0 345.0 4.0 \n", - "1 237.0 6.0 \n", - "2 2738.0 81.0 \n", - "3 3258.0 74.0 \n", - "4 353.0 10.0 \n", - "\n", - " state_total_nightskiing_ac \n", - "0 580.0 \n", - "1 80.0 \n", - "2 587.0 \n", - "3 428.0 \n", - "4 256.0 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 27#\n", - "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n", - "#call them 'state_total_days_open', 'state_total_terrain_parks', and 'state_total_nightskiing_ac',\n", - "#respectively\n", - "#Finally, add a call to the reset_index() method (we recommend you experiment with and without this to see\n", - "#what it does)\n", - "state_summary = ski_data.groupby('state').agg(\n", - " resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n", - " state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n", - " state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n", - " state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n", - " state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n", - ").reset_index()\n", - "state_summary.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.8 Drop Rows With No Price Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 82.317073\n", - "2 14.329268\n", - "1 3.353659\n", - "dtype: float64" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", - "missing_price.value_counts()/len(missing_price) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 28#\n", - "#Use `missing_price` to remove rows from ski_data where both price values are missing\n", - "ski_data = ski_data[missing_price != 2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.9 Review distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ski_data.hist(figsize=(15, 10))\n", - "plt.subplots_adjust(hspace=0.5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values. Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.10 Population data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 29#\n", - "#Use pandas' `read_html` method to read the table from the URL below\n", - "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n", - "usa_states = pd.read_html(states_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "list" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(usa_states)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(usa_states)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Name &postal abbs. [1]CitiesEstablished[A]Population[B][3]Total area[4]Land area[4]Water area[4]Numberof Reps.
Name &postal abbs. [1]Name &postal abbs. [1].1CapitalLargest[5]Established[A]Population[B][3]mi2km2mi2km2mi2km2Numberof Reps.
0AlabamaALMontgomeryBirminghamDec 14, 181949031855242013576750645131171177545977
1AlaskaAKJuneauAnchorageJan 3, 195973154566538417233375706411477953947432453841
2ArizonaAZPhoenixPhoenixFeb 14, 1912727871711399029523411359429420739610269
3ArkansasARLittle RockLittle RockJun 15, 183630178045317913773252035134771114329614
4CaliforniaCASacramentoLos AngelesSep 9, 18503951222316369542396715577940346679162050153
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" - ], - "text/plain": [ - " Name &postal abbs. [1] Cities \\\n", - " Name &postal abbs. [1] Name &postal abbs. [1].1 Capital Largest[5] \n", - "0 Alabama AL Montgomery Birmingham \n", - "1 Alaska AK Juneau Anchorage \n", - "2 Arizona AZ Phoenix Phoenix \n", - "3 Arkansas AR Little Rock Little Rock \n", - "4 California CA Sacramento Los Angeles \n", - "\n", - " Established[A] Population[B][3] Total area[4] Land area[4] \\\n", - " Established[A] Population[B][3] mi2 km2 mi2 \n", - "0 Dec 14, 1819 4903185 52420 135767 50645 \n", - "1 Jan 3, 1959 731545 665384 1723337 570641 \n", - "2 Feb 14, 1912 7278717 113990 295234 113594 \n", - "3 Jun 15, 1836 3017804 53179 137732 52035 \n", - "4 Sep 9, 1850 39512223 163695 423967 155779 \n", - "\n", - " Water area[4] Numberof Reps. \n", - " km2 mi2 km2 Numberof Reps. \n", - "0 131171 1775 4597 7 \n", - "1 1477953 94743 245384 1 \n", - "2 294207 396 1026 9 \n", - "3 134771 1143 2961 4 \n", - "4 403466 7916 20501 53 " - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "usa_states = usa_states[0]\n", - "usa_states.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 30#\n", - "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n", - "#It should be a column of dates\n", - "established = usa_states.iloc[:, 4]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Dec 14, 1819\n", - "1 Jan 3, 1959\n", - "2 Feb 14, 1912\n", - "3 Jun 15, 1836\n", - "4 Sep 9, 1850\n", - "5 Aug 1, 1876\n", - "6 Jan 9, 1788\n", - "7 Dec 7, 1787\n", - "8 Mar 3, 1845\n", - "9 Jan 2, 1788\n", - "10 Aug 21, 1959\n", - "11 Jul 3, 1890\n", - "12 Dec 3, 1818\n", - "13 Dec 11, 1816\n", - "14 Dec 28, 1846\n", - "15 Jan 29, 1861\n", - "16 Jun 1, 1792\n", - "17 Apr 30, 1812\n", - "18 Mar 15, 1820\n", - "19 Apr 28, 1788\n", - "20 Feb 6, 1788\n", - "21 Jan 26, 1837\n", - "22 May 11, 1858\n", - "23 Dec 10, 1817\n", - "24 Aug 10, 1821\n", - "25 Nov 8, 1889\n", - "26 Mar 1, 1867\n", - "27 Oct 31, 1864\n", - "28 Jun 21, 1788\n", - "29 Dec 18, 1787\n", - "30 Jan 6, 1912\n", - "31 Jul 26, 1788\n", - "32 Nov 21, 1789\n", - "33 Nov 2, 1889\n", - "34 Mar 1, 1803\n", - "35 Nov 16, 1907\n", - "36 Feb 14, 1859\n", - "37 Dec 12, 1787\n", - "38 May 29, 1790\n", - "39 May 23, 1788\n", - "40 Nov 2, 1889\n", - "41 Jun 1, 1796\n", - "42 Dec 29, 1845\n", - "43 Jan 4, 1896\n", - "44 Mar 4, 1791\n", - "45 Jun 25, 1788\n", - "46 Nov 11, 1889\n", - "47 Jun 20, 1863\n", - "48 May 29, 1848\n", - "49 Jul 10, 1890\n", - "Name: (Established[A], Established[A]), dtype: object" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "established" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extract the state name, population, and total area (square miles) columns." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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statestate_populationstate_area_sq_miles
0Alabama490318552420
1Alaska731545665384
2Arizona7278717113990
3Arkansas301780453179
4California39512223163695
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" - ], - "text/plain": [ - " state state_population state_area_sq_miles\n", - "0 Alabama 4903185 52420\n", - "1 Alaska 731545 665384\n", - "2 Arizona 7278717 113990\n", - "3 Arkansas 3017804 53179\n", - "4 California 39512223 163695" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 31#\n", - "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n", - "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n", - "#respectively.\n", - "usa_states_sub = usa_states.iloc[:, [0, 5, 6]].copy()\n", - "usa_states_sub.columns = ['state', 'state_population', 'state_area_sq_miles']\n", - "usa_states_sub.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do you have all the ski data states accounted for?" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 32#\n", - "#Find the states in `state_summary` that are not in `usa_states_sub`\n", - "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n", - "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", - "missing_states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "No?? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20 Massachusetts[C]\n", - "37 Pennsylvania[C]\n", - "38 Rhode Island[D]\n", - "45 Virginia[C]\n", - "47 West Virginia\n", - "Name: state, dtype: object" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Delete square brackets and their contents and try again:" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20 Massachusetts\n", - "37 Pennsylvania\n", - "38 Rhode Island\n", - "45 Virginia\n", - "47 West Virginia\n", - "Name: state, dtype: object" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 33#\n", - "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n", - "#with the empty string. Do this inplace, so you need to specify the arguments:\n", - "#to_replace='\\[.*\\]' #literal square bracket followed by anything or nothing followed by literal closing bracket\n", - "#value='' #empty string as replacement\n", - "#regex=True #we used a regex in our `to_replace` argument\n", - "#inplace=True #Do this \"in place\"\n", - "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n", - "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "set()" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 34#\n", - "#And now verify none of our states are missing by checking that there are no states in\n", - "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n", - "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", - "missing_states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acstate_populationstate_area_sq_miles
0Alaska32280.0345.04.0580.0731545665384
1Arizona21577.0237.06.080.07278717113990
2California2125948.02738.081.0587.039512223163695
3Colorado2243682.03258.074.0428.05758736104094
4Connecticut5358.0353.010.0256.035652785543
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" - ], - "text/plain": [ - " state resorts_per_state state_total_skiable_area_ac \\\n", - "0 Alaska 3 2280.0 \n", - "1 Arizona 2 1577.0 \n", - "2 California 21 25948.0 \n", - "3 Colorado 22 43682.0 \n", - "4 Connecticut 5 358.0 \n", - "\n", - " state_total_days_open state_total_terrain_parks \\\n", - "0 345.0 4.0 \n", - "1 237.0 6.0 \n", - "2 2738.0 81.0 \n", - "3 3258.0 74.0 \n", - "4 353.0 10.0 \n", - "\n", - " state_total_nightskiing_ac state_population state_area_sq_miles \n", - "0 580.0 731545 665384 \n", - "1 80.0 7278717 113990 \n", - "2 587.0 39512223 163695 \n", - "3 428.0 5758736 104094 \n", - "4 256.0 3565278 5543 " - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 35#\n", - "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n", - "#specify the arguments how='left' and on='state'\n", - "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n", - "state_summary.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.11 Target Feature" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Code task 36#\n", - "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n", - "#'AdultWeekend' on the y-axis\n", - "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " AdultWeekend AdultWeekday\n", - "141 42.0 42.0\n", - "142 63.0 63.0\n", - "143 49.0 49.0\n", - "144 48.0 48.0\n", - "145 46.0 46.0\n", - "146 39.0 39.0\n", - "147 50.0 50.0\n", - "148 67.0 67.0\n", - "149 47.0 47.0\n", - "150 39.0 39.0\n", - "151 81.0 81.0" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Code task 37#\n", - "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n", - "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Is there any reason to prefer weekend or weekday prices? Which is missing the least?" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AdultWeekend 4\n", - "AdultWeekday 7\n", - "dtype: int64" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "ski_data.drop(columns='AdultWeekday', inplace=True)\n", - "ski_data.dropna(subset=['AdultWeekend'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(277, 25)" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Perform a final quick check on the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.11.1 Number Of Missing Values By Row - Resort" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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39416.0
\n", - "
" - ], - "text/plain": [ - " count %\n", - "329 5 20.0\n", - "62 5 20.0\n", - "141 5 20.0\n", - "86 5 20.0\n", - "74 5 20.0\n", - "146 5 20.0\n", - "184 4 16.0\n", - "108 4 16.0\n", - "198 4 16.0\n", - "39 4 16.0" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n", - "missing.columns=['count', '%']\n", - "missing.sort_values(by='count', ascending=False).head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These seem possibly curiously quantized..." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0., 4., 8., 12., 16., 20.])" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missing['%'].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Yes, the percentage of missing values per row appear in multiples of 4." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0 107\n", - "4.0 94\n", - "8.0 45\n", - "12.0 15\n", - "16.0 10\n", - "20.0 6\n", - "Name: %, dtype: int64" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missing['%'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 277 entries, 0 to 329\n", - "Data columns (total 25 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Name 277 non-null object \n", - " 1 Region 277 non-null object \n", - " 2 state 277 non-null object \n", - " 3 summit_elev 277 non-null int64 \n", - " 4 vertical_drop 277 non-null int64 \n", - " 5 base_elev 277 non-null int64 \n", - " 6 trams 277 non-null int64 \n", - " 7 fastSixes 277 non-null int64 \n", - " 8 fastQuads 277 non-null int64 \n", - " 9 quad 277 non-null int64 \n", - " 10 triple 277 non-null int64 \n", - " 11 double 277 non-null int64 \n", - " 12 surface 277 non-null int64 \n", - " 13 total_chairs 277 non-null int64 \n", - " 14 Runs 274 non-null float64\n", - " 15 TerrainParks 233 non-null float64\n", - " 16 LongestRun_mi 272 non-null float64\n", - " 17 SkiableTerrain_ac 275 non-null float64\n", - " 18 Snow Making_ac 240 non-null float64\n", - " 19 daysOpenLastYear 233 non-null float64\n", - " 20 yearsOpen 277 non-null float64\n", - " 21 averageSnowfall 268 non-null float64\n", - " 22 AdultWeekend 277 non-null float64\n", - " 23 projectedDaysOpen 236 non-null float64\n", - " 24 NightSkiing_ac 163 non-null float64\n", - "dtypes: float64(11), int64(11), object(3)\n", - "memory usage: 56.3+ KB\n" - ] - } - ], - "source": [ - "ski_data.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are still some missing values, and it's good to be aware of this, but leave them as is for now." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.12 Save data" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(277, 25)" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ski_data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'save_file' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/wsuser/ipykernel_154/3665361625.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# save the data to a new csv file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdatapath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msave_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mski_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ski_data_cleaned.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatapath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'save_file' is not defined" - ] - } - ], - "source": [ - "# save the data to a new csv file\n", - "datapath = '../data'\n", - "save_file(ski_data, 'ski_data_cleaned.csv', datapath)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "# save the state_summary separately.\n", - "datapath = '../data'\n", - "save_file(state_summary, 'state_summary.csv', datapath)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.13 Summary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A: 3** Your answer here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, a dataset for multiple ski resorts accross the United States was taken from a github respository and cleaned/transformed to make it more suitable for analysis and possibly feeding it into a model. The dataset had 330 records with 27 features including the record for 'Big Mountain Resort' which is located in Montana and is the resort we are looking to develop a pricing model for. The dataset appeared to have quite a few missing values, but fortunatedly, the record for Big Mountain Resort had no missing values. With that said, some records were missing values for the features 'AdultWeekday' and 'AdultWeekend' which represent the prices for each resort and were decidedly the target variables for the study. The feature with the most missing values was 'fastEight' feature with about 50% of records missing a value for this feature. This feature would eventually be dropped along with the only resort to have been open for over 100 years\n", - "\n", - "The records mostly consisted of numerical variables with the only categorical variables being 'name', 'state', and 'region'. Sometimes, state and region can have the same name on the same record and regions can have the same names in different states. But there are no records that have the exact same name, state, and region meaning there are no duplicate records. \n", - "\n", - "The states Colorado, Michigan, and New York had the highest number of resorts " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 73ddd980c804af3da7cb915c14ca85ca2c56a65a Mon Sep 17 00:00:00 2001 From: Thapelo Date: Sun, 27 Mar 2022 20:21:01 +0200 Subject: [PATCH 3/7] Work done on data wrangling and exploratory data analysis. Includes the output files. --- Notebooks/02_data_wrangling.ipynb | 3160 ++++++++++++++---- Notebooks/03_exploratory_data_analysis.ipynb | 1405 +++++--- data/ski_data_cleaned.csv | 278 ++ data/ski_data_step3_features.csv | 278 ++ data/state_summary.csv | 36 + 5 files changed, 4165 insertions(+), 992 deletions(-) create mode 100644 data/ski_data_cleaned.csv create mode 100644 data/ski_data_step3_features.csv create mode 100644 data/state_summary.csv diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb index a52eb6c24..a8ffe9873 100644 --- a/Notebooks/02_data_wrangling.ipynb +++ b/Notebooks/02_data_wrangling.ipynb @@ -120,18 +120,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Code task 1#\n", "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n", - "import ___ as pd\n", - "import ___ as plt\n", - "import ___ as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "import os\n", "\n", - "from library.sb_utils import save_file\n" + "import lxml\n", + "\n", + "from library.sb_utils import save_file'\n" ] }, { @@ -179,13 +181,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 330 entries, 0 to 329\n", + "Data columns (total 27 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 330 non-null object \n", + " 1 Region 330 non-null object \n", + " 2 state 330 non-null object \n", + " 3 summit_elev 330 non-null int64 \n", + " 4 vertical_drop 330 non-null int64 \n", + " 5 base_elev 330 non-null int64 \n", + " 6 trams 330 non-null int64 \n", + " 7 fastEight 164 non-null float64\n", + " 8 fastSixes 330 non-null int64 \n", + " 9 fastQuads 330 non-null int64 \n", + " 10 quad 330 non-null int64 \n", + " 11 triple 330 non-null int64 \n", + " 12 double 330 non-null int64 \n", + " 13 surface 330 non-null int64 \n", + " 14 total_chairs 330 non-null int64 \n", + " 15 Runs 326 non-null float64\n", + " 16 TerrainParks 279 non-null float64\n", + " 17 LongestRun_mi 325 non-null float64\n", + " 18 SkiableTerrain_ac 327 non-null float64\n", + " 19 Snow Making_ac 284 non-null float64\n", + " 20 daysOpenLastYear 279 non-null float64\n", + " 21 yearsOpen 329 non-null float64\n", + " 22 averageSnowfall 316 non-null float64\n", + " 23 AdultWeekday 276 non-null float64\n", + " 24 AdultWeekend 279 non-null float64\n", + " 25 projectedDaysOpen 283 non-null float64\n", + " 26 NightSkiing_ac 187 non-null float64\n", + "dtypes: float64(13), int64(11), object(3)\n", + "memory usage: 65.8+ KB\n" + ] + } + ], "source": [ "#Code task 2#\n", "#Call the info method on ski_data to see a summary of the data\n", - "ski_data.___" + "ski_data.info()" ] }, { @@ -204,211 +247,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "scrolled": true }, - "outputs": [], - "source": [ - "#Code task 3#\n", - "#Call the head method on ski_data to print the first several rows of the data\n", - "ski_data.___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.6 Explore The Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.1 Find Your Resort Of Interest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your resort of interest is called Big Mountain Resort. Check it's in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 4#\n", - "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", - "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", - "#transpose method, but you can access this conveniently with the `T` property.\n", - "ski_data[ski_data.Name == ___].___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's good that your resort doesn't appear to have any missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.2 Number Of Missing Values By Column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Count the number of missing values in each column and sort them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 5#\n", - "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", - "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", - "#Order them (increasing or decreasing) using sort_values\n", - "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", - "missing = ___([ski_data.___.___, 100 * ski_data.___.___], axis=1)\n", - "missing.columns=[___, ___]\n", - "missing.___(by=___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.3 Categorical Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 6#\n", - "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", - "ski_data.___(___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", - "\n", - "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", - "* Is `Region` always the same as `state`?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.1 Unique Resort Names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 7#\n", - "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", - "ski_data['Name'].___.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have a duplicated resort name: Crystal Mountain." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 8#\n", - "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", - "(ski_data[___] + ', ' + ski_data[___]).___.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 9#\n", - "#Concatenate 'Name' and 'state' and count the values again (as above)\n", - "(ski_data[___] + ', ' + ski_data[___]).___.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A: 1** Your answer here" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, "outputs": [ { "data": { @@ -456,57 +298,1012 @@ " \n", " \n", " \n", - " 104\n", - " Crystal Mountain\n", - " Michigan\n", - " Michigan\n", - " 1132\n", - " 375\n", - " 757\n", - " 0\n", + " 0\n", + " Alyeska Resort\n", + " Alaska\n", + " Alaska\n", + " 3939\n", + " 2500\n", + " 250\n", + " 1\n", " 0.0\n", " 0\n", - " 1\n", + " 2\n", " ...\n", - " 0.3\n", - " 102.0\n", - " 96.0\n", - " 120.0\n", - " 63.0\n", - " 132.0\n", - " 54.0\n", - " 64.0\n", - " 135.0\n", - " 56.0\n", + " 1.0\n", + " 1610.0\n", + " 113.0\n", + " 150.0\n", + " 60.0\n", + " 669.0\n", + " 65.0\n", + " 85.0\n", + " 150.0\n", + " 550.0\n", " \n", " \n", - " 295\n", - " Crystal Mountain\n", - " Washington\n", - " Washington\n", - " 7012\n", - " 3100\n", - " 4400\n", - " 1\n", - " NaN\n", - " 2\n", - " 2\n", + " 1\n", + " Eaglecrest Ski Area\n", + " Alaska\n", + " Alaska\n", + " 2600\n", + " 1540\n", + " 1200\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0\n", " ...\n", - " 2.5\n", - " 2600.0\n", - " 10.0\n", - " NaN\n", - " 57.0\n", - " 486.0\n", - " 99.0\n", - " 99.0\n", - " NaN\n", + " 2.0\n", + " 640.0\n", + " 60.0\n", + " 45.0\n", + " 44.0\n", + " 350.0\n", + " 47.0\n", + " 53.0\n", + " 90.0\n", " NaN\n", " \n", - " \n", - "\n", - "

2 rows × 27 columns

\n", - "" + " \n", + " 2\n", + " Hilltop Ski Area\n", + " Alaska\n", + " Alaska\n", + " 2090\n", + " 294\n", + " 1796\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0\n", + " ...\n", + " 1.0\n", + " 30.0\n", + " 30.0\n", + " 150.0\n", + " 36.0\n", + " 69.0\n", + " 30.0\n", + " 34.0\n", + " 152.0\n", + " 30.0\n", + " \n", + " \n", + " 3\n", + " Arizona Snowbowl\n", + " Arizona\n", + " Arizona\n", + " 11500\n", + " 2300\n", + " 9200\n", + " 0\n", + " 0.0\n", + " 1\n", + " 0\n", + " ...\n", + " 2.0\n", + " 777.0\n", + " 104.0\n", + " 122.0\n", + " 81.0\n", + " 260.0\n", + " 89.0\n", + " 89.0\n", + " 122.0\n", + " NaN\n", + " \n", + " \n", + " 4\n", + " Sunrise Park Resort\n", + " Arizona\n", + " Arizona\n", + " 11100\n", + " 1800\n", + " 9200\n", + " 0\n", + " NaN\n", + " 0\n", + " 1\n", + " ...\n", + " 1.2\n", + " 800.0\n", + " 80.0\n", + " 115.0\n", + " 49.0\n", + " 250.0\n", + " 74.0\n", + " 78.0\n", + " 104.0\n", + " 80.0\n", + " \n", + " \n", + "\n", + "

5 rows × 27 columns

\n", + "" + ], + "text/plain": [ + " Name Region state summit_elev vertical_drop \\\n", + "0 Alyeska Resort Alaska Alaska 3939 2500 \n", + "1 Eaglecrest Ski Area Alaska Alaska 2600 1540 \n", + "2 Hilltop Ski Area Alaska Alaska 2090 294 \n", + "3 Arizona Snowbowl Arizona Arizona 11500 2300 \n", + "4 Sunrise Park Resort Arizona Arizona 11100 1800 \n", + "\n", + " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", + "0 250 1 0.0 0 2 ... 1.0 \n", + "1 1200 0 0.0 0 0 ... 2.0 \n", + "2 1796 0 0.0 0 0 ... 1.0 \n", + "3 9200 0 0.0 1 0 ... 2.0 \n", + "4 9200 0 NaN 0 1 ... 1.2 \n", + "\n", + " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", + "0 1610.0 113.0 150.0 60.0 \n", + "1 640.0 60.0 45.0 44.0 \n", + "2 30.0 30.0 150.0 36.0 \n", + "3 777.0 104.0 122.0 81.0 \n", + "4 800.0 80.0 115.0 49.0 \n", + "\n", + " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", + "0 669.0 65.0 85.0 150.0 \n", + "1 350.0 47.0 53.0 90.0 \n", + "2 69.0 30.0 34.0 152.0 \n", + "3 260.0 89.0 89.0 122.0 \n", + "4 250.0 74.0 78.0 104.0 \n", + "\n", + " NightSkiing_ac \n", + "0 550.0 \n", + "1 NaN \n", + "2 30.0 \n", + "3 NaN \n", + "4 80.0 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 3#\n", + "#Call the head method on ski_data to print the first several rows of the data\n", + "ski_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.6 Explore The Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.1 Find Your Resort Of Interest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your resort of interest is called Big Mountain Resort. Check it's in the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameBig Mountain Resort
RegionMontana
stateMontana
summit_elev6817
vertical_drop2353
base_elev4464
trams0
fastEight0.0
fastSixes0
fastQuads3
quad2
triple6
double0
surface3
total_chairs14
Runs105.0
TerrainParks4.0
LongestRun_mi3.3
SkiableTerrain_ac3000.0
Snow Making_ac600.0
daysOpenLastYear123.0
yearsOpen72.0
averageSnowfall333.0
AdultWeekday81.0
AdultWeekend81.0
projectedDaysOpen123.0
NightSkiing_ac600.0
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" + ], + "text/plain": [ + " 151\n", + "Name Big Mountain Resort\n", + "Region Montana\n", + "state Montana\n", + "summit_elev 6817\n", + "vertical_drop 2353\n", + "base_elev 4464\n", + "trams 0\n", + "fastEight 0.0\n", + "fastSixes 0\n", + "fastQuads 3\n", + "quad 2\n", + "triple 6\n", + "double 0\n", + "surface 3\n", + "total_chairs 14\n", + "Runs 105.0\n", + "TerrainParks 4.0\n", + "LongestRun_mi 3.3\n", + "SkiableTerrain_ac 3000.0\n", + "Snow Making_ac 600.0\n", + "daysOpenLastYear 123.0\n", + "yearsOpen 72.0\n", + "averageSnowfall 333.0\n", + "AdultWeekday 81.0\n", + "AdultWeekend 81.0\n", + "projectedDaysOpen 123.0\n", + "NightSkiing_ac 600.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 4#\n", + "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", + "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", + "#transpose method, but you can access this conveniently with the `T` property.\n", + "ski_data[ski_data.Name == 'Big Mountain Resort'].T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's good that your resort doesn't appear to have any missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.2 Number Of Missing Values By Column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count the number of missing values in each column and sort them." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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count%
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fastQuads00.000000
fastSixes00.000000
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averageSnowfall144.242424
Snow Making_ac4613.939394
projectedDaysOpen4714.242424
TerrainParks5115.454545
daysOpenLastYear5115.454545
AdultWeekend5115.454545
AdultWeekday5416.363636
NightSkiing_ac14343.333333
fastEight16650.303030
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" + ], + "text/plain": [ + " count %\n", + "Name 0 0.000000\n", + "total_chairs 0 0.000000\n", + "double 0 0.000000\n", + "triple 0 0.000000\n", + "quad 0 0.000000\n", + "fastQuads 0 0.000000\n", + "fastSixes 0 0.000000\n", + "surface 0 0.000000\n", + "trams 0 0.000000\n", + "base_elev 0 0.000000\n", + "vertical_drop 0 0.000000\n", + "summit_elev 0 0.000000\n", + "state 0 0.000000\n", + "Region 0 0.000000\n", + "yearsOpen 1 0.303030\n", + "SkiableTerrain_ac 3 0.909091\n", + "Runs 4 1.212121\n", + "LongestRun_mi 5 1.515152\n", + "averageSnowfall 14 4.242424\n", + "Snow Making_ac 46 13.939394\n", + "projectedDaysOpen 47 14.242424\n", + "TerrainParks 51 15.454545\n", + "daysOpenLastYear 51 15.454545\n", + "AdultWeekend 51 15.454545\n", + "AdultWeekday 54 16.363636\n", + "NightSkiing_ac 143 43.333333\n", + "fastEight 166 50.303030" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 5#\n", + "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", + "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", + "#Order them (increasing or decreasing) using sort_values\n", + "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", + "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n", + "missing.columns=['count', '%']\n", + "missing.sort_values(by='count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.3 Categorical Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameRegionstate
0Alyeska ResortAlaskaAlaska
1Eaglecrest Ski AreaAlaskaAlaska
2Hilltop Ski AreaAlaskaAlaska
3Arizona SnowbowlArizonaArizona
4Sunrise Park ResortArizonaArizona
............
325Meadowlark Ski LodgeWyomingWyoming
326Sleeping Giant Ski ResortWyomingWyoming
327Snow King ResortWyomingWyoming
328Snowy Range Ski & Recreation AreaWyomingWyoming
329White Pine Ski AreaWyomingWyoming
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330 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Name Region state\n", + "0 Alyeska Resort Alaska Alaska\n", + "1 Eaglecrest Ski Area Alaska Alaska\n", + "2 Hilltop Ski Area Alaska Alaska\n", + "3 Arizona Snowbowl Arizona Arizona\n", + "4 Sunrise Park Resort Arizona Arizona\n", + ".. ... ... ...\n", + "325 Meadowlark Ski Lodge Wyoming Wyoming\n", + "326 Sleeping Giant Ski Resort Wyoming Wyoming\n", + "327 Snow King Resort Wyoming Wyoming\n", + "328 Snowy Range Ski & Recreation Area Wyoming Wyoming\n", + "329 White Pine Ski Area Wyoming Wyoming\n", + "\n", + "[330 rows x 3 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 6#\n", + "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", + "ski_data.select_dtypes('object')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", + "\n", + "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", + "* Is `Region` always the same as `state`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.1 Unique Resort Names" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Crystal Mountain 2\n", + "Alyeska Resort 1\n", + "Brandywine 1\n", + "Boston Mills 1\n", + "Alpine Valley 1\n", + "Name: Name, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 7#\n", + "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", + "ski_data['Name'].value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have a duplicated resort name: Crystal Mountain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 8#\n", + "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Code task 9#\n", + "#Concatenate 'Name' and 'state' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A: 1** Your answer here" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameRegionstatesummit_elevvertical_dropbase_elevtramsfastEightfastSixesfastQuads...LongestRun_miSkiableTerrain_acSnow Making_acdaysOpenLastYearyearsOpenaverageSnowfallAdultWeekdayAdultWeekendprojectedDaysOpenNightSkiing_ac
104Crystal MountainMichiganMichigan113237575700.001...0.3102.096.0120.063.0132.054.064.0135.056.0
295Crystal MountainWashingtonWashington7012310044001NaN22...2.52600.010.0NaN57.0486.099.099.0NaNNaN
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2 rows × 27 columns

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" ], "text/plain": [ " Name Region state summit_elev vertical_drop \\\n", @@ -571,13 +1368,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 10#\n", "#Calculate the number of times Region does not equal state\n", - "(ski_data.Region ___ ski_data.state).___" + "(ski_data.Region != ski_data.state).sum()" ] }, { @@ -604,34 +1412,34 @@ "New Hampshire 16\n", "Vermont 15\n", "Minnesota 14\n", - "Montana 12\n", "Idaho 12\n", + "Montana 12\n", "Massachusetts 11\n", "Washington 10\n", - "Maine 9\n", "New Mexico 9\n", + "Maine 9\n", "Wyoming 8\n", "Utah 7\n", - "Oregon 6\n", "Salt Lake City 6\n", "North Carolina 6\n", + "Oregon 6\n", "Connecticut 5\n", "Ohio 5\n", - "West Virginia 4\n", "Virginia 4\n", - "Mt. Hood 4\n", + "West Virginia 4\n", "Illinois 4\n", + "Mt. Hood 4\n", "Alaska 3\n", "Iowa 3\n", - "Missouri 2\n", + "South Dakota 2\n", "Arizona 2\n", + "Nevada 2\n", + "Missouri 2\n", "Indiana 2\n", - "South Dakota 2\n", "New Jersey 2\n", - "Nevada 2\n", "Rhode Island 1\n", - "Maryland 1\n", "Tennessee 1\n", + "Maryland 1\n", "Northern California 1\n", "Name: Region, dtype: int64" ] @@ -654,15 +1462,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "state Region \n", + "California Sierra Nevada 20\n", + " Northern California 1\n", + "Nevada Sierra Nevada 2\n", + "Oregon Mt. Hood 4\n", + "Utah Salt Lake City 6\n", + "Name: Region, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 11#\n", "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n", "#group that by 'state' and perform `value_counts` on the 'Region'\n", - "(ski_data[ski_data.___ ___ ski_data.___]\n", - " .groupby(___)[___]\n", + "(ski_data[ski_data.Region != ski_data.state]\n", + " .groupby('state')['Region']\n", " .value_counts())" ] }, @@ -682,14 +1507,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Region 38\n", + "state 35\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 12#\n", "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n", "#the number of unique values in each\n", - "ski_data[[___, ___]].___" + "ski_data[['Region', 'state']].nunique()" ] }, { @@ -715,27 +1553,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 13#\n", "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n", - "fig, ax = plt.subplots(___, ___, figsize=(___))\n", + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,8))\n", "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.Region.value_counts().plot(kind=___, ax=ax[0])\n", + "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n", "#Give the plot a helpful title of 'Region'\n", - "ax[0].set_title(___)\n", + "ax[0].set_title('Region')\n", "#Label the xaxis 'Count'\n", - "ax[0].set_xlabel(___)\n", + "ax[0].set_xlabel('Count')\n", "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.state.value_counts().plot(kind=___, ax=ax[1])\n", + "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n", "#Give the plot a helpful title of 'state'\n", - "ax[1].set_title(___)\n", + "ax[1].set_title('State')\n", "#Label the xaxis 'Count'\n", - "ax[1].set_xlabel(___)\n", + "ax[1].set_xlabel('Count')\n", "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n", - "plt.subplots_adjust(wspace=___);\n", + "plt.subplots_adjust(wspace=0.5);\n", "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n", "# as the importance of easy-to-read and informative figures is frequently understated\n", "# and you will find the ability to tweak figures invaluable later on" @@ -771,14 +1622,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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AdultWeekdayAdultWeekend
state
Illinois35.00000043.333333
Iowa35.66666741.666667
Tennessee36.00000065.000000
Massachusetts40.90000057.200000
North Carolina41.83333364.166667
\n", + "
" + ], + "text/plain": [ + " AdultWeekday AdultWeekend\n", + "state \n", + "Illinois 35.000000 43.333333\n", + "Iowa 35.666667 41.666667\n", + "Tennessee 36.000000 65.000000\n", + "Massachusetts 40.900000 57.200000\n", + "North Carolina 41.833333 64.166667" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 14#\n", "# Calculate average weekday and weekend price by state and sort by the average of the two\n", "# Hint: use the pattern dataframe.groupby()[].mean()\n", - "state_price_means = ski_data.___(___)[[___, ___]].mean()\n", + "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean().sort_values(by='AdultWeekday')\n", "state_price_means.head()" ] }, @@ -789,7 +1715,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -815,14 +1741,19 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -839,7 +1770,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -849,11 +1780,11 @@ "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", "#call the resultant price column 'Price' via the `value_name` argument,\n", "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", - "ticket_prices = pd.melt(ski_data[[___, ___, ___]], \n", - " id_vars=___, \n", - " var_name=___, \n", - " value_vars=[___, ___], \n", - " value_name=___)" + "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], \n", + " id_vars='state', \n", + " var_name='Ticket', \n", + " value_vars=['AdultWeekday', 'AdultWeekend'], \n", + " value_name='Price')" ] }, { @@ -895,258 +1826,826 @@ " 65.0\n", " \n", " \n", - " 1\n", - " Alaska\n", - " AdultWeekday\n", - " 47.0\n", + " 1\n", + " Alaska\n", + " AdultWeekday\n", + " 47.0\n", + " \n", + " \n", + " 2\n", + " Alaska\n", + " AdultWeekday\n", + " 30.0\n", + " \n", + " \n", + " 3\n", + " Arizona\n", + " AdultWeekday\n", + " 89.0\n", + " \n", + " \n", + " 4\n", + " Arizona\n", + " AdultWeekday\n", + " 74.0\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " state Ticket Price\n", + "0 Alaska AdultWeekday 65.0\n", + "1 Alaska AdultWeekday 47.0\n", + "2 Alaska AdultWeekday 30.0\n", + "3 Arizona AdultWeekday 89.0\n", + "4 Arizona AdultWeekday 74.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ticket_prices.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Code task 16#\n", + "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", + "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", + "#This will use boxplot's x, y, hue, and data arguments.\n", + "plt.subplots(figsize=(12, 8))\n", + "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n", + "plt.xticks(rotation='vertical')\n", + "plt.ylabel('Price ($)')\n", + "plt.xlabel('State');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", + "\n", + "* disregard State completely\n", + "* retain all State information\n", + "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", + "\n", + "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus we currently have two main questions you want to resolve:\n", + "\n", + "* What do you do about the two types of ticket price?\n", + "* What do you do about the state information?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.4 Numeric Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.4.1 Numeric data summary" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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summit_elev330.04591.8181823735.535934315.01403.753127.57806.0013487.0
vertical_drop330.01215.427273947.86455760.0461.25964.51800.004425.0
base_elev330.03374.0000003117.12162170.0869.001561.56325.2510800.0
trams330.00.1727270.5599460.00.000.00.004.0
fastEight164.00.0060980.0780870.00.000.00.001.0
fastSixes330.00.1848480.6516850.00.000.00.006.0
fastQuads330.01.0181822.1982940.00.000.01.0015.0
quad330.00.9333331.3122450.00.000.01.008.0
triple330.01.5000001.6191300.00.001.02.008.0
double330.01.8333331.8150280.01.001.03.0014.0
surface330.02.6212122.0596360.01.002.03.0015.0
total_chairs330.08.2666675.7986830.05.007.010.0041.0
Runs326.048.21472446.3640773.019.0033.060.00341.0
TerrainParks279.02.8207892.0081131.01.002.04.0014.0
LongestRun_mi325.01.4332311.1561710.00.501.02.006.0
SkiableTerrain_ac327.0739.8012231816.1674418.085.00200.0690.0026819.0
Snow Making_ac284.0174.873239261.3361252.050.00100.0200.503379.0
daysOpenLastYear279.0115.10394335.0632513.097.00114.0135.00305.0
yearsOpen329.063.656535109.4299286.050.0058.069.002019.0
averageSnowfall316.0185.316456136.35684218.069.00150.0300.00669.0
AdultWeekday276.057.91695726.14012615.040.0050.071.00179.0
2AlaskaAdultWeekday30.0AdultWeekend279.064.16681024.55458417.047.0060.077.50179.0
3ArizonaAdultWeekday89.0projectedDaysOpen283.0120.05300431.04596330.0100.00120.0139.50305.0
4ArizonaAdultWeekday74.0NightSkiing_ac187.0100.395722105.1696202.040.0072.0114.00650.0
\n", "
" ], "text/plain": [ - " state Ticket Price\n", - "0 Alaska AdultWeekday 65.0\n", - "1 Alaska AdultWeekday 47.0\n", - "2 Alaska AdultWeekday 30.0\n", - "3 Arizona AdultWeekday 89.0\n", - "4 Arizona AdultWeekday 74.0" + " count mean std min 25% 50% \\\n", + "summit_elev 330.0 4591.818182 3735.535934 315.0 1403.75 3127.5 \n", + "vertical_drop 330.0 1215.427273 947.864557 60.0 461.25 964.5 \n", + "base_elev 330.0 3374.000000 3117.121621 70.0 869.00 1561.5 \n", + "trams 330.0 0.172727 0.559946 0.0 0.00 0.0 \n", + "fastEight 164.0 0.006098 0.078087 0.0 0.00 0.0 \n", + "fastSixes 330.0 0.184848 0.651685 0.0 0.00 0.0 \n", + "fastQuads 330.0 1.018182 2.198294 0.0 0.00 0.0 \n", + "quad 330.0 0.933333 1.312245 0.0 0.00 0.0 \n", + "triple 330.0 1.500000 1.619130 0.0 0.00 1.0 \n", + "double 330.0 1.833333 1.815028 0.0 1.00 1.0 \n", + "surface 330.0 2.621212 2.059636 0.0 1.00 2.0 \n", + "total_chairs 330.0 8.266667 5.798683 0.0 5.00 7.0 \n", + "Runs 326.0 48.214724 46.364077 3.0 19.00 33.0 \n", + "TerrainParks 279.0 2.820789 2.008113 1.0 1.00 2.0 \n", + "LongestRun_mi 325.0 1.433231 1.156171 0.0 0.50 1.0 \n", + "SkiableTerrain_ac 327.0 739.801223 1816.167441 8.0 85.00 200.0 \n", + "Snow Making_ac 284.0 174.873239 261.336125 2.0 50.00 100.0 \n", + "daysOpenLastYear 279.0 115.103943 35.063251 3.0 97.00 114.0 \n", + "yearsOpen 329.0 63.656535 109.429928 6.0 50.00 58.0 \n", + "averageSnowfall 316.0 185.316456 136.356842 18.0 69.00 150.0 \n", + "AdultWeekday 276.0 57.916957 26.140126 15.0 40.00 50.0 \n", + "AdultWeekend 279.0 64.166810 24.554584 17.0 47.00 60.0 \n", + "projectedDaysOpen 283.0 120.053004 31.045963 30.0 100.00 120.0 \n", + "NightSkiing_ac 187.0 100.395722 105.169620 2.0 40.00 72.0 \n", + "\n", + " 75% max \n", + "summit_elev 7806.00 13487.0 \n", + "vertical_drop 1800.00 4425.0 \n", + "base_elev 6325.25 10800.0 \n", + "trams 0.00 4.0 \n", + "fastEight 0.00 1.0 \n", + "fastSixes 0.00 6.0 \n", + "fastQuads 1.00 15.0 \n", + "quad 1.00 8.0 \n", + "triple 2.00 8.0 \n", + "double 3.00 14.0 \n", + "surface 3.00 15.0 \n", + "total_chairs 10.00 41.0 \n", + "Runs 60.00 341.0 \n", + "TerrainParks 4.00 14.0 \n", + "LongestRun_mi 2.00 6.0 \n", + "SkiableTerrain_ac 690.00 26819.0 \n", + "Snow Making_ac 200.50 3379.0 \n", + "daysOpenLastYear 135.00 305.0 \n", + "yearsOpen 69.00 2019.0 \n", + "averageSnowfall 300.00 669.0 \n", + "AdultWeekday 71.00 179.0 \n", + "AdultWeekend 77.50 179.0 \n", + "projectedDaysOpen 139.50 305.0 \n", + "NightSkiing_ac 114.00 650.0 " ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ticket_prices.head()" + "#Code task 17#\n", + "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", + "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", + "#will be useful again\n", + "ski_data.describe().T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." + "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 82.424242\n", + "2 14.242424\n", + "1 3.333333\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#Code task 16#\n", - "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", - "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", - "#This will use boxplot's x, y, hue, and data arguments.\n", - "plt.subplots(figsize=(12, 8))\n", - "sns.boxplot(x=___, y=___, hue=___, data=ticket_prices)\n", - "plt.xticks(rotation='vertical')\n", - "plt.ylabel('Price ($)')\n", - "plt.xlabel('State');" + "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", + "missing_price.value_counts()/len(missing_price) * 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", - "\n", - "* disregard State completely\n", - "* retain all State information\n", - "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", - "\n", - "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." + "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Thus we currently have two main questions you want to resolve:\n", - "\n", - "* What do you do about the two types of ticket price?\n", - "* What do you do about the state information?" + "#### 2.6.4.2 Distributions Of Feature Values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2.6.4 Numeric Features" + "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." + "#Code task 18#\n", + "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", + "#Try passing it an argument figsize=(15,10)\n", + "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", + "#It's important you create legible and easy-to-read plots\n", + "ski_data.hist(figsize=(15,10))\n", + "plt.subplots_adjust(hspace=0.5);\n", + "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2.6.4.1 Numeric data summary" + "What features do we have possible cause for concern about and why?\n", + "\n", + "* SkiableTerrain_ac because values are clustered down the low end,\n", + "* Snow Making_ac for the same reason,\n", + "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", + "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", + "* trams also may get an amber flag for the same reason,\n", + "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.4.2.1 SkiableTerrain_ac" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "39 26819.0\n", + "Name: SkiableTerrain_ac, dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#Code task 17#\n", - "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", - "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", - "#will be useful again\n", - "ski_data.___.___" + "#Code task 19#\n", + "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", + "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." + "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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39
NameSilverton Mountain
RegionColorado
stateColorado
summit_elev13487
vertical_drop3087
base_elev10400
trams0
fastEight0.0
fastSixes0
fastQuads0
quad0
triple0
double1
surface0
total_chairs1
RunsNaN
TerrainParksNaN
LongestRun_mi1.5
SkiableTerrain_ac26819.0
Snow Making_acNaN
daysOpenLastYear175.0
yearsOpen17.0
averageSnowfall400.0
AdultWeekday79.0
AdultWeekend79.0
projectedDaysOpen181.0
NightSkiing_acNaN
\n", + "
" + ], "text/plain": [ - "0 82.424242\n", - "2 14.242424\n", - "1 3.333333\n", - "dtype: float64" + " 39\n", + "Name Silverton Mountain\n", + "Region Colorado\n", + "state Colorado\n", + "summit_elev 13487\n", + "vertical_drop 3087\n", + "base_elev 10400\n", + "trams 0\n", + "fastEight 0.0\n", + "fastSixes 0\n", + "fastQuads 0\n", + "quad 0\n", + "triple 0\n", + "double 1\n", + "surface 0\n", + "total_chairs 1\n", + "Runs NaN\n", + "TerrainParks NaN\n", + "LongestRun_mi 1.5\n", + "SkiableTerrain_ac 26819.0\n", + "Snow Making_ac NaN\n", + "daysOpenLastYear 175.0\n", + "yearsOpen 17.0\n", + "averageSnowfall 400.0\n", + "AdultWeekday 79.0\n", + "AdultWeekend 79.0\n", + "projectedDaysOpen 181.0\n", + "NightSkiing_ac NaN" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", - "missing_price.value_counts()/len(missing_price) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.4.2 Distributions Of Feature Values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 18#\n", - "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", - "#Try passing it an argument figsize=(15,10)\n", - "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", - "#It's important you create legible and easy-to-read plots\n", - "ski_data.___(___)\n", - "#plt.subplots_adjust(hspace=___);\n", - "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What features do we have possible cause for concern about and why?\n", - "\n", - "* SkiableTerrain_ac because values are clustered down the low end,\n", - "* Snow Making_ac for the same reason,\n", - "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", - "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", - "* trams also may get an amber flag for the same reason,\n", - "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.1 SkiableTerrain_ac" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 19#\n", - "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", - "ski_data.___[ski_data.___ > ___]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "#Code task 20#\n", "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n", "#Hint: don't forget the transpose will be helpful here\n", - "ski_data[ski_data.___ > ___].___" + "ski_data[ski_data.SkiableTerrain_ac > 10000].T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**A: 2** Your answer here" + "**A: 2** Your answer here: Silverton Mountain" ] }, { @@ -1179,35 +2678,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "26819.0" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 21#\n", "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n", - "ski_data.___[39, 'SkiableTerrain_ac']" + "ski_data.loc[39, 'SkiableTerrain_ac']" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "#Code task 22#\n", "#Use the .loc accessor again to modify this value with the correct value of 1819\n", - "ski_data.___[39, 'SkiableTerrain_ac'] = ___" + "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1819.0" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 23#\n", "#Use the .loc accessor a final time to verify that the value has been modified\n", - "ski_data.___[39, 'SkiableTerrain_ac']" + "ski_data.loc[39, 'SkiableTerrain_ac']" ] }, { @@ -1231,7 +2752,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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P1TIzsxGmbYEpIhZHxB15+DnSk6Inkh5NcV6e7TzSu2HI6edHcguwQf5nOTMzG0GG5BqTpMmkl2LdSnrS9OI86VFWPD5/Iis/Tn4hzb8awszMVhNtvysvP5n7MtKbKp9ND8lOIiIkDeg/fPPzz6YDdHV1UalUWipXb29vy8uOBK6fxlw/jbl+GnP9NNbWwJQfu34ZcEFEXJ6TH5M0ISIW5666x3P6IlZ+d8pm1HinSX7+2QyA7u7uaPWWy07frll2rp/GXD+NuX4ac/001s678kR6LP99EfHtwqSrSe+2J/+9qpB+aL47bzdgaaHLz8zMRoh2njHtTno3y12S5uS0k4DTgEskHUl6V8gBedrPSK/enkt6QVa9dzqZmdlqrG2BKSJuov5rePeoToj0NNlPtKs8tUw+4adNzTf/tH3bXBIzM+vjJz+YmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmpODCZmVmptC0wSTpH0uOS7i6kXSxpTv7MlzQnp0+W9LfCtLPaVS4zMyu30W3M+1zge8D5fQkR8eG+YUmnA0sL8z8UEVPbWB4zMxsG2haYIuJGSZNrTZMk4ADgHe1av5mZDU+KiPZlngLTtRGxQ1X624BvR0R3Yb57gAeBZ4FTIuK3dfKcDkwH6Orq2nnmzJktla23t5d5S19uat4dJ45vaR3DWW9vL2PHju10MUrL9dOY66exTtfPtGnTZvf9/pZRO7vyGjkIuKgwvhjYPCKekrQzcKWk7SPi2eoFI2IGMAOgu7s7enp6WipApVLh9JuWNTXv/INbW8dwVqlUaLVuRwLXT2Oun8ZcP40N+V15kkYDHwAu7kuLiBci4qk8PBt4CNh6qMtmZmad14nbxfcE7o+IhX0JkjaRNCoPbwVMAR7uQNnMzKzD2nm7+EXAzcA2khZKOjJPOpCVu/EA3gbcmW8fvxQ4JiKeblfZzMysvNp5V95BddIPr5F2GXBZu8piZmbDh5/8YGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpeLAZGZmpdLOV6ufI+lxSXcX0k6VtEjSnPzZpzDtRElzJT0g6d3tKpeZmZVbO8+YzgX2qpF+RkRMzZ+fAUjaDjgQ2D4v831Jo9pYNjMzK6m2BaaIuBF4usnZ9wdmRsQLETEPmAu8sV1lMzOz8hrdgXV+UtKhwCzguIhYAkwEbinMszCnvYqk6cB0gK6uLiqVSkuF6O3t5bgdX25q3lbXMZz19vaOyO1uluunMddPY66fxoY6MJ0JfA2I/Pd04IiBZBARM4AZAN3d3dHT09NSQSqVCqfftKypeecf3No6hrNKpUKrdTsSuH4ac/005vppbEjvyouIxyLi5Yj4O/ADVnTXLQImFWbdLKeZmdkIM6SBSdKEwuj7gb479q4GDpS0lqQtgSnAbUNZNjMzK4e2deVJugjoATaWtBD4MtAjaSqpK28+cDRARNwj6RLgXmA58ImIaO4CkJmZrVbaFpgi4qAayWc3mP/rwNfbVR4zMxse/OQHMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrFQcmMzMrlbYFJknnSHpc0t2FtG9Kul/SnZKukLRBTp8s6W+S5uTPWe0ql5mZlVs7z5jOBfaqSrsO2CEiXgc8CJxYmPZQREzNn2PaWC4zMyuxtgWmiLgReLoq7VcRsTyP3gJs1q71m5nZ8KSIaF/m0mTg2ojYoca0a4CLI+LHeb57SGdRzwKnRMRv6+Q5HZgO0NXVtfPMmTNbKltvby/zlr7c1Lw7Thzf0jqGs97eXsaOHdvpYpSW66cx109jna6fadOmzY6I7o4VoB+jO7FSSScDy4ELctJiYPOIeErSzsCVkraPiGerl42IGcAMgO7u7ujp6WmpDJVKhdNvWtbUvPMPbm0dw1mlUqHVuh0JXD+NuX4ac/00NuR35Uk6HNgPODjy6VpEvBART+Xh2cBDwNZDXTYzM+u8IQ1MkvYCvgC8NyL+WkjfRNKoPLwVMAV4eCjLZmZm5dC2rjxJFwE9wMaSFgJfJt2FtxZwnSSAW/IdeG8DvirpJeDvwDER8XTNjM3MbLXWtsAUEQfVSD67zryXAZe1qyxmZjZ8+MkPZmZWKg5MZmZWKg5MZmZWKg5MZmZWKg5MZmZWKk0FJkm7N5NmZma2qpo9Y/rPJtPMzMxWScP/Y5L0JuDNwCaSPleYtD4wqp0FMzOzkam/f7AdA4zN840rpD8LfLBdhTIzs5GrYWCKiN8Av5F0bkQ8MkRlMjOzEazZRxKtJWkGMLm4TES8ox2FMjOzkavZwPQT4Czgh0Bzb9czMzNrQbOBaXlEnNnWkpiZmdH87eLXSPq4pAmSNur7tLVkZmY2IjV7xnRY/vv5QloAWw1ucczMbKRrKjBFxJbtLoiZmRk0GZgkHVorPSLOH9zimJnZSNfsNaZdCp+3AqcC7+1vIUnnSHpc0t2FtI0kXSfpT/nvhjldkr4raa6kOyXtNOCtMTOzYa+pwBQRnyp8PgbsRHoiRH/OBfaqSjsBuD4ipgDX53GAvYEp+TMd8F2AZmYjUKuvvVgG9HvdKSJuBJ6uSt4fOC8Pnwe8r5B+fiS3ABtImtBi+czMbJhq9hrTNaS78CA9vHVb4JIW19kVEYvz8KNAVx6eCCwozLcwpy0upCFpOumMiq6uLiqVSkuF6O3t5bgdm/tf4VbXMZz19vaOyO1uluunMddPY66fxpq9XfxbheHlwCMRsXBVVx4RISn6n3OlZWYAMwC6u7ujp6enpXVXKhVOv2lZU/POP7i1dQxnlUqFVut2JHD9NOb6acz101iz15h+A9xPesL4hsCLq7DOx/q66PLfx3P6ImBSYb7NcpqZmY0gzb7B9gDgNuBDwAHArZJafe3F1az4h93DgKsK6Yfmu/N2A5YWuvzMzGyEaLYr72Rgl4h4HEDSJsCvgUsbLSTpIqAH2FjSQuDLwGnAJZKOBB4hBTqAnwH7AHOBvwIfHdCWmJnZaqHZwLRGX1DKnqKJs62IOKjOpD1qzBvAJ5osj5mZraaaDUy/kPRL4KI8/mHSGY6ZmdmgahiYJL2GdHv35yV9AHhLnnQzcEG7C2dmZiNPf2dM3wFOBIiIy4HLASTtmKe9p41lMzOzEai/60RdEXFXdWJOm9yWEpmZ2YjWX2DaoMG0dQaxHGZmZkD/gWmWpI9VJ0o6CpjdniKZmdlI1t81pmOBKyQdzIpA1A2MAd7fxnKZmdkI1TAwRcRjwJslTQN2yMk/jYj/bXvJzMxsRGr21eo3ADe0uSxmZmYtv4/JzMysLRyYzMysVByYzMysVByYzMysVByYzMysVByYzMysVByYzMysVByYzMysVJp9UeCgkbQNcHEhaSvgS6QHxn4MeCKnnxQRfhmhmdkIM+SBKSIeAKYCSBoFLAKuAD4KnBER3xrqMpmZWXl0uitvD+ChiHikw+UwM7OS6HRgOhC4qDD+SUl3SjpH0oadKpSZmXWOIqIzK5bGAH8Bto+IxyR1AU8CAXwNmBARR9RYbjowHaCrq2vnmTNntrT+3t5e5i19ual5d5w4vqV1DGe9vb2MHTu208UoLddPY66fxjpdP9OmTZsdEd0dK0A/OhmY9gc+ERHvqjFtMnBtROzwqgULuru7Y9asWS2tv1KpcPgvljU17/zT9m1pHcNZpVKhp6en08UoLddPY66fxjpdP5JKHZg62ZV3EIVuPEkTCtPeD9w95CUyM7OOG/K78gAkrQe8Ezi6kPwNSVNJXXnzq6aZmdkI0ZHAFBHLgH+oSjukE2UxM7Ny6fRdeWZmZitxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1JxYDIzs1LpyKvVASTNB54DXgaWR0S3pI2Ai4HJwHzggIhY0qkympnZ0Ov0GdO0iJgaEd15/ATg+oiYAlyfx83MbATpdGCqtj9wXh4+D3hf54piZmadoIjozIqlecASIID/jogZkp6JiA3ydAFL+sYLy00HpgN0dXXtPHPmzJbW39vby7ylLzc1744Tx7e0juGst7eXsWPHdroYpeX6acz101in62fatGmzCz1VpdOxa0zAWyJikaT/A1wn6f7ixIgISa+KmhExA5gB0N3dHT09PS2tvFKpcPpNy5qad/7Bra1jOKtUKrRatyOB66cx109jrp/GOtaVFxGL8t/HgSuANwKPSZoAkP8+3qnymZlZZ3QkMElaT9K4vmHgXcDdwNXAYXm2w4CrOlE+MzPrnE515XUBV6TLSIwGLoyIX0i6HbhE0pHAI8ABHSqfmZl1SEcCU0Q8DLy+RvpTwB5DXyIzMyuLst0ubmZmI5wDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlUon38c0bEw+4adNzTf/tH3bXBIzs9Wfz5jMzKxUHJjMzKxUHJjMzKxUHJjMzKxUHJjMzKxUhjwwSZok6QZJ90q6R9JncvqpkhZJmpM/+wx12czMrPM6cbv4cuC4iLhD0jhgtqTr8rQzIuJbHSiTmZmVxJAHpohYDCzOw89Jug+YONTlMDOzclJEdG7l0mTgRmAH4HPA4cCzwCzSWdWSGstMB6YDdHV17Txz5syW1t3b28u8pS+3tGw9O04cP6j5dVJvby9jx47tdDFKy/XTmOunsU7Xz7Rp02ZHRHfHCtCPjgUmSWOB3wBfj4jLJXUBTwIBfA2YEBFHNMqju7s7Zs2a1dL6K5UKh/9iWUvL1rM6PfmhUqnQ09PT6WKUluunMddPY52uH0mlDkwdeSSRpDWBy4ALIuJygIh4rDD9B8C1nSjbqvCji8zMVl0n7soTcDZwX0R8u5A+oTDb+4G7h7psZmbWeZ04Y9odOAS4S9KcnHYScJCkqaSuvPnA0R0om5mZdVgn7sq7CVCNST8b6rKYmVn5+MkPZmZWKg5MZmZWKg5MZmZWKg5MZmZWKg5MZmZWKg5MZmZWKg5MZmZWKh15JJENLj8KycxWJw5MHeBAYmZWn7vyzMysVByYzMysVByYzMysVHyNqcSavRbVDnctWsrhTazf18HMbLD5jMnMzErFgcnMzErFXXkjyEC6Bo/bcXDzbLbLz7fSm5nPmMzMrFR8xmRDopM3cpjZ8FK6MyZJe0l6QNJcSSd0ujxmZja0SnXGJGkU8F/AO4GFwO2Sro6IeztbMisbX4syW32VKjABbwTmRsTDAJJmAvsDDkzWkk4GsE7dGNJJZT8QGA4HNMOhjO2miOh0GV4h6YPAXhFxVB4/BNg1Ij5ZmGc6MD2PbgM80OLqNgaeXIXiru5cP425fhpz/TTW6frZIiI26eD6GyrbGVO/ImIGMGNV85E0KyK6B6FIqyXXT2Oun8ZcP425fhor280Pi4BJhfHNcpqZmY0QZQtMtwNTJG0paQxwIHB1h8tkZmZDqFRdeRGxXNIngV8Co4BzIuKeNq1ulbsDV3Oun8ZcP425fhpz/TRQqpsfzMzMytaVZ2ZmI5wDk5mZlcqIDEwj8bFHkiZJukHSvZLukfSZnL6RpOsk/Sn/3TCnS9J3cx3dKWmnQl6H5fn/JOmwTm1TO0gaJekPkq7N41tKujXXw8X5phwkrZXH5+bpkwt5nJjTH5D07g5tyqCTtIGkSyXdL+k+SW9y+1lB0mfzvnW3pIskre3206KIGFEf0k0VDwFbAWOAPwLbdbpcQ7DdE4Cd8vA44EFgO+AbwAk5/QTg3/PwPsDPAQG7Abfm9I2Ah/PfDfPwhp3evkGsp88BFwLX5vFLgAPz8FnAP+XhjwNn5eEDgYvz8Ha5Ta0FbJnb2qhOb9cg1c15wFF5eAywgdvPK3UzEZgHrFNoN4e7/bT2GYlnTK889igiXgT6Hnu0WouIxRFxRx5+DriPtDPtT/rBIf99Xx7eHzg/kluADSRNAN4NXBcRT0fEEuA6YK+h25L2kbQZsC/wwzwu4B3ApXmW6vrpq7dLgT3y/PsDMyPihYiYB8wltblhTdJ44G3A2QAR8WJEPIPbT9FoYB1Jo4F1gcW4/bRkJAamicCCwvjCnDZi5G6DNwC3Al0RsThPehToysP16ml1rr/vAF8A/p7H/wF4JiKW5/Hitr5SD3n60jz/6lo/WwJPAP+Tuzp/KGk93H4AiIhFwLeAP5MC0lJgNm4/LRmJgWlEkzQWuAw4NiKeLU6L1JcwIv9/QNJ+wOMRMbvTZSmp0cBOwJkR8QZgGanr7hUjvP1sSDrb2RLYFFiP1edMcMiNxMA0Yh97JGlNUlC6ICIuz8mP5S4W8t/Hc3q9elpd62934L2S5pO6d98B/AepC6rvH9GL2/pKPeTp44GnWH3rZyGwMCJuzeOXkgKV20+yJzAvIp6IiJeAy0ltyu2nBSMxMI3Ixx7l/uuzgfsi4tuFSVcDfXdGHQZcVUg/NN9dtRuwNHfZ/BJ4l6QN81Hiu3LasBYRJ0bEZhExmdQm/jciDgZuAD6YZ6uun756+2CeP3L6gfmuqy2BKcBtQ7QZbRMRjwILJG2Tk/YgvY7G7Sf5M7CbpHXzvtZXP24/rej03Red+JDuGHqQdMfLyZ0uzxBt81tI3Sx3AnPyZx9Sv/b1wJ+AXwMb5flFemnjQ8BdQHchryNIF2XnAh/t9La1oa56WHFX3lakH4a5wE+AtXL62nl8bp6+VWH5k3O9PQDs3entGcR6mQrMym3oStJddW4/K7brK8D9wN3Aj0h31rn9tPDxI4nMzKxURmJXnpmZlZgDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDk5mZlYoDkw07kk7Orxe4U9IcSbtKmi9p4xrz/r6fvCZLurvOtIqk7gbLXpHXP1fS0jw8R9KbB75VddexqaRL+5/TbPUxuv9ZzMpD0puA/Uiv8HghB6Mx9eaPiEELEjXyfn8uUw9wfETs18xykkbHigd7vmq8ah1/YcWTA8xGBJ8x2XAzAXgyIl4AiIgn8483AJLWkfRzSR/L473571hJ10u6Q9JdkoqvOhkt6QKll99dKmnd6pVKepekm/PyP8kPw30VSZtIukzS7fmze04/VdKPJP0O+FGN8cmSfpvzv6PvrKt4RifpcEmXS/qF0kv2vtGooiSdKWlWPrv8SiF9F0m/l/RHSbdJGtdMxZsNmU4/esIffwbyAcaSHqf0IPB94O05fT4wmfRYnEML8/fmv6OB9fPwxqRHwSgvE8Duedo5pLMfgArQnee/EVgvp38R+FJhHT2seITRhcBb8vDmpGcTApxKeg3COnXG1wXWzsNTgFl5eDJwdx4+nPRivfGkR9o8AkxqUFd9jwcalbfldaSzy4eBXfK09YHRnf5e/fGn+HFXng0rEdEraWfgrcA04GJJfa9fuAr4RkRcUGNRAf8q6W2k9y1NZMW7gxZExO/y8I+BT5PerdNnN9KbRX+Xns/JGODmOkXcE9guzwewfuHs6uqI+Fth3uL4msD3JE0FXga2rpP/9RGxFEDSvcAWrPz+nqIDJE0nBeUJeRsCWBwRtwNE1atPzMrAgcmGnYh4mXQGUJF0Fyue0vw7YC9JF0ZE9UMgDwY2AXaOiJfy6y3W7suyehVV4yK9dfWgJoq3BrBbRDy/UgYpUC2rmrc4/lngMeD1OY/nqe2FwvDL1NmH85OpjyedGS2RdC4rttes1HyNyYYVSdtImlJImkrq0gL4ErCE9FTrauNJLwJ8SdI00plGn83zTRUA/wjcVLXsLcDukl6Ty7CepHpnNL8CPlUo79R+N2pF+RZHxN+BQ0jdb6tifVLgWyqpC9g7pz8ATJC0Sy7fOK14X5BZKTgw2XAzFjhP0r2S7iR1T51amP4ZYJ0aNwZcAHTnM6xDSa8n6PMA8AlJ95Fe5XBmccGIeIJ0feeivM6bgdfWKd+n83ruzF1txzS5Xd8HDpP0x5x39dnVgETEH4E/kLbzQtLZJBHxIvBh4D/zuq7DZ1JWMn7thZmZlYrPmMzMrFTct2w2zEm6lfS21KJDIuKuTpTHbFW5K8/MzErFXXlmZlYqDkxmZlYqDkxmZlYqDkxmZlYq/x/QLACFuukgcAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -1345,7 +2866,7 @@ " \n", " \n", " fastEight\n", - " 0\n", + " 0.0\n", " \n", " \n", " fastSixes\n", @@ -1377,11 +2898,11 @@ " \n", " \n", " Runs\n", - " 97\n", + " 97.0\n", " \n", " \n", " TerrainParks\n", - " 3\n", + " 3.0\n", " \n", " \n", " LongestRun_mi\n", @@ -1389,23 +2910,23 @@ " \n", " \n", " SkiableTerrain_ac\n", - " 4800\n", + " 4800.0\n", " \n", " \n", " Snow Making_ac\n", - " 3379\n", + " 3379.0\n", " \n", " \n", " daysOpenLastYear\n", - " 155\n", + " 155.0\n", " \n", " \n", " yearsOpen\n", - " 64\n", + " 64.0\n", " \n", " \n", " averageSnowfall\n", - " 360\n", + " 360.0\n", " \n", " \n", " AdultWeekday\n", @@ -1417,7 +2938,7 @@ " \n", " \n", " projectedDaysOpen\n", - " 157\n", + " 157.0\n", " \n", " \n", " NightSkiing_ac\n", @@ -1436,7 +2957,7 @@ "vertical_drop 3500\n", "base_elev 7170\n", "trams 2\n", - "fastEight 0\n", + "fastEight 0.0\n", "fastSixes 2\n", "fastQuads 7\n", "quad 1\n", @@ -1444,17 +2965,17 @@ "double 3\n", "surface 8\n", "total_chairs 28\n", - "Runs 97\n", - "TerrainParks 3\n", + "Runs 97.0\n", + "TerrainParks 3.0\n", "LongestRun_mi 5.5\n", - "SkiableTerrain_ac 4800\n", - "Snow Making_ac 3379\n", - "daysOpenLastYear 155\n", - "yearsOpen 64\n", - "averageSnowfall 360\n", + "SkiableTerrain_ac 4800.0\n", + "Snow Making_ac 3379.0\n", + "daysOpenLastYear 155.0\n", + "yearsOpen 64.0\n", + "averageSnowfall 360.0\n", "AdultWeekday NaN\n", "AdultWeekend NaN\n", - "projectedDaysOpen 157\n", + "projectedDaysOpen 157.0\n", "NightSkiing_ac NaN" ] }, @@ -1553,13 +3074,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#Code task 24#\n", "#Drop the 'fastEight' column from ski_data. Use inplace=True\n", - "ski_data.drop(columns=___, inplace=___)" + "ski_data.drop(columns='fastEight', inplace=True)" ] }, { @@ -1571,13 +3092,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "34 104.0\n", + "115 2019.0\n", + "Name: yearsOpen, dtype: float64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 25#\n", "#Filter the 'yearsOpen' column for values greater than 100\n", - "ski_data.___[ski_data.___ > ___]" + "ski_data.yearsOpen[ski_data.yearsOpen > 100]" ] }, { @@ -1596,14 +3130,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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COPGbmRXGid/MrDBO/GZmhak88UvaVNKtkq7Ow7tLuknSPZIuk7R51TGYmdnzmtHi/wBwZ83w54EvR8SewCrglCbEYGZmWaWJX9LOwFHA+XlYwKHAwjzJPOC4KmMwM7P1KSKqW7i0EPgcsA1wGnAS8Kvc2kfSLsCPImJaL/POBmYDtLe3HzB//vzK4hxJuru7aWtra3UYTTda671k+ZqGpps+eUKv5aO13kPlejfHjBkzFkdER335uKpWKOloYGVELJbUOdj5I2IuMBego6MjOjsHvYhRqauri1LqWmu01vukM65paLplJ3T2Wj5a6z1UrndrVZb4gYOBN0h6PbAlsC3wFWCipHERsQ7YGVheYQxmZlansj7+iPhIROwcEVOA44GfRsQJwPXAm/Nks4Arq4rBzMw21Irr+P8V+JCke4AdgAtaEIOZWbGq7Op5TkR0AV359VLgwGas18zMNuRf7pqZFcaJ38ysME78ZmaFceI3MyuME7+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRWmocQv6eBGyszMbORrtMX/1QbLzMxshOv3Xj2SXgG8EthJ0odqRm0LbFplYGZmVo2BbtK2OdCWp9umpvxvPH9rZTMzG0X6TfwRcQNwg6QLI+K+JsVkZmYVavS2zFtImgtMqZ0nIg6tIigzM6tOo4n/e8B5wPnAM9WFY2ZmVWs08a+LiG9UGomZmTVFo5dz/kDSeyVNkrR9z1+lkZmZWSUabfHPyv9PrykLYI/hDcfMzKrWUOKPiN2rDsTMzJqjocQv6R29lUfERcMbjpmZVa3Rrp6X1bzeEjgM+A3gxG/WgClnXNNr+Zzp6zipbtyyc45qRkhWsEa7ek6tHZY0EZhfRUBmZlatjb0t81rA/f5mZqNQo338PyBdxQPp5mwvBhZUFZSZmVWn0T7+L9a8XgfcFxEP9DeDpC2BG4Et8noWRsSnJO1O6ibaAVgMnBgRTw06cjMz2ygNdfXkm7XdRbpD53ZAI4n6SeDQiNgX2A84QtJBwOeBL0fEnsAq4JSNiNvMzDZSo0/gmgncDLwFmAncJKnf2zJH0p0HN8t/ARwKLMzl84DjBh+2mZltLEXEwBNJvwMOj4iVeXgn4H9ya76/+TYldefsCfwn8AXgV7m1j6RdgB9FxLRe5p0NzAZob28/YP78Mi4i6u7upq2trdVhNN1orfeS5WuGNH/7VrDiifXLpk+eMKRljgajdX8PVbPrPWPGjMUR0VFf3mgf/yY9ST97lAa+LUTEM8B++fLPK4C9G1wfETEXmAvQ0dERnZ2djc46qnV1dVFKXWuN1nrXX4M/WHOmr+PcJeu/DZed0DmkZY4Go3V/D9VIqXejif9aST8GLs3DbwV+2OhKImK1pOuBVwATJY2LiHXAzsDywQRsZmZD02+rXdKekg6OiNOBbwIvyX+/JLfG+5l3p9zSR9JWwOHAncD1PP/YxlnAlUOpgJmZDc5ALf5/Bz4CEBGXA5cDSJqexx3Tz7yTgHm5n38TYEFEXC3pDmC+pLOBW4ELhlIBMzMbnIESf3tELKkvjIglkqb0N2NE3Abs30v5UuDAwQRpZmbDZ6ATtBP7GbfVMMZhZmZNMlCL/xZJ746Ib9UWSnoX6TJNszGpr7tpmo0FAyX+DwJXSDqB5xN9B7A58MYK4zIzs4r0m/gjYgXwSkkzgJ4fWV0TET+tPDIzM6tEo/fjv550GaaZmY1yG3s/fjMzG6Wc+M3MCuPEb2ZWmEbv1WNmVonBXDrrB9EPD7f4zcwK48RvZlYYJ34zs8I48ZuZFcaJ38ysME78ZmaFceI3MyuME7+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGN2kzs0r0d/O1OdPXcZKfa9wybvGbmRXGid/MrDBO/GZmhamsj1/SLsBFQDsQwNyI+Iqk7YHLgCnAMmBmRKyqKg6z0abRB5P4oSS2saps8a8D5kTEPsBBwPsk7QOcASyKiKnAojxsZmZNUlnij4iHIuI3+fVjwJ3AZOBYYF6ebB5wXFUxmJnZhhQR1a9EmgLcCEwD/hwRE3O5gFU9w3XzzAZmA7S3tx8wf/78yuMcCbq7u2lra2t1GE031HovWb6moemmT54wrMsbqvatYMUTGzdvo3Vplf624cbWe6TXeSDNfn/PmDFjcUR01JdXnvgltQE3AJ+NiMslra5N9JJWRcR2/S2jo6MjbrnllkrjHCm6urro7OxsdRhNN9R6D3e/+GAeAD4Uc6av49wlG3eqbaT38Q90Hf/G1Huk13kgzX5/S+o18Vd6VY+kzYDvA9+NiMtz8QpJk/L4ScDKKmMwM7P1VZb4czfOBcCdEfGlmlFXAbPy61nAlVXFYGZmG6rylg0HAycCSyT9Npd9FDgHWCDpFOA+YGaFMZiZWZ3KEn9E/BxQH6MPq2q9ZmbWP/9y18ysME78ZmaFceI3MyuME7+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGid/MrDBO/GZmhXHiNzMrTJU3aTOzEcDP8LV6bvGbmRXGid/MrDDu6rGiNOuRis0wlupizeUWv5lZYZz4zcwK48RvZlYYJ34zs8I48ZuZFcaJ38ysML6c00Y0X7LYPP6Fbznc4jczK4wTv5lZYZz4zcwKU1nil/RtSSsl3V5Ttr2k6yT9Mf/frqr1m5lZ76ps8V8IHFFXdgawKCKmAovysJmZNVFliT8ibgT+Wld8LDAvv54HHFfV+s3MrHeKiOoWLk0Bro6IaXl4dURMzK8FrOoZ7mXe2cBsgPb29gPmz59fWZxVWLJ8TcPTTp884bnX3d3dtLW1VRHSiNZXvQezHUej9q1gxROtjmJwao/X/vS37za23o2ue6Rq9vt7xowZiyOio768ZdfxR0RI6vNTJyLmAnMBOjo6orOzs1mhDYuTBnH9+bITOp973dXVxWir63Doq96D2Y6j0Zzp6zh3yej6OU3t8dqf/vbdxta70XWPVCPl/d3sq3pWSJoEkP+vbPL6zcyK1+zEfxUwK7+eBVzZ5PWbmRWvyss5LwV+CbxI0gOSTgHOAQ6X9EfgtXnYzMyaqLLOxYh4Wx+jDqtqnWZmNjD/ctfMrDCj63ICG9F8d0ez0cEtfjOzwjjxm5kVxl091nS9dQnNmb5uzP9Yy2ykcIvfzKwwTvxmZoVxV48NyM+9NRtb3OI3MyuME7+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGid/MrDBO/GZmhXHiNzMrjBO/mVlhxvwtG/xwELOxw+/n4eEWv5lZYZz4zcwKM+a7eoZbFXeqrF1mMx9I4q/DtjF8t9bRzy1+M7PCOPGbmRXGXT1ZiV9fS6yz2cYY7quJWn11Ukta/JKOkHS3pHskndGKGMzMStX0xC9pU+A/gSOBfYC3Sdqn2XGYmZWqFS3+A4F7ImJpRDwFzAeObUEcZmZFUkQ0d4XSm4EjIuJdefhE4OUR8f666WYDs/Pgi4C7mxpo6+wIPNLqIFrA9S6L690cu0XETvWFI/bkbkTMBea2Oo5mk3RLRHS0Oo5mc73L4nq3Viu6epYDu9QM75zLzMysCVqR+H8NTJW0u6TNgeOBq1oQh5lZkZre1RMR6yS9H/gxsCnw7Yj4fbPjGMGK697KXO+yuN4t1PSTu2Zm1lq+ZYOZWWGc+M3MCuPE3yKSdpF0vaQ7JP1e0gdy+faSrpP0x/x/u1bHWgVJm0q6VdLVeXh3STfl23hclk/8jymSJkpaKOkuSXdKekUJ+1vSv+Rj/HZJl0racqzub0nflrRS0u01Zb3uYyX/kbfBbZJe2qw4nfhbZx0wJyL2AQ4C3pdvXXEGsCgipgKL8vBY9AHgzprhzwNfjog9gVXAKS2JqlpfAa6NiL2BfUn1H9P7W9Jk4J+BjoiYRrqg43jG7v6+EDiirqyvfXwkMDX/zQa+0aQYnfhbJSIeiojf5NePkZLAZNLtK+blyeYBx7UkwApJ2hk4Cjg/Dws4FFiYJxlz9ZY0AXg1cAFARDwVEaspYH+Trh7cStI4YGvgIcbo/o6IG4G/1hX3tY+PBS6K5FfAREmTmhGnE/8IIGkKsD9wE9AeEQ/lUQ8D7a2Kq0L/DnwYeDYP7wCsjoh1efgB0ofgWLI78BfgO7mL63xJ4xnj+zsilgNfBP5MSvhrgMWM/f1dq699PBm4v2a6pm0HJ/4Wk9QGfB/4YET8rXZcpGttx9T1tpKOBlZGxOJWx9Jk44CXAt+IiP2BtdR164zR/b0dqWW7O/ACYDwbdoUUY6TsYyf+FpK0GSnpfzciLs/FK3q+7uX/K1sVX0UOBt4gaRnpzqyHkvq+J+auABibt/F4AHggIm7KwwtJHwRjfX+/Frg3Iv4SEU8Dl5OOgbG+v2v1tY9bdvsaJ/4Wyf3aFwB3RsSXakZdBczKr2cBVzY7tipFxEciYueImEI6yffTiDgBuB54c55sLNb7YeB+SS/KRYcBdzDG9zepi+cgSVvnY76n3mN6f9fpax9fBbwjX91zELCmpkuoUv7lbotIOgT4GbCE5/u6P0rq518A7ArcB8yMiPqTRWOCpE7gtIg4WtIepG8A2wO3Am+PiCdbGN6wk7Qf6YT25sBS4GRS42tM729JnwbeSrqS7VbgXaS+7DG3vyVdCnSSbr+8AvgU8N/0so/zB+HXSF1fjwMnR8QtTYnTid/MrCzu6jEzK4wTv5lZYZz4zcwK48RvZlYYJ34zs8I48duYk6+L/rmkI2vK3iLp2lbGZTZS+HJOG5MkTQO+R7oH0jjSteJHRMSfNmJZ42ruK2M26jnx25gl6f+S7okzPv/fDZgGbAacGRFX5hvkXZynAXh/RPwi/7jsLNItg/cmfYAsIP2sflPgrIi4rG59+wHnke5A+SfgnRGxSlIX8DvgNaQPoXdGxM35Jm1f7SWmk4A35OW8ELgiIj48nNvGyubEb2NWTqy/AZ4CrgZ+HxGXSJoI3ExK5gE8GxF/lzQVuDQiOnLivwaYFhH3SnoT6Rv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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 26#\n", "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n", "#Pass the argument bins=30 to hist(), but feel free to explore other values\n", - "ski_data.___[ski_data.___ < ___].hist(___)\n", + "ski_data.yearsOpen[ski_data.yearsOpen < 2000].hist(bins=30)\n", "plt.xlabel('Years open')\n", "plt.ylabel('Count')\n", "plt.title('Distribution of years open excluding 2019');" @@ -1720,9 +3267,116 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_ac
0Alaska32280.0345.04.0580.0
1Arizona21577.0237.06.080.0
2California2125948.02738.081.0587.0
3Colorado2243682.03258.074.0428.0
4Connecticut5358.0353.010.0256.0
\n", + "
" + ], + "text/plain": [ + " state resorts_per_state state_total_skiable_area_ac \\\n", + "0 Alaska 3 2280.0 \n", + "1 Arizona 2 1577.0 \n", + "2 California 21 25948.0 \n", + "3 Colorado 22 43682.0 \n", + "4 Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "0 345.0 4.0 \n", + "1 237.0 6.0 \n", + "2 2738.0 81.0 \n", + "3 3258.0 74.0 \n", + "4 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac \n", + "0 580.0 \n", + "1 80.0 \n", + "2 587.0 \n", + "3 428.0 \n", + "4 256.0 " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 27#\n", "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n", @@ -1733,10 +3387,10 @@ "state_summary = ski_data.groupby('state').agg(\n", " resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n", " state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n", - " state_total_days_open=pd.NamedAgg(column=__, aggfunc='sum'),\n", - " ___=pd.NamedAgg(column=___, aggfunc=___),\n", - " ___=pd.NamedAgg(column=___, aggfunc=___)\n", - ").___\n", + " state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n", + " state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n", + " state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n", + ").reset_index()\n", "state_summary.head()" ] }, @@ -1787,13 +3441,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "#Code task 28#\n", "#Use `missing_price` to remove rows from ski_data where both price values are missing\n", - "ski_data = ski_data[___ != 2]" + "ski_data = ski_data[missing_price != 2]" ] }, { @@ -1810,7 +3464,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -1849,14 +3503,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "#Code task 29#\n", "#Use pandas' `read_html` method to read the table from the URL below\n", "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n", - "usa_states = pd.___(___)" + "usa_states = pd.read_html(states_url)" ] }, { @@ -1927,8 +3581,8 @@ " \n", " Name &postal abbs. [1]\n", " Cities\n", - " Established[upper-alpha 1]\n", - " Population[upper-alpha 2][3]\n", + " Established[A]\n", + " Population[B][3]\n", " Total area[4]\n", " Land area[4]\n", " Water area[4]\n", @@ -1940,8 +3594,8 @@ " Name &postal abbs. [1].1\n", " Capital\n", " Largest[5]\n", - " Established[upper-alpha 1]\n", - " Population[upper-alpha 2][3]\n", + " Established[A]\n", + " Population[B][3]\n", " mi2\n", " km2\n", " mi2\n", @@ -2045,21 +3699,21 @@ "3 Arkansas AR Little Rock Little Rock \n", "4 California CA Sacramento Los Angeles \n", "\n", - " Established[upper-alpha 1] Population[upper-alpha 2][3] Total area[4] \\\n", - " Established[upper-alpha 1] Population[upper-alpha 2][3] mi2 \n", - "0 Dec 14, 1819 4903185 52420 \n", - "1 Jan 3, 1959 731545 665384 \n", - "2 Feb 14, 1912 7278717 113990 \n", - "3 Jun 15, 1836 3017804 53179 \n", - "4 Sep 9, 1850 39512223 163695 \n", + " Established[A] Population[B][3] Total area[4] Land area[4] \\\n", + " Established[A] Population[B][3] mi2 km2 mi2 \n", + "0 Dec 14, 1819 4903185 52420 135767 50645 \n", + "1 Jan 3, 1959 731545 665384 1723337 570641 \n", + "2 Feb 14, 1912 7278717 113990 295234 113594 \n", + "3 Jun 15, 1836 3017804 53179 137732 52035 \n", + "4 Sep 9, 1850 39512223 163695 423967 155779 \n", "\n", - " Land area[4] Water area[4] Numberof Reps. \n", - " km2 mi2 km2 mi2 km2 Numberof Reps. \n", - "0 135767 50645 131171 1775 4597 7 \n", - "1 1723337 570641 1477953 94743 245384 1 \n", - "2 295234 113594 294207 396 1026 9 \n", - "3 137732 52035 134771 1143 2961 4 \n", - "4 423967 155779 403466 7916 20501 53 " + " Water area[4] Numberof Reps. \n", + " km2 mi2 km2 Numberof Reps. \n", + "0 131171 1775 4597 7 \n", + "1 1477953 94743 245384 1 \n", + "2 294207 396 1026 9 \n", + "3 134771 1143 2961 4 \n", + "4 403466 7916 20501 53 " ] }, "execution_count": 47, @@ -2081,14 +3735,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "#Code task 30#\n", "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n", "#It should be a column of dates\n", - "established = usa_sates.___[:, 4]" + "established = usa_states.iloc[:, 4]" ] }, { @@ -2149,37 +3803,112 @@ "47 Jun 20, 1863\n", "48 May 29, 1848\n", "49 Jul 10, 1890\n", - "Name: (Established[upper-alpha 1], Established[upper-alpha 1]), dtype: object" + "Name: (Established[A], Established[A]), dtype: object" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "established" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the state name, population, and total area (square miles) columns." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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statestate_populationstate_area_sq_miles
0Alabama490318552420
1Alaska731545665384
2Arizona7278717113990
3Arkansas301780453179
4California39512223163695
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" + ], + "text/plain": [ + " state state_population state_area_sq_miles\n", + "0 Alabama 4903185 52420\n", + "1 Alaska 731545 665384\n", + "2 Arizona 7278717 113990\n", + "3 Arkansas 3017804 53179\n", + "4 California 39512223 163695" ] }, - "execution_count": 49, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "established" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extract the state name, population, and total area (square miles) columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "#Code task 31#\n", "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n", "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n", "#respectively.\n", - "usa_states_sub = usa_states.___[:, [___]].copy()\n", - "usa_states_sub.columns = [___]\n", + "usa_states_sub = usa_states.iloc[:, [0, 5, 6]].copy()\n", + "usa_states_sub.columns = ['state', 'state_population', 'state_area_sq_miles']\n", "usa_states_sub.head()" ] }, @@ -2192,14 +3921,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 32#\n", "#Find the states in `state_summary` that are not in `usa_states_sub`\n", "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n", - "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n", + "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", "missing_states" ] }, @@ -2225,11 +3965,11 @@ { "data": { "text/plain": [ - "20 Massachusetts[upper-alpha 3]\n", - "37 Pennsylvania[upper-alpha 3]\n", - "38 Rhode Island[upper-alpha 4]\n", - "45 Virginia[upper-alpha 3]\n", - "47 West Virginia\n", + "20 Massachusetts[C]\n", + "37 Pennsylvania[C]\n", + "38 Rhode Island[D]\n", + "45 Virginia[C]\n", + "47 West Virginia\n", "Name: state, dtype: object" ] }, @@ -2251,9 +3991,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "20 Massachusetts\n", + "37 Pennsylvania\n", + "38 Rhode Island\n", + "45 Virginia\n", + "47 West Virginia\n", + "Name: state, dtype: object" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 33#\n", "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n", @@ -2262,20 +4018,31 @@ "#value='' #empty string as replacement\n", "#regex=True #we used a regex in our `to_replace` argument\n", "#inplace=True #Do this \"in place\"\n", - "usa_states_sub.state.___(to_replace=___, value=__, regex=___, inplace=___)\n", + "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n", "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 34#\n", "#And now verify none of our states are missing by checking that there are no states in\n", "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n", - "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n", + "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", "missing_states" ] }, @@ -2288,14 +4055,133 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acstate_populationstate_area_sq_miles
0Alaska32280.0345.04.0580.0731545665384
1Arizona21577.0237.06.080.07278717113990
2California2125948.02738.081.0587.039512223163695
3Colorado2243682.03258.074.0428.05758736104094
4Connecticut5358.0353.010.0256.035652785543
\n", + "
" + ], + "text/plain": [ + " state resorts_per_state state_total_skiable_area_ac \\\n", + "0 Alaska 3 2280.0 \n", + "1 Arizona 2 1577.0 \n", + "2 California 21 25948.0 \n", + "3 Colorado 22 43682.0 \n", + "4 Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "0 345.0 4.0 \n", + "1 237.0 6.0 \n", + "2 2738.0 81.0 \n", + "3 3258.0 74.0 \n", + "4 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac state_population state_area_sq_miles \n", + "0 580.0 731545 665384 \n", + "1 80.0 7278717 113990 \n", + "2 587.0 39512223 163695 \n", + "3 428.0 5758736 104094 \n", + "4 256.0 3565278 5543 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 35#\n", "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n", "#specify the arguments how='left' and on='state'\n", - "state_summary = state_summary.___(usa_states_sub, ___=___, ___=___)\n", + "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n", "state_summary.head()" ] }, @@ -2322,14 +4208,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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The dataset had 330 records with 27 features including the record for 'Big Mountain Resort' which is located in Montana and is the resort we are looking to develop a pricing model for. The dataset appeared to have quite a few missing values, but fortunatedly, the record for Big Mountain Resort had no missing values. With that said, some records were missing values for the features 'AdultWeekday' and 'AdultWeekend' which represent the prices for each resort and were decidedly the target variables for the study. The feature with the most missing values was 'fastEight' feature with about 50% of records missing a value for this feature. Also, all the records except one had a value of zero under fastEight. This feature would eventually be dropped along with the only resort to have been open for over 100 years.\n", + "\n", + "The silverton mountain skiable area was a huge outlier in the data. After researching online about the skiable area for that resort, it was found that the skiable area was much lower. In the data it was 26819, but online it was 1819. It is possible that there may have been an error in recording the data. A decision was made to replace the original value with 1819.\n", + "\n", + "The records mostly consisted of numerical variables with the only categorical variables being 'name', 'state', and 'region'. Sometimes, state and region can have the same name on the same record and regions can have the same names in different states. But there are no records that have the exact same name, state, and region meaning there are no duplicate records. \n", + "\n", + "The states Colorado, Michigan, and New York had the highest number of resorts which could be because of the high populations in those states. \n", + "\n", + "The Data will be explored some more in due course." + ] } ], "metadata": { @@ -2763,7 +4799,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.8.5" }, "toc": { "base_numbering": 1, diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb index c1746d2e4..4df033550 100644 --- a/Notebooks/03_exploratory_data_analysis.ipynb +++ b/Notebooks/03_exploratory_data_analysis.ipynb @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2020-10-07T07:04:19.124917Z", @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -154,7 +154,7 @@ " 23 projectedDaysOpen 236 non-null float64\n", " 24 NightSkiing_ac 163 non-null float64\n", "dtypes: float64(11), int64(11), object(3)\n", - "memory usage: 54.2+ KB\n" + "memory usage: 50.9+ KB\n" ] } ], @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -369,7 +369,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 4, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -417,7 +417,7 @@ " 6 state_population 35 non-null int64 \n", " 7 state_area_sq_miles 35 non-null int64 \n", "dtypes: float64(4), int64(3), object(1)\n", - "memory usage: 2.3+ KB\n" + "memory usage: 2.1+ KB\n" ] } ], @@ -427,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -546,7 +546,7 @@ "4 256.0 3565278 5543 " ] }, - "execution_count": 7, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -594,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -609,7 +609,7 @@ "Name: state_area_sq_miles, dtype: int64" ] }, - "execution_count": 9, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -649,7 +649,7 @@ "Name: state_population, dtype: int64" ] }, - "execution_count": 10, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -674,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -689,7 +689,7 @@ "Name: resorts_per_state, dtype: int64" ] }, - "execution_count": 11, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -714,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -729,7 +729,7 @@ "Name: state_total_skiable_area_ac, dtype: float64" ] }, - "execution_count": 12, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -769,7 +769,7 @@ "Name: state_total_nightskiing_ac, dtype: float64" ] }, - "execution_count": 13, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -794,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -809,7 +809,7 @@ "Name: state_total_days_open, dtype: float64" ] }, - "execution_count": 14, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -843,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -960,7 +960,7 @@ "4 256.0 0.140242 90.203861 " ] }, - "execution_count": 15, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -989,12 +989,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1013,12 +1013,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1051,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1066,7 +1066,7 @@ "Name: resorts_per_100kcapita, dtype: float64" ] }, - "execution_count": 18, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1077,7 +1077,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1092,7 +1092,7 @@ "Name: resorts_per_100ksq_mile, dtype: float64" ] }, - "execution_count": 19, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1152,17 +1152,151 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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resorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acresorts_per_100kcapitaresorts_per_100ksq_mile
state
Alaska32280.0345.04.0580.00.4100910.450867
Arizona21577.0237.06.080.00.0274771.754540
California2125948.02738.081.0587.00.05314812.828736
Colorado2243682.03258.074.0428.00.38202821.134744
Connecticut5358.0353.010.0256.00.14024290.203861
\n", + "
" + ], + "text/plain": [ + " resorts_per_state state_total_skiable_area_ac \\\n", + "state \n", + "Alaska 3 2280.0 \n", + "Arizona 2 1577.0 \n", + "California 21 25948.0 \n", + "Colorado 22 43682.0 \n", + "Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "state \n", + "Alaska 345.0 4.0 \n", + "Arizona 237.0 6.0 \n", + "California 2738.0 81.0 \n", + "Colorado 3258.0 74.0 \n", + "Connecticut 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac resorts_per_100kcapita \\\n", + "state \n", + "Alaska 580.0 0.410091 \n", + "Arizona 80.0 0.027477 \n", + "California 587.0 0.053148 \n", + "Colorado 428.0 0.382028 \n", + "Connecticut 256.0 0.140242 \n", + "\n", + " resorts_per_100ksq_mile \n", + "state \n", + "Alaska 0.450867 \n", + "Arizona 1.754540 \n", + "California 12.828736 \n", + "Colorado 21.134744 \n", + "Connecticut 90.203861 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 1#\n", "#Create a new dataframe, `state_summary_scale` from `state_summary` whilst setting the index to 'state'\n", - "state_summary_scale = state_summary.set_index(___)\n", + "state_summary_scale = state_summary.set_index('state')\n", "#Save the state labels (using the index attribute of `state_summary_scale`) into the variable 'state_summary_index'\n", - "state_summary_index = state_summary_scale.___\n", + "state_summary_index = state_summary_scale.index\n", "#Save the column names (using the `columns` attribute) of `state_summary_scale` into the variable 'state_summary_columns'\n", - "state_summary_columns = state_summary_scale.___\n", + "state_summary_columns = state_summary_scale.columns\n", "state_summary_scale.head()" ] }, @@ -1177,7 +1311,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1193,13 +1327,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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resorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acresorts_per_100kcapitaresorts_per_100ksq_mile
0-0.806912-0.392012-0.689059-0.8161180.0694100.139593-0.689999
1-0.933558-0.462424-0.819038-0.726994-0.701326-0.644706-0.658125
21.4727061.9785742.1909332.6151410.080201-0.592085-0.387368
31.5993513.7548112.8167572.303209-0.1648930.082069-0.184291
4-0.553622-0.584519-0.679431-0.548747-0.430027-0.4135571.504408
\n", + "
" + ], + "text/plain": [ + " resorts_per_state state_total_skiable_area_ac state_total_days_open \\\n", + "0 -0.806912 -0.392012 -0.689059 \n", + "1 -0.933558 -0.462424 -0.819038 \n", + "2 1.472706 1.978574 2.190933 \n", + "3 1.599351 3.754811 2.816757 \n", + "4 -0.553622 -0.584519 -0.679431 \n", + "\n", + " state_total_terrain_parks state_total_nightskiing_ac \\\n", + "0 -0.816118 0.069410 \n", + "1 -0.726994 -0.701326 \n", + "2 2.615141 0.080201 \n", + "3 2.303209 -0.164893 \n", + "4 -0.548747 -0.430027 \n", + "\n", + " resorts_per_100kcapita resorts_per_100ksq_mile \n", + "0 0.139593 -0.689999 \n", + "1 -0.644706 -0.658125 \n", + "2 -0.592085 -0.387368 \n", + "3 0.082069 -0.184291 \n", + "4 -0.413557 1.504408 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 2#\n", "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n", - "state_summary_scaled_df = pd.DataFrame(___, columns=___)\n", + "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n", "state_summary_scaled_df.head()" ] }, @@ -1226,13 +1473,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "resorts_per_state -7.295751e-17\n", + "state_total_skiable_area_ac -4.163336e-17\n", + "state_total_days_open 7.692260e-17\n", + "state_total_terrain_parks 4.599495e-17\n", + "state_total_nightskiing_ac 7.612958e-17\n", + "resorts_per_100kcapita 5.075305e-17\n", + "resorts_per_100ksq_mile 5.075305e-17\n", + "dtype: float64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 3#\n", "#Call `state_summary_scaled_df`'s `mean()` method\n", - "state_summary_scaled_df.___" + "state_summary_scaled_df.mean()" ] }, { @@ -1251,13 +1516,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "resorts_per_state 1.014599\n", + "state_total_skiable_area_ac 1.014599\n", + "state_total_days_open 1.014599\n", + "state_total_terrain_parks 1.014599\n", + "state_total_nightskiing_ac 1.014599\n", + "resorts_per_100kcapita 1.014599\n", + "resorts_per_100ksq_mile 1.014599\n", + "dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 4#\n", "#Call `state_summary_scaled_df`'s `std()` method\n", - "state_summary_scaled_df.___" + "state_summary_scaled_df.std()" ] }, { @@ -1271,13 +1554,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "resorts_per_state 1.0\n", + "state_total_skiable_area_ac 1.0\n", + "state_total_days_open 1.0\n", + "state_total_terrain_parks 1.0\n", + "state_total_nightskiing_ac 1.0\n", + "resorts_per_100kcapita 1.0\n", + "resorts_per_100ksq_mile 1.0\n", + "dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 5#\n", "#Repeat the previous call to `std()` but pass in ddof=0 \n", - "state_summary_scaled_df.___(___)" + "state_summary_scaled_df.std(ddof=0)" ] }, { @@ -1303,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1319,9 +1620,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 6#\n", "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n", @@ -1330,10 +1644,10 @@ "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n", "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n", "plt.subplots(figsize=(10, 6))\n", - "plt.plot(state_pca.explained_variance_ratio_.___)\n", - "plt.xlabel(___)\n", - "plt.ylabel(___)\n", - "plt.title(___);" + "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n", + "plt.xlabel('Component #')\n", + "plt.ylabel('Cumulative ratio variance')\n", + "plt.title('Cumulative variance ratio explained by PCA components for state/resort summary statistics');" ] }, { @@ -1359,18 +1673,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#Code task 7#\n", "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n", - "state_pca_x = state_pca.___(___)" + "state_pca_x = state_pca.transform(state_summary_scale)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1379,7 +1693,7 @@ "(35, 7)" ] }, - "execution_count": 29, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1406,12 +1720,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1452,24 +1766,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "state\n", + "Alaska 57.333333\n", + "Arizona 83.500000\n", + "California 81.416667\n", + "Colorado 90.714286\n", + "Connecticut 56.800000\n", + "Name: AdultWeekend, dtype: float64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 8#\n", "#Calculate the average 'AdultWeekend' ticket price by state\n", - "state_avg_price = ski_data.groupby(___)[___].___\n", + "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n", "state_avg_price.head()" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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PC1PC2
state
Alaska-1.336533-0.182208
Arizona-1.839049-0.387959
California3.537857-1.282509
Colorado4.402210-0.898855
Connecticut-0.9880271.020218
\n", + "
" + ], + "text/plain": [ + " PC1 PC2\n", + "state \n", + "Alaska -1.336533 -0.182208\n", + "Arizona -1.839049 -0.387959\n", + "California 3.537857 -1.282509\n", + "Colorado 4.402210 -0.898855\n", + "Connecticut -0.988027 1.020218" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 9#\n", "#Create a dataframe containing the values of the first two PCA components\n", "#Remember the first component was given by state_pca_x[:, 0],\n", "#and the second by state_pca_x[:, 1]\n", "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n", - "pca_df = pd.DataFrame({'PC1': ___, 'PC2': ___}, index=__)\n", + "pca_df = pd.DataFrame({'PC1': state_pca_x[:,0], 'PC2': state_pca_x[:,1]}, index=state_summary_index)\n", "pca_df.head()" ] }, @@ -1525,7 +1931,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1540,7 +1946,7 @@ "Name: AdultWeekend, dtype: float64" ] }, - "execution_count": 34, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1552,7 +1958,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1618,7 +2024,7 @@ "Connecticut 56.800000" ] }, - "execution_count": 35, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1637,14 +2043,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PC1PC2AdultWeekend
state
Alaska-1.336533-0.18220857.333333
Arizona-1.839049-0.38795983.500000
California3.537857-1.28250981.416667
Colorado4.402210-0.89885590.714286
Connecticut-0.9880271.02021856.800000
\n", + "
" + ], + "text/plain": [ + " PC1 PC2 AdultWeekend\n", + "state \n", + "Alaska -1.336533 -0.182208 57.333333\n", + "Arizona -1.839049 -0.387959 83.500000\n", + "California 3.537857 -1.282509 81.416667\n", + "Colorado 4.402210 -0.898855 90.714286\n", + "Connecticut -0.988027 1.020218 56.800000" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 10#\n", "#Use pd.concat to concatenate `pca_df` and `state_avg_price` along axis 1\n", "# remember, pd.concat will align on index\n", - "pca_df = ___([___, ___], axis=___)\n", + "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n", "pca_df.head()" ] }, @@ -1657,7 +2145,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1686,6 +2174,13 @@ " AdultWeekend\n", " Quartile\n", " \n", + " \n", + " state\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1729,6 +2224,7 @@ ], "text/plain": [ " PC1 PC2 AdultWeekend Quartile\n", + "state \n", "Alaska -1.336533 -0.182208 57.333333 (53.1, 60.4]\n", "Arizona -1.839049 -0.387959 83.500000 (78.4, 93.0]\n", "California 3.537857 -1.282509 81.416667 (78.4, 93.0]\n", @@ -1736,7 +2232,7 @@ "Connecticut -0.988027 1.020218 56.800000 (53.1, 60.4]" ] }, - "execution_count": 37, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1748,7 +2244,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1761,7 +2257,7 @@ "dtype: object" ] }, - "execution_count": 38, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1781,7 +2277,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1810,6 +2306,13 @@ " AdultWeekend\n", " Quartile\n", " \n", + " \n", + " state\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1825,10 +2328,11 @@ ], "text/plain": [ " PC1 PC2 AdultWeekend Quartile\n", + "state \n", "Rhode Island -1.843646 0.761339 NaN NaN" ] }, - "execution_count": 39, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1853,20 +2357,20 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PC1 -1.84365\n", - "PC2 0.761339\n", - "AdultWeekend 64.1244\n", - "Quartile NA\n", + "PC1 -1.843646\n", + "PC2 0.761339\n", + "AdultWeekend 64.124388\n", + "Quartile NA\n", "Name: Rhode Island, dtype: object" ] }, - "execution_count": 40, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1949,9 +2453,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 11#\n", "#Create a seaborn scatterplot by calling `sns.scatterplot`\n", @@ -1966,8 +2483,8 @@ "plt.subplots(figsize=(12, 10))\n", "# Note the argument below to make sure we get the colours in the ascending\n", "# order we intuitively expect!\n", - "sns.___(x=___, y=___, size=___, hue=___, \n", - " hue_order=___, data=pca_df)\n", + "sns.scatterplot(x=x, y=y, size='AdultWeekend', hue='Quartile', \n", + " hue_order=pca_df.Quartile.cat.categories, data=pca_df)\n", "#and we can still annotate with the state labels\n", "for s, x, y in zip(state, x, y):\n", " plt.annotate(s, (x, y)) \n", @@ -2380,11 +2897,11 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "***Since there does not seem to be any categorical patterns in the data especially from the features that contribute most to the variance of the data, we may consider treating all states the same in our analysis. Fortunately, we have been able to identify some key features in the data that can help predict the price of the resorts in our dataset.***" + ] }, { "cell_type": "markdown", @@ -2395,7 +2912,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -2541,91 +3058,91 @@ " \n", " \n", " Runs\n", - " 76\n", - " 36\n", - " 13\n", - " 55\n", - " 65\n", + " 76.0\n", + " 36.0\n", + " 13.0\n", + " 55.0\n", + " 65.0\n", " \n", " \n", " TerrainParks\n", - " 2\n", - " 1\n", - " 1\n", - " 4\n", - " 2\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 4.0\n", + " 2.0\n", " \n", " \n", " LongestRun_mi\n", - " 1\n", - " 2\n", - " 1\n", - " 2\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", " 1.2\n", " \n", " \n", " SkiableTerrain_ac\n", - " 1610\n", - " 640\n", - " 30\n", - " 777\n", - " 800\n", + " 1610.0\n", + " 640.0\n", + " 30.0\n", + " 777.0\n", + " 800.0\n", " \n", " \n", " Snow Making_ac\n", - " 113\n", - " 60\n", - " 30\n", - " 104\n", - " 80\n", + " 113.0\n", + " 60.0\n", + " 30.0\n", + " 104.0\n", + " 80.0\n", " \n", " \n", " daysOpenLastYear\n", - " 150\n", - " 45\n", - " 150\n", - " 122\n", - " 115\n", + " 150.0\n", + " 45.0\n", + " 150.0\n", + " 122.0\n", + " 115.0\n", " \n", " \n", " yearsOpen\n", - " 60\n", - " 44\n", - " 36\n", - " 81\n", - " 49\n", + " 60.0\n", + " 44.0\n", + " 36.0\n", + " 81.0\n", + " 49.0\n", " \n", " \n", " averageSnowfall\n", - " 669\n", - " 350\n", - " 69\n", - " 260\n", - " 250\n", + " 669.0\n", + " 350.0\n", + " 69.0\n", + " 260.0\n", + " 250.0\n", " \n", " \n", " AdultWeekend\n", - " 85\n", - " 53\n", - " 34\n", - " 89\n", - " 78\n", + " 85.0\n", + " 53.0\n", + " 34.0\n", + " 89.0\n", + " 78.0\n", " \n", " \n", " projectedDaysOpen\n", - " 150\n", - " 90\n", - " 152\n", - " 122\n", - " 104\n", + " 150.0\n", + " 90.0\n", + " 152.0\n", + " 122.0\n", + " 104.0\n", " \n", " \n", " NightSkiing_ac\n", - " 550\n", + " 550.0\n", " NaN\n", - " 30\n", + " 30.0\n", " NaN\n", - " 80\n", + " 80.0\n", " \n", " \n", "\n", @@ -2647,17 +3164,17 @@ "double 0 4 0 \n", "surface 2 0 2 \n", "total_chairs 7 4 3 \n", - "Runs 76 36 13 \n", - "TerrainParks 2 1 1 \n", - "LongestRun_mi 1 2 1 \n", - "SkiableTerrain_ac 1610 640 30 \n", - "Snow Making_ac 113 60 30 \n", - "daysOpenLastYear 150 45 150 \n", - "yearsOpen 60 44 36 \n", - "averageSnowfall 669 350 69 \n", - "AdultWeekend 85 53 34 \n", - "projectedDaysOpen 150 90 152 \n", - "NightSkiing_ac 550 NaN 30 \n", + "Runs 76.0 36.0 13.0 \n", + "TerrainParks 2.0 1.0 1.0 \n", + "LongestRun_mi 1.0 2.0 1.0 \n", + "SkiableTerrain_ac 1610.0 640.0 30.0 \n", + "Snow Making_ac 113.0 60.0 30.0 \n", + "daysOpenLastYear 150.0 45.0 150.0 \n", + "yearsOpen 60.0 44.0 36.0 \n", + "averageSnowfall 669.0 350.0 69.0 \n", + "AdultWeekend 85.0 53.0 34.0 \n", + "projectedDaysOpen 150.0 90.0 152.0 \n", + "NightSkiing_ac 550.0 NaN 30.0 \n", "\n", " 3 4 \n", "Name Arizona Snowbowl Sunrise Park Resort \n", @@ -2674,20 +3191,20 @@ "double 1 1 \n", "surface 2 0 \n", "total_chairs 8 7 \n", - "Runs 55 65 \n", - "TerrainParks 4 2 \n", - "LongestRun_mi 2 1.2 \n", - "SkiableTerrain_ac 777 800 \n", - "Snow Making_ac 104 80 \n", - "daysOpenLastYear 122 115 \n", - "yearsOpen 81 49 \n", - "averageSnowfall 260 250 \n", - "AdultWeekend 89 78 \n", - "projectedDaysOpen 122 104 \n", - "NightSkiing_ac NaN 80 " + "Runs 55.0 65.0 \n", + "TerrainParks 4.0 2.0 \n", + "LongestRun_mi 2.0 1.2 \n", + "SkiableTerrain_ac 777.0 800.0 \n", + "Snow Making_ac 104.0 80.0 \n", + "daysOpenLastYear 122.0 115.0 \n", + "yearsOpen 81.0 49.0 \n", + "averageSnowfall 260.0 250.0 \n", + "AdultWeekend 89.0 78.0 \n", + "projectedDaysOpen 122.0 104.0 \n", + "NightSkiing_ac NaN 80.0 " ] }, - "execution_count": 46, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2712,7 +3229,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -2829,7 +3346,7 @@ "4 256.0 0.140242 90.203861 " ] }, - "execution_count": 47, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -2840,7 +3357,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2986,91 +3503,91 @@ " \n", " \n", " Runs\n", - " 76\n", - " 36\n", - " 13\n", - " 55\n", - " 65\n", + " 76.0\n", + " 36.0\n", + " 13.0\n", + " 55.0\n", + " 65.0\n", " \n", " \n", " TerrainParks\n", - " 2\n", - " 1\n", - " 1\n", - " 4\n", - " 2\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 4.0\n", + " 2.0\n", " \n", " \n", " LongestRun_mi\n", - " 1\n", - " 2\n", - " 1\n", - " 2\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", " 1.2\n", " \n", " \n", " SkiableTerrain_ac\n", - " 1610\n", - " 640\n", - " 30\n", - " 777\n", - " 800\n", + " 1610.0\n", + " 640.0\n", + " 30.0\n", + " 777.0\n", + " 800.0\n", " \n", " \n", " Snow Making_ac\n", - " 113\n", - " 60\n", - " 30\n", - " 104\n", - " 80\n", + " 113.0\n", + " 60.0\n", + " 30.0\n", + " 104.0\n", + " 80.0\n", " \n", " \n", " daysOpenLastYear\n", - " 150\n", - " 45\n", - " 150\n", - " 122\n", - " 115\n", + " 150.0\n", + " 45.0\n", + " 150.0\n", + " 122.0\n", + " 115.0\n", " \n", " \n", " yearsOpen\n", - " 60\n", - " 44\n", - " 36\n", - " 81\n", - " 49\n", + " 60.0\n", + " 44.0\n", + " 36.0\n", + " 81.0\n", + " 49.0\n", " \n", " \n", " averageSnowfall\n", - " 669\n", - " 350\n", - " 69\n", - " 260\n", - " 250\n", + " 669.0\n", + " 350.0\n", + " 69.0\n", + " 260.0\n", + " 250.0\n", " \n", " \n", " AdultWeekend\n", - " 85\n", - " 53\n", - " 34\n", - " 89\n", - " 78\n", + " 85.0\n", + " 53.0\n", + " 34.0\n", + " 89.0\n", + " 78.0\n", " \n", " \n", " projectedDaysOpen\n", - " 150\n", - " 90\n", - " 152\n", - " 122\n", - " 104\n", + " 150.0\n", + " 90.0\n", + " 152.0\n", + " 122.0\n", + " 104.0\n", " \n", " \n", " NightSkiing_ac\n", - " 550\n", + " 550.0\n", " NaN\n", - " 30\n", + " 30.0\n", " NaN\n", - " 80\n", + " 80.0\n", " \n", " \n", " resorts_per_state\n", @@ -3082,43 +3599,43 @@ " \n", " \n", " state_total_skiable_area_ac\n", - " 2280\n", - " 2280\n", - " 2280\n", - " 1577\n", - " 1577\n", + " 2280.0\n", + " 2280.0\n", + " 2280.0\n", + " 1577.0\n", + " 1577.0\n", " \n", " \n", " state_total_days_open\n", - " 345\n", - " 345\n", - " 345\n", - " 237\n", - " 237\n", + " 345.0\n", + " 345.0\n", + " 345.0\n", + " 237.0\n", + " 237.0\n", " \n", " \n", " state_total_terrain_parks\n", - " 4\n", - " 4\n", - " 4\n", - " 6\n", - " 6\n", + " 4.0\n", + " 4.0\n", + " 4.0\n", + " 6.0\n", + " 6.0\n", " \n", " \n", " state_total_nightskiing_ac\n", - " 580\n", - " 580\n", - " 580\n", - " 80\n", - " 80\n", + " 580.0\n", + " 580.0\n", + " 580.0\n", + " 80.0\n", + " 80.0\n", " \n", " \n", " resorts_per_100kcapita\n", " 0.410091\n", " 0.410091\n", " 0.410091\n", - " 0.0274774\n", - " 0.0274774\n", + " 0.027477\n", + " 0.027477\n", " \n", " \n", " resorts_per_100ksq_mile\n", @@ -3148,22 +3665,22 @@ "double 0 4 \n", "surface 2 0 \n", "total_chairs 7 4 \n", - "Runs 76 36 \n", - "TerrainParks 2 1 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 1610 640 \n", - "Snow Making_ac 113 60 \n", - "daysOpenLastYear 150 45 \n", - "yearsOpen 60 44 \n", - "averageSnowfall 669 350 \n", - "AdultWeekend 85 53 \n", - "projectedDaysOpen 150 90 \n", - "NightSkiing_ac 550 NaN \n", + "Runs 76.0 36.0 \n", + "TerrainParks 2.0 1.0 \n", + "LongestRun_mi 1.0 2.0 \n", + "SkiableTerrain_ac 1610.0 640.0 \n", + "Snow Making_ac 113.0 60.0 \n", + "daysOpenLastYear 150.0 45.0 \n", + "yearsOpen 60.0 44.0 \n", + "averageSnowfall 669.0 350.0 \n", + "AdultWeekend 85.0 53.0 \n", + "projectedDaysOpen 150.0 90.0 \n", + "NightSkiing_ac 550.0 NaN \n", "resorts_per_state 3 3 \n", - "state_total_skiable_area_ac 2280 2280 \n", - "state_total_days_open 345 345 \n", - "state_total_terrain_parks 4 4 \n", - "state_total_nightskiing_ac 580 580 \n", + "state_total_skiable_area_ac 2280.0 2280.0 \n", + "state_total_days_open 345.0 345.0 \n", + "state_total_terrain_parks 4.0 4.0 \n", + "state_total_nightskiing_ac 580.0 580.0 \n", "resorts_per_100kcapita 0.410091 0.410091 \n", "resorts_per_100ksq_mile 0.450867 0.450867 \n", "\n", @@ -3182,23 +3699,23 @@ "double 0 1 \n", "surface 2 2 \n", "total_chairs 3 8 \n", - "Runs 13 55 \n", - "TerrainParks 1 4 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 30 777 \n", - "Snow Making_ac 30 104 \n", - "daysOpenLastYear 150 122 \n", - "yearsOpen 36 81 \n", - "averageSnowfall 69 260 \n", - "AdultWeekend 34 89 \n", - "projectedDaysOpen 152 122 \n", - "NightSkiing_ac 30 NaN \n", + "Runs 13.0 55.0 \n", + "TerrainParks 1.0 4.0 \n", + "LongestRun_mi 1.0 2.0 \n", + "SkiableTerrain_ac 30.0 777.0 \n", + "Snow Making_ac 30.0 104.0 \n", + "daysOpenLastYear 150.0 122.0 \n", + "yearsOpen 36.0 81.0 \n", + "averageSnowfall 69.0 260.0 \n", + "AdultWeekend 34.0 89.0 \n", + "projectedDaysOpen 152.0 122.0 \n", + "NightSkiing_ac 30.0 NaN \n", "resorts_per_state 3 2 \n", - "state_total_skiable_area_ac 2280 1577 \n", - "state_total_days_open 345 237 \n", - "state_total_terrain_parks 4 6 \n", - "state_total_nightskiing_ac 580 80 \n", - "resorts_per_100kcapita 0.410091 0.0274774 \n", + "state_total_skiable_area_ac 2280.0 1577.0 \n", + "state_total_days_open 345.0 237.0 \n", + "state_total_terrain_parks 4.0 6.0 \n", + "state_total_nightskiing_ac 580.0 80.0 \n", + "resorts_per_100kcapita 0.410091 0.027477 \n", "resorts_per_100ksq_mile 0.450867 1.75454 \n", "\n", " 4 \n", @@ -3216,27 +3733,27 @@ "double 1 \n", "surface 0 \n", "total_chairs 7 \n", - "Runs 65 \n", - "TerrainParks 2 \n", + "Runs 65.0 \n", + "TerrainParks 2.0 \n", "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800 \n", - "Snow Making_ac 80 \n", - "daysOpenLastYear 115 \n", - "yearsOpen 49 \n", - "averageSnowfall 250 \n", - "AdultWeekend 78 \n", - "projectedDaysOpen 104 \n", - "NightSkiing_ac 80 \n", + "SkiableTerrain_ac 800.0 \n", + "Snow Making_ac 80.0 \n", + "daysOpenLastYear 115.0 \n", + "yearsOpen 49.0 \n", + "averageSnowfall 250.0 \n", + "AdultWeekend 78.0 \n", + "projectedDaysOpen 104.0 \n", + "NightSkiing_ac 80.0 \n", "resorts_per_state 2 \n", - "state_total_skiable_area_ac 1577 \n", - "state_total_days_open 237 \n", - "state_total_terrain_parks 6 \n", - "state_total_nightskiing_ac 80 \n", - "resorts_per_100kcapita 0.0274774 \n", + "state_total_skiable_area_ac 1577.0 \n", + "state_total_days_open 237.0 \n", + "state_total_terrain_parks 6.0 \n", + "state_total_nightskiing_ac 80.0 \n", + "resorts_per_100kcapita 0.027477 \n", "resorts_per_100ksq_mile 1.75454 " ] }, - "execution_count": 48, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -3264,7 +3781,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -3293,15 +3810,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 12#\n", "#Show a seaborn heatmap of correlations in ski_data\n", "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n", "plt.subplots(figsize=(12,10))\n", - "sns.___(ski_data.___);" + "sns.heatmap(ski_data.corr());" ] }, { @@ -3333,7 +3863,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -3355,26 +3885,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "#Code task 13#\n", "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n", "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n", - "features = [___ for ___ in ski_data.columns if ___ not in [___, ___, ___, ___]]" + "features = [i for i in ski_data.columns if i not in ['name', 'Region', 'state', 'AdultWeekend']]" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 74, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": { @@ -3403,7 +3933,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -3415,12 +3945,12 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -3468,7 +3998,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -3614,91 +4144,91 @@ " \n", " \n", " Runs\n", - " 76\n", - " 36\n", - " 13\n", - " 55\n", - " 65\n", + " 76.0\n", + " 36.0\n", + " 13.0\n", + " 55.0\n", + " 65.0\n", " \n", " \n", " TerrainParks\n", - " 2\n", - " 1\n", - " 1\n", - " 4\n", - " 2\n", + " 2.0\n", + " 1.0\n", + " 1.0\n", + " 4.0\n", + " 2.0\n", " \n", " \n", " LongestRun_mi\n", - " 1\n", - " 2\n", - " 1\n", - " 2\n", + " 1.0\n", + " 2.0\n", + " 1.0\n", + " 2.0\n", " 1.2\n", " \n", " \n", " SkiableTerrain_ac\n", - " 1610\n", - " 640\n", - " 30\n", - " 777\n", - " 800\n", + " 1610.0\n", + " 640.0\n", + " 30.0\n", + " 777.0\n", + " 800.0\n", " \n", " \n", " Snow Making_ac\n", - " 113\n", - " 60\n", - " 30\n", - " 104\n", - " 80\n", + " 113.0\n", + " 60.0\n", + " 30.0\n", + " 104.0\n", + " 80.0\n", " \n", " \n", " daysOpenLastYear\n", - " 150\n", - " 45\n", - " 150\n", - " 122\n", - " 115\n", + " 150.0\n", + " 45.0\n", + " 150.0\n", + " 122.0\n", + " 115.0\n", " \n", " \n", " yearsOpen\n", - " 60\n", - " 44\n", - " 36\n", - " 81\n", - " 49\n", + " 60.0\n", + " 44.0\n", + " 36.0\n", + " 81.0\n", + " 49.0\n", " \n", " \n", " averageSnowfall\n", - " 669\n", - " 350\n", - " 69\n", - " 260\n", - " 250\n", + " 669.0\n", + " 350.0\n", + " 69.0\n", + " 260.0\n", + " 250.0\n", " \n", " \n", " AdultWeekend\n", - " 85\n", - " 53\n", - " 34\n", - " 89\n", - " 78\n", + " 85.0\n", + " 53.0\n", + " 34.0\n", + " 89.0\n", + " 78.0\n", " \n", " \n", " projectedDaysOpen\n", - " 150\n", - " 90\n", - " 152\n", - " 122\n", - " 104\n", + " 150.0\n", + " 90.0\n", + " 152.0\n", + " 122.0\n", + " 104.0\n", " \n", " \n", " NightSkiing_ac\n", - " 550\n", + " 550.0\n", " NaN\n", - " 30\n", + " 30.0\n", " NaN\n", - " 80\n", + " 80.0\n", " \n", " \n", " resorts_per_state\n", @@ -3713,8 +4243,8 @@ " 0.410091\n", " 0.410091\n", " 0.410091\n", - " 0.0274774\n", - " 0.0274774\n", + " 0.027477\n", + " 0.027477\n", " \n", " \n", " resorts_per_100ksq_mile\n", @@ -3728,7 +4258,7 @@ " resort_skiable_area_ac_state_ratio\n", " 0.70614\n", " 0.280702\n", - " 0.0131579\n", + " 0.013158\n", " 0.492708\n", " 0.507292\n", " \n", @@ -3752,13 +4282,13 @@ " resort_night_skiing_state_ratio\n", " 0.948276\n", " NaN\n", - " 0.0517241\n", + " 0.051724\n", " NaN\n", - " 1\n", + " 1.0\n", " \n", " \n", " total_chairs_runs_ratio\n", - " 0.0921053\n", + " 0.092105\n", " 0.111111\n", " 0.230769\n", " 0.145455\n", @@ -3766,7 +4296,7 @@ " \n", " \n", " total_chairs_skiable_ratio\n", - " 0.00434783\n", + " 0.004348\n", " 0.00625\n", " 0.1\n", " 0.010296\n", @@ -3774,18 +4304,18 @@ " \n", " \n", " fastQuads_runs_ratio\n", - " 0.0263158\n", - " 0\n", - " 0\n", - " 0\n", - " 0.0153846\n", + " 0.026316\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.015385\n", " \n", " \n", " fastQuads_skiable_ratio\n", - " 0.00124224\n", - " 0\n", - " 0\n", - " 0\n", + " 0.001242\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " 0.00125\n", " \n", " \n", @@ -3808,17 +4338,17 @@ "double 0 4 \n", "surface 2 0 \n", "total_chairs 7 4 \n", - "Runs 76 36 \n", - "TerrainParks 2 1 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 1610 640 \n", - "Snow Making_ac 113 60 \n", - "daysOpenLastYear 150 45 \n", - "yearsOpen 60 44 \n", - "averageSnowfall 669 350 \n", - "AdultWeekend 85 53 \n", - "projectedDaysOpen 150 90 \n", - "NightSkiing_ac 550 NaN \n", + "Runs 76.0 36.0 \n", + "TerrainParks 2.0 1.0 \n", + "LongestRun_mi 1.0 2.0 \n", + "SkiableTerrain_ac 1610.0 640.0 \n", + "Snow Making_ac 113.0 60.0 \n", + "daysOpenLastYear 150.0 45.0 \n", + "yearsOpen 60.0 44.0 \n", + "averageSnowfall 669.0 350.0 \n", + "AdultWeekend 85.0 53.0 \n", + "projectedDaysOpen 150.0 90.0 \n", + "NightSkiing_ac 550.0 NaN \n", "resorts_per_state 3 3 \n", "resorts_per_100kcapita 0.410091 0.410091 \n", "resorts_per_100ksq_mile 0.450867 0.450867 \n", @@ -3826,10 +4356,10 @@ "resort_days_open_state_ratio 0.434783 0.130435 \n", "resort_terrain_park_state_ratio 0.5 0.25 \n", "resort_night_skiing_state_ratio 0.948276 NaN \n", - "total_chairs_runs_ratio 0.0921053 0.111111 \n", - "total_chairs_skiable_ratio 0.00434783 0.00625 \n", - "fastQuads_runs_ratio 0.0263158 0 \n", - "fastQuads_skiable_ratio 0.00124224 0 \n", + "total_chairs_runs_ratio 0.092105 0.111111 \n", + "total_chairs_skiable_ratio 0.004348 0.00625 \n", + "fastQuads_runs_ratio 0.026316 0.0 \n", + "fastQuads_skiable_ratio 0.001242 0.0 \n", "\n", " 2 3 \\\n", "Name Hilltop Ski Area Arizona Snowbowl \n", @@ -3846,28 +4376,28 @@ "double 0 1 \n", "surface 2 2 \n", "total_chairs 3 8 \n", - "Runs 13 55 \n", - "TerrainParks 1 4 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 30 777 \n", - "Snow Making_ac 30 104 \n", - "daysOpenLastYear 150 122 \n", - "yearsOpen 36 81 \n", - "averageSnowfall 69 260 \n", - "AdultWeekend 34 89 \n", - "projectedDaysOpen 152 122 \n", - "NightSkiing_ac 30 NaN \n", + "Runs 13.0 55.0 \n", + "TerrainParks 1.0 4.0 \n", + "LongestRun_mi 1.0 2.0 \n", + "SkiableTerrain_ac 30.0 777.0 \n", + "Snow Making_ac 30.0 104.0 \n", + "daysOpenLastYear 150.0 122.0 \n", + "yearsOpen 36.0 81.0 \n", + "averageSnowfall 69.0 260.0 \n", + "AdultWeekend 34.0 89.0 \n", + "projectedDaysOpen 152.0 122.0 \n", + "NightSkiing_ac 30.0 NaN \n", "resorts_per_state 3 2 \n", - "resorts_per_100kcapita 0.410091 0.0274774 \n", + "resorts_per_100kcapita 0.410091 0.027477 \n", "resorts_per_100ksq_mile 0.450867 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.0131579 0.492708 \n", + "resort_skiable_area_ac_state_ratio 0.013158 0.492708 \n", "resort_days_open_state_ratio 0.434783 0.514768 \n", "resort_terrain_park_state_ratio 0.25 0.666667 \n", - "resort_night_skiing_state_ratio 0.0517241 NaN \n", + "resort_night_skiing_state_ratio 0.051724 NaN \n", "total_chairs_runs_ratio 0.230769 0.145455 \n", "total_chairs_skiable_ratio 0.1 0.010296 \n", - "fastQuads_runs_ratio 0 0 \n", - "fastQuads_skiable_ratio 0 0 \n", + "fastQuads_runs_ratio 0.0 0.0 \n", + "fastQuads_skiable_ratio 0.0 0.0 \n", "\n", " 4 \n", "Name Sunrise Park Resort \n", @@ -3884,31 +4414,31 @@ "double 1 \n", "surface 0 \n", "total_chairs 7 \n", - "Runs 65 \n", - "TerrainParks 2 \n", + "Runs 65.0 \n", + "TerrainParks 2.0 \n", "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800 \n", - "Snow Making_ac 80 \n", - "daysOpenLastYear 115 \n", - "yearsOpen 49 \n", - "averageSnowfall 250 \n", - "AdultWeekend 78 \n", - "projectedDaysOpen 104 \n", - "NightSkiing_ac 80 \n", + "SkiableTerrain_ac 800.0 \n", + "Snow Making_ac 80.0 \n", + "daysOpenLastYear 115.0 \n", + "yearsOpen 49.0 \n", + "averageSnowfall 250.0 \n", + "AdultWeekend 78.0 \n", + "projectedDaysOpen 104.0 \n", + "NightSkiing_ac 80.0 \n", "resorts_per_state 2 \n", - "resorts_per_100kcapita 0.0274774 \n", + "resorts_per_100kcapita 0.027477 \n", "resorts_per_100ksq_mile 1.75454 \n", "resort_skiable_area_ac_state_ratio 0.507292 \n", "resort_days_open_state_ratio 0.485232 \n", "resort_terrain_park_state_ratio 0.333333 \n", - "resort_night_skiing_state_ratio 1 \n", + "resort_night_skiing_state_ratio 1.0 \n", "total_chairs_runs_ratio 0.107692 \n", "total_chairs_skiable_ratio 0.00875 \n", - "fastQuads_runs_ratio 0.0153846 \n", + "fastQuads_runs_ratio 0.015385 \n", "fastQuads_skiable_ratio 0.00125 " ] }, - "execution_count": 56, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -3919,15 +4449,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing file. \"../data\\ski_data_step3_features.csv\"\n" + ] + } + ], "source": [ "# Save the data \n", "\n", "datapath = '../data'\n", "save_file(ski_data, 'ski_data_step3_features.csv', datapath)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -3946,7 +4491,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.5" }, "toc": { "base_numbering": 1, diff --git a/data/ski_data_cleaned.csv b/data/ski_data_cleaned.csv new file mode 100644 index 000000000..4259e45d8 --- /dev/null +++ b/data/ski_data_cleaned.csv @@ -0,0 +1,278 @@ +Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac +Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0 +Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0, +Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0 +Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0, +Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0 +Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0, +Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0, +Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0, +Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,, +Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0, +Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0 +Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0, +Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0, +Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0 +Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0, +Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0, +Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0, +Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0, +Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0, +Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0, +Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0, +Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0 +Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0, +Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0, +Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0, +Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0, +Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0, +Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0 +Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0, +Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0, +Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0, +Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0 +Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0 +Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0 +Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0 +Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0 +Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0 +Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,, +Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,, +Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0, +Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0, +Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0 +Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0 +Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0 +Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,, +Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0, +Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0 +Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0 +Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0 +Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0 +Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0 +Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0 +Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0 +Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0 +Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0, +Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0 +Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0 +Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0, +New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0 +Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0 +Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0, +Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0 +Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0 +Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0 +Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0 +Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,, +Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0 +Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,, +Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0 +Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0 +Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0 +Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0, +Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0 +Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0 +Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0 +Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0, +Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0 +Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0, +Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0 +Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0 +Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0, +Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0 +Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0, +Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0 +Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0, +Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0 +Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0 +Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0 +Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0 +Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0 +Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0 +Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0 +Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0 +Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0 +Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0 +Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0 +The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0 +Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0 +Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0 +Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0 +Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0 +Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0 +Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0 +Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0 +Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0 +Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0 +Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0, +Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0 +Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0 +Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0 +Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0 +Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0 +Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0 +Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0 +Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,, +Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0, +Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0, +Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0 +Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0, +Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,, +Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0 +Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0, +Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0, +Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0, +Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0 +Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0, +Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0, +Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0, +Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0, +Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0, +Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0, +Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0 +Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0, +Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0 +Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0, +Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0 +King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0 +Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0, +Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0 +Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0, +Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0, +Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0 +Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0, +Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0 +Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0 +Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0, +Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0, +Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,, +Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0, +Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0, +Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0, +Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0, +Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0, +Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0, +Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,, +Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0 +Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0 +Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0 +Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0 +Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0 +Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0 +Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0 +Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0 +Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0, +Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0 +Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0, +Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0 +Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0 +Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0 +McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0, +Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0 +Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0 +Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0 +Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0, +Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,, +Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0 +Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0 +Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0 +Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0 +Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0 +Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0 +West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0 +Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0, +Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0 +Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0 +Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0 +Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0 +Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0 +Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0 +Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0 +Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0 +Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0 +Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0 +Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0 +Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0 +Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0 +Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0 +Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0, +Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0, +Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0 +Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0 +Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0, +Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0 +Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0, +Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0 +Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0 +Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0 +Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0 +Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0 +Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0 +Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0 +Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0, +Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0 +Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0 +Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0 +Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0 +Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0 +Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0 +Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0 +Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0 +Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0, +Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0, +Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0, +Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0, +Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0, +Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0, +Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0 +Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,, +Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0, +Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0 +Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0, +Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0 +Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0, +Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0, +Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0 +Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0 +Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0, +Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0, +Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0, +Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,, +Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0, +Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,, +Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0, +Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0, +Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0, +Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0 +49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0 +Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0, +Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,, +Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0, +Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0 +The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0 +White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0 +Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0, +Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0 +Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0 +Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0 +Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0 +Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0 +Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0, +Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0 +Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0 +Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0 +Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0 +Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0 +Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0 +Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0 +Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0 +Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0 +Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0, +Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0 +Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0, +Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0, +Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0, +Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0 +Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,, +White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,, diff --git a/data/ski_data_step3_features.csv b/data/ski_data_step3_features.csv new file mode 100644 index 000000000..6895fd09a --- /dev/null +++ b/data/ski_data_step3_features.csv @@ -0,0 +1,278 @@ +Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac,resorts_per_state,resorts_per_100kcapita,resorts_per_100ksq_mile,resort_skiable_area_ac_state_ratio,resort_days_open_state_ratio,resort_terrain_park_state_ratio,resort_night_skiing_state_ratio,total_chairs_runs_ratio,total_chairs_skiable_ratio,fastQuads_runs_ratio,fastQuads_skiable_ratio +Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0,3,0.4100909718472548,0.45086746901037594,0.706140350877193,0.43478260869565216,0.5,0.9482758620689655,0.09210526315789473,0.004347826086956522,0.02631578947368421,0.0012422360248447205 +Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0,,3,0.4100909718472548,0.45086746901037594,0.2807017543859649,0.13043478260869565,0.25,,0.1111111111111111,0.00625,0.0,0.0 +Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0,3,0.4100909718472548,0.45086746901037594,0.013157894736842105,0.43478260869565216,0.25,0.05172413793103448,0.23076923076923078,0.1,0.0,0.0 +Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0,,2,0.027477369981550318,1.7545398719185894,0.49270767279644895,0.5147679324894515,0.6666666666666666,,0.14545454545454545,0.010296010296010296,0.0,0.0 +Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0,2,0.027477369981550318,1.7545398719185894,0.507292327203551,0.48523206751054854,0.3333333333333333,1.0,0.1076923076923077,0.00875,0.015384615384615385,0.00125 +Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0,,21,0.05314811064920341,12.828736369467608,0.0033913981809773393,0.04017531044558072,0.024691358024691357,,0.5,0.056818181818181816,0.0,0.0 +Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0,,21,0.05314811064920341,12.828736369467608,0.033220286727300756,,0.06172839506172839,,0.1791044776119403,0.013921113689095127,0.0,0.0 +Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0,,21,0.05314811064920341,12.828736369467608,0.019462000924926778,0.059532505478451424,0.024691358024691357,,0.15384615384615385,0.015841584158415842,0.0,0.0 +Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,,,21,0.05314811064920341,12.828736369467608,0.1348851549252351,0.0887509130752374,0.08641975308641975,,0.16233766233766234,0.007142857142857143,0.05844155844155844,0.0025714285714285713 +Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0,,21,0.05314811064920341,12.828736369467608,0.016378911669492832,0.05113221329437546,0.024691358024691357,,0.125,0.009411764705882352,0.0,0.0 +Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0,21,0.05314811064920341,12.828736369467608,0.01117619855094805,0.04309715120525932,0.012345679012345678,0.12436115843270869,0.23728813559322035,0.04827586206896552,0.03389830508474576,0.006896551724137931 +Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0,,21,0.05314811064920341,12.828736369467608,0.015415446277169724,0.06391526661796933,,,0.15384615384615385,0.01,0.0,0.0 +Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0,,21,0.05314811064920341,12.828736369467608,0.05395406197009404,0.05113221329437546,0.012345679012345678,,0.24444444444444444,0.007857142857142858,0.0,0.0 +Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0,21,0.05314811064920341,12.828736369467608,0.009249267766301835,0.04054054054054054,0.07407407407407407,0.27938671209540034,0.42857142857142855,0.05,0.0,0.0 +Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0,,21,0.05314811064920341,12.828736369467608,0.007707723138584862,0.0547845142439737,,,0.2222222222222222,0.02,0.0,0.0 +Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0,,21,0.05314811064920341,12.828736369467608,0.06358871589332511,0.05514974433893353,0.037037037037037035,,0.11428571428571428,0.007272727272727273,0.047619047619047616,0.0030303030303030303 +Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0,,21,0.05314811064920341,12.828736369467608,0.004624633883150917,0.0547845142439737,0.024691358024691357,,0.35714285714285715,0.041666666666666664,0.0,0.0 +Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0,,22,0.3820282784277661,21.13474359713336,0.032690810860308596,0.07059545733578883,0.04054054054054054,,0.06206896551724138,0.0063025210084033615,0.006896551724137931,0.0007002801120448179 +Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0,,22,0.3820282784277661,21.13474359713336,0.12629916212627626,0.0423572744014733,0.13513513513513514,,0.11904761904761904,0.0072503172013775605,0.044642857142857144,0.0027188689505165853 +Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0,,22,0.3820282784277661,21.13474359713336,0.05784991529691864,0.05033763044812769,0.08108108108108109,,0.16,0.009497427779976256,0.02666666666666667,0.0015829046299960427 +Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0,,22,0.3820282784277661,21.13474359713336,0.03674282313080903,0.03990178023327195,0.12162162162162163,,0.1188118811881188,0.007476635514018692,0.019801980198019802,0.0012461059190031153 +Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0,22,0.3820282784277661,21.13474359713336,0.0011446362346046427,0.030693677102516883,0.013513513513513514,0.02336448598130841,0.23529411764705882,0.08,0.0,0.0 +Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0,,22,0.3820282784277661,21.13474359713336,0.041206904445767134,0.06292203806015961,0.013513513513513514,,0.10638297872340426,0.005555555555555556,0.010638297872340425,0.0005555555555555556 +Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0,,22,0.3820282784277661,21.13474359713336,0.018314179753674283,0.04389195825659914,0.02702702702702703,,0.109375,0.00875,0.0,0.0 +Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0,,22,0.3820282784277661,21.13474359713336,0.036628359507348565,0.03406998158379374,0.02702702702702703,,0.11904761904761904,0.003125,0.023809523809523808,0.000625 +Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0,,22,0.3820282784277661,21.13474359713336,0.041641866214916896,0.053713934929404544,,,,0.0005497526113249038,,0.0 +Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0,,22,0.3820282784277661,21.13474359713336,0.009157089876837141,0.03990178023327195,0.013513513513513514,,0.0975609756097561,0.01,0.0,0.0 +Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0,22,0.3820282784277661,21.13474359713336,0.009294446224989698,0.03560466543891958,0.013513513513513514,0.2336448598130841,0.125,0.012315270935960592,0.05,0.0049261083743842365 +Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0,,22,0.3820282784277661,21.13474359713336,0.01556705279062314,0.030693677102516883,0.013513513513513514,,0.04477611940298507,0.004411764705882353,0.0,0.0 +Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0,,22,0.3820282784277661,21.13474359713336,0.0457854493841857,0.040208717004297116,0.04054054054054054,,0.11486486486486487,0.0085,0.04054054054054054,0.003 +Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0,,22,0.3820282784277661,21.13474359713336,0.036628359507348565,0.03990178023327195,,,0.08333333333333333,0.00625,0.025,0.001875 +Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0,5,0.14024151833321272,90.20386072523904,0.2988826815642458,,,0.25,0.32,0.07476635514018691,0.0,0.0 +Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0,5,0.14024151833321272,90.20386072523904,0.1424581005586592,0.17847025495750707,0.2,0.19921875,0.5,0.13725490196078433,0.0,0.0 +Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0,5,0.14024151833321272,90.20386072523904,0.22346368715083798,0.22662889518413598,0.4,0.15625,0.2631578947368421,0.0625,0.0,0.0 +Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0,5,0.14024151833321272,90.20386072523904,0.19553072625698323,0.23796033994334279,0.2,0.2578125,0.3125,0.07142857142857142,0.0,0.0 +Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0,5,0.14024151833321272,90.20386072523904,0.13966480446927373,0.35694050991501414,0.2,0.13671875,0.4166666666666667,0.1,0.0,0.0 +Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0,12,0.6714920833881253,14.359391640440833,0.15857526225908758,0.11795774647887323,0.1111111111111111,0.39759036144578314,0.12087912087912088,0.004230769230769231,0.03296703296703297,0.001153846153846154 +Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,,,12,0.6714920833881253,14.359391640440833,0.11710173212978775,0.11091549295774648,0.07407407407407407,,0.11764705882352941,0.003125,0.0196078431372549,0.0005208333333333333 +Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,,,12,0.6714920833881253,14.359391640440833,0.0451329592583557,,0.037037037037037035,,0.11764705882352941,0.008108108108108109,0.0,0.0 +Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0,,12,0.6714920833881253,14.359391640440833,0.032934862161502806,0.0994718309859155,0.07407407407407407,,0.11428571428571428,0.007407407407407408,0.0,0.0 +Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0,,12,0.6714920833881253,14.359391640440833,0.017077335935594046,0.05721830985915493,,,0.2727272727272727,0.010714285714285714,0.0,0.0 +Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0,12,0.6714920833881253,14.359391640440833,0.0670895340326909,0.07482394366197183,0.07407407407407407,0.07228915662650602,0.05555555555555555,0.0027272727272727275,0.0,0.0 +Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0,12,0.6714920833881253,14.359391640440833,0.17687240790436692,0.11971830985915492,0.1111111111111111,0.24096385542168675,0.09782608695652174,0.003103448275862069,0.021739130434782608,0.000689655172413793 +Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0,12,0.6714920833881253,14.359391640440833,0.09758477677482313,0.11443661971830986,0.07407407407407407,0.04819277108433735,0.0875,0.004375,0.0,0.0 +Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,,,12,0.6714920833881253,14.359391640440833,0.06965113442303,0.0528169014084507,,,0.08333333333333333,0.002626970227670753,0.0,0.0 +Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0,,12,0.6714920833881253,14.359391640440833,0.062210295193949744,,0.1111111111111111,,0.125,0.0058823529411764705,0.041666666666666664,0.00196078431372549 +Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0,4,0.03156610245678186,6.906792830749041,0.7277486910994765,0.3936651583710407,0.5,0.7277486910994765,0.4090909090909091,0.06474820143884892,0.0,0.0 +Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0,4,0.03156610245678186,6.906792830749041,0.14659685863874344,0.25339366515837103,0.16666666666666666,0.14659685863874344,0.4,0.21428571428571427,0.0,0.0 +Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0,4,0.03156610245678186,6.906792830749041,0.07853403141361257,,0.16666666666666666,0.07853403141361257,1.0,0.4666666666666667,0.0,0.0 +Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0,2,0.02970788680522722,5.491488193300384,0.3939393939393939,0.47770700636942676,0.5,0.3939393939393939,0.5333333333333333,0.12307692307692308,0.06666666666666667,0.015384615384615385 +Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0,2,0.02970788680522722,5.491488193300384,0.6060606060606061,0.5222929936305732,0.5,0.6060606060606061,0.4782608695652174,0.11,0.0,0.0 +Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0,3,0.0950850535804277,5.3311534839088015,0.35714285714285715,,0.2,0.35714285714285715,0.18181818181818182,0.04,0.0,0.0 +Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0,3,0.0950850535804277,5.3311534839088015,0.25,1.0,0.4,0.25,0.36363636363636365,0.11428571428571428,0.0,0.0 +Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0,3,0.0950850535804277,5.3311534839088015,0.39285714285714285,,0.4,0.39285714285714285,0.2857142857142857,0.10909090909090909,0.0,0.0 +Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0,,9,0.6695372456130432,25.438100621820237,,0.07745664739884393,,,0.034482758620689655,,0.0, +Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0,9,0.6695372456130432,25.438100621820237,0.03109452736318408,0.07861271676300578,0.11764705882352941,0.12371134020618557,0.11538461538461539,0.03,0.0,0.0 +Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0,9,0.6695372456130432,25.438100621820237,0.013992537313432836,0.10057803468208093,0.11764705882352941,0.11597938144329897,0.18181818181818182,0.08888888888888889,0.0,0.0 +Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0,,9,0.6695372456130432,25.438100621820237,0.19900497512437812,0.13872832369942195,0.058823529411764705,,0.09259259259259259,0.0078125,0.0,0.0 +New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0,9,0.6695372456130432,25.438100621820237,0.021766169154228857,0.11791907514450867,,0.11597938144329897,0.15,0.04285714285714286,0.0,0.0 +Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0,9,0.6695372456130432,25.438100621820237,0.07431592039800995,0.11213872832369942,0.17647058823529413,0.28350515463917525,0.13953488372093023,0.02510460251046025,0.0,0.0 +Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0,,9,0.6695372456130432,25.438100621820237,0.3855721393034826,0.1838150289017341,0.23529411764705882,,0.08024691358024691,0.010483870967741936,0.012345679012345678,0.0016129032258064516 +Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0,9,0.6695372456130432,25.438100621820237,0.27052238805970147,0.1907514450867052,0.29411764705882354,0.36082474226804123,0.1111111111111111,0.017241379310344827,0.02962962962962963,0.004597701149425287 +Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0,1,0.016540736525916026,8.060615831049493,1.0,1.0,1.0,1.0,0.35294117647058826,0.06976744186046512,0.0,0.0 +Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0,11,0.1595936918707181,104.22588592003032,0.15437392795883362,0.17883755588673622,0.1111111111111111,0.137221269296741,0.1276595744680851,0.03333333333333333,0.0,0.0 +Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0,11,0.1595936918707181,104.22588592003032,0.11320754716981132,,0.1111111111111111,0.12006861063464837,0.1724137931034483,0.03787878787878788,0.0,0.0 +Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,,,11,0.1595936918707181,104.22588592003032,0.051457975986277875,,0.05555555555555555,,0.25,0.06666666666666667,0.0,0.0 +Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0,11,0.1595936918707181,104.22588592003032,0.17152658662092624,,0.05555555555555555,0.17152658662092624,0.21739130434782608,0.025,0.0,0.0 +Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,,,11,0.1595936918707181,104.22588592003032,0.0411663807890223,,0.05555555555555555,,0.6666666666666666,0.20833333333333334,0.0,0.0 +Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0,11,0.1595936918707181,104.22588592003032,0.1432246998284734,0.18032786885245902,0.16666666666666666,0.1783876500857633,0.2,0.05389221556886228,0.0,0.0 +Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0,11,0.1595936918707181,104.22588592003032,0.044596912521440824,0.16691505216095381,0.1111111111111111,0.08919382504288165,0.6470588235294118,0.21153846153846154,0.0,0.0 +Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0,11,0.1595936918707181,104.22588592003032,0.051457975986277875,0.15797317436661698,0.05555555555555555,0.060034305317324184,0.36363636363636365,0.06666666666666667,0.0,0.0 +Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0,,11,0.1595936918707181,104.22588592003032,0.09433962264150944,0.15946348733233978,0.1111111111111111,,0.5,0.1,0.0,0.0 +Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0,11,0.1595936918707181,104.22588592003032,0.09605488850771869,,0.1111111111111111,0.1783876500857633,0.2962962962962963,0.07142857142857142,0.1111111111111111,0.026785714285714284 +Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,,0.047619047619047616,0.051387461459403906,0.56,0.14,0.0,0.0 +Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0,28,0.28036848830417815,28.951341067477305,0.018157058556513846,,,0.041109969167523124,0.5,0.075,0.0,0.0 +Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0,,28,0.28036848830417815,28.951341067477305,0.057421697684975036,0.041858518208455424,0.031746031746031744,,0.2222222222222222,0.039525691699604744,0.0,0.0 +Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,0.033486814566764334,0.031746031746031744,0.051387461459403906,0.6,0.12,0.0,0.0 +Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0,,28,0.28036848830417815,28.951341067477305,0.03858374943259192,0.03976559229803265,0.031746031746031744,,0.23076923076923078,0.03529411764705882,0.0,0.0 +Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0,28,0.28036848830417815,28.951341067477305,0.09872900590104403,0.04060276266220176,0.06349206349206349,0.07708119218910586,0.18181818181818182,0.022988505747126436,0.01818181818181818,0.0022988505747126436 +Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0,28,0.28036848830417815,28.951341067477305,0.04539264639128461,0.0493930514859774,0.031746031746031744,0.07708119218910586,0.14705882352941177,0.025,0.0,0.0 +Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0,,28,0.28036848830417815,28.951341067477305,0.022696323195642305,0.041858518208455424,0.07936507936507936,,0.47619047619047616,0.1,0.0,0.0 +Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0,28,0.28036848830417815,28.951341067477305,0.02315024965955515,0.05023022185014651,0.047619047619047616,0.02877697841726619,0.13793103448275862,0.0784313725490196,0.017241379310344827,0.00980392156862745 +Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0,,28,0.28036848830417815,28.951341067477305,0.05447117566954154,0.05023022185014651,0.031746031746031744,,0.28125,0.0375,0.0,0.0 +Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0,28,0.28036848830417815,28.951341067477305,0.02541988197911938,0.04771871075763918,0.031746031746031744,0.051387461459403906,0.16,0.03571428571428571,0.0,0.0 +Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0,,28,0.28036848830417815,28.951341067477305,0.1327734906945075,0.034742570113018,,,,0.003418803418803419,,0.0 +Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0,28,0.28036848830417815,28.951341067477305,0.029505220154335,0.046462955211385513,0.07936507936507936,0.06680369989722508,0.52,0.1,0.0,0.0 +Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0,28,0.28036848830417815,28.951341067477305,0.010213345438039038,0.041858518208455424,,0.023124357656731757,0.3333333333333333,0.08888888888888889,0.0,0.0 +Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,,,0.051387461459403906,0.6842105263157895,0.13,0.05263157894736842,0.01 +Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0,28,0.28036848830417815,28.951341067477305,0.0022696323195642307,,,0.0051387461459403904,0.8333333333333334,0.5,0.0,0.0 +Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0,28,0.28036848830417815,28.951341067477305,0.04221516114389469,0.046044370029300966,0.015873015873015872,0.020554984583761562,0.35294117647058826,0.03225806451612903,0.0,0.0 +Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0,28,0.28036848830417815,28.951341067477305,0.05628688152519292,0.05567182921724571,0.047619047619047616,0.08221993833504625,0.18867924528301888,0.04032258064516129,0.0,0.0 +Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0,28,0.28036848830417815,28.951341067477305,0.03631411711302769,0.046044370029300966,0.015873015873015872,0.041109969167523124,0.14285714285714285,0.025,0.0,0.0 +Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0,28,0.28036848830417815,28.951341067477305,0.015887426236949616,0.039347007115948095,0.047619047619047616,0.03597122302158273,0.19047619047619047,0.11428571428571428,0.0,0.0 +Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0,28,0.28036848830417815,28.951341067477305,0.03404448479346346,0.06864796986186689,0.047619047619047616,0.020554984583761562,0.7058823529411765,0.08,0.0,0.0 +Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0,28,0.28036848830417815,28.951341067477305,0.009078529278256923,,0.031746031746031744,0.020554984583761562,0.5,0.15,0.0,0.0 +Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0,28,0.28036848830417815,28.951341067477305,0.013617793917385384,0.03725408120552533,0.031746031746031744,0.030832476875642344,0.6363636363636364,0.11666666666666667,0.0,0.0 +The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0,28,0.28036848830417815,28.951341067477305,0.003631411711302769,0.019673503557974047,0.015873015873015872,0.008221993833504625,0.3333333333333333,0.3125,0.0,0.0 +Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0,28,0.28036848830417815,28.951341067477305,0.011348161597821153,0.033486814566764334,0.031746031746031744,0.025693730729701953,0.5,0.16,0.0,0.0 +Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0,14,0.24824314777985515,16.103800496917273,0.16025641025641027,0.09060402684563758,0.1724137931034483,0.24509803921568626,0.4583333333333333,0.088,0.0,0.0 +Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0,14,0.24824314777985515,16.103800496917273,0.022435897435897436,0.06711409395973154,0.06896551724137931,0.03431372549019608,0.4,0.17142857142857143,0.0,0.0 +Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0,14,0.24824314777985515,16.103800496917273,0.028846153846153848,0.07718120805369127,,0.04411764705882353,0.5,0.17777777777777778,0.0,0.0 +Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0,14,0.24824314777985515,16.103800496917273,0.019230769230769232,0.03825503355704698,,0.029411764705882353,0.35294117647058826,0.2,0.0,0.0 +Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0,14,0.24824314777985515,16.103800496917273,0.02564102564102564,0.03825503355704698,0.034482758620689655,0.03431372549019608,0.21428571428571427,0.075,0.0,0.0 +Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0,14,0.24824314777985515,16.103800496917273,0.009615384615384616,0.07046979865771812,,0.014705882352941176,1.0,0.2,0.0,0.0 +Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0,14,0.24824314777985515,16.103800496917273,0.1294871794871795,0.08053691275167785,0.06896551724137931,0.11862745098039215,0.2,0.034653465346534656,0.02857142857142857,0.0049504950495049506 +Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0,14,0.24824314777985515,16.103800496917273,0.022435897435897436,0.0738255033557047,0.034482758620689655,0.03431372549019608,0.5714285714285714,0.22857142857142856,0.0,0.0 +Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0,,14,0.24824314777985515,16.103800496917273,0.2519230769230769,0.09060402684563758,0.06896551724137931,,0.12903225806451613,0.020356234096692113,0.0,0.0 +Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0,14,0.24824314777985515,16.103800496917273,0.035256410256410256,0.07718120805369127,0.13793103448275862,0.049019607843137254,0.5263157894736842,0.18181818181818182,0.0,0.0 +Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0,14,0.24824314777985515,16.103800496917273,0.038461538461538464,0.06510067114093959,0.13793103448275862,0.058823529411764705,0.4,0.1,0.0,0.0 +Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0,14,0.24824314777985515,16.103800496917273,0.11217948717948718,0.06711409395973154,0.10344827586206896,0.1411764705882353,0.3181818181818182,0.04,0.045454545454545456,0.005714285714285714 +Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0,14,0.24824314777985515,16.103800496917273,0.08012820512820513,0.07651006711409396,0.034482758620689655,0.09803921568627451,0.2,0.08,0.0,0.0 +Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0,14,0.24824314777985515,16.103800496917273,0.0641025641025641,0.087248322147651,0.13793103448275862,0.09803921568627451,0.3076923076923077,0.08,0.0,0.0 +Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0,2,0.03258694032744661,2.8691523089503206,0.5,,,0.3617021276595745,0.4117647058823529,0.23333333333333334,0.0,0.0 +Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0,2,0.03258694032744661,2.8691523089503206,0.5,1.0,1.0,0.6382978723404256,0.35714285714285715,0.16666666666666666,0.0,0.0 +Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,,,12,1.1227776020838753,8.161044613710555,0.046707146193367584,,,,0.14814814814814814,0.004,0.0,0.0 +Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0,,12,1.1227776020838753,8.161044613710555,0.09341429238673517,0.12828601472134596,0.07407407407407407,,0.10476190476190476,0.0055,0.0,0.0 +Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0,,12,1.1227776020838753,8.161044613710555,0.1120971508640822,0.12197686645636173,0.037037037037037035,,0.10810810810810811,0.0033333333333333335,0.0,0.0 +Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0,12,1.1227776020838753,8.161044613710555,0.07473143390938813,0.09884332281808622,0.2222222222222222,0.14084507042253522,0.05454545454545454,0.00375,0.0,0.0 +Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0,,12,1.1227776020838753,8.161044613710555,0.08407286314806166,0.08832807570977919,0.07407407407407407,,0.11594202898550725,0.0044444444444444444,0.0,0.0 +Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,,,12,1.1227776020838753,8.161044613710555,0.011910322279308735,,,,0.09090909090909091,0.00784313725490196,0.0,0.0 +Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0,12,1.1227776020838753,8.161044613710555,0.044371788883699206,,,0.014084507042253521,0.10810810810810811,0.004210526315789474,0.0,0.0 +Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0,,12,1.1227776020838753,8.161044613710555,0.076366184026156,0.14931650893796003,0.07407407407407407,,0.1,0.004281345565749235,0.02857142857142857,0.0012232415902140672 +Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0,,12,1.1227776020838753,8.161044613710555,0.029892573563755253,0.0904311251314406,0.037037037037037035,,0.1111111111111111,0.00625,0.0,0.0 +Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0,,12,1.1227776020838753,8.161044613710555,0.015413358243811303,0.04206098843322818,0.037037037037037035,,0.06976744186046512,0.00909090909090909,0.0,0.0 +Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0,12,1.1227776020838753,8.161044613710555,0.14012143858010276,0.12933753943217666,0.14814814814814814,0.8450704225352113,0.13333333333333333,0.004666666666666667,0.02857142857142857,0.001 +Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0,,4,0.12986355236552954,3.61755236407047,0.3104265402843602,0.24096385542168675,0.3333333333333333,,0.23333333333333334,0.010687022900763359,0.03333333333333333,0.0015267175572519084 +Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0,,4,0.12986355236552954,3.61755236407047,0.02843601895734597,0.04578313253012048,,,0.2,0.03333333333333333,0.0,0.0 +Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0,,4,0.12986355236552954,3.61755236407047,0.0924170616113744,0.3469879518072289,0.1111111111111111,,0.125,0.015384615384615385,0.0,0.0 +Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0,,4,0.12986355236552954,3.61755236407047,0.5687203791469194,0.36626506024096384,0.5555555555555556,,0.12307692307692308,0.006666666666666667,0.0,0.0 +Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0,,16,1.1767206413715856,171.14129853460264,0.09074992704989787,0.062263129399025445,0.06976744186046512,,0.1323529411764706,0.028938906752411574,0.029411764705882353,0.006430868167202572 +Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0,,16,1.1767206413715856,171.14129853460264,0.04172745841844178,0.05955603681645912,,,0.1111111111111111,0.03496503496503497,0.0,0.0 +Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0,16,1.1767206413715856,171.14129853460264,0.13539538955354538,0.09745533297238766,0.046511627906976744,0.1196808510638298,0.14285714285714285,0.01939655172413793,0.06349206349206349,0.008620689655172414 +Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0,,16,1.1767206413715856,171.14129853460264,0.083163116428363,0.06713589604764483,0.06976744186046512,,0.1134020618556701,0.03859649122807018,0.010309278350515464,0.0035087719298245615 +Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0,16,1.1767206413715856,171.14129853460264,0.029180040852057193,0.0568489442338928,0.06976744186046512,0.26595744680851063,0.2,0.05,0.04,0.01 +Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0,,16,1.1767206413715856,171.14129853460264,0.031222643711701196,0.056307525717379535,0.023255813953488372,,0.14285714285714285,0.037383177570093455,0.0,0.0 +Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0,16,1.1767206413715856,171.14129853460264,0.06623869273416982,0.057390362750406064,0.09302325581395349,0.1595744680851064,0.10909090909090909,0.02643171806167401,0.01818181818181818,0.004405286343612335 +King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0,16,1.1767206413715856,171.14129853460264,0.014006419608987453,0.0568489442338928,0.046511627906976744,0.061170212765957445,0.35294117647058826,0.125,0.0,0.0 +Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0,,16,1.1767206413715856,171.14129853460264,0.06769769477677269,0.07038440714672442,0.09302325581395349,,0.15151515151515152,0.04310344827586207,0.030303030303030304,0.008620689655172414 +Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0,16,1.1767206413715856,171.14129853460264,0.03355704697986577,0.05901461829994586,0.06976744186046512,0.2473404255319149,0.39285714285714285,0.09565217391304348,0.0,0.0 +Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0,,16,1.1767206413715856,171.14129853460264,0.07295010213014298,,0.06976744186046512,,0.10526315789473684,0.024,0.017543859649122806,0.004 +Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0,,16,1.1767206413715856,171.14129853460264,0.07732710825795155,0.0768814293448836,0.09302325581395349,,0.1774193548387097,0.04150943396226415,0.03225806451612903,0.007547169811320755 +Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0,16,1.1767206413715856,171.14129853460264,0.024803034724248614,0.0568489442338928,0.023255813953488372,0.14627659574468085,0.13333333333333333,0.047058823529411764,0.0,0.0 +Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0,,16,1.1767206413715856,171.14129853460264,0.06565509191712868,0.0844612885760693,,,0.10416666666666667,0.022222222222222223,0.020833333333333332,0.0044444444444444444 +Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0,2,0.022516969351027167,22.927891780350798,0.8789473684210526,0.5294117647058824,0.75,0.9226519337016574,0.21739130434782608,0.059880239520958084,0.043478260869565216,0.011976047904191617 +Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0,9,0.4292195500920676,7.401924500370097,0.10721807390388666,0.10455486542443064,0.16666666666666666,1.0,0.08641975308641975,0.0125,0.024691358024691357,0.0035714285714285713 +Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0,,9,0.4292195500920676,7.401924500370097,0.11487650775416428,0.13457556935817805,,,0.0,0.0,0.0,0.0 +Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0,,9,0.4292195500920676,7.401924500370097,0.14359563469270534,0.09213250517598344,0.1111111111111111,,0.13333333333333333,0.008,0.0,0.0 +Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,,,9,0.4292195500920676,7.401924500370097,0.04001531686770055,0.11387163561076605,0.16666666666666666,,0.1111111111111111,0.03349282296650718,0.0,0.0 +Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0,,9,0.4292195500920676,7.401924500370097,0.03829216925138809,0.033126293995859216,0.05555555555555555,,0.1282051282051282,0.025,0.0,0.0 +Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0,,9,0.4292195500920676,7.401924500370097,0.03829216925138809,0.13146997929606624,0.2222222222222222,,0.14285714285714285,0.03,0.0,0.0 +Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0,,9,0.4292195500920676,7.401924500370097,0.14359563469270534,0.13768115942028986,0.16666666666666666,,0.2,0.014666666666666666,0.0,0.0 +Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0,,9,0.4292195500920676,7.401924500370097,0.1263641585295807,0.11076604554865424,0.05555555555555555,,0.08433734939759036,0.010606060606060607,0.0,0.0 +Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0,,9,0.4292195500920676,7.401924500370097,0.24775033505648095,0.14182194616977226,0.05555555555555555,,0.12612612612612611,0.010819165378670788,0.009009009009009009,0.0007727975270479134 +Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0,,33,0.16963475221837276,60.48941435248832,0.03173739571998549,0.06459731543624161,0.027777777777777776,,0.16,0.045714285714285714,0.02,0.005714285714285714 +Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,,,33,0.16963475221837276,60.48941435248832,0.003627130939426913,,,,0.5,0.25,0.0,0.0 +Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0,33,0.16963475221837276,60.48941435248832,0.029017047515415305,0.054110738255033555,0.041666666666666664,0.054301833568406205,0.17647058823529413,0.0375,0.058823529411764705,0.0125 +Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0,33,0.16963475221837276,60.48941435248832,0.040805223068552776,,0.013888888888888888,0.03526093088857546,0.13953488372093023,0.02666666666666667,0.0,0.0 +Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0,33,0.16963475221837276,60.48941435248832,0.024120420747188974,0.04194630872483222,0.06944444444444445,0.019393511988716503,0.19444444444444445,0.05263157894736842,0.0,0.0 +Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0,33,0.16963475221837276,60.48941435248832,0.006347479143997099,,0.013888888888888888,0.009167842031029619,0.42857142857142855,0.08571428571428572,0.0,0.0 +Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0,33,0.16963475221837276,60.48941435248832,0.07961552412042075,0.05956375838926174,0.09722222222222222,0.005289139633286318,0.12727272727272726,0.03189066059225513,0.01818181818181818,0.004555808656036446 +Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0,33,0.16963475221837276,60.48941435248832,0.03989844033369604,0.04614093959731544,0.05555555555555555,0.06170662905500705,0.14285714285714285,0.03636363636363636,0.0,0.0 +Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0,33,0.16963475221837276,60.48941435248832,0.00671019223793979,0.031459731543624164,,0.01304654442877292,0.4444444444444444,0.10810810810810811,0.0,0.0 +Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0,33,0.16963475221837276,60.48941435248832,0.05259339862169024,0.04865771812080537,0.06944444444444445,0.06664315937940761,0.21666666666666667,0.04482758620689655,0.05,0.010344827586206896 +Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0,,33,0.16963475221837276,60.48941435248832,0.024483133841131665,0.04614093959731544,0.041666666666666664,,0.1509433962264151,0.05925925925925926,0.018867924528301886,0.007407407407407408 +Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0,33,0.16963475221837276,60.48941435248832,0.07254261878853827,,0.013888888888888888,0.14104372355430184,0.15789473684210525,0.0075,0.0,0.0 +Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0,,33,0.16963475221837276,60.48941435248832,0.05803409503083061,0.06208053691275168,0.05555555555555555,,0.19402985074626866,0.040625,0.014925373134328358,0.003125 +Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0,33,0.16963475221837276,60.48941435248832,0.12694958287994196,0.04320469798657718,0.06944444444444445,0.22919605077574048,0.2564102564102564,0.014285714285714285,0.0,0.0 +Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0,33,0.16963475221837276,60.48941435248832,0.04533913674283642,,0.013888888888888888,0.06346967559943582,0.17391304347826086,0.016,0.0,0.0 +Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0,33,0.16963475221837276,60.48941435248832,0.004533913674283642,,,0.007052186177715092,0.3,0.12,0.0,0.0 +McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0,,33,0.16963475221837276,60.48941435248832,0.012694958287994197,0.044043624161073824,0.013888888888888888,,0.21739130434782608,0.07142857142857142,0.0,0.0 +Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0,33,0.16963475221837276,60.48941435248832,0.012513601741022852,0.04194630872483222,0.013888888888888888,0.024330042313117067,0.35714285714285715,0.07246376811594203,0.0,0.0 +Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0,33,0.16963475221837276,60.48941435248832,0.008342401160681901,,0.013888888888888888,0.004231311706629055,0.18181818181818182,0.08695652173913043,0.0,0.0 +Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0,33,0.16963475221837276,60.48941435248832,0.01994922016684802,0.04614093959731544,0.05555555555555555,0.038787023977433006,0.37037037037037035,0.09090909090909091,0.0,0.0 +Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0,,33,0.16963475221837276,60.48941435248832,0.01994922016684802,0.02726510067114094,0.013888888888888888,,0.10526315789473684,0.03636363636363636,0.0,0.0 +Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,,,33,0.16963475221837276,60.48941435248832,0.006347479143997099,,,,0.21428571428571427,0.08571428571428572,0.0,0.0 +Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0,33,0.16963475221837276,60.48941435248832,0.023576351106274936,0.030620805369127518,0.027777777777777776,0.014104372355430184,0.2857142857142857,0.046153846153846156,0.0,0.0 +Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0,33,0.16963475221837276,60.48941435248832,0.016866158868335146,0.037751677852348994,,0.02468265162200282,0.20833333333333334,0.053763440860215055,0.0,0.0 +Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0,33,0.16963475221837276,60.48941435248832,0.023576351106274936,0.04278523489932886,0.041666666666666664,0.028208744710860368,0.14285714285714285,0.038461538461538464,0.0,0.0 +Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0,33,0.16963475221837276,60.48941435248832,0.018135654697134566,0.05075503355704698,,0.03526093088857546,0.2,0.06,0.0,0.0 +Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0,33,0.16963475221837276,60.48941435248832,0.03627130939426913,0.04236577181208054,0.041666666666666664,0.02468265162200282,0.2,0.05,0.0,0.0 +Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0,33,0.16963475221837276,60.48941435248832,0.015415306492564382,,0.027777777777777776,0.025740479548660086,0.22727272727272727,0.058823529411764705,0.0,0.0 +West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0,33,0.16963475221837276,60.48941435248832,0.022488211824446862,,0.013888888888888888,0.03702397743300423,0.1724137931034483,0.04032258064516129,0.0,0.0 +Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0,,33,0.16963475221837276,60.48941435248832,0.05223068552774755,0.051174496644295304,0.06944444444444445,,0.13953488372093023,0.041666666666666664,0.011627906976744186,0.003472222222222222 +Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0,33,0.16963475221837276,60.48941435248832,0.009067827348567283,0.03565436241610738,,0.01234132581100141,0.3125,0.1,0.0,0.0 +Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0,33,0.16963475221837276,60.48941435248832,0.051686615886833515,0.051593959731543626,0.08333333333333333,0.019746121297602257,0.2222222222222222,0.042105263157894736,0.037037037037037035,0.007017543859649123 +Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0,33,0.16963475221837276,60.48941435248832,0.004533913674283642,,,0.005289139633286318,0.2857142857142857,0.24,0.0,0.0 +Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0,6,0.057207779800390615,11.148479161634366,0.07297297297297298,0.1976284584980237,0.3333333333333333,0.08059701492537313,0.4166666666666667,0.18518518518518517,0.0,0.0 +Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0,6,0.057207779800390615,11.148479161634366,0.13513513513513514,0.27865612648221344,0.2222222222222222,0.14925373134328357,0.2777777777777778,0.1,0.0,0.0 +Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0,6,0.057207779800390615,11.148479161634366,0.021621621621621623,0.10474308300395258,0.1111111111111111,0.023880597014925373,,0.375,,0.0 +Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0,6,0.057207779800390615,11.148479161634366,0.25675675675675674,0.19367588932806323,0.1111111111111111,0.2835820895522388,0.47058823529411764,0.08421052631578947,0.0,0.0 +Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0,6,0.057207779800390615,11.148479161634366,0.33783783783783783,0.22529644268774704,0.1111111111111111,0.2835820895522388,0.38095238095238093,0.064,0.0,0.0 +Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0,6,0.057207779800390615,11.148479161634366,0.17567567567567569,,0.1111111111111111,0.1791044776119403,0.26666666666666666,0.06153846153846154,0.0,0.0 +Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0,5,0.042774892848893416,11.154240842368269,0.171021377672209,0.2147239263803681,0.08333333333333333,0.171021377672209,0.45454545454545453,0.06944444444444445,0.0,0.0 +Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0,5,0.042774892848893416,11.154240842368269,0.09501187648456057,0.18813905930470348,0.16666666666666666,0.09501187648456057,1.1428571428571428,0.2,0.0,0.0 +Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0,5,0.042774892848893416,11.154240842368269,0.20190023752969122,0.18813905930470348,0.16666666666666666,0.20190023752969122,1.4545454545454546,0.18823529411764706,0.0,0.0 +Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0,5,0.042774892848893416,11.154240842368269,0.342042755344418,0.20245398773006135,0.3333333333333333,0.342042755344418,0.6,0.08333333333333333,0.0,0.0 +Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0,5,0.042774892848893416,11.154240842368269,0.19002375296912113,0.2065439672801636,0.25,0.19002375296912113,0.5294117647058824,0.1125,0.0,0.0 +Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0,,10,0.23709396768930827,10.164770936886937,0.09342619330728724,0.0635593220338983,0.09090909090909091,,0.14285714285714285,0.0027272727272727275,0.0,0.0 +Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0,,10,0.23709396768930827,10.164770936886937,0.004246645150331238,0.06610169491525424,,,0.2,0.04,0.0,0.0 +Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0,10,0.23709396768930827,10.164770936886937,0.06845591982333957,0.06779661016949153,,0.1774622892635315,0.14705882352941177,0.00620347394540943,0.0,0.0 +Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0,10,0.23709396768930827,10.164770936886937,0.018685238661457448,0.07966101694915254,0.09090909090909091,0.0354924578527063,0.21739130434782608,0.022727272727272728,0.0,0.0 +Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0,,10,0.23709396768930827,10.164770936886937,0.36674027518260577,0.15677966101694915,0.22727272727272727,,0.10891089108910891,0.0025474756831866605,0.07920792079207921,0.0018527095877721167 +Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0,10,0.23709396768930827,10.164770936886937,0.08153558688635977,0.1059322033898305,0.09090909090909091,0.28127772848269744,0.13846153846153847,0.009375,0.0,0.0 +Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0,,10,0.23709396768930827,10.164770936886937,0.047137761168676746,0.002542372881355932,,,0.1724137931034483,0.009009009009009009,0.0,0.0 +Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0,19,0.14841443778775315,41.255916967038694,0.045550847457627115,0.06481481481481481,0.06382978723404255,0.056282722513089,0.2608695652173913,0.06976744186046512,0.0,0.0 +Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0,19,0.14841443778775315,41.255916967038694,0.013771186440677966,0.053418803418803416,0.02127659574468085,0.017015706806282723,0.3333333333333333,0.23076923076923078,0.0,0.0 +Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0,19,0.14841443778775315,41.255916967038694,0.02913135593220339,0.05413105413105413,0.1702127659574468,0.03599476439790576,0.5,0.14545454545454545,0.0,0.0 +Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.06196581196581197,0.02127659574468085,0.0274869109947644,0.17647058823529413,0.06,0.0,0.0 +Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0,19,0.14841443778775315,41.255916967038694,0.08686440677966102,0.0868945868945869,0.10638297872340426,0.10732984293193717,0.41025641025641024,0.0975609756097561,0.02564102564102564,0.006097560975609756 +Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0,19,0.14841443778775315,41.255916967038694,0.08792372881355932,0.07122507122507123,0.10638297872340426,0.10471204188481675,0.43243243243243246,0.0963855421686747,0.05405405405405406,0.012048192771084338 +Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0,19,0.14841443778775315,41.255916967038694,0.09533898305084745,,0.0425531914893617,0.058900523560209424,0.25925925925925924,0.03888888888888889,0.0,0.0 +Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0,,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.06837606837606838,0.02127659574468085,,0.45,0.09,0.0,0.0 +Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.07621082621082621,0.06382978723404255,0.06544502617801047,0.5,0.08,0.0,0.0 +Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0,19,0.14841443778775315,41.255916967038694,0.0211864406779661,0.053418803418803416,,0.022905759162303665,0.2,0.05,0.0,0.0 +Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0,19,0.14841443778775315,41.255916967038694,0.05455508474576271,,0.0425531914893617,0.06544502617801047,0.4,0.07766990291262135,0.0,0.0 +Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0,19,0.14841443778775315,41.255916967038694,0.15095338983050846,0.07051282051282051,0.14893617021276595,0.13089005235602094,0.42424242424242425,0.04912280701754386,0.0,0.0 +Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0,19,0.14841443778775315,41.255916967038694,0.06620762711864407,0.07122507122507123,0.0425531914893617,0.07853403141361257,0.43478260869565216,0.08,0.043478260869565216,0.008 +Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0,19,0.14841443778775315,41.255916967038694,0.007944915254237288,,0.02127659574468085,0.00981675392670157,0.35714285714285715,0.3333333333333333,0.0,0.0 +Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0,19,0.14841443778775315,41.255916967038694,0.020127118644067795,0.07122507122507123,0.02127659574468085,0.01963350785340314,0.625,0.13157894736842105,0.0,0.0 +Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0,19,0.14841443778775315,41.255916967038694,0.0635593220338983,0.08262108262108261,0.0425531914893617,0.07853403141361257,0.34782608695652173,0.06666666666666667,0.043478260869565216,0.008333333333333333 +Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0,,2,0.22607581000136776,2.593495513252762,0.5263157894736842,0.3770491803278688,0.6666666666666666,,0.06349206349206349,0.008,0.0,0.0 +Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0,,2,0.22607581000136776,2.593495513252762,0.47368421052631576,0.6229508196721312,0.3333333333333333,,0.16666666666666666,0.011111111111111112,0.1,0.006666666666666667 +Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0,,1,0.014643059321669063,2.3728170083523157,,1.0,1.0,,0.4,,0.0, +Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0,,13,0.4054950189615709,15.312673003757494,0.08568244394912809,0.09715025906735751,,,0.05172413793103448,0.0022953328232593728,0.02586206896551724,0.0011476664116296864 +Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0,,13,0.4054950189615709,15.312673003757494,0.015209125475285171,0.07772020725388601,0.07692307692307693,,0.10416666666666667,0.010775862068965518,0.0,0.0 +Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0,,13,0.4054950189615709,15.312673003757494,0.02130588698046414,0.09650259067357513,0.07692307692307693,,0.11267605633802817,0.012307692307692308,0.014084507042253521,0.0015384615384615385 +Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0,13,0.4054950189615709,15.312673003757494,0.03441720204536515,0.08937823834196891,0.15384615384615385,0.3115264797507788,0.10606060606060606,0.006666666666666667,0.045454545454545456,0.002857142857142857 +Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,,,13,0.4054950189615709,15.312673003757494,0.06640881080372361,,,,0.20388349514563106,0.010365251727541954,0.1262135922330097,0.006416584402764067 +Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0,,13,0.4054950189615709,15.312673003757494,0.02130588698046414,,0.038461538461538464,,0.125,0.007692307692307693,0.0,0.0 +Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0,13,0.4054950189615709,15.312673003757494,0.27743542677330535,0.07772020725388601,0.07692307692307693,0.4672897196261682,0.05389221556886228,0.0010633270321361058,0.005988023952095809,0.00011814744801512288 +Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0,,13,0.4054950189615709,15.312673003757494,0.09833486298675757,0.09261658031088082,0.15384615384615385,,0.102803738317757,0.0036666666666666666,0.018691588785046728,0.0006666666666666666 +Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0,13,0.4054950189615709,15.312673003757494,0.08194571915563131,0.12176165803108809,0.038461538461538464,0.003115264797507788,0.08235294117647059,0.0056,0.03529411764705882,0.0024 +Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0,,13,0.4054950189615709,15.312673003757494,0.03933394519470303,0.10427461139896373,,,0.1125,0.0075,0.05,0.0033333333333333335 +Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0,,13,0.4054950189615709,15.312673003757494,0.014750229448013635,0.08290155440414508,0.038461538461538464,,0.1111111111111111,0.011111111111111112,0.0,0.0 +Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0,13,0.4054950189615709,15.312673003757494,0.004588960272715353,0.06800518134715026,0.038461538461538464,0.21806853582554517,0.17391304347826086,0.02857142857142857,0.0,0.0 +Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0,15,2.4038885300862676,155.99001663893512,0.04144218814753419,0.07484524479459764,0.06,1.0,0.08450704225352113,0.02,0.0,0.0 +Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0,,15,2.4038885300862676,155.99001663893512,0.02458903163420362,,0.02,,0.19148936170212766,0.05056179775280899,0.02127659574468085,0.0056179775280898875 +Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0,,15,2.4038885300862676,155.99001663893512,0.02458903163420362,0.061902082160945414,0.06,,0.12,0.033707865168539325,0.04,0.011235955056179775 +Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0,,15,2.4038885300862676,155.99001663893512,0.05318414145600221,0.08722566122678672,0.04,,0.11392405063291139,0.023376623376623377,0.012658227848101266,0.0025974025974025974 +Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,,,15,2.4038885300862676,155.99001663893512,0.20928305014504767,0.1080472706809229,0.12,,0.14193548387096774,0.014521452145214522,0.03225806451612903,0.0033003300330033004 +Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0,,15,2.4038885300862676,155.99001663893512,0.028318828567481698,0.04276871131119865,0.02,,0.12,0.02926829268292683,0.0,0.0 +Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,,,15,2.4038885300862676,155.99001663893512,0.0359165630611963,,0.02,,0.11864406779661017,0.026923076923076925,0.03389830508474576,0.007692307692307693 +Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0,,15,2.4038885300862676,155.99001663893512,0.1381406271584473,0.07653348339898705,0.12,,0.10256410256410256,0.008,0.0,0.0 +Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0,,15,2.4038885300862676,155.99001663893512,0.08025970437905788,0.08441193021947102,0.08,,0.14414414414414414,0.027538726333907058,0.04504504504504504,0.008605851979345954 +Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0,,15,2.4038885300862676,155.99001663893512,0.01381406271584473,0.056274620146314014,,,0.125,0.03,0.0,0.0 +Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0,4,0.04686299684881493,9.35125657510228,0.09293680297397769,0.273224043715847,,0.14814814814814814,0.875,0.28,0.0,0.0 +49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0,10,0.13132160885254723,14.02563886785043,0.15166340508806261,0.09882583170254403,0.047619047619047616,0.12518778167250877,0.07865168539325842,0.003010752688172043,0.0,0.0 +Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0,,10,0.13132160885254723,14.02563886785043,0.023157208088714937,0.0684931506849315,0.09523809523809523,,0.125,0.008450704225352112,0.0,0.0 +Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,,,10,0.13132160885254723,14.02563886785043,0.16960208741030658,,0.047619047619047616,,0.17543859649122806,0.0038461538461538464,0.03508771929824561,0.0007692307692307692 +Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0,,10,0.13132160885254723,14.02563886785043,0.06523157208088715,0.13992172211350293,,,0.2631578947368421,0.01,0.0,0.0 +Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0,10,0.13132160885254723,14.02563886785043,0.1111545988258317,0.09784735812133072,0.14285714285714285,0.022533800701051578,0.1346153846153846,0.004107981220657277,0.0,0.0 +The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0,10,0.13132160885254723,14.02563886785043,0.13007175472928897,0.11741682974559686,0.23809523809523808,0.27090635953930897,0.24107142857142858,0.01354062186559679,0.026785714285714284,0.0015045135406218655 +White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0,10,0.13132160885254723,14.02563886785043,0.09145466405740378,0.14481409001956946,0.09523809523809523,0.045067601402103155,0.17777777777777778,0.005706134094151213,0.044444444444444446,0.0014265335235378032 +Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0,,4,0.22319597666932456,16.50846058605035,0.1752767527675277,,0.1111111111111111,,0.0851063829787234,0.042105263157894736,0.0,0.0 +Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0,4,0.22319597666932456,16.50846058605035,0.474169741697417,0.3654970760233918,0.5555555555555556,0.45989304812834225,0.23333333333333334,0.054474708171206226,0.05,0.011673151750972763 +Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0,4,0.22319597666932456,16.50846058605035,0.18450184501845018,0.28362573099415206,0.1111111111111111,0.1443850267379679,0.1,0.04,0.0,0.0 +Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0,4,0.22319597666932456,16.50846058605035,0.16605166051660517,0.3508771929824561,0.2222222222222222,0.39572192513368987,0.3333333333333333,0.1,0.0,0.0 +Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0,15,0.2576242169511926,22.90216196408941,0.05142857142857143,0.06583278472679395,0.075,0.08450704225352113,0.5714285714285714,0.13333333333333333,0.14285714285714285,0.03333333333333333 +Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0,15,0.2576242169511926,22.90216196408941,0.022857142857142857,0.027649769585253458,0.05,0.028169014084507043,0.4166666666666667,0.125,0.0,0.0 +Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0,,15,0.2576242169511926,22.90216196408941,0.1,0.07899934167215274,0.1,,0.2553191489361702,0.06857142857142857,0.0425531914893617,0.011428571428571429 +Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0,15,0.2576242169511926,22.90216196408941,0.025714285714285714,0.06056616194865043,0.1,0.03286384976525822,0.2,0.13333333333333333,0.0,0.0 +Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0,15,0.2576242169511926,22.90216196408941,0.14857142857142858,0.07241606319947334,0.025,0.18779342723004694,0.4444444444444444,0.046153846153846156,0.0,0.0 +Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0,15,0.2576242169511926,22.90216196408941,0.017142857142857144,0.05924950625411455,0.025,0.028169014084507043,0.3,0.2,0.0,0.0 +Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0,15,0.2576242169511926,22.90216196408941,0.12571428571428572,0.08953258722843976,0.1,0.18779342723004694,0.09333333333333334,0.031818181818181815,0.02666666666666667,0.00909090909090909 +Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0,15,0.2576242169511926,22.90216196408941,0.05714285714285714,0.07570770243581304,0.025,0.08450704225352113,0.21052631578947367,0.04,0.0,0.0 +Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0,15,0.2576242169511926,22.90216196408941,0.03428571428571429,0.04476629361421988,0.1,0.056338028169014086,0.3888888888888889,0.11666666666666667,0.0,0.0 +Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0,15,0.2576242169511926,22.90216196408941,0.021142857142857144,0.065174456879526,0.1,0.03474178403755868,0.6923076923076923,0.24324324324324326,0.0,0.0 +Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0,15,0.2576242169511926,22.90216196408941,0.04914285714285714,0.08558262014483213,0.1,0.08075117370892018,0.3333333333333333,0.09302325581395349,0.0,0.0 +Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0,15,0.2576242169511926,22.90216196408941,0.018285714285714287,0.07373271889400922,0.125,0.03004694835680751,0.2777777777777778,0.15625,0.0,0.0 +Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0,,15,0.2576242169511926,22.90216196408941,0.22857142857142856,0.06912442396313365,0.025,,0.11627906976744186,0.0125,0.0,0.0 +Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0,15,0.2576242169511926,22.90216196408941,0.07714285714285714,0.08229098090849243,0.05,0.1267605633802817,0.43478260869565216,0.07407407407407407,0.0,0.0 +Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0,,8,1.3822679215355613,8.17887192908918,0.3988962133987429,0.2122905027932961,0.07142857142857142,,0.05263157894736842,0.001921598770176787,0.021052631578947368,0.0007686395080707148 +Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0,,8,1.3822679215355613,8.17887192908918,0.01410393990495171,0.16899441340782123,0.07142857142857142,,0.07142857142857142,0.021739130434782608,0.0,0.0 +Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0,,8,1.3822679215355613,8.17887192908918,0.02820787980990342,0.08519553072625698,0.07142857142857142,,0.0625,0.016304347826086956,0.0,0.0 +Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0,8,1.3822679215355613,8.17887192908918,0.06132147784761613,0.16899441340782123,0.14285714285714285,1.0,0.09375,0.0075,0.0,0.0 +Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,,,8,1.3822679215355613,8.17887192908918,0.011497777096428024,0.1829608938547486,0.14285714285714285,,0.15151515151515152,0.06666666666666667,0.0,0.0 +White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,,,8,1.3822679215355613,8.17887192908918,0.05672236700904492,,,,0.08,0.005405405405405406,0.0,0.0 diff --git a/data/state_summary.csv b/data/state_summary.csv new file mode 100644 index 000000000..53979a710 --- /dev/null +++ b/data/state_summary.csv @@ -0,0 +1,36 @@ +state,resorts_per_state,state_total_skiable_area_ac,state_total_days_open,state_total_terrain_parks,state_total_nightskiing_ac,state_population,state_area_sq_miles +Alaska,3,2280.0,345.0,4.0,580.0,731545,665384 +Arizona,2,1577.0,237.0,6.0,80.0,7278717,113990 +California,21,25948.0,2738.0,81.0,587.0,39512223,163695 +Colorado,22,43682.0,3258.0,74.0,428.0,5758736,104094 +Connecticut,5,358.0,353.0,10.0,256.0,3565278,5543 +Idaho,12,16396.0,1136.0,27.0,415.0,1787065,83569 +Illinois,4,191.0,221.0,6.0,191.0,12671821,57914 +Indiana,2,165.0,157.0,4.0,165.0,6732219,36420 +Iowa,3,140.0,100.0,5.0,140.0,3155070,56273 +Maine,9,3216.0,865.0,17.0,388.0,1344212,35380 +Maryland,1,172.0,121.0,3.0,118.0,6045680,12406 +Massachusetts,11,1166.0,671.0,18.0,583.0,6892503,10554 +Michigan,28,4406.0,2389.0,63.0,1946.0,9986857,96714 +Minnesota,14,1560.0,1490.0,29.0,1020.0,5639632,86936 +Missouri,2,60.0,69.0,2.0,47.0,6137428,69707 +Montana,12,21410.0,951.0,27.0,710.0,1068778,147040 +Nevada,4,2110.0,415.0,9.0,0.0,3080156,110572 +New Hampshire,16,3427.0,1847.0,43.0,376.0,1359711,9349 +New Jersey,2,190.0,170.0,4.0,181.0,8882190,8723 +New Mexico,9,5223.0,966.0,18.0,50.0,2096829,121590 +New York,33,5514.0,2384.0,72.0,2836.0,19453561,54555 +North Carolina,6,370.0,506.0,9.0,335.0,10488084,53819 +Ohio,5,421.0,489.0,12.0,421.0,11689100,44826 +Oregon,10,11774.0,1180.0,22.0,1127.0,4217737,98379 +Pennsylvania,19,1888.0,1404.0,47.0,1528.0,12801989,46054 +Rhode Island,1,30.0,100.0,1.0,30.0,1059361,1545 +South Dakota,2,950.0,183.0,3.0,0.0,884659,77116 +Tennessee,1,0.0,83.0,1.0,0.0,6829174,42144 +Utah,13,30508.0,1544.0,26.0,642.0,3205958,84897 +Vermont,15,7239.0,1777.0,50.0,50.0,623989,9616 +Virginia,4,269.0,366.0,4.0,135.0,8535519,42775 +Washington,10,15330.0,1022.0,21.0,1997.0,7614893,71298 +West Virginia,4,542.0,342.0,9.0,187.0,1792147,24230 +Wisconsin,15,1750.0,1519.0,40.0,1065.0,5822434,65496 +Wyoming,8,6523.0,716.0,14.0,110.0,578759,97813 From c6e9da803954a3a337cac3d3b5cf7a64e90e16e5 Mon Sep 17 00:00:00 2001 From: ituser Date: Tue, 29 Mar 2022 12:28:40 +0200 Subject: [PATCH 4/7] Saving work done with data wrangling and data preprocessing/training --- Notebooks/03_exploratory_data_analysis.ipynb | 25 +- Notebooks/04_preprocessing_and_training.ipynb | 470 ++---------------- 2 files changed, 50 insertions(+), 445 deletions(-) diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb index c1746d2e4..d80c00198 100644 --- a/Notebooks/03_exploratory_data_analysis.ipynb +++ b/Notebooks/03_exploratory_data_analysis.ipynb @@ -3463,7 +3463,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**A: 1** Your answer here" + "**A: 1** Your answer here: Montana is the third largest state in the list of states although it doesn't have a high population relative to the other states, meaning it's less densely populated. The state with the highest population is California. The state hosting the most resorts is New York. Montana is in the top five for largest skiable areas and New York is not. Another interesting statistic is that New York tops the Night Skiing Area.\n", + "\n", + "It was decided that resort density (resorts per state/state population & resorts per state/state size) could be useful in predicting price of tickets in a state. Possibly, high competition could be repressing ticket price. Vermont ranks high in both of these new features.\n", + "\n", + "Since the dataset had a lot of dimensions making it very complicated to analyse, principle components analysis (PCA) was used to bring the data back down to a lower dimension and make it more suitable to fit into a model. To do this, the data was first scaled, then the PCA transformation was fitted using the scaled data, the transformation was applied to the data to get derived features, and those features were plotted on a scatterplot of the two most significant components with the points color coded based on the ticket price.\n", + "\n", + "The results of the plot showed no obvious patterns for the price. This tempts us to treat all states the same in our analysis. Two states stood out as outliers in the plot for both components: New Hampshire and Vermont. Also, in analysis of the 2 components from the PCA, it was found that the second component was heavily influenced by resorts_per_100kcapita and resorts_per_100ksq_mile (0.662458 and 0.637691 respectively). These two states are both more than three standard deviations from the mean for the two features.The two outlier states mentioned above are both more than three standard deviations from the mean for the two features.\n", + "\n", + "Next, after analysing the data based on state, some more analysis was done based on resort data. This analysis was to incorporate the data that was taken from both resorts and state to get some information on how each resort in a state was able to share the state's resources such as skiable area and population(market). \n", + "\n", + "From a heatmap, it was discovered that the ratio between resort night skiing and total night skiing for the state was the most correlated with ticket price. This suggests that a greater share of night skiing capacity can lead to a higher price for the tickets for that resort. Other features that correlated well with ticket price were Runs and total_chairs. Another feature with positive correlation to price is vertical drop.\n", + "\n", + "After observing the correlations with ticket price for a number of features, a scatterplots were made for each feature with ticket price on the y-axis. This was to get a more clear view of the relationships of these features with ticket price. From the scatterplots, it was clear that there was a strong correlation of ticket price with vertical drop. Other features once again included fastQuads, Runs, and total_chairs. \n", + "\n", + "Ticket price at a low Resorts_per_100kcapita value seemed have quite a lot of variance, but as the value rose, the ticket price rose as well. It could possibly be because the more resorts there are in an area, the more popular that area is for skiing. This is just speculation ofcourse. \n", + "\n", + "Finally, the final step of the analysis was to visualize the relationship between chairs to runs ration and ticket price for the resorts. The relationship seemed to be negative although it wasn't a very strong correlation. Basically, the less chairs there were, the higher the ticket price. It is important to note that this doesn't necessarily mean more revenue was generated from less chairs. This could mean chairs were more expensive due to a shortage of chairs and that less customers were able to occupy the chairs. It could've been useful to have data about the number of customers per year.\n", + "\n", + "\n", + "\n" ] }, { @@ -3932,7 +3951,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -3946,7 +3965,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.9.7" }, "toc": { "base_numbering": 1, diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb index 94ff2aeba..bc71da72b 100644 --- a/Notebooks/04_preprocessing_and_training.ipynb +++ b/Notebooks/04_preprocessing_and_training.ipynb @@ -99,7 +99,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -139,445 +147,23 @@ }, "outputs": [ { - "data": { - "text/html": [ - "
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01234
NameAlyeska ResortEaglecrest Ski AreaHilltop Ski AreaArizona SnowbowlSunrise Park Resort
RegionAlaskaAlaskaAlaskaArizonaArizona
stateAlaskaAlaskaAlaskaArizonaArizona
summit_elev3939260020901150011100
vertical_drop2500154029423001800
base_elev2501200179692009200
trams10000
fastSixes00010
fastQuads20001
quad20022
triple00123
double04011
surface20220
total_chairs74387
Runs7636135565
TerrainParks21142
LongestRun_mi12121.2
SkiableTerrain_ac161064030777800
Snow Making_ac113603010480
daysOpenLastYear15045150122115
yearsOpen6044368149
averageSnowfall66935069260250
AdultWeekend8553348978
projectedDaysOpen15090152122104
NightSkiing_ac550NaN30NaN80
resorts_per_state33322
resorts_per_100kcapita0.4100910.4100910.4100910.02747740.0274774
resorts_per_100ksq_mile0.4508670.4508670.4508671.754541.75454
resort_skiable_area_ac_state_ratio0.706140.2807020.01315790.4927080.507292
resort_days_open_state_ratio0.4347830.1304350.4347830.5147680.485232
resort_terrain_park_state_ratio0.50.250.250.6666670.333333
resort_night_skiing_state_ratio0.948276NaN0.0517241NaN1
total_chairs_runs_ratio0.09210530.1111110.2307690.1454550.107692
total_chairs_skiable_ratio0.004347830.006250.10.0102960.00875
fastQuads_runs_ratio0.02631580000.0153846
fastQuads_skiable_ratio0.001242240000.00125
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" - ], - "text/plain": [ - " 0 1 \\\n", - "Name Alyeska Resort Eaglecrest Ski Area \n", - "Region Alaska Alaska \n", - "state Alaska Alaska \n", - "summit_elev 3939 2600 \n", - "vertical_drop 2500 1540 \n", - "base_elev 250 1200 \n", - "trams 1 0 \n", - "fastSixes 0 0 \n", - "fastQuads 2 0 \n", - "quad 2 0 \n", - "triple 0 0 \n", - "double 0 4 \n", - "surface 2 0 \n", - "total_chairs 7 4 \n", - "Runs 76 36 \n", - "TerrainParks 2 1 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 1610 640 \n", - "Snow Making_ac 113 60 \n", - "daysOpenLastYear 150 45 \n", - "yearsOpen 60 44 \n", - "averageSnowfall 669 350 \n", - "AdultWeekend 85 53 \n", - "projectedDaysOpen 150 90 \n", - "NightSkiing_ac 550 NaN \n", - "resorts_per_state 3 3 \n", - "resorts_per_100kcapita 0.410091 0.410091 \n", - "resorts_per_100ksq_mile 0.450867 0.450867 \n", - "resort_skiable_area_ac_state_ratio 0.70614 0.280702 \n", - "resort_days_open_state_ratio 0.434783 0.130435 \n", - "resort_terrain_park_state_ratio 0.5 0.25 \n", - "resort_night_skiing_state_ratio 0.948276 NaN \n", - "total_chairs_runs_ratio 0.0921053 0.111111 \n", - "total_chairs_skiable_ratio 0.00434783 0.00625 \n", - "fastQuads_runs_ratio 0.0263158 0 \n", - "fastQuads_skiable_ratio 0.00124224 0 \n", - "\n", - " 2 3 \\\n", - "Name Hilltop Ski Area Arizona Snowbowl \n", - "Region Alaska Arizona \n", - "state Alaska Arizona \n", - "summit_elev 2090 11500 \n", - "vertical_drop 294 2300 \n", - "base_elev 1796 9200 \n", - "trams 0 0 \n", - "fastSixes 0 1 \n", - "fastQuads 0 0 \n", - "quad 0 2 \n", - "triple 1 2 \n", - "double 0 1 \n", - "surface 2 2 \n", - "total_chairs 3 8 \n", - "Runs 13 55 \n", - "TerrainParks 1 4 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 30 777 \n", - "Snow Making_ac 30 104 \n", - "daysOpenLastYear 150 122 \n", - "yearsOpen 36 81 \n", - "averageSnowfall 69 260 \n", - "AdultWeekend 34 89 \n", - "projectedDaysOpen 152 122 \n", - "NightSkiing_ac 30 NaN \n", - "resorts_per_state 3 2 \n", - "resorts_per_100kcapita 0.410091 0.0274774 \n", - "resorts_per_100ksq_mile 0.450867 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.0131579 0.492708 \n", - "resort_days_open_state_ratio 0.434783 0.514768 \n", - "resort_terrain_park_state_ratio 0.25 0.666667 \n", - "resort_night_skiing_state_ratio 0.0517241 NaN \n", - "total_chairs_runs_ratio 0.230769 0.145455 \n", - "total_chairs_skiable_ratio 0.1 0.010296 \n", - "fastQuads_runs_ratio 0 0 \n", - "fastQuads_skiable_ratio 0 0 \n", - "\n", - " 4 \n", - "Name Sunrise Park Resort \n", - "Region Arizona \n", - "state Arizona \n", - "summit_elev 11100 \n", - "vertical_drop 1800 \n", - "base_elev 9200 \n", - "trams 0 \n", - "fastSixes 0 \n", - "fastQuads 1 \n", - "quad 2 \n", - "triple 3 \n", - "double 1 \n", - "surface 0 \n", - "total_chairs 7 \n", - "Runs 65 \n", - "TerrainParks 2 \n", - "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800 \n", - "Snow Making_ac 80 \n", - "daysOpenLastYear 115 \n", - "yearsOpen 49 \n", - "averageSnowfall 250 \n", - "AdultWeekend 78 \n", - "projectedDaysOpen 104 \n", - "NightSkiing_ac 80 \n", - "resorts_per_state 2 \n", - "resorts_per_100kcapita 0.0274774 \n", - "resorts_per_100ksq_mile 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.507292 \n", - "resort_days_open_state_ratio 0.485232 \n", - "resort_terrain_park_state_ratio 0.333333 \n", - "resort_night_skiing_state_ratio 1 \n", - "total_chairs_runs_ratio 0.107692 \n", - "total_chairs_skiable_ratio 0.00875 \n", - "fastQuads_runs_ratio 0.0153846 \n", - "fastQuads_skiable_ratio 0.00125 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '../data/ski_data_step3_features.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_29600/1057605783.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mski_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/ski_data_step3_features.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mski_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 481\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 813\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1038\u001b[0m )\n\u001b[0;32m 1039\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1042\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 52\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 221\u001b[0m \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 223\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 700\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 701\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 703\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/ski_data_step3_features.csv'" + ] } ], "source": [ @@ -3536,7 +3122,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -3550,7 +3136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.9.7" }, "toc": { "base_numbering": 1, From e4df79df85d2bee0ca8e78ee292f857cf0fefc34 Mon Sep 17 00:00:00 2001 From: ituser Date: Thu, 31 Mar 2022 18:39:25 +0200 Subject: [PATCH 5/7] Saving all changes before uploading all my answers. --- Notebooks/04_preprocessing_and_training.ipynb | 1041 +++++++++++++---- 1 file changed, 825 insertions(+), 216 deletions(-) diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb index 3b8bc2dc8..db4cee86e 100644 --- a/Notebooks/04_preprocessing_and_training.ipynb +++ b/Notebooks/04_preprocessing_and_training.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -133,29 +133,451 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '../data/ski_data_step3_features.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13636/1057605783.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mski_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../data/ski_data_step3_features.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m 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\u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 700\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 701\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 703\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/ski_data_step3_features.csv'" - ] + "data": { + "text/html": [ + "
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01234
NameAlyeska ResortEaglecrest Ski AreaHilltop Ski AreaArizona SnowbowlSunrise Park Resort
RegionAlaskaAlaskaAlaskaArizonaArizona
stateAlaskaAlaskaAlaskaArizonaArizona
summit_elev3939260020901150011100
vertical_drop2500154029423001800
base_elev2501200179692009200
trams10000
fastSixes00010
fastQuads20001
quad20022
triple00123
double04011
surface20220
total_chairs74387
Runs76.036.013.055.065.0
TerrainParks2.01.01.04.02.0
LongestRun_mi1.02.01.02.01.2
SkiableTerrain_ac1610.0640.030.0777.0800.0
Snow Making_ac113.060.030.0104.080.0
daysOpenLastYear150.045.0150.0122.0115.0
yearsOpen60.044.036.081.049.0
averageSnowfall669.0350.069.0260.0250.0
AdultWeekend85.053.034.089.078.0
projectedDaysOpen150.090.0152.0122.0104.0
NightSkiing_ac550.0NaN30.0NaN80.0
resorts_per_state33322
resorts_per_100kcapita0.4100910.4100910.4100910.0274770.027477
resorts_per_100ksq_mile0.4508670.4508670.4508671.754541.75454
resort_skiable_area_ac_state_ratio0.706140.2807020.0131580.4927080.507292
resort_days_open_state_ratio0.4347830.1304350.4347830.5147680.485232
resort_terrain_park_state_ratio0.50.250.250.6666670.333333
resort_night_skiing_state_ratio0.948276NaN0.051724NaN1.0
total_chairs_runs_ratio0.0921050.1111110.2307690.1454550.107692
total_chairs_skiable_ratio0.0043480.006250.10.0102960.00875
fastQuads_runs_ratio0.0263160.00.00.00.015385
fastQuads_skiable_ratio0.0012420.00.00.00.00125
\n", + "
" + ], + "text/plain": [ + " 0 1 \\\n", + "Name Alyeska Resort Eaglecrest Ski Area \n", + "Region Alaska Alaska \n", + "state Alaska Alaska \n", + "summit_elev 3939 2600 \n", + "vertical_drop 2500 1540 \n", + "base_elev 250 1200 \n", + "trams 1 0 \n", + "fastSixes 0 0 \n", + "fastQuads 2 0 \n", + "quad 2 0 \n", + "triple 0 0 \n", + "double 0 4 \n", + "surface 2 0 \n", + "total_chairs 7 4 \n", + "Runs 76.0 36.0 \n", + "TerrainParks 2.0 1.0 \n", + "LongestRun_mi 1.0 2.0 \n", + "SkiableTerrain_ac 1610.0 640.0 \n", + "Snow Making_ac 113.0 60.0 \n", + "daysOpenLastYear 150.0 45.0 \n", + "yearsOpen 60.0 44.0 \n", + "averageSnowfall 669.0 350.0 \n", + "AdultWeekend 85.0 53.0 \n", + "projectedDaysOpen 150.0 90.0 \n", + "NightSkiing_ac 550.0 NaN \n", + "resorts_per_state 3 3 \n", + "resorts_per_100kcapita 0.410091 0.410091 \n", + "resorts_per_100ksq_mile 0.450867 0.450867 \n", + "resort_skiable_area_ac_state_ratio 0.70614 0.280702 \n", + "resort_days_open_state_ratio 0.434783 0.130435 \n", + "resort_terrain_park_state_ratio 0.5 0.25 \n", + "resort_night_skiing_state_ratio 0.948276 NaN \n", + "total_chairs_runs_ratio 0.092105 0.111111 \n", + "total_chairs_skiable_ratio 0.004348 0.00625 \n", + "fastQuads_runs_ratio 0.026316 0.0 \n", + "fastQuads_skiable_ratio 0.001242 0.0 \n", + "\n", + " 2 3 \\\n", + "Name Hilltop Ski Area Arizona Snowbowl \n", + "Region Alaska Arizona \n", + "state Alaska Arizona \n", + "summit_elev 2090 11500 \n", + "vertical_drop 294 2300 \n", + "base_elev 1796 9200 \n", + "trams 0 0 \n", + "fastSixes 0 1 \n", + "fastQuads 0 0 \n", + "quad 0 2 \n", + "triple 1 2 \n", + "double 0 1 \n", + "surface 2 2 \n", + "total_chairs 3 8 \n", + "Runs 13.0 55.0 \n", + "TerrainParks 1.0 4.0 \n", + "LongestRun_mi 1.0 2.0 \n", + "SkiableTerrain_ac 30.0 777.0 \n", + "Snow Making_ac 30.0 104.0 \n", + "daysOpenLastYear 150.0 122.0 \n", + "yearsOpen 36.0 81.0 \n", + "averageSnowfall 69.0 260.0 \n", + "AdultWeekend 34.0 89.0 \n", + "projectedDaysOpen 152.0 122.0 \n", + "NightSkiing_ac 30.0 NaN \n", + "resorts_per_state 3 2 \n", + "resorts_per_100kcapita 0.410091 0.027477 \n", + "resorts_per_100ksq_mile 0.450867 1.75454 \n", + "resort_skiable_area_ac_state_ratio 0.013158 0.492708 \n", + "resort_days_open_state_ratio 0.434783 0.514768 \n", + "resort_terrain_park_state_ratio 0.25 0.666667 \n", + "resort_night_skiing_state_ratio 0.051724 NaN \n", + "total_chairs_runs_ratio 0.230769 0.145455 \n", + "total_chairs_skiable_ratio 0.1 0.010296 \n", + "fastQuads_runs_ratio 0.0 0.0 \n", + "fastQuads_skiable_ratio 0.0 0.0 \n", + "\n", + " 4 \n", + "Name Sunrise Park Resort \n", + "Region Arizona \n", + "state Arizona \n", + "summit_elev 11100 \n", + "vertical_drop 1800 \n", + "base_elev 9200 \n", + "trams 0 \n", + "fastSixes 0 \n", + "fastQuads 1 \n", + "quad 2 \n", + "triple 3 \n", + "double 1 \n", + "surface 0 \n", + "total_chairs 7 \n", + "Runs 65.0 \n", + "TerrainParks 2.0 \n", + "LongestRun_mi 1.2 \n", + "SkiableTerrain_ac 800.0 \n", + "Snow Making_ac 80.0 \n", + "daysOpenLastYear 115.0 \n", + "yearsOpen 49.0 \n", + "averageSnowfall 250.0 \n", + "AdultWeekend 78.0 \n", + "projectedDaysOpen 104.0 \n", + "NightSkiing_ac 80.0 \n", + "resorts_per_state 2 \n", + "resorts_per_100kcapita 0.027477 \n", + "resorts_per_100ksq_mile 1.75454 \n", + "resort_skiable_area_ac_state_ratio 0.507292 \n", + "resort_days_open_state_ratio 0.485232 \n", + "resort_terrain_park_state_ratio 0.333333 \n", + "resort_night_skiing_state_ratio 1.0 \n", + "total_chairs_runs_ratio 0.107692 \n", + "total_chairs_skiable_ratio 0.00875 \n", + "fastQuads_runs_ratio 0.015385 \n", + "fastQuads_skiable_ratio 0.00125 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -179,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -274,11 +696,11 @@ " \n", " \n", " Runs\n", - " 105\n", + " 105.0\n", " \n", " \n", " TerrainParks\n", - " 4\n", + " 4.0\n", " \n", " \n", " LongestRun_mi\n", @@ -286,35 +708,35 @@ " \n", " \n", " SkiableTerrain_ac\n", - " 3000\n", + " 3000.0\n", " \n", " \n", " Snow Making_ac\n", - " 600\n", + " 600.0\n", " \n", " \n", " daysOpenLastYear\n", - " 123\n", + " 123.0\n", " \n", " \n", " yearsOpen\n", - " 72\n", + " 72.0\n", " \n", " \n", " averageSnowfall\n", - " 333\n", + " 333.0\n", " \n", " \n", " AdultWeekend\n", - " 81\n", + " 81.0\n", " \n", " \n", " projectedDaysOpen\n", - " 123\n", + " 123.0\n", " \n", " \n", " NightSkiing_ac\n", - " 600\n", + " 600.0\n", " \n", " \n", " resorts_per_state\n", @@ -322,11 +744,11 @@ " \n", " \n", " resorts_per_100kcapita\n", - " 1.12278\n", + " 1.122778\n", " \n", " \n", " resorts_per_100ksq_mile\n", - " 8.16104\n", + " 8.161045\n", " \n", " \n", " resort_skiable_area_ac_state_ratio\n", @@ -350,11 +772,11 @@ " \n", " \n", " total_chairs_skiable_ratio\n", - " 0.00466667\n", + " 0.004667\n", " \n", " \n", " fastQuads_runs_ratio\n", - " 0.0285714\n", + " 0.028571\n", " \n", " \n", " fastQuads_skiable_ratio\n", @@ -380,31 +802,31 @@ "double 0\n", "surface 3\n", "total_chairs 14\n", - "Runs 105\n", - "TerrainParks 4\n", + "Runs 105.0\n", + "TerrainParks 4.0\n", "LongestRun_mi 3.3\n", - "SkiableTerrain_ac 3000\n", - "Snow Making_ac 600\n", - "daysOpenLastYear 123\n", - "yearsOpen 72\n", - "averageSnowfall 333\n", - "AdultWeekend 81\n", - "projectedDaysOpen 123\n", - "NightSkiing_ac 600\n", + "SkiableTerrain_ac 3000.0\n", + "Snow Making_ac 600.0\n", + "daysOpenLastYear 123.0\n", + "yearsOpen 72.0\n", + "averageSnowfall 333.0\n", + "AdultWeekend 81.0\n", + "projectedDaysOpen 123.0\n", + "NightSkiing_ac 600.0\n", "resorts_per_state 12\n", - "resorts_per_100kcapita 1.12278\n", - "resorts_per_100ksq_mile 8.16104\n", + "resorts_per_100kcapita 1.122778\n", + "resorts_per_100ksq_mile 8.161045\n", "resort_skiable_area_ac_state_ratio 0.140121\n", "resort_days_open_state_ratio 0.129338\n", "resort_terrain_park_state_ratio 0.148148\n", "resort_night_skiing_state_ratio 0.84507\n", "total_chairs_runs_ratio 0.133333\n", - "total_chairs_skiable_ratio 0.00466667\n", - "fastQuads_runs_ratio 0.0285714\n", + "total_chairs_skiable_ratio 0.004667\n", + "fastQuads_runs_ratio 0.028571\n", "fastQuads_skiable_ratio 0.001" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -485,16 +907,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(193.2, 82.8)" + "(193.89999999999998, 83.1)" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -505,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -516,16 +938,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((193, 35), (83, 35))" + "((193, 35), (84, 35))" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -536,16 +958,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((193,), (83,))" + "((193,), (84,))" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -556,41 +978,138 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "((193, 32), (84, 32))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 1#\n", "#Save the 'Name', 'state', and 'Region' columns from the train/test data into names_train and names_test\n", "#Then drop those columns from `X_train` and `X_test`. Use 'inplace=True'\n", "names_list = ['Name', 'state', 'Region']\n", - "names_train = X_train[___]\n", - "names_test = X_test[___]\n", - "X_train.___(columns=names_list, inplace=___)\n", - "X_test.___(columns=names_list, inplace=___)\n", + "names_train = X_train[names_list]\n", + "names_test = X_test[names_list]\n", + "X_train.drop(columns=names_list, inplace=True)\n", + "X_test.drop(columns=names_list, inplace=True)\n", "X_train.shape, X_test.shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "summit_elev int64\n", + "vertical_drop int64\n", + "base_elev int64\n", + "trams int64\n", + "fastSixes int64\n", + "fastQuads int64\n", + "quad int64\n", + "triple int64\n", + "double int64\n", + "surface int64\n", + "total_chairs int64\n", + "Runs float64\n", + "TerrainParks float64\n", + "LongestRun_mi float64\n", + "SkiableTerrain_ac float64\n", + "Snow Making_ac float64\n", + "daysOpenLastYear float64\n", + "yearsOpen float64\n", + "averageSnowfall float64\n", + "projectedDaysOpen float64\n", + "NightSkiing_ac float64\n", + "resorts_per_state int64\n", + "resorts_per_100kcapita float64\n", + "resorts_per_100ksq_mile float64\n", + "resort_skiable_area_ac_state_ratio float64\n", + "resort_days_open_state_ratio float64\n", + "resort_terrain_park_state_ratio float64\n", + "resort_night_skiing_state_ratio float64\n", + "total_chairs_runs_ratio float64\n", + "total_chairs_skiable_ratio float64\n", + "fastQuads_runs_ratio float64\n", + "fastQuads_skiable_ratio float64\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 2#\n", "#Check the `dtypes` attribute of `X_train` to verify all features are numeric\n", - "X_train.___" + "X_train.dtypes" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "summit_elev int64\n", + "vertical_drop int64\n", + "base_elev int64\n", + "trams int64\n", + "fastSixes int64\n", + "fastQuads int64\n", + "quad int64\n", + "triple int64\n", + "double int64\n", + "surface int64\n", + "total_chairs int64\n", + "Runs float64\n", + "TerrainParks float64\n", + "LongestRun_mi float64\n", + "SkiableTerrain_ac float64\n", + "Snow Making_ac float64\n", + "daysOpenLastYear float64\n", + "yearsOpen float64\n", + "averageSnowfall float64\n", + "projectedDaysOpen float64\n", + "NightSkiing_ac float64\n", + "resorts_per_state int64\n", + "resorts_per_100kcapita float64\n", + "resorts_per_100ksq_mile float64\n", + "resort_skiable_area_ac_state_ratio float64\n", + "resort_days_open_state_ratio float64\n", + "resort_terrain_park_state_ratio float64\n", + "resort_night_skiing_state_ratio float64\n", + "total_chairs_runs_ratio float64\n", + "total_chairs_skiable_ratio float64\n", + "fastQuads_runs_ratio float64\n", + "fastQuads_skiable_ratio float64\n", + "dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 3#\n", "#Repeat this check for the test split in `X_test`\n", - "X_test.___" + "X_test.dtypes" ] }, { @@ -616,13 +1135,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "63.961398963730566" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 4#\n", "#Calculate the mean of `y_train`\n", - "train_mean = y_train.___\n", + "train_mean = y_train.mean()\n", "train_mean" ] }, @@ -635,17 +1165,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[63.96139896]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 5#\n", "#Fit the dummy regressor on the training data\n", "#Hint, call its `.fit()` method with `X_train` and `y_train` as arguments\n", "#Then print the object's `constant_` attribute and verify it's the same as the mean above\n", "dumb_reg = DummyRegressor(strategy='mean')\n", - "dumb_reg.___(___, ___)\n", - "dumb_reg.___" + "dumb_reg.fit(X_train, y_train)\n", + "dumb_reg.constant_" ] }, { @@ -702,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -718,9 +1259,9 @@ " ypred -- the predicted values\n", " \"\"\"\n", " ybar = np.sum(y) / len(y) #yes, we could use np.mean(y)\n", - " sum_sq_tot = np.___((y - ybar)**2) #total sum of squares error\n", - " sum_sq_res = np.___((y - ypred)**2) #residual sum of squares error\n", - " R2 = 1.0 - ___ / ___\n", + " sum_sq_tot = np.sum((y - ybar)**2) #total sum of squares error\n", + " sum_sq_res = np.sum((y - ypred)**2) #residual sum of squares error\n", + " R2 = 1.0 - sum_sq_res / sum_sq_tot\n", " return R2" ] }, @@ -739,7 +1280,7 @@ { "data": { "text/plain": [ - "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])" + "array([63.96139896, 63.96139896, 63.96139896, 63.96139896, 63.96139896])" ] }, "execution_count": 18, @@ -767,7 +1308,7 @@ { "data": { "text/plain": [ - "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])" + "array([63.96139896, 63.96139896, 63.96139896, 63.96139896, 63.96139896])" ] }, "execution_count": 19, @@ -829,7 +1370,7 @@ { "data": { "text/plain": [ - "-0.0031235200417913944" + "-0.0015324079131544543" ] }, "execution_count": 21, @@ -874,7 +1415,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -889,8 +1430,8 @@ " y -- the observed values\n", " ypred -- the predicted values\n", " \"\"\"\n", - " abs_error = np.abs(___ - ___)\n", - " mae = np.mean(___)\n", + " abs_error = np.abs(y - ypred)\n", + " mae = np.mean(abs_error)\n", " return mae" ] }, @@ -902,7 +1443,7 @@ { "data": { "text/plain": [ - "17.923463717146785" + "17.73548283175387" ] }, "execution_count": 23, @@ -922,7 +1463,7 @@ { "data": { "text/plain": [ - "19.136142081278486" + "19.58960461386627" ] }, "execution_count": 24, @@ -959,7 +1500,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -976,8 +1517,8 @@ " y -- the observed values\n", " ypred -- the predicted values\n", " \"\"\"\n", - " sq_error = (___ - ___)**2\n", - " mse = np.mean(___)\n", + " sq_error = (y - ypred)**2\n", + " mse = np.mean(sq_error)\n", " return mse" ] }, @@ -989,7 +1530,7 @@ { "data": { "text/plain": [ - "614.1334096969057" + "558.7754824988588" ] }, "execution_count": 26, @@ -1009,7 +1550,7 @@ { "data": { "text/plain": [ - "581.4365441953481" + "704.8367793503885" ] }, "execution_count": 27, @@ -1036,7 +1577,7 @@ { "data": { "text/plain": [ - "array([24.78171523, 24.11299534])" + "array([23.63843232, 26.54876229])" ] }, "execution_count": 28, @@ -1077,7 +1618,7 @@ { "data": { "text/plain": [ - "(0.0, -0.0031235200417913944)" + "(0.0, -0.0015324079131544543)" ] }, "execution_count": 29, @@ -1104,7 +1645,7 @@ { "data": { "text/plain": [ - "(17.92346371714677, 19.136142081278486)" + "(17.735482831753874, 19.58960461386627)" ] }, "execution_count": 30, @@ -1131,7 +1672,7 @@ { "data": { "text/plain": [ - "(614.1334096969046, 581.4365441953483)" + "(558.775482498859, 704.8367793503884)" ] }, "execution_count": 31, @@ -1172,7 +1713,7 @@ { "data": { "text/plain": [ - "(0.0, -3.041041349306602e+30)" + "(0.0, -1.1067688684101517e+31)" ] }, "execution_count": 32, @@ -1194,7 +1735,7 @@ { "data": { "text/plain": [ - "(-0.0031235200417913944, 0.0)" + "(-0.0015324079131544543, -3.490182681275203e+30)" ] }, "execution_count": 33, @@ -1216,7 +1757,7 @@ { "data": { "text/plain": [ - "(0.0, -3.041041349306602e+30)" + "(0.0, -1.1067688684101517e+31)" ] }, "execution_count": 34, @@ -1235,18 +1776,10 @@ "execution_count": 35, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/guy/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in double_scalars\n", - " from ipykernel import kernelapp as app\n" - ] - }, { "data": { "text/plain": [ - "(-0.0031235200417913944, -inf)" + "(-0.0015324079131544543, -3.490182681275203e+30)" ] }, "execution_count": 35, @@ -1315,48 +1848,48 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "summit_elev 2215.000000\n", - "vertical_drop 750.000000\n", + "summit_elev 2250.000000\n", + "vertical_drop 800.000000\n", "base_elev 1300.000000\n", "trams 0.000000\n", "fastSixes 0.000000\n", "fastQuads 0.000000\n", - "quad 1.000000\n", + "quad 0.000000\n", "triple 1.000000\n", "double 1.000000\n", "surface 2.000000\n", "total_chairs 7.000000\n", - "Runs 28.000000\n", + "Runs 30.000000\n", "TerrainParks 2.000000\n", "LongestRun_mi 1.000000\n", - "SkiableTerrain_ac 170.000000\n", - "Snow Making_ac 96.500000\n", - "daysOpenLastYear 109.000000\n", - "yearsOpen 57.000000\n", - "averageSnowfall 120.000000\n", + "SkiableTerrain_ac 178.000000\n", + "Snow Making_ac 100.000000\n", + "daysOpenLastYear 110.000000\n", + "yearsOpen 58.000000\n", + "averageSnowfall 125.000000\n", "projectedDaysOpen 115.000000\n", "NightSkiing_ac 70.000000\n", "resorts_per_state 15.000000\n", "resorts_per_100kcapita 0.248243\n", "resorts_per_100ksq_mile 22.902162\n", - "resort_skiable_area_ac_state_ratio 0.051458\n", - "resort_days_open_state_ratio 0.071225\n", + "resort_skiable_area_ac_state_ratio 0.051687\n", + "resort_days_open_state_ratio 0.071821\n", "resort_terrain_park_state_ratio 0.069444\n", "resort_night_skiing_state_ratio 0.077081\n", "total_chairs_runs_ratio 0.200000\n", - "total_chairs_skiable_ratio 0.040323\n", + "total_chairs_skiable_ratio 0.040000\n", "fastQuads_runs_ratio 0.000000\n", "fastQuads_skiable_ratio 0.000000\n", "dtype: float64" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1376,15 +1909,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "#Code task 9#\n", "#Call `X_train` and `X_test`'s `fillna()` method, passing `X_defaults_median` as the values to use\n", "#Assign the results to `X_tr` and `X_te`, respectively\n", - "X_tr = X_train.___(___)\n", - "X_te = X_test.___(___)" + "X_tr = X_train.fillna(X_defaults_median)\n", + "X_te = X_test.fillna(X_defaults_median)" ] }, { @@ -1403,7 +1936,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1412,9 +1945,9 @@ "#then use it's `transform()` method to apply the scaling to both the train and test split\n", "#data (`X_tr` and `X_te`), naming the results `X_tr_scaled` and `X_te_scaled`, respectively\n", "scaler = StandardScaler()\n", - "scaler.___(X_tr)\n", - "X_tr_scaled = scaler.___(X_tr)\n", - "X_te_scaled = scaler.___(X_te)" + "scaler.fit(X_tr)\n", + "X_tr_scaled = scaler.transform(X_tr)\n", + "X_te_scaled = scaler.transform(X_te)" ] }, { @@ -1426,7 +1959,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1442,15 +1975,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "#Code task 11#\n", "#Call the `predict()` method of the model (`lm`) on both the (scaled) train and test data\n", "#Assign the predictions to `y_tr_pred` and `y_te_pred`, respectively\n", - "y_tr_pred = lm.___(X_tr_scaled)\n", - "y_te_pred = lm.___(X_te_scaled)" + "y_tr_pred = lm.predict(X_tr_scaled)\n", + "y_te_pred = lm.predict(X_te_scaled)" ] }, { @@ -1462,16 +1995,16 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.8177988515690604, 0.7209725843435142)" + "(0.8144386003347039, 0.7588675340576477)" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1491,15 +2024,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(8.168087397673386, 10.045694095532316)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 12#\n", "#Now calculate the mean absolute error scores using `sklearn`'s `mean_absolute_error` function\n", "# as we did above for R^2\n", "# MAE - train, test\n", - "median_mae = ___(y_train, y_tr_pred), ___(y_test, y_te_pred)\n", + "median_mae = mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)\n", "median_mae" ] }, @@ -1512,14 +2056,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(103.68716063113943, 169.69898262779168)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 13#\n", "#And also do the same using `sklearn`'s `mean_squared_error`\n", "# MSE - train, test\n", - "median_mse = ___(___, ___), ___(___, ___)\n", + "median_mse = mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)\n", "median_mse" ] }, @@ -1546,14 +2101,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "summit_elev 4103.155440\n", + "vertical_drop 1085.886010\n", + "base_elev 2999.854922\n", + "trams 0.098446\n", + "fastSixes 0.056995\n", + "fastQuads 0.740933\n", + "quad 0.937824\n", + "triple 1.445596\n", + "double 1.792746\n", + "surface 2.590674\n", + "total_chairs 7.663212\n", + "Runs 43.366492\n", + "TerrainParks 2.444444\n", + "LongestRun_mi 1.339267\n", + "SkiableTerrain_ac 480.272251\n", + "Snow Making_ac 132.935673\n", + "daysOpenLastYear 111.777778\n", + "yearsOpen 56.948187\n", + "averageSnowfall 165.951872\n", + "projectedDaysOpen 116.766467\n", + "NightSkiing_ac 91.564103\n", + "resorts_per_state 16.424870\n", + "resorts_per_100kcapita 0.442261\n", + "resorts_per_100ksq_mile 42.539036\n", + "resort_skiable_area_ac_state_ratio 0.096123\n", + "resort_days_open_state_ratio 0.121879\n", + "resort_terrain_park_state_ratio 0.113350\n", + "resort_night_skiing_state_ratio 0.163529\n", + "total_chairs_runs_ratio 0.260567\n", + "total_chairs_skiable_ratio 0.068081\n", + "fastQuads_runs_ratio 0.011186\n", + "fastQuads_skiable_ratio 0.001760\n", + "dtype: float64" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 14#\n", "#As we did for the median above, calculate mean values for imputing missing values\n", "# These are the values we'll use to fill in any missing values\n", - "X_defaults_mean = X_train.___()\n", + "X_defaults_mean = X_train.mean()\n", "X_defaults_mean" ] }, @@ -1573,7 +2171,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1590,7 +2188,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1609,7 +2207,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1625,7 +2223,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1642,16 +2240,16 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.8170154093990025, 0.716381471695996)" + "(0.8153961204004764, 0.7516735399853209)" ] }, - "execution_count": 49, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1662,16 +2260,16 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.536884040670973, 9.416375625789271)" + "(8.171846911392267, 10.145495313823073)" ] }, - "execution_count": 50, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1682,16 +2280,16 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(112.37695054778276, 164.3926930952436)" + "(103.1521218943851, 174.7618159146046)" ] }, - "execution_count": 51, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -1746,7 +2344,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -1759,7 +2357,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1768,7 +2366,7 @@ "sklearn.pipeline.Pipeline" ] }, - "execution_count": 53, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1779,7 +2377,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1788,7 +2386,7 @@ "(True, True)" ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1813,13 +2411,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('linearregression', LinearRegression())])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 15#\n", "#Call the pipe's `fit()` method with `X_train` and `y_train` as arguments\n", - "pipe.___(___, ___)" + "pipe.fit(X_train, y_train)" ] }, { @@ -1831,7 +2442,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1848,16 +2459,16 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.8177988515690604, 0.7209725843435142)" + "(0.8144386003347039, 0.7588675340576477)" ] }, - "execution_count": 57, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1875,16 +2486,16 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.8177988515690604, 0.7209725843435142)" + "(0.8144386003347039, 0.7588675340576477)" ] }, - "execution_count": 58, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1895,16 +2506,16 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.547850301825427, 9.40702011858132)" + "(8.168087397673386, 10.045694095532316)" ] }, - "execution_count": 59, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1914,26 +2525,24 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "Compare with your earlier result:" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.547850301825427, 9.40702011858132)" + "(8.168087397673386, 10.045694095532316)" ] }, - "execution_count": 60, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1944,16 +2553,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(111.89581253658478, 161.73156451192284)" + "(103.68716063113943, 169.69898262779168)" ] }, - "execution_count": 61, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -1971,16 +2580,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(111.89581253658478, 161.73156451192284)" + "(103.68716063113943, 169.69898262779168)" ] }, - "execution_count": 62, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -2027,7 +2636,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2037,7 +2646,7 @@ "pipe = make_pipeline(\n", " SimpleImputer(strategy='median'), \n", " StandardScaler(),\n", - " ___(___),\n", + " SelectKBest(f_regression),\n", " LinearRegression()\n", ")" ] @@ -2051,7 +2660,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -2060,11 +2669,11 @@ "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", " ('standardscaler', StandardScaler()),\n", " ('selectkbest',\n", - " SelectKBest(score_func=)),\n", + " SelectKBest(score_func=)),\n", " ('linearregression', LinearRegression())])" ] }, - "execution_count": 64, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -2082,7 +2691,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -2092,16 +2701,16 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.7674914326052744, 0.6259877354190833)" + "(0.76754436691681, 0.6523454604324264)" ] }, - "execution_count": 66, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -2112,16 +2721,16 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9.501495079727484, 11.201830190332057)" + "(9.223528160840567, 11.49927307517182)" ] }, - "execution_count": 67, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -2146,7 +2755,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -2155,7 +2764,7 @@ "pipe15 = make_pipeline(\n", " SimpleImputer(strategy='median'), \n", " StandardScaler(),\n", - " ___(___, k=___),\n", + " SelectKBest(f_regression, k=15),\n", " LinearRegression()\n", ")" ] @@ -2169,7 +2778,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -2179,11 +2788,11 @@ " ('standardscaler', StandardScaler()),\n", " ('selectkbest',\n", " SelectKBest(k=15,\n", - " score_func=)),\n", + " score_func=)),\n", " ('linearregression', LinearRegression())])" ] }, - "execution_count": 69, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -2201,7 +2810,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -2211,16 +2820,16 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.7924096060483825, 0.6376199973170795)" + "(0.7719217266659402, 0.6354911802705958)" ] }, - "execution_count": 71, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -2231,16 +2840,16 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9.211767769307116, 10.488246867294356)" + "(9.087070639150939, 11.799373665384277)" ] }, - "execution_count": 72, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } From 39fcb95548e160d063c819c1acf791e4dfc6ef36 Mon Sep 17 00:00:00 2001 From: Thaps Date: Fri, 1 Apr 2022 07:46:44 +0200 Subject: [PATCH 6/7] Update 03_exploratory_data_analysis.ipynb --- Notebooks/03_exploratory_data_analysis.ipynb | 1407 ++++++------------ 1 file changed, 429 insertions(+), 978 deletions(-) diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb index bd2be300f..d80c00198 100644 --- a/Notebooks/03_exploratory_data_analysis.ipynb +++ b/Notebooks/03_exploratory_data_analysis.ipynb @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2020-10-07T07:04:19.124917Z", @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -154,7 +154,7 @@ " 23 projectedDaysOpen 236 non-null float64\n", " 24 NightSkiing_ac 163 non-null float64\n", "dtypes: float64(11), int64(11), object(3)\n", - "memory usage: 50.9+ KB\n" + "memory usage: 54.2+ KB\n" ] } ], @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -369,7 +369,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 11, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -417,7 +417,7 @@ " 6 state_population 35 non-null int64 \n", " 7 state_area_sq_miles 35 non-null int64 \n", "dtypes: float64(4), int64(3), object(1)\n", - "memory usage: 2.1+ KB\n" + "memory usage: 2.3+ KB\n" ] } ], @@ -427,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -546,7 +546,7 @@ "4 256.0 3565278 5543 " ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -594,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -609,7 +609,7 @@ "Name: state_area_sq_miles, dtype: int64" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -649,7 +649,7 @@ "Name: state_population, dtype: int64" ] }, - "execution_count": 17, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -674,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -689,7 +689,7 @@ "Name: resorts_per_state, dtype: int64" ] }, - "execution_count": 18, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -714,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -729,7 +729,7 @@ "Name: state_total_skiable_area_ac, dtype: float64" ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -769,7 +769,7 @@ "Name: state_total_nightskiing_ac, dtype: float64" ] }, - "execution_count": 20, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -794,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -809,7 +809,7 @@ "Name: state_total_days_open, dtype: float64" ] }, - "execution_count": 21, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -843,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -960,7 +960,7 @@ "4 256.0 0.140242 90.203861 " ] }, - "execution_count": 22, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -989,12 +989,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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2M4HfSLIX+DfgrHX0JgjgacDLgF1Jbmyf+9/Aj8C63//91D7w/nc4CknSmj9NJElaBYaBJMkwkCQZBpIkDANJEobBmpekkryt5/Frk7xpldZ9UZIzV2NdB2nnhUluSXL1sNtapv3XJDmmi7bb9t+S5GtJ5hc9v+yIu0le1z5/a5LntM9NLTeK6Si1I2ZefpB5npTkeT2PT8sSo+5qdAyDte9e4IzD7dul7be9+/UK4H9U1ZZVWl/fkmwAXgOMJAyWqeOTNIMOLvYK4NtV9ePAHwN/0K7jJJrvy/wE8Fzgz9s61pIn0XwHAICq2l5VF3TXHRkGa99emnue/ubiFxa/s19459m+c/tMksuS3JbkgiRnJ7kuya4kP9azmmcn2ZHkH5M8v11+Q5I/THJ9OwDer/Ws9++SbAduXqI/L2nXf1OShT9sbwCeDrwnyR8umn+/9R2g3eOTXJNm3PabkvzX5dpb2A5J3pbkC8DvAI8Crk4zRvyGdrvd1C673HZ992ptl6r63DLfjF1uxN3Tgdmqureq/onmS1X7hUmSxyb5fJKfWmKbXpPkivao4t1Jjuhje/1xmrHzr0pyXPv8XJLpdnpTkt1LbKunJPls25d/SHJimm/Lvxl4cbvPXpzmvhvvapeZSvLpdhteleRHerb7n7bruS0jOGodK12Pze3PIY9tPg8cC+ymGXfptcCb2tcuAs7snbf9dwb4Ds246A+hGa/ld9vXzgP+pGf5T9G8aXgczaiPDwW2Aq9v53kIsAN4TLvee4DHLNHPRwFfBY6j+eb7p4EXtK/NAdNLLLPf+g7Q7m8Bv9M+v4FmjPcDtVfAi3ra2Q1saqdPAa7see0RS/Rr1bbL4n256PFNwAk9j78CbALeBby05/n30HzjdKpd5kTg88ATl9mm36O5B8YGmsELz+xje53dTr+B9v4Yvfut7dfunjYub6ePBY5sp58NXNJOn0vPfTZ6H9McKZ3TTr8cuLRnu3+83e4n0Qzf3vn/wfXys6aHo1Cjqu5O8gHg1TRfPe/H9dW+G03yFeBv2+d3Ab2naz5WVfcDX0pyG/Cfgf8OPKHnndlGmj+K9wHXVfNudbGfAuaq6httmx+muUHLpQfpZ+/6lmv3euC9aQbvurSqbkzyzAO09wOaQb6Wchvw2CTvBK7o2S6LrdZ2WW3HAZcBZ1TVg45CWtdV1W3wwLAWTwe+z/Lb637go+2yHwKWGhRuORuB9yd5HE2oHNXHMj8NnNFOfxB4a89rl7bb/eYkkwP0QwdhGKwff0IzbO37ep7bS3sqsD0VcHTPa/f2TN/f8/h+9v+9WDxeSdHcOepVVfU3vS8kmaF5B7yaete3ZLtt288ATgUuSvJ2mvtWLOd7VfWDpV6oqm8neSLwHODXgRfRvDt90KxLPF7t7bIw6ubt2X/E3QONxrmH5h3+01nilNQB+j6Ihfkf+P2iOTJayu8BV1fVL6S5AD43YFuL9f7erpebVB0WvGawTlTVt4CP0Vx0XLCbfUNWn0Z/78oWe2GSI9rrCI8FbqUZGO832nfiJPlPSR52kPVcB/xse255A/AS4DMD9mXJdpP8KPAvVfUXNMN0P3nA9r5Lc2qJNBfij6iqS4DXt+taymptlwNZbsTd7cBZaT5t9Biao4/r2vnuA36BZrTOX1pmvU9JM9LtEcCLgb/nwNvriLZ9gF9q54f9f7+WO3+/kX1BdW7P8w9s8yX8A/sGlDwb+Ltl5tMq8shgfXkb8Mqex38BXNZeKP0UK3t3+lWaPxTHAr9eVd9LciHN+ekb2gua3wBecKCVVNWdaT46eDXNO7orquqyAfuyXLszwG8n+T7NNZRfHrC9bcCnkvwzzSeL3rdwURV43TLLrMp2AUjyVpo/ssekGYH0wqp6E8uMuFvNyLQfo3nnvxf4X1X1g7R3c62qe9qL2lcmma9m9Npe19Ncd/jxdvv8ZVXdf4DtdQ9NgLweuIsmQAD+CPhYkq00p9SW8laa00SvXzTP1cD5aUbd/P1Fy7yKZh/8Ns02XC+jjR7WHLVUGlCSi2gukF7cdV8G1Z6yem1VPX+AZearamJondJhwdNEkiSPDCRJHhlIkjAMJEkYBpIkDANJEoaBJAn4d6b0m7ol4pfOAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -1013,12 +1013,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1051,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1066,7 +1066,7 @@ "Name: resorts_per_100kcapita, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1077,7 +1077,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1092,7 +1092,7 @@ "Name: resorts_per_100ksq_mile, dtype: float64" ] }, - "execution_count": 26, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1152,151 +1152,17 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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resorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acresorts_per_100kcapitaresorts_per_100ksq_mile
state
Alaska32280.0345.04.0580.00.4100910.450867
Arizona21577.0237.06.080.00.0274771.754540
California2125948.02738.081.0587.00.05314812.828736
Colorado2243682.03258.074.0428.00.38202821.134744
Connecticut5358.0353.010.0256.00.14024290.203861
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" - ], - "text/plain": [ - " resorts_per_state state_total_skiable_area_ac \\\n", - "state \n", - "Alaska 3 2280.0 \n", - "Arizona 2 1577.0 \n", - "California 21 25948.0 \n", - "Colorado 22 43682.0 \n", - "Connecticut 5 358.0 \n", - "\n", - " state_total_days_open state_total_terrain_parks \\\n", - "state \n", - "Alaska 345.0 4.0 \n", - "Arizona 237.0 6.0 \n", - "California 2738.0 81.0 \n", - "Colorado 3258.0 74.0 \n", - "Connecticut 353.0 10.0 \n", - "\n", - " state_total_nightskiing_ac resorts_per_100kcapita \\\n", - "state \n", - "Alaska 580.0 0.410091 \n", - "Arizona 80.0 0.027477 \n", - "California 587.0 0.053148 \n", - "Colorado 428.0 0.382028 \n", - "Connecticut 256.0 0.140242 \n", - "\n", - " resorts_per_100ksq_mile \n", - "state \n", - "Alaska 0.450867 \n", - "Arizona 1.754540 \n", - "California 12.828736 \n", - "Colorado 21.134744 \n", - "Connecticut 90.203861 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 1#\n", "#Create a new dataframe, `state_summary_scale` from `state_summary` whilst setting the index to 'state'\n", - "state_summary_scale = state_summary.set_index('state')\n", + "state_summary_scale = state_summary.set_index(___)\n", "#Save the state labels (using the index attribute of `state_summary_scale`) into the variable 'state_summary_index'\n", - "state_summary_index = state_summary_scale.index\n", + "state_summary_index = state_summary_scale.___\n", "#Save the column names (using the `columns` attribute) of `state_summary_scale` into the variable 'state_summary_columns'\n", - "state_summary_columns = state_summary_scale.columns\n", + "state_summary_columns = state_summary_scale.___\n", "state_summary_scale.head()" ] }, @@ -1311,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1327,126 +1193,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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resorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acresorts_per_100kcapitaresorts_per_100ksq_mile
0-0.806912-0.392012-0.689059-0.8161180.0694100.139593-0.689999
1-0.933558-0.462424-0.819038-0.726994-0.701326-0.644706-0.658125
21.4727061.9785742.1909332.6151410.080201-0.592085-0.387368
31.5993513.7548112.8167572.303209-0.1648930.082069-0.184291
4-0.553622-0.584519-0.679431-0.548747-0.430027-0.4135571.504408
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" - ], - "text/plain": [ - " resorts_per_state state_total_skiable_area_ac state_total_days_open \\\n", - "0 -0.806912 -0.392012 -0.689059 \n", - "1 -0.933558 -0.462424 -0.819038 \n", - "2 1.472706 1.978574 2.190933 \n", - "3 1.599351 3.754811 2.816757 \n", - "4 -0.553622 -0.584519 -0.679431 \n", - "\n", - " state_total_terrain_parks state_total_nightskiing_ac \\\n", - "0 -0.816118 0.069410 \n", - "1 -0.726994 -0.701326 \n", - "2 2.615141 0.080201 \n", - "3 2.303209 -0.164893 \n", - "4 -0.548747 -0.430027 \n", - "\n", - " resorts_per_100kcapita resorts_per_100ksq_mile \n", - "0 0.139593 -0.689999 \n", - "1 -0.644706 -0.658125 \n", - "2 -0.592085 -0.387368 \n", - "3 0.082069 -0.184291 \n", - "4 -0.413557 1.504408 " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 2#\n", "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n", - "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n", + "state_summary_scaled_df = pd.DataFrame(___, columns=___)\n", "state_summary_scaled_df.head()" ] }, @@ -1473,31 +1226,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "resorts_per_state -7.295751e-17\n", - "state_total_skiable_area_ac -4.163336e-17\n", - "state_total_days_open 7.692260e-17\n", - "state_total_terrain_parks 4.599495e-17\n", - "state_total_nightskiing_ac 7.612958e-17\n", - "resorts_per_100kcapita 5.075305e-17\n", - "resorts_per_100ksq_mile 5.075305e-17\n", - "dtype: float64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 3#\n", "#Call `state_summary_scaled_df`'s `mean()` method\n", - "state_summary_scaled_df.mean()" + "state_summary_scaled_df.___" ] }, { @@ -1516,31 +1251,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "resorts_per_state 1.014599\n", - "state_total_skiable_area_ac 1.014599\n", - "state_total_days_open 1.014599\n", - "state_total_terrain_parks 1.014599\n", - "state_total_nightskiing_ac 1.014599\n", - "resorts_per_100kcapita 1.014599\n", - "resorts_per_100ksq_mile 1.014599\n", - "dtype: float64" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 4#\n", "#Call `state_summary_scaled_df`'s `std()` method\n", - "state_summary_scaled_df.std()" + "state_summary_scaled_df.___" ] }, { @@ -1554,31 +1271,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "resorts_per_state 1.0\n", - "state_total_skiable_area_ac 1.0\n", - "state_total_days_open 1.0\n", - "state_total_terrain_parks 1.0\n", - "state_total_nightskiing_ac 1.0\n", - "resorts_per_100kcapita 1.0\n", - "resorts_per_100ksq_mile 1.0\n", - "dtype: float64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 5#\n", "#Repeat the previous call to `std()` but pass in ddof=0 \n", - "state_summary_scaled_df.std(ddof=0)" + "state_summary_scaled_df.___(___)" ] }, { @@ -1604,7 +1303,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1620,22 +1319,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Code task 6#\n", "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n", @@ -1644,10 +1330,10 @@ "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n", "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n", "plt.subplots(figsize=(10, 6))\n", - "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n", - "plt.xlabel('Component #')\n", - "plt.ylabel('Cumulative ratio variance')\n", - "plt.title('Cumulative variance ratio explained by PCA components for state/resort summary statistics');" + "plt.plot(state_pca.explained_variance_ratio_.___)\n", + "plt.xlabel(___)\n", + "plt.ylabel(___)\n", + "plt.title(___);" ] }, { @@ -1673,18 +1359,18 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Code task 7#\n", "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n", - "state_pca_x = state_pca.transform(state_summary_scale)" + "state_pca_x = state_pca.___(___)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1693,7 +1379,7 @@ "(35, 7)" ] }, - "execution_count": 36, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1720,12 +1406,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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+m5YtW3LffffRpUsXEhISWLt2LcnJyVx44YU899xzADjnSElJISoqiujoaGbNmgVAWloaSUlJXH/99bRp04bBgwfjnGPatGns3r2b3r1707t379AcGJEiQtlDdy6w3MzWAauABc6590JYj4iIVEONGzemY8eOvPee9ydi5syZXHLJJUyaNInFixezdu1aEhISeOKJJwLb1KlTh+XLlzNw4EAALrjgAj788EN69OjB8OHDeeedd/joo4946KGHAJgzZw6ZmZmsW7eOxYsXk5KSwpdffglARkYGU6dOZfPmzXz22WesWLGC0aNH07RpU1JTU0lNTa3iIyJyvJDdtsQ59xkQG6r9i4hI+Cgcdv3Vr37FzJkzufbaa/nnP/9Jt27dAPjhhx/o0qVLYP0bb7yx2PZXX301ANHR0ezfv5/69etTv3596tSpQ05ODsuXL2fQoEFERERw7rnn0qtXL1avXk2DBg3o2LEjzZs3ByAuLo6srCy6d+9eRZ9cJDjV4T50IiIix5mbsYvHFm5ld04e50b+jI8X/pu1a9eSl5dHfHw8l112GTNmzChx27p16xabrl27NgA1atQIvC+cPnLkCM65Uusoun5ERARHjhw5lY8lUilCfZWriIjIceZm7GL8nA3sysnDAV/lgTvvYq4bNJRBgwbRuXNnVqxYwaeffgrAwYMH+eSTT056fz179mTWrFkUFBSQnZ3N0qVL6djxuOv0iqlfvz779u076X2KVCQFOhERqXYeW7iVvPziV6jWadODrE82M3DgQJo0acKrr77KoEGDiImJoXPnzmzZsuWk93fNNdcQExNDbGwsffr04dFHH+W8884rc5uRI0fSr18/XRQh1YKV1c1c3SQkJLj09PRQlyEiIpWs1b0LKOmvkwE7Jvev6nJEToqZramq59Srh05ERKqdpg0jyzVf5HSnQCciItVOSvJFRNaKKDYvslYEKckXhagikepNV7mKiEi1MyC+GUDgKtemDSNJSb4oMF9EilOgExGRamlAfDMFOJEgachVREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYC3mgM7MIM8sws/mhrkVEREQkHIU80AFjgI9DXYSIiIhIuAppoDOz5kB/4G+hrENEREQknIW6h24q8HvgaGkrmNlIM0s3s/Ts7Oyqq0xEREQkTIQs0JnZlcA3zrk1Za3nnHvBOZfgnEto0qRJFVUnIiIiEj5C2UPXDbjazLKAmUAfM/t7COsRERERCUshC3TOufHOuebOuZbAQGCJc25IqOoRERERCVehPodORERERE5RzVAXAOCcSwPSQlyGiIiISFhSD52IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhERCZqZMW7cuMD0lClTmDhxYoW1n5WVRVRUVLF5EydOZMqUKRW2j2CVtd+uXbtWcTUiZVOgExGRoNWuXZs5c+awZ8+eUJcSUitXrjxuXkFBQQgqEfEo0ImISNBq1qzJyJEjefLJJ49blp2dzXXXXUdiYiKJiYmsWLECgOjoaHJycnDO0bhxY15//XUAhg4dyuLFi8u1/xdffJHExERiY2O57rrrOHjwIADDhw/njjvuoHfv3rRu3ZoPPviAW2+9lbZt2zJ8+PDA9vXq1WPcuHG0b9+eSy65hOzsbACmTZvGxRdfTExMDAMHDgysv3nzZpKSkmjdujXTpk0r1g5AWloavXv35qabbiI6OpqCggJSUlJITEwkJiaG559/vlyfT+RkKdCJiEi5jBo1iunTp5Obm1ts/pgxYxg7diyrV69m9uzZjBgxAoBu3bqxYsUKNm3aROvWrVm2bBkAH330EZ07dz6u/e3btxMXFxd4Pffcc4Fl1157LatXr2bdunW0bduWl156KbDsu+++Y8mSJTz55JNcddVVjB07lk2bNrFhwwYyMzMBOHDgAO3bt2ft2rX06tWLhx9+GIDJkyeTkZHB+vXri+1vy5YtLFy4kFWrVvHwww+Tn59/XL2rVq1i0qRJbN68mZdeeomzzjqL1atXs3r1al588UV27NhxsodaJGg1Q12AiIiElwYNGnDzzTczbdo0IiMjA/MXL17M5s2bA9Pff/89+/bto0ePHixdupQWLVpwxx138MILL7Br1y4aNWoU6Okq6sILLwwEMKDYOXobN27kgQceICcnh/3795OcnBxYdtVVV2FmREdHc+655xIdHQ1Au3btyMrKIi4ujho1anDjjTcCMGTIEK699loAYmJiGDx4MAMGDGDAgAGBNvv370/t2rWpXbs255xzDl9//TXNmzcvVm/Hjh1p1aoVAIsWLWL9+vW88847AOTm5rJt27bAcpHKokAnIiJlmpuxi8cWbmV3Th55+QXMzdjFXXfdRfv27bnlllsC6x09epQPP/ywWMgD6NmzJ8888ww7d+5k0qRJvPvuu7zzzjv06NGj3LUMHz6cuXPnEhsby6uvvkpaWlpgWe3atQGoUaNG4H3h9JEjR0psz8wAWLBgAUuXLuUf//gHf/jDH9i0aVOxNgEiIiJKbKdu3bqB9845nn766WJBU6QqaMhVRERKNTdjF+PnbGBXTh4OcA7Gz9nA0s/z+PWvf11syLNv37785S9/CUwX9rJdcMEF7Nmzh23bttG6dWu6d+/OlClTTirQ7du3j/PPP5/8/HymT59e7u2PHj0a6D1788036d69O0ePHuWLL76gd+/ePProo4Hev5ORnJzMX//618DQ7CeffMKBAwdOqi2R8lAPnYiIlOqxhVvJyy9+9WZefgGPLdzKnHHjigW4adOmMWrUKGJiYjhy5Ag9e/YMnI/WqVOnwFWgPXr0YPz48XTv3r3c9fzhD3+gU6dOtGjRgujoaPbt21eu7evWrcumTZvo0KEDZ511FrNmzaKgoIAhQ4aQm5uLc46xY8fSsGHDctcGMGLECLKysmjfvj3OOZo0acLcuXNPqi2R8jDnXKhrCFpCQoJLT08PdRkiIqeNVvcuoKS/EgbsmNy/qss5ZfXq1Tvp3jeR8jKzNc65hKrYl4ZcRUSkVE0bRpZrvoiEhgKdiIiUKiX5IiJrRRSbF1krgpTki0JU0alR75z8VOkcOhERKdWA+GYAgatcmzaMJCX5osB8EakeFOhERKRMA+KbKcCJVHMachUREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMyFLNCZWR0zW2Vm68xsk5k9HKpaRERERMJZzRDu+zDQxzm338xqAcvN7F/OuY9CWJOIiIhI2AlZoHPOOWC/P1nLf7lQ1SMiIiISrkJ6Dp2ZRZhZJvAN8G/n3H9CWY+IiIhIOAppoHPOFTjn4oDmQEczizp2HTMbaWbpZpaenZ1d9UWKiIiIVHPV4ipX51wOkAZcXsKyF5xzCc65hCZNmlR5bSIiIiLVXSivcm1iZg3995HApcCWUNUjIiIiEq5CeZXr+cBrZhaBFyzfcs7ND2E9IiIiImEplFe5rgfiQ7V/ERERkZ+KanEOnYiIiIicPAU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImDthoDOz/w1mnoiIiIiERjA9dJeVMK9fRRciIiIiIienZmkLzOwO4LdAazNbX2RRfWBFZRcmIiIiIsEpNdABbwL/Av4M3Ftk/j7n3LeVWpWIiIiIBK3UQOecywVygUFmFgGc669fz8zqOed2VlGNIiIiIlKGsnroADCzO4GJwNfAUX+2A2IqrywRERERCdYJAx1wF3CRc25vZRcjIiIiIuUXzFWuX+ANvYqIiIhINRRMD91nQJqZLQAOF850zj1RaVWJiIiISNCCCXQ7/dcZ/ktEREREqpETBjrn3MMAZlbXOXeg8ksSERERkfII5tFfXcxsM/CxPx1rZs9WemUiIiIiEpRgLoqYCiQDewGcc+uAnpVZlIiIiIgEL5hAh3Pui2NmFVRCLSIiIiJyEoK5KOILM+sKODM7AxiNP/wqIiIiIqEXTA/d7cAooBnwXyDOnxYRERGRaiCYq1z3AIOroBYREREROQnBPMu1CXAb0LLo+s65WyuvLBEREREJVjDn0M0DlgGL0cUQIiIiItVOMIHuTOfcPZVeiYiIiIiclGAuiphvZldUeiUiIiIiclKCCXRj8ELdITPb57++r+zCRERERCQ4wVzlWr8qChERERGRkxPMOXSY2dX8+LivNOfc/MorSURERETK44RDrmY2GW/YdbP/GuPPExEREZFqIJgeuiuAOOfcUQAzew3IAO49lR2b2QXA68B5wFHgBefcU6fSpoiIiMjpKJiLIgAaFnl/VgXt+wgwzjnXFugMjDKziyuobREREZHTRjA9dH8GMswsFTC8c+nGn+qOnXNfAl/67/eZ2cd4z4vdfKpti4iIiJxOgrnKdYaZpQGJ/qx7nHNfVWQRZtYSiAf+U8KykcBIgJ///OcVuVsRERGRn4Rgh1y7AElAL/99hTGzesBs4C7n3HH3t3POveCcS3DOJTRp0qQidy0iIiLykxDMVa7PArcDG4CNwG/M7JmK2LmZ1cILc9Odc3Mqok0RERGR000w59D1AqKccw4CV7luONUdm5kBLwEfO+eeONX2RERERE5XwQy5bgWKnrx2AbC+AvbdDRgK9DGzTP+lZ8aKiIiIlFMwPXSNgY/NbJU/nQh8aGb/AHDOXX0yO3bOLce7alZERERETkEwge6hSq9CRERERE5aMLct+QDAzBoUXd85920l1iUiIiIiQTphoPPvA/cHIA/vEV0GOKB15ZYmIiIiIsEIZsg1BWjnnNtT2cWIiIiISPkFc5XrduBgZRciIiIiIicnmB668cBKM/sPcLhwpnNudKVVJSIiIiJBCybQPQ8swbuZ8NHKLUdEREREyiuYQHfEOXd3pVciIiIiIiclmHPoUs1spJmdb2aNCl+VXpmIiIiIBCWYHrqb/P+OLzJPty0RERERqSaCubFwq6ooREREREROTjA3Fq4F3AH09GelAc875/IrsS4RERERCVIwQ65/BWoBz/rTQ/15IyqrKBEREREJXjCBLtE5F1tkeomZrausgkRERESkfIK5yrXAzC4snDCz1kBB5ZUkIiIiIuUR7LNcU83sM8CAFsAtlVqViIiIiAQtmKtc300IHdMAACAASURBVDezXwAX4QW6Lc65wyfYTERERESqyAmHXM1sFBDpnFvvnFsHnGlmv6380kREREQkGMGcQ3ebcy6ncMI59x1wW+WVJCIiIiLlEUygq2FmVjhhZhHAGZVXkoiIiIiURzAXRSwE3jKz5/Ae+XU78F6lViUiIiIiQQsm0N0DjMR7WoQBi4C/VWZRIiIiIhK8YK5yPQo8579EREREpJoJ5hw6EREREanGFOhEREREwpwCnYiIiEiYK/UcOjP7J95VrSVyzl1dKRWJiIiISLmU1UM3BXgc2AHkAS/6r/3AxsovTSR8jB07lqlTpwamk5OTGTFiRGB63LhxPPHEE5Wy7xEjRrB58+ZKaVtERMJDqYHOOfeBc+4DIN45d6Nz7p/+6yage9WVKFL9de3alZUrVwJw9OhR9uzZw6ZNmwLLV65cSbdu3Spl33/729+4+OKLK6VtEREJD8GcQ9fEzFoXTphZK6BJ5ZUkEn66desWCHSbNm0iKiqK+vXr891333H48GE+/vhj7rrrLjIzM4tts379er799lsGDBhATEwMnTt3Zv369QBMnDiRYcOG0bdvX1q2bMmcOXP4/e9/T3R0NJdffjn5+fkAJCUlkZ6eDkC9evW4//77iY2NpXPnznz99dcAbN++nc6dO5OYmMhDDz1EvXr1qvLwiIhIJQsm0I0F0swszczSgFTgrkqtSiTMNG3alJo1a7Jz505WrlxJly5d6NSpEx9++CHp6enExMRw++238+qrrwLwySefcPjwYWJiYpgwYQLx8fGsX7+eP/3pT9x8882Bdrdv386CBQuYN28eQ4YMoXfv3mzYsIHIyEgWLFhwXB0HDhygc+fOrFu3jp49e/Liiy8CMGbMGMaMGcPq1atp2rRplRwTERGpOicMdM6594BfAGP810XOuYWVXZhIuCnspSsMdF26dAlMd+3alRtuuIH58+eTn5/Pyy+/zPDhwwFYvnw5Q4cOBaBPnz7s3buX3NxcAPr160etWrWIjo6moKCAyy+/HIDo6GiysrKOq+GMM87gyiuvBKBDhw6BdT788ENuuOEGAG666aZKPAoiIhIKwTz6C6AD0NJfP9bMcM69XmlViYSBuRm7eGzhVnbn5NG0YSTtLriYlStXsmHDBqKiorjgggt4/PHHadCgAbfeeitnnnkml112GfPmzeOtt94KDJM6d/zF5GYGQO3atQGoUaMGtWrVCsyvUaMGR44cOW67outERESUuI6IiPz0nLCHzszewLvitTuQ6L8SKrkukWptbsYuxs/ZwK6cPBywKyePf+9tyFtz5tGoUSMiIiJo1KgROTk5fPjhh3Tp0gXwrkgdPXo0iYmJNGrUCICePXsyffp0ANLS0jj77LNp0KBBhdbbuXNnZs+eDcDMmTMrtG0REQm9YHroEoCLXUndCCKnqccWbiUvv6DYvKM/u4A9e/bQufPQwLzo6Gj279/P2WefDXjDoA0aNOCWW24JrDNx4kRuueUWYmJiOPPMM3nttdcqvN6pU6cyZMgQHn/8cfr3789ZZ51V4fsQEZHQsRPlNDN7GxjtnPuyakoqXUJCgiscphIJpVb3LijxrtsG7Jjcv9Ttdu/eTVJSElu2bKFGjap7UMvBgweJjIzEzJg5cyYzZsxg3rx5VbZ/EZHTkZmtcc5VyahmMD10ZwObzWwVcLhwpp4UIaezpg0j2ZWTV+L80rz++uvcf//9PPHEE1Ua5gDWrFnDnXfeiXOOhg0b8vLLL1fp/kVEpHIF00PXq6T5/k2Hq5R66KS6KDyHruiwa2StCP58bTQD4puFsDIREakuqlUPnXPuAzM7F+9iCIBVzrlvKrcskeqtMLQVvco1JfkihTkREQmJEwY6M/s18BiQhneK0NNmluKce6eSaxOp1gbEN1OAExGRaiGYc+juBxILe+XMrAmwGFCgExEREakGgjkzu8YxQ6x7g9xORERERKpAMD1075nZQmCGP30j8K/KK0lEREREyiOYiyJSzOxavCdFGPCCc+7dSq9MRERERIISzEURrYD/c87N8acjzaylcy6rsosTERERkRML5ly4t4GjRaYL/HkiIiIiUg0EE+hqOud+KJzw359ReSWJiIiISHkEE+iyzSzwmC8z+xWwp/JKEhEREZHyCCbQ3Q7cZ2ZfmNlO4B7gN5VblpwOvvrqKwYOHMiFF17IxRdfzBVXXMEnn3wSsnqmTp3KwYMHA9NXXHEFOTk55W4nKyuLN998syJLExERKdMJA51zbrtzrjPQFmjnnOvqnPu08kuTnzLnHNdccw1JSUls376dzZs386c//Ymvv/46ZDUdG+j+7//+j4YNG5a7HQU6ERGpaicMdGZ2rpm9BLztnNtnZheb2f+rgtrkJyw1NZVatWpx++23B+bFxcXRvXt3UlJSiIqKIjo6mlmzZgGQlpZGUlIS119/PW3atGHw4ME45wBo2bIlEyZMoH379kRHR7NlyxYADhw4wK233kpiYiLx8fHMmzcPgIKCAn73u98RHR1NTEwMTz/9NNOmTWP37t307t2b3r17B9rds8c7u+D1118nJiaG2NhYhg4dCsDw4cN5550fH5hSr149AO69916WLVtGXFwcTz75ZGUeRhERESC4IddXgYVAU3/6E+CuyipITg8bN26kQ4cOx82fM2cOmZmZrFu3jsWLF5OSksKXX34JQEZGBlOnTmXz5s189tlnrFixIrDd2Wefzdq1a7njjjuYMmUKAJMmTaJPnz6sXr2a1NRUUlJSOHDgAC+88AI7duwgIyOD9evXM3jwYEaPHk3Tpk1JTU0lNTW1WE2bNm1i0qRJLFmyhHXr1vHUU0+V+dkmT55Mjx49yMzMZOzYsad6qDCzQIgEOHLkCE2aNOHKK6885bZPRlpaWqXuOycnh2effTYwrR5PEZETCybQne2cewv/1iXOuSN4ty4RqXDLly9n0KBBREREcO6559KrVy9Wr14NQMeOHWnevDk1atQgLi6OrKyswHbXXnstAB06dAjMX7RoEZMnTyYuLo6kpCQOHTrEzp07Wbx4Mbfffjs1a3q3YWzUqFGZNS1ZsoTrr7+es88+O6j1K1rdunXZuHEjeXl5APz73/+mWbNmVVpDVVKgExEpv2AC3QEzaww4ADPrDORWxM7N7GUz+8bMNlZEe1L9zc3YRbfJS/jTyn28PPd95mbsKra8cBi1JLVr1w68j4iI4MiRI8ctKzrfOcfs2bPJzMwkMzOTnTt30rZtW5xzmFnQNZe2fs2aNTl69GhgnR9++OG4dSpKv379WLBgAQAzZsxg0KBBgWWrVq2ia9euxMfH07VrV7Zu3Qp4PYsdO3YkLi6OmJgYtm3bxoEDB+jfvz+xsbFERUUFhrQfeeQREhMTiYqKYuTIkYGfw6effsqll15KbGws7du3Z/v27QDs37+/1OHvwmHq9PR0kpKSAPjggw+Ii4sjLi6O+Ph49u3bB8Bjjz1GYmIiMTExTJgwAfCGrLdv305cXBwpKSnHDWGX9LlERE53wQS6u4F/ABea2QrgdeD/q6D9vwpcXkFtSTU3N2MX4+dsYFdOHrVbxHLo8GHueODRQKhbvXo1P/vZz5g1axYFBQVkZ2ezdOlSOnbseFL7S05O5umnnw6EjYyMDAD69u3Lc889Fwh+3377LQD169cPBI2iLrnkEt566y327t1bbP2WLVuyZs0aAObNm0d+fn6Z7ZyKgQMHMnPmTA4dOsT69evp1KlTYFmbNm1YunQpGRkZPPLII9x3330APPfcc4wZM4bMzEzS09Np3rw57733Hk2bNmXdunVs3LiRyy/3fv3uvPNOVq9eHegJnD9/PgCDBw9m1KhRrFu3jpUrV3L++ecDZQ9/l2TKlCk888wzZGZmsmzZMiIjI1m0aBHbtm1j1apVZGZmsmbNGpYuXcrkyZO58MILyczM5LHHHjtuCLukzyUicroL5irXtUAvoCve7UraOefWV8TOnXNLgW8roi2p/h5buJW8fG+03sxocs39fL99LQMvTaRdu3ZMnDiRm266KXDxQZ8+fXj00Uc577zzTmp/Dz74IPn5+cTExBAVFcWDDz4IwIgRI/j5z38e2E/hcN7IkSPp169f4KKIQu3ateP++++nV69exMbGcvfddwNw22238cEHH9CxY0f+85//ULduXQBiYmKoWbMmsbGxJ31RRGFPZqt7F5CXX8BnBY3JyspixowZXHHFFcXWzc3N5YYbbiAqKoqxY8eyadMmALp06cKf/vQn/vd//5fPP/+cyMhIoqOjWbx4Mffccw/Lli3jrLPOAryLVDp16kR0dDRLlixh06ZN7Nu3j127dnHNNdcAUKdOHc4880yg7OHvknTr1o27776badOmkZOTQ82aNVm0aBGLFi0iPj6e9u3bs2XLlqB620r6XCIipz3nXIkvIBE4r8j0zcA8YBrQqLTtyvsCWgIbg1m3Q4cOTsJXy3vmuxYlvFreMz/UpVUr7679r2vzwL8Cx8dq1XFtHviXG3j7ONeoUSO3fv16l5qa6vr37++cc27YsGHuqaeecs45t2PHDteiRYtAW59++ql76qmnXKtWrdz777/vnHNu79697o033nDdunVzDz/8sMvLy3PnnHOO27lzp3POuQkTJrgJEya43Nxc16xZs+PqK7pv55wbNWqUe+WVV5xzzl144YXu66+/ds45t2zZMterV6/AeuvXr3eTJ092zZo1cx9//LG7++673XPPPXdc+zt27HDt2rUrdX+lfS4RkeoGSHcVlJdO9Cqrh+554AcAM+sJTMYbbs0FXqiUdFkCMxtpZulmlp6dnV1Vu5VK0LRhyT0ppc0/XRXtySyUl1/AtoaJPPTQQ0RHRxdblpubG7hI4tVXXw3M/+yzz2jdujWjR4/m6quvZv369ezevZszzzyTIUOG8Lvf/Y61a9dy6NAhwLtSeP/+/YFbsTRo0IDmzZszd+5cAA4fPlzsPn0lKToMPXv27MD87du3Ex0dzT333ENCQgJbtmwhOTmZl19+mf379wOwa9cuvvnmm+OGrI+dLulziYic7soKdBHOucLh0BuBF5xzs51zDwL/U/mleZxzLzjnEpxzCU2aNKmq3UolSEm+iMhaEcXmRdaKICX5ohBVVD3tzskrcf5eV48xY8YcN//3v/8948ePp1u3bhQU/BgEZ82aRVRUFHFxcWzZsoWbb76ZDRs2BC4omDRpEg888AANGzbktttuIzo6mgEDBpCYmBho44033mDatGnExMTQtWtXvvrqqzJrnzBhAmPGjKFHjx5ERPz4s546dSpRUVHExsYSGRlJv3796Nu3LzfddBNdunQhOjqa66+/nn379tG4cWO6detGVFQUKSkpxw1hl/S5REROd+ZKuarQv/I0zjl3xMy2ACOdd84bZrbRORdVIQWYtQTmB9NeQkKCS09Pr4jdSojMzdjFYwu3sjsnj6YNI0lJvogB8T/dW3CcjG6Tl7CrhFDXrGEkK+7tE4KKRETkZJjZGudcQlXsq2YZy2YAH5jZHiAPWOYX9z9U3G1LZgBJwNlm9l9ggnPupYpoW6qnAfHNFOBOICX5IsbP2VBs2FU9mSIiUpZSA51zbpKZvQ+cDyxyP3bl1aCCblvinBt04rVETi+FgVc9mSIiEqyyeuhwzn1UwrxPKq8cEQH1ZIqISPkEc2NhEREREanGFOhCKCIigri4OKKiorjqqqvIyckBTv3h5+XdPisri6iosq9JqVev3knXU959iYiISPko0IVQZGQkmZmZbNy4kUaNGvHMM8+EuiQREREJQwp01USXLl3YtevHB9WX9vDz999/n/j4eKKjo7n11ls5fPgwAO+99x5t2rShe/fuzJkzJ9DOgQMHuPXWW0lMTCQ+Pp558+aVWceJHny+f/9+LrnkEtq3b090dHSgvaysLNq2bcttt91Gu3bt6Nu3L3l53q031qxZQ2xsLF26dFFoFRERqQQKdNVAQUEB77//PldffXVgXkkPPz906BDDhw9n1qxZbNiwgSNHjvDXv/6VQ4cOcdttt/HPf/6TZcuWFbv566RJk+jTpw+rV68mNTWVlJQUDhw4UGotJ3rweZ06dXj33XdZu3YtqampjBs3LhA2t23bxqhRo9i0aRMNGzYMPCnglltuYdq0aXz44YcVedhERETEp0AXQnl5ecTFxdG4cWO+/fZbLrvsssCykh5+vnXrVlq1asUvf/lLAIYNG8bSpUvZsmULrVq14he/+AVmxpAhQwLtLFq0iMmTJxMXF0dSUhKHDh1i586dpdZ0ogefO+e47777iImJ4dJLL2XXrl18/fXXALRq1Yq4uDgAOnToQFZWFrm5ueTk5NCrVy8Ahg4dWjEHT0RERAIU6KrY3IxddJu8hFb3LoCaZzDxlQV8/vnn/PDDD8WGI2vXrh14HxERwZEjRyjtqR4AZlbifOccs2fPJjMzk8zMTHbu3Enbtm1Lbeemm27iH//4B5GRkSQnJ7NkyZJiy6dPn052djZr1qwhMzOTc889N/As0NJqLq02ERERqRgKdFVobsYuxs/ZwK6cPBzgHIyfs4HUz/Yzbdo0pkyZQn5+fqnbt2nThqysLD799FPAe85mr169aNOmDTt27GD79u0AzJgxI7BNcnIyTz/9dCAMZmRklFnjiR58npubyznnnEOtWrVITU3l888/L7O9hg0bctZZZ7F8+XLAC4QiIiJSsRToqtBjC7cWe5wTQF5+AY8t3Ep8fDyxsbHMnDmz1O3r1KnDK6+8wg033EB0dDQ1atTg9ttvp06dOrzwwgv079+f7t2706JFi8A2Dz74IPn5+cTExBAVFcWDDz5YZo0nevD54MGDSU9PJyEhgenTp9OmTZsTfu5XXnmFUaNG0aVLl+OGcEVEROTUWVnDeNVNQkKCS09PD3UZJ63VvQso6WgbsGNy/6ouR0RERCqRma1xziVUxb7UQ1eFmjYsuXeqtPkiIiIiwVCgq0IpyRcRWSui2LzIWhGkJF8UoopERETkp6BmqAs4nRQ+bP2xhVvZnZNH04aRpCRfpIewi4iIyClRoKtiA+KbKcCJiIhIhdKQq4iIiEiYU6ATERERCXMKdCKVxMyKPersyJEjNGnShCuvvLLM7dLT0xk9enRllyciIj8hOodOpJLUrVuXjRs3kpeXR2RkJP/+979p1uzE508mJCSQkFAlty0SEZGfCPXQiVSifv36sWDBAsB7JNugQYMCy1atWkXXrl2Jj4+na9eubN26FYC0tLRAL97EiRO59dZbSUpKonXr1kybNi2w/d///nc6duxIXFwcv/nNbygoKP4UEhEROX0o0IlUooEDBzJz5kwOHTrE+vXr6dSpU2BZmzZtWLp0KRkZGTzyyCPcd999JbaxZcsWFi5cyKpVq3j44YfJz8/n448/ZtasWaxYsYLMzEwiIiL0nFwRkdOYhlxFKtDcjF2B+wzm5RfwWUFjsrKymDFjBldccUWxdXNzcxk2bBjbtm3DzMjPzy+xzf79+1O7dm1q167NOeecw9dff83777/PmjVrSExMBCAvL49zzjmn0j+fiIhUTwp0IhVkbsYuxs/ZQF6+N/TpHIyfs4G4xCR+97vfkZaWxt69ewPrP/jgg/Tu3Zt3332XrKwskpKSSmy3du3agfcREREcOXIE5xzDhg3jz3/+c6V+JhERCQ8achWpII8t3BoIc4Xy8gvY1jCRhx56iOjo6GLLcnNzAxdJvPrqq+Xa1yWXXMI777zDN998A8C3337L559/fvLFi4hIWFOgE6kgu3PySpy/19VjzJgxx83//e9/z/jx4+nWrVu5L2i4+OKL+eMf/0jfvn2JiYnhsssu48svvzypukVEJPyZcy7UNQQtISHBpaenh7oMkRJ1m7yEXSWEumYNI1lxb58QVHR6MzOGDBnCG2+8AXj3ATz//PPp1KkT8+fPL3d7OTk5vPnmm/z2t7+t6FJF5CfKzNY456rkPlTqoROpICnJFxFZK6LYvMhaEaQkXxSiik5vRe8DCAR9H8DS5OTk8Oyzz1ZUeSIiFUqBTqSCDIhvxp+vjaZZw0gMr2fuz9dGMyD+5EOEnJqy7gP47bffMmDAAGJiYujcuTPr168HSr/337333sv27duJi4sjJSWF/fv3c8kll9C+fXuio6OZN28eAFlZWbRt25bbbruNdu3a0bdv30CofPHFF0lMTCQ2NpbrrruOgwcPVuXhEJGfMudc2Lw6dOjgRESCUbduXbdu3Tp33XXXuby8PBcbG+tSU1Nd//79nXPO3XnnnW7ixInOOefef/99Fxsb65xzbsKECa5Lly7u0KFDLjs72zVq1Mj98MMPbseOHa5du3aB9vPz811ubq5zzrns7Gx34YUXuqNHj7odO3a4iIgIl5GR4Zxz7oYbbnBvvPGGc865PXv2BLa///773bRp0yr/QIhIyADprooykm5bIiI/WTExMaXeB3D58uXMnj0bgD59+rB3715yc3OBku/9dyznHPfddx9Lly6lRo0a7Nq1K7Beq1atiIuLA6BDhw5kZWUBsHHjRh544AFycnLYv38/ycnJlfXRReQ0o0AnIj8Zx97YeW7GLq6++uoS7wPoSrggzMyAku/9d6zp06eTnZ3NmjVrqFWrFi1btuTQoUMlbl845Dp8+HDmzp1LbGwsr776KmlpaRXyuUVEdA6diPwkFN7YeVdOHo4fb+zctGO/Eu8D2LNnz8Dj0tLS0jj77LNp0KBBqe3Xr1+fffv2BaZzc3M555xzqFWrFqmpqUHdB3Dfvn2cf/755Ofn61FtIlKh1EMnIj8Jpd3Y+ZXMfay49/j7AE6cOJFbbrmFmJgYzjzzTF577bUy22/cuDHdunUjKiqKfv36cc8993DVVVeRkJBAXFwcbdq0OWGNf/jDH+jUqRMtWrQgOjq6WEAUETkVug+diPwktLp3ASX938yAHZP7V3U5IiK6D52ISHk1bRhZrvkiIj8lCnQi8pOgGzuLyOlM59CJyE9C4Q2cC69ybdowkpTki3RjZxE5LSjQichPxoD4ZgpwInJa0pCriIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyJVql69eiXOHz58OO+8806Z2yYlJaHH/4mIHE+BTqQMZsa4ceMC01OmTGHixIkV1n5WVhZmxoMPPhiYt2fPHmrVqsWdd955Um0+9NBDLF68uKJKFBGRMKBAJ1KG2rVrM2fOHPbs2VNp+2jdujXz588PTL/99tu0a9fupNt75JFHuPTSSyuitErlnOPOO+/k4osvpn///nzzzTeBZY888giJiYlERUUxcuRInHOBZW+//TYdO3bkl7/8JcuWLQPg0KFD3HLLLURHRxMfH09qamqVfx4RkVBSoBMpQ82aNRk5ciRPPvnkccuys7O57rrrSExMJDExkRUrVgAQHR1NTk4OzjkaN27M66+/DsDQoUNL7DmLjIykbdu2gaHEWbNm8etf//qE+/nVr34VaPv5559n8ODBQPGhy9WrV9O1a1diY2Pp2LEj+/btqzbh591332Xr1q1s2LCBF198kZUrVwaW3XnnnaxevZqNGzeSl5dXLPAeOXKEVatWMXXqVB5++GEAnnnmGQA2bNjAjBkzGDZsGIcOHaraDyQiEkIKdKepsWPHMnXq1MB0cnIyI0aMCEyPGzeOJ554olxtpqWlFfujXCgrK4vmzZtz9OjRYvPj4uJYtWoVI0aMYPPmzeXa13PPPRcIM6VJT09n9OjR5Wq3JKNGjWL69Onk5uYWmz9mzBjGjh3L6tWrmT17duD4devWjRUrVrBp0yZat24d6EX66KOP6Ny5c4n7GDhwIDNnzuS///0vERERNG3a9IT7eeGFF3jkkUdYtmwZjz/+OE8//XSxNn/44QduvPFGnnrqKdatW8fixYuJjIysNuFn6dKlDBo0KPB5+/TpE1iWmppKp06diI6OZsmSJWzatCmw7NprrwWgQ4cOZGVlAbB8+XKGDh0KQJs2bWjRogWffPJJ1X0YEZEQ07NcT1Ndu3bl7bff5q677uLo0aPs2bOH77//PrB85cqVxQJfMNLS0qhXrx5du3YtNr9ly5ZccMEFLFu2jF69egGwZcsW9u3bx//P3p2HVVWtDxz/bg7DYVZBURRRnAg4h0EURRHUCqcMp5yuSl71Os+WVhbaoCmmUd1Mfw6lpORsDpkkiFMJyCCYQyqoOIQKyCwc9u8PLjsQMDVFrfV5Hp/L2Wfvtdfe3ut9WcP7tm3blrZt21bZnk6nQ6VSVfnd2LFj/7Q/np6eeHp6PtQzVMXCwoLhw4cTEhKCsbGxcjw8PLxCIHrnzh2ys7Px8fEhKioKe3t7xo0bx4oVK0hLS6NOnTrVbgjo1q0bc+fOxcbGhoEDB1b4rrr72NjYMH/+fDp37sy2bduoU6dOhevOnDlDgwYNaNOmjfIcUBr8TJo0CagY/Gi12r/wlu5ve1wai/ed4WpmPvlFOrbHpQGlaxTvVVBQwPjx44mJicHOzo6goKAKAaeRkREAKpWK4uJigApTsoIgCP9EYoTuH6pDhw7KaFpycjIuLi6Ym5uTkZFBYWEhv/76K+7u7sTGxuLr60vr1q3x9/fn2rVrAISEhODk5IRWq2XQoEGkpKSwfPlyli5dipubmzIqVWbw4MFs3LhR+bxx40YGDx4MVNy5aGZmxrvvvouXlxfHjh1j1apVtGzZEj8/P0aPHq1sFAgKCiI4OFi5/s0336y0rioyMpJevXoBcPz4cby9vXF3d8fb25szZ85U+262x6XRYeEBms7erQQfU6dOZdWqVeTm5irnlZSUcOzYMeLj44mPjyctLQ1zc3M6derEoUOHOHToEH5+ftStW5fNmzfj4+NT7T0NDQ1p3bo1S5YsoV+/fhW+q+4+UDrKZmVlxdWrVyu1Kctyv9HJ7QAAIABJREFUlQFTTQc/2+PSmLP1JGmZ+ciALMOcrScxauTMxo0b0el0XLt2TZn6LQverK2tycnJ+dOdrwCdOnUiNDQUgLNnz3Lp0iVatWr1xJ5JEAThWSMCun8oW1tb9PX1uXTpEkePHqV9+/ZKEBUTE4NWq0WSJCZNmsTmzZuJjY1l5MiRvP322wAsXLiQuLg4EhMTWb58OU2aNGHs2LFMmzaN+Pj4SsHLa6+9xvbt25URlbCwMAYNGlSpX7m5ubi4uPDLL7/g4ODA+++/z88//8z+/fs5ffp0tc9T1bqq8hwdHYmKiiIuLo758+fz1ltvVdlOdcFHVGo+r732GqtWrVLOffnll/n888+Vz/Hx8QDY2dlx8+ZNzp07h4ODAx07diQ4OPi+AR2UTnN//PHHWFlZVThe3X2OHz/O3r17iYuLIzg4mIsXL1Z65qtXrxIdHQ1AdnY2xcXFNR78LN53hvwiXYVj+UU6DhU50KJFCzQaDePGjVNGb2vVqsXo0aPRaDQEBAQoI4z3M378eHQ6HRqNhoEDB7J27VplJE8QBOGfQEy5/oOVjdIdPXqU6dOnk5aWxtGjR7G0tFRGsZKSknjppZeA0inQBg0aAKDVahk6dCgBAQEEBAT86b3q16+Ps7MzP/30EzY2NhgYGODi4lLpPJVKpYxQHT9+HF9fX2UqccCAAdWui6pqXVV5WVlZjBgxgnPnziFJEkVFRVW2U13wsXjfGbbOmFEhsAoJCWHChAlotVolUFq+fDkAXl5e6HSl7fj4+DBnzhw6dux4v1eEs7Nzlbtbq7rPp59+yujRo1mzZg22trYsWbKEkSNHcuDAAeU6Q0NDwsLCmDRpEvn5+RgbGxMeHs748eMZO3YsGo0GfX39Jx78XM3Mr/C58fTSEbdrWQUV3md5H3zwAR988EGl45GRkcrP1tbWyt+1Wq1m7dq1j6W/giAIzyMR0NWQDz/8kG+//RaVSoWenh5fffUVXl5eD91OZGQkhoaGyjq1wMBAevXqRf/+/e97nUqlQqPRcDs7n1t5xRi90JlaZnXI2vEjV8+cxMXFBTs7O5YsWYKFhQUjR47k+PHjqFQqZUSovN27dxMVFcXOnTt5//33SU5O5ueff6ZTp07V9qFs2tXGxkaZbr2XWq1W1s09zNRgVeuqyps7d66y1iwlJQU/P78q26ku+LiamY+NjQ15eXnKd9bW1oSFhVXZzrp165Sfvb29K20IKdOkSROSkpIqHQ8MDCQwMPC+90lISFB+7t27N7179waoENi0adOGn3/+udK1NRn82NYyJu2e91p2XBAEQXg8nuqUqyRJ3SRJOiNJ0m+SJM1+mn15ko4dO8auXbs4ceIEiYmJhIeHY2dn90htVbeT9M8YGxsTtGY3pkM+pU7/+eRfiCH90m/8+MNedAamqFQq6tSpQ2ZmJseOHaN9+/bY2dlx9+5djh07BkBRURHJycmUlJRw+fJlOnfuzKJFi8jMzCQnJ4fjx4+TkZFRbR/69evHnj17qp1uvVfbtm05ePAgGRkZFBcXs2XLlod+7jJZWVk0bNgQuH8wU12QIYKPRzfLvxXGBhU3txgbqJjlL9a4CYIgPC5PLaCTJEkFfAF0B5yAwZIkOT2t/jxJ165dw9raWhlFsra2VtJS/PTTT7i7u6PRaBg5ciSFhYVA6chNWTLbmJgY/Pz8qt14EBUVhbe3Nw4ODvddQF42nagyrUWdbpPIPX2Y4rwsfldZ4ePjg4eHBykpKRgaGmJtbY2BgQEeHh68+eabtGjRAgsLC3bs2MH+/ftxcXFBrVZTr149Jk2axDfffEN2djafffYZZmZmHDp0iHHjxuHp6YmzszPvvfcetWrVol27dtjY2NC0adM/fW8NGzbkrbfewsvLixdffBEnJycsLS0f6e/gjTfeYM6cOXTo0EGZCq2KCD4evwD3hizoq6FhLWMkoGEtYxb01RDg3vBpd00QBOHvQ5blp/IHaA/sK/d5DjDnfte0bt1afh5lZ2fLrq6ucosWLeRx48bJkZGRsizLcn5+vtyoUSMZkKdPny4PGzZMXrp0qbx48WLZ0tJSTk9Pl2VZlqOjo2VfX19ZlmX5vffekxcvXqy0PWLECLl///6yTqeTk5OT5WbNmlW6/8WLF2U9PT25yZu7ZPtyf/SMTOVGE9fJjadvlvPz82VZluWzZ8/KZe85IiJC7tmzp3zkyBHZw8NDTk1NVfp85swZWZZlpc+yLMv29vZKn2VZlm/duiXLsiwXFxfLvr6+ckJCwiO9O1mW5aKiIrlXr17y1q1bH7qNh7XtxBXZe8FPcpM3d8neC36St5248sTvKQiCIPz9ADFyDcVVT3MNXUPgcrnPV4CHX1T2DCufe6vBa4vpXzeToitJDBw4kIULF+Lu7k7Tpk1JT09n69atLFmyhPXr11fK4/ZnAgIC0NPTw8nJiRs3blR73r1rmcpWqNU3N2D06NHEx8ejUqkqbDz49ddfGTNmDD/++CO2trYkJCTQtGlTWrZsCcCIESP44osvmDp1aqX7fffdd6xYsYLi4mKuXbvGqVOnHjrXWVBQEOHh4RQUFPDyyy8/0AaMvyrAvaEYPRIEQRCeK09zDV3lBFl/xBh/nCRJYyRJipEkKSY9Pb0GuvV43Jv+4uqdu4ReMsc9YAyff/45W7ZsURb9l5WXKj9dqqenR0lJCenp6cyYMYPY2FjatGnDpUuXgD/KS0Fpkt2yqgkFBQXVFmaf5d8KtQoyIlZzddVE5Lv53P3tF5pei+Tu3btYWVnh6OhIfn4+ubm5zJ49m+vXr3P+/Hk+/fRToDRnXUJCgpKX7tatW+Tm5uLh4aHc59y5c7i4uBAcHMxPP/1EYmIiPXv2fKRqBMHBwcTHx3P69GlCQkKqzKsmCIIgCP90TzOguwKU3xnQCKiUHVWW5RWyLHvKsuxZt27dGuvcX1U+/UXRrSsU3U5T0l/Ex8djb2+Po6MjKSkplJSUMGHCBHbu3KlUTahVqxaxsbFMmTIFGxsbWrduzZYtW9i1axfZ2dlKeamMjAzq1aunrKfT6XTVlpcKcG9IFykJM2MjVGa1aeg7CPPUKEyKMrGysiI6OloJzH744QesrKzo3LkzKSkp7N69m/DwcEJCQjA3NycsLEzJS9ejRw8sLS0xMDAgOzubNWvW0KtXL0xNTbG0tOTGjRvs3bu3Bt66IAiCIPwzPc0p12ighSRJTYE0YBAw5Cn257Eqn/6ipKiAjP3LKSnM5aqeCpuO7gRMnEfXZUe56/0f8r+bi6Z1W5o3b05+fj7m5ub4+voyZcoULl68SO3atcnLy6N3795IksSWLVvIzs7mzp073LhxA39/f06cOEFaWhqSJFVZXqqkpAQ3NzcuXLhAYWEhVlZWWN3WcSf/Dj4+PnzwwQeoVCoyMjIwNTVFo9EQGxuLqakpZ8+eZc+ePXTp0oVr165Rv359NBqNUnx+7NixWFtbs3LlSrp3705qaipXrlzh+vXrODs74+DgQIcOHWry9QuCIAjCP8pTC+hkWS6WJGkisA9QAatlWU7+k8ueG+XXqxnVb079YaVlqhrWMma4fyvmbD1JfpEOdRM3JH0jTAYtY0bXRswN7MXrr7+Ovb09a9aswdramtTU1Ao1RAEuX77MwIEDadq0KTNnzmTKlCls3ryZadOmVdkfZ2dn4uPj6devH2PGjMHf37/C902bNiU4OJgFCxawYMECtm3bxo0bN/j444+ZM2eOMnKo1WqVNCbl9evXj3nz5rF48WJCQ0OxsrJS0oOYmZmRk5Pzl96nIAiCIAjVe6p56GRZ3iPLcktZlpvJsvzh0+zL43a/9BfVVSNY/vPvT7y8lL+/P19++aVSKeHs2bMV6pOWWb16Nd7e3uTl5TFz5kySkpIwNDQkPT29Ul46KE0I7O/vz7hx43j99dcf9DUJgiAIgvAYiFquT8j9cm/dW42gzNXMfGbMmKHkn4PSsk9ltVWdnJyU0lJQWl6qbLepj48PaWlpVZaXKi4uVnLgjRo1CicnJzw8PHBxceE///lPpcoKOTk5HD16lJs3b7Jw4UI+/PBDJk6ciJ6eHps3b2bKlCmYmppiYWFB9+7dlUTHZZskZs+ejYuLi7Kur8zNmzdp3749u3fvJiUlRcl95+Hh8UjJkgVBEARBKCWV7bR8Hnh6esoxMTFPuxt/WYeFB6oshdSwljFHZnd5bPeRJInp06crxdjbtm1LTk4OQUFB971u/fr1REREsGrVKry9vfn888+pU6cOvXr1Iikpiby8PPT09FCr1Zw7d47BgwcTExPDK6+8wv79+7ly5Yqy7s/c3BwzMzPOnz9P7969+eCDD3jppZeqbUMQBEEQ/i4kSYqVZdmzJu4lRuiegpqqRmBkZMSqVat4++23mTNnzgNft2HDBqU016BBg9iwYUOF74uKihg9ejQajYYBAwZw6tQp+vTpw6lTpyguLmbRokWcPHkSc3Nz5fyuXbuyaNEiXnrppWrbEARBEATh0TzNXa7/WGVJa/u1a07jaZuxrWXMLP9Wjz2Zrb6+Pm+++SY5OTm4u7vz008/Kd+lp6czduxYJa9d3/Fv8/01M44vHExJXhbRJxIwMdLnypUr2NjY0KpVKy5cuIC7uztZWVn4+/uTkJBAeno69evXJyUlhRdffJHdu3dTv359hg0bhqGhIVAavJmbm7Nv3z58fX0BWLp0KTY2NiQkJFBSUoJarX6szy4IgiAI/yRihO4pCXBviLGBiosLe3JkdpcHDub8/PzYt29fhWPLli3DwcGBhQsXVjp/woQJhIaGkpWVVeH4lClTGDZsGPb29oyZ9wVBsyZz9U4h+rVtMbRthdWo/2P8/M/w8/MjPz8fCwsLmjZtSlxcHM2aNePUqVPo6ekxdOhQAOLi4mjXrh1paWkMHz6cf//737Rp04bY2FiMjY3Jzs4mISFB6WNWVhYNGjRAT0+PdevW3be+qiAIgiAI9ydG6B4DSZL417/+xbp164DSTQgNGjTAy8uLXbt2/en1sizzxhtvsHfvXiRJ4p133mHgwIGMHz+ebt26YWFhwaBBg+jRoweDBw9m/vz5HDp0iA8++ICAgAD2799P3bp1qVOnToVyY/lFOvafyWD48OGEhIRgbGxMfHw8EydOJDw8XJnm3DtqCLrCXEoK8ygpyEGS9Mgv0rFsxdfMGzGQ3r17ExISQmpqKhqNhtzcXNLT02nXrh0XL15UUqro6emhUqno3LkztWrVwsPDA1dXVwoKCrh8+TJffvklH3zwARYWFowfP55+/fqxadMmOnfujKmp6ZP7CxIEQRCEv7uaKhr7OP6UFY1/km7evCm7urrKrq6uso2NjWxra6t8LiwsVM4D5H/961+yLMuyqamp7OrqKltbW8s9e/aU9+zZIzdp0kRu1arVfe9lamoqy7Isb968We7SpYtcXFwsX79+Xbazs5OvXr0qb9iwQZ45c6YcEREhW1payl5eXvLNmzdlIyMjeefOnbIsy3JcXJxsZ2cnL1++XDazqCXX7TpKNmrkJJt7vioj6cm1vfrKi9fvkQ0NDWVbW1u5ZcuWsr6+vmxlZSX/+uuvsrOzs9zkzV2yVY+psnHL9rK6qYeMnko28+gp61vayDdv3pTt7e1lb29veceOHfKrr74qt2jRQjYxMZG/+uor2dXVVb5w4YLyTLVr15bT09PliIgIuUOHDnJubq4sy7Ls6+srR0REPM6/KkEQBEF4pgExcg3FSGKE7h5WVlZKrregoCDMzMyYOXNmpfNMTU1JSkoiP790t2qrVq2U2qobNmxg7NixStqO48ePM3XqVPLz88kvUWHy4iQyDKzJKyjE+6VeXD9/CiMjIwIDA+nfvz++vr5ER0cTFhbGqVOncHZ2xtzcHBsbG3755ReKioqYNGkSCxYsQJZl8vPzmTNnDjl3Mrl76hBFty6hy74NKgMs/Eby3syJGBgYcOvWLVSq0s0YL7/8Ml9//TVQmgQ5OSudohsXaPB6CJmHvyUn4QfMGzliZWUFwJ07d2jYsCGrV69mxowZ/Pbbb4SEhNC+fXtCQ0N555132Lt3LxkZGUDplGrt2rUxMTHh9OnT/Pzzz0/wb00QBEEQ/tlEQPcAYmNjmT59Ojk5OVhbWysVEG7fvs3gwYPJz89n9+7d1KtXj5KSEiIjI/nll1+4dOkSLVq0oEWLFvz888/YN3ck7ffb6P47ET3T2si6Yo79tBczcws83LQcOnSITZs2Icsy+/bt4/bt26hUKiZOnEhubi5TpkwhISEBAwMDfv/9d+7evcu1a9cYNmwY+/fvB6A4Iw25pJjinNtIkkRxzm3yrl8kPj6O9u3bK2vVQkJCCAwM5LfffqPuirHcLTFGbe+KnpEppk5+ZMfs4KXOnZR38MYbbzBgwACKi4spKCggLy8PlUrFkiVLWLJkCR4eHvj6+tK4cWMAunXrxvLly9FqtbRq1ara+rKCIAiCIPx1YlPEn5BlmUmTJrF582ZiY2OVgvS6EplbOjW7o44jqwwwtazDjRs3SEtLQ6vVcuPGDerWrcvJkyeVYOt6Zg5FuZlIxhYY27sCEgZ1m2DT9d/ExMQQFhYGQO3atenVqxc6nQ4fHx+MjIwwNFLzzQVj3g76gMJimYKCAkpKSoDSOq3NmzcHQN2sDQZW9kgqfWr5BXL36hkklT7Ozs7k5eUxZcoUAKytrfn8889p3rw5ly+cZdTrgZiZlCZBdnhBS+uOXZk4rK/yHrp3787q1atp3LgxKSkpys5ZIyMjfvzxR06cOMHSpUtJTU3F2toaIyMj9u7dS2JiIps2bSIyMhI/P78a+3sTBEEQhH8SMUL3P+U3E5SlEQEoLCwkKSlJyZ+m0+lQmdWhsLgEQz1DkPSQS3QU2XogZ/7Er7/+yoABA7h8+TJZWVmo1WoMDQ3Jy8vDoJGWwsyf0GVcJSf7JiBTdOsyubUdMDU1pVOnTty9exedTseePXswMzMjKSmJrKw76EpKOLlyBhQXAiADv//+OwCbNm1SniMvObL0W0kP3Z10TGrXQ1+lR4sWLTAxMcHExKTK529qASa/n+T8wp4A9Dr8SaVzxDSqIAiCIDybxAgdpcHcnK0nScvMRwbSMvOZs/Ukp6/dQZZlpbB90JrdWAz9lEzfN5Rrjew0oCvGsGV7iiQVLVq0oFGjRujr/xErm5ubo1KpMNTlIhffBcB29AosO41A0tMnfcObpKeno1KpaNasGdnZ2RQVFVG/fn2GDBmCun5TkEtH4/RMa2PQoCWSoTGyLCNJEm3atMHS0hIDAwP6/3sSJg2aAdDQpR3zRwVQp3Yt6tSpg1qtJiEhQckPV17t2rXp3r37fd9Tt27dKC4uRpIkevfurUyjBgcH/2n1iXtFRkZWKPcVGBjI5s2b//S669evM2jQIJo1a4aTkxM9evTg7NmzD3XvqqSkpODi4gJATEwMkydP/sttCoIgCEJNEQEdsHjfGfKLKuZByy/SceT8LYyMjEhPT2fh2p3M2XqSK7eyuZueqpxn0twL9PQxsLKjBImmTZtWar9hw4bodDpq6zJAlkFSIZcUo6c2Rd+sNmbGRtjY2GBpaYmhoSE6nY4+ffpw9epVhgwZgp5taaCBXIK6iTtF184i381HUpsjyzI6nY7mzZujp6fHhbjDzJs+FpWeRP2Le/m/oIlkZWWRkJDAmTNnKCkpQZIkXFxceOWVV5g7dy5Qmt8uMjISgOTkZH7//XemTp2KVqtl//79FaZRjYyMKCoqYvPmzY80jVpcXFwpoHsQsizTp08f/Pz8OH/+PKdOneKjjz7ixo0bD3x92TT1/Xh6ehISEvJQfRMEQRCEp0kEdMDVKuqqAmQXFCkF6RfMe4fzX43j2prJFKb9qpyjMq2F/aztpT9LknK8Vq1aOHn50WHhAeLPXACgsKCAF9zaoDJUc339TDJ/WomqMIucrExMTU25ceMGt2/fpqSkhPXr13P37l06duxI4Zn/FbnXFZH3axRIpTtV5cJcJElClmWlzFZcXBzz588HIDc3lytXrihBoiRJGBoaYm9vz6RJk1CpVAwbNowePXoou3UBli9fzpQpU4iPjycmJoZGjRpVeC/6+vqMGTOGpUuXVnpnqampdO3aFa1WS9euXZVKFIGBgUyfPp3OnTszcOBAli9fztKlS3Fzc1N2A0dFReHt7Y2Dg0OVo3UREREYGBgwduxY5Zibmxs+Pj7k5OTQtWtXPDw80Gg07NixAygdeXvhhRcYP348Hh4eXL58mVmzZuHi4oJGo1HWLZYXGRlJr169gNKdziNHjsTPzw8HB4cKgV5AQACtW7fG2dmZFStWVGpHEARBEGqKCOgoTdtRFedeo5g5cyZubm7UGbgA25GfYzvqv5i7daPx9M3UH7IQowYtADCzrMN3USfZtWsXgYGBTAr+hostB5GWmY/dxPVIBmpMBy9jyPiZdHvRD11OBiXFd+no3R5vb2+Sk5NRqVTodDrGjRtHcXExPj4+bDx2HpvOI0BPhWSgxsC6MXX7voWhlR2mZuY0b94cAwMD4uLi6NmzJ6NHj8bOzg4TExP8/f1JS0vDzc0NMzMzOnToQGFhIT179qR///7Ex8czcOBAzMzMKgQ27du356OPPuLjjz8mNTVVSRxcXnUVKCZOnMjw4cNJTExk6NChFaYuz549S3h4OFu2bGHs2LFMmzaN+Ph4fHx8ALh27RqHDx9m165dzJ49u9I9k5KSaN26dZV/V2q1mm3btnHixAkiIiKYMWMGpSmA4MyZMwwfPpy4uDhiYmKIj48nISGB8PBwZs2axbVr1+73Xw9Onz7Nvn37OH78OPPmzaOoqAiA1atXExsbS0xMDCEhIdy6deu+7QiCIAjCkyICOmCWfyuMDVQVjhkbqJSNEVB90AfQsJYxC/pqKpTvqm4ad2P05QrHzp07h5eXF2q1mlGjRlFSUsLBgwextbXlTpEec7aeJK9cO7U6DiFz/3KKMtLw6dAeExMTbt++zQsvvIC+vj4GBgb07duX4uJiDAwMaNq0KSYmJuTm5nL16lXMzMyA0uDIx8eH77//noMHD3Lu3DnlHkOGDGHnzp0YGxvj7+/PgQMHKj2zhYWFUoEiMzOTjRs30qJFC/bs2UNMTAx3795FlmV++OEH5ZoBAwYoefBCQ0MrjApC6YiXnp4eTk5ODzyNWkaWZd566y20Wi0vvvgiaWlpShv29vbKer/Dhw8zePBgVCoVNjY2Ss6/++nZsydGRkZYW1tTr149pd2QkBBcXV1p164dly9frvAOBUEQBKEmiV2uoARi9+5yLR+gzfJvxZytJysEacYGqkqBXJl7p3EbTy+dQsyt04pR4wbSYeEBLl+7QVraNb5ev4FNmzah0+kwNTUlJCSEJUuWkNFxOjcz8zHTvMjt/csBMGnRDtW1ZApPR7J3714iIyOZMGECvr6+ODg4EBMTA4BGo8HKygq1Ws3evXsxMzNj+PDhREdHk5CQwHfffceaNWuUqdCyaU+ACxcu4ODgwOTJk7lw4QKJiYncqd2qQkmx7XFpTJ06FXd3d4qLi/H09GTHjh1YWVmRk5PD22+/jaOjI1K5aejy5b2GDh1aaeTPyMhI+blsdK08Z2fnajdOhIaGkp6eTmxsLAYGBjRp0oSCgoJK962q3T9Tvl8qlUpZAxgeHs6xY8cwMTHBz89PuZ8gCIIg1DQxQvc/Ae4NOTK7CxcX9uTI7C6VgrQA94Ys6KuhYa3SXG1VjcqVV92IXi0TA2VHbe6ZI5g4d8Fq1P+xbNsRLl++TNOmTTl8+DBQ9dq+vN+Oc/t8Ag0aNFCO1alTh+3bt1NYWEhRURHbtm3Dxsamyvs7Ojqi1Wq5cuUK48eP56OPPqpUbzYsLAwXFxfc3Nw4ffo0Vm4vVtgFLMswZ+tJolLzadeuHbdu3cLd3R2ADh060L59e1avXs3BgwepXbs23bp1Y+vWrUqtWygd3Sob6frkk0/YsWMH06dPZ9myZVX2G6BLly4UFhaycuVK5Vh0dDQHDx4kKyuLevXqYWBgQEREBKmpqVW20alTJ8LCwtDpdKSnpxMVFUXbtm2rvWd1RAoXQRAE4VkiArqH8GdBX3nVTePKMsooX+6pg5i0bE9+kY7F+84A0K9fP7799lugclDYePpm7kRvQ7qbi4GBAW5ubhw4cIBDhw4RGBjIZ599xpEjRxg1ahQ7d+6kR48eyrU5OTkASJLE4sWL+eyzzygoKGDlypU4OjoqOe8A5syZQ3JyMvHx8fzwww8s//n3KqePF+87g1arpbi4WDkeEhLCxo0bycvL4+eff0ZfX5+wsDBeffVVjh49yuXLpVPOJiYm7N69m5YtW/L555/To0cPPvzwQ1auXElcXFyV71SSJLZt28b+/ftp1qwZzs7OBAUFYWtry9ChQ4mJicHT05PQ0FAcHR2rbKNPnz5otVpcXV3p0qULixYton79+tX+PVanLIWLVqtl7ty5ohKGIAiC8FRJjzIF9bR4enrKZVOKz4OqkhVPC4unqjcuARf/l9S3/PUPM837pDSdvbvKPgNIyXvQWBaxc33FXZ5ubm78+9//JjExURlR6969O2+//TYdO3akSZMmxMTEEBoayq1bt5SduXPnzqVu3boiD5wgCILw3JMkKVaWZc+auJdYQ/cEBbg3rBR4Ld53hrQqplKrmqJ9kLV9NcG2lnGVfQbIM7UlPGoj2+PSlH7duXOHy5cvo1Kpqlx/Vt7z9AuFIAiCIDyrxJRrDXuQHbXlPcw075NSVZ/LqO1d0RUVMGvBZ0BpabQZM2YQGBhYbZmx8jp16sT27dvJy8sjNzeXbdu2KWlMBEEQBEF4MCKgq2EPu7niWVC+z/eSJIm6fd4m7cQBWrRoQcuWLVGr1Xz00UcP1LaHhweBgYG0bdsWLy8vRo0apWywEIQnYdq0aRU23/imoneMAAAgAElEQVT7+zNq1Cjl84wZM5g/fz4LFy58Gt3j6tWr9O/f/6ncWxCE55dYQyc8lA4LD1Q5/dqwljFHZnd5Cj0ShIezadMmNm3axHfffUdJSQlt2rTB0NCQY8eOAaWJtZctW4aXl9dT7qkgCM+7mlxDJ0bohIfysFPGgvCs6dChg1JHODk5GRcXF8zNzcnIyKCwsJBff/2VhIQEJk6cCJQGgC4uLri6utKpUyegdGnBzJkz0Wg0aLVaPvusdMnBTz/9hLu7OxqNhpEjR1JYWAhAkyZNeO+995TSdKdPnwbg4MGDuLm54ebmhru7O9nZ2aSkpODiUlq/ee3atfTt25du3brRokUL3njjjRp9V4IgPD/EpgjhoTwrGzWEvw9JkvjXv/6l5CksLi6mQYMGeHl5sWvXLnbu3MmpU6eqLAf3KGxtbdHX1+fSpUscPXqU9u3bk5aWxrFjx7C0tESr1WJoaKicP3/+fN577z0aNmyIk5MTACtWrODixYvExcWhr6/P7du3KSgoYNiwYZiamnLu3DmGDx/Ol19+SWZmJnfu3OH8+fPs2rWL7du3ExwczP/93/8RHBzMF198QYcOHcjJyUGtVjNo0KAKVVTi4+OJi4vDyMiIVq1aMWnSJOzs7B7LuxAE4e9DBHTCQ6tq964gPCpTU1OSkpLIz8/H2NiY/fv307DhH//96t27N7179/7L9ymfRii3VnM+/XYXN3/9henTp5OWlsbRo0extLTE29u7wnUdOnRg9uzZeHh4sHx5acWW8PBwxo4di75+6T+hderUISEhgUaNGpGXlwfAiBEj+OKLL9BqtQCcP3+eq1ev0rp1a7Zu3aq0PX36dIYOHUrfvn1p1KhRpX537doVS0tLAJycnEhNTRUBnSAIlYiAThCEp6579+7s3r2b/v37s2HDBgYPHqyUo1u7di0xMTF8/vnnBAYGYmFhQUxMDNevX2fRokX079+fyMhIgoKCsLa2JikpidatW7N+/XokSSI2NpbhYyZw/uotJLU5Vj2nUVKvBV9++SXF189x4sQJmjdvzp07d1Cr1dy6dYvQ0FCKiooYM2YMs2fPZtOmTfzwww80aNCArVu3cvXqVSZNmoSxsTFWVlaEhob+aQqexMREhg4diizL1K9fn/nz5/P999+TkZHB+vXrWbRoEeHh4UBp6p+2bdty6dKlCgFmVal/BEEQQKyhEwThGTBo0CA2btxIQUEBiYmJ992QcO3aNQ4fPsyuXbsqTMPGxcWxbNkyTp06xYULFzhy5AhFRUVMmjQJ0+5vUH/EMsy0L5EZ9Q1GDZ3Iv3wK40YvcPLkSdasWUNmZiaRkZF06tSJ999/n3bt2jF8+HB0Oh2TJk3ivffew9nZmUaNGjFgwABcXFyIjo5m0KBBzJs3D0dHR9LS0pR1c+vWrcPX11fpn5ubG6GhoXz77bfo6ekxceJENm7cyG+//UarVq1o1KiRsrZOlmWOHz/O4MGDiY6OfkJvXRCEvxMR0AmC8NRptVpSUlLYsGFDhZJ1VQkICEBPTw8nJyelHjBA27ZtadSoEXp6eri5uZGSksKZM2dISkoifuVMrq6ZRNbRMHTZtzCoaw9AXuZN1q9fj76+PhqNBp1Ox9ixYwFo1KgRt27dYurUqXz55ZcEBwfTqVMnXF1d6dy5M9HR0ZiZmTFhwgQiIiJQq9UsXryYK1euoNFo0NPTU9qqSkREBN7e3qjVajZu3Eh+fj7du3cHwMLCAijdTHHnzp2/9G4FQfhnEFOugiDUuPLr2fKLdGyPS6N3797MnDmTyMhIbt26Ve215auPlJ/mrKoqiSzLODs7o/fqh5XS7TSeuQ3zjHPExsby/vvvK/WLAQIDAwkMDGTHjh2EhobyySefYGZmxsyZM4HSXHafffYZvXv3JjIykokz59Bh4QGu/G5IiaEp73/zg7LO9Pbt2yxdupQ1a9YA4OnpyQ8//IC9vT0xMTHY2dkRFBSkPINarea7774DYOjQoSxdulTp865dux76XQuC8M8gRugEQahRZTWK0zLzkQFZhjlbT2LbtjvvvvsuGo3msd2rVatWpKen80r9HIwNVMi6Yu6mpyLLJejnZ/DB+EEsWrSIzMxMcnJy6NSpE6GhoQBERkZibW2NhYUF5ubmZGdnK+1mZWUpGzfe/+RLLqTnkpaZj2RoDCa1mbzkG7bHpXH79m1++OEHOnbsWKGNgoICAKytrcnJyWHz5s2P7ZkFQfhnEgGdIAg1avG+M+QX6Socyy/SsSY+mylTpjzWexkaGrJ582b2rA6mIGw66d9M4W7ar9haGGF4+AvmDu+Gu7s706ZNo1atWgQFBRETE4NWq2X27Nl8/fXXALzyyits27YNNzc3Dh06RFBQEAMGDMDHx4fTmVBSbqTQqud0fj+0gaE9fenSpQvvvfcezZo1IzAwkLFjx+Lm5oaRkRGjR49Go9EQEBBAmzZtHuh5VCoVbm5uuLi4MGDAAGVH7dOwdu1aJVff4yKqZAjCoxOVIgRBqFFNZ++mqn91JODiwp413Z2/rCafx8zMjJycHKB0OrZ169ZMnz79sd7jQZXffSwIQtVEpQhBEP62bKuoCXy/48+6p/U8Pj4+/Pbbb+Tm5jJy5EjatGmDu7s7O3bsAO5fZcLMzIy3334bV1dX2rVrp2wuqaoqho+Pj7K2EEpz5yUmJiqfs7KyaNKkCSUlJQDk5eVhZ2dHUVERK1eupE2bNri6utKvXz9lRDEwMJDJkyfj7e2Ng4ODMuVcvkpGSkoKPj4+eHh44OHhoVT3EAShaiKgEwShRv3dysc9jecpLi5m7969aDQaPvzwQ7p06UJ0dDQRERHMmjWL3NxcoLTKRFhYGCdPniQsLIzLly8DkJubS7t27UhISKBTp06sXLkSKK2KsW/fPhISEti5cycAo0aNYu3atQCcPXuWwsJCJVkygKWlJa6urhw8eBCA77//Hn9/fwwMDOjbty/R0dEkJCTwwgsvsGrVKuW66tLPlKlXrx779+/nxIkThIWFMXny5Mf/IgXhb0QEdIIg1KgA94Ys6KuhYS1jJKBhLWMW9NU8t9VHavJ58vPzcXNzw9PTk8aNG/Pvf/+bH3/8kYULF+Lm5oafnx8FBQVcunQJ+KPKhFqtVqpMQOnawl69egHQunVrUlJSgNLRt8DAQFauXIlOV7rOccCAAezatYuioiJWr15NYGBgpX4NHDiQsLAwADZu3MjAgQMBSEpKwsfHB41GQ2hoKMnJyX+8t2rSz5QpKipS1hkOGDCAU6dOPZ6XKAh/UyJtiSAINe7vVj7uST5P+RQv6BsStGZ3hXvJssyWLVto1ariiOAvv/xSZSoXAAMDAyRJqnR8+fLl/PLLL+zevRs3Nzfi4+OxsrLipZdeYseOHXz33XdUtY65d+/ezJkzh9u3bxMbG0uXLl2A0qnV7du34+rqytq1a4mMjFSuqS79TJmlS5diY2NDQkICJSUlqNXqh311gvCPIkboBEEQnlHVpXjZHpemnOPv789nn32mBEVxcXGPfL/z58/j5eXF/Pnzsba2VqZoR40axeTJk2nTpg116tSpdJ2ZmRlt27ZlypQp9OrVC5WqdAo6OzubBg0aUFRUpKSDeVBZWVk0aNAAPT091q1bp4wYCoJQNRHQCYIgPKOqS/GyeN8Z5fPcuXMpKipCq9Xi4uLC3LlzH/l+s2bNQqPR4OLiolTFgNJpWQsLC15//fVqrx04cCDr169XplsB3n//fby8vHjppZdwdHR8qL6MHz+er7/+mnbt2nH27FlMTU0f7aEE4R9CpC0RBOEvuXLlChMmTODUqVOUlJTQq1cvFi9ejKGh4dPu2nPvWUnxcvXqVfz8/Dh9+jR6emIcQBAelEhbIgjCc0GWZfr27UtAQADnzp3j7Nmz5OTk8Pbbb1c4r2yNlvBwnoUUL9988w1eXl58+OGHIpgThGeY+F+nIAiP7MCBA6jVamUqTqVSsXTpUlavXs1///tfBgwYwCuvvMLLL79cbb60vLw8XnvtNbRaLQMHDsTLy0tZeL9hwwZlCvDNN99U7ltdHrW/m2chxcvw4cO5fPkyAwYMqLF7CoLw8ERAJwjCI0tOTqZ169YVjllYWNC4cWOKi4s5duwYX3/9NQcOHKg2X9p///tfateuTWJiInPnziU2NhYoneZ78803OXDgAPHx8URHR7N9+3ag+jxqfzd/txQvgvC4XL9+nUGDBtGsWTOcnJzo0aMHZ8+erfLc8gmrH7egoCCCg4OfSNsPS6QtEQThkcmyrKS/qOr4Sy+9pOyK/PHHH9m5c6fyj19ZvrTDhw8rNVxdXFyUpLXR0dH4+flRt25doLTUVVRUFAEBAZXyqO3fv/+JP+vT8ndL8SIIf5Usy/Tp04cRI0awceNGoDSJ9o0bN2jZsuVfbr+4uBh9/ecvPHr+eiwIwlNVPi+ayc27cKJiSaY7d+5w+fJlVCpVhZ2J1eVLq25j1v02bFWXR00QhL+/iIgIDAwMGDt2rHLMzc0NWZaZNWsWe/fuRZIk3nnnnQq7rqH0F8lx48YRExODvr4+n3zyCZ07d2bt2rXs3r2bgoICcnNz2blzJ6+++ioZGRkUFRXxwQcf8OqrrwLw4Ycf8s0332BnZ0fdunWVWYr4+HjGjh1LXl4ezZo1Y/Xq1TX3UhBTroIgPIR786LlWL3Aheu3mfL+pwDodDpmzJhBYGAgJiYmFa6tLl9ax44d+e677wA4deoUJ0+eBMDLy4uDBw9y8+ZNdDodGzZswNfXt4aeVBCEZ1VSUlKlpR4AW7duJT4+noSEBMLDw5k1axbXrl2rcM4XX3wBwMmTJ9mwYQMjRoygoKAAoMISEbVazbZt2zhx4gQRERHMmDEDWZaJjY1l48aNxMXFsXXrVqKjo5W2hw8fzscff0xiYiIajYZ58+Y9wbdQmQjoBEF4YPfmRZMkCauAt1j37UZatGhBy5YtUavVfPTRR5WurS5f2vjx40lPT0er1fLxxx+j1WqxtLSkQYMGLFiwgM6dO+Pq6oqHh4fyG7IgCMK9Dh8+zODBg1GpVNjY2ODr61sh4Co7Z9iwYQA4Ojpib2+vrL0rv0RElmXeeusttFotL774Imlpady4cYNDhw7Rp08fTExMsLCwoHfv3kBpIuzMzEzll84RI0YQFRVVU48OiClXQRAewtXM/ErH9C3qYvnqO5y7Jy9aYGBghbqfxsbGfPXVV5WuV6vVrF+/HrVazfnz5+natSv29vYADBkyhCFDhlS6JicnR/m5f//+9O/f/1EfSRCE50DlpR7HKp3zIHl173dO+SUioaGhpKenExsbi4GBAU2aNFFG8qpaN/wsECN0giA8sCeRFy0vL4+OHTvi6upKnz59+PLLL0VSYkEQFJWXejhy4UYm4+cuUs6Jjo6mdu3ahIWFodPpSE9PJyoqirZt21Zoq1OnTkoZurNnz3Lp0qVK63qhdMStXr16GBgYEBERQWpqqnL9tm3byM/PJzs7m++//x4AS0tLateuzaFDhwBYt25djS8RESN0giA8sFn+rZiz9WSFade/mhfN3Ny8yoLvgiAIUP1Sj+92rGbft1+hVqtp0qQJy5YtIycnB1dXVyRJYtGiRdSvX5+UlBTl2vHjxzN27Fg0Gg36+vqsXbsWIyOjSvccOnQor7zyCp6enri5uSml6zw8PBg4cCBubm7Y29vj4+OjXPP1118rmyIcHBxYs2YNy5Yte3Iv5h6i9JcgCA+l/NSHbS1jZvm3Emk1BEF4Yp6VEniPoiZLf4kROkEQHorIiyYIQk2yrWVMWhXrd2uyBN7z4KmsoZMkaYAkScmSJJVIklQjkasgCIIgCM+fZ6EE3vPgaY3QJQF9gcpb3gRBEARBEP6nbEZALPW4v6cS0Mmy/Cs8u1t/BUEQBEF4doilHn9OpC0RBEEQBEF4zj2xETpJksKB+lV89bYsyzseop0xwBiAxo0bP6beCYIgCIIg/H08sYBOluUXH1M7K4AVUJq25HG0KQiCIAiC8HciplwFQRAEQRCec08rbUkfSZKuAO2B3ZIk7Xsa/RAEQRCE55EkSUqReYDi4mLq1q1Lr169ANi5cycLFy6s9vqUlBRcXFyq/O7dd98lPDz88XZYeOKe1i7XbcC2p3FvQRAEQXjemZqakpSURH5+PsbGxuzfv5+GDf/YBdq7d2969+79SG3Pnz//cXVTqEFiylUQBEEQnkPdu3dn9+7dAGzYsIHBgwcr361du5aJEycCcOPGDfr06YOrqyuurq4cPXoUAJ1Ox+jRo3F2dubll18mP7+0GkNgYCCbN28GYM+ePTg6OtKxY0cmT56sjAAeP34cb29v3N3d8fb25syZM8p9+/btS7du3WjRogVvvPFGzbwMQQR0giAIgvA8GjRoEBs3bqSgoIDExES8vLyqPG/y5Mn4+vqSkJDAiRMncHZ2BuDcuXNMmDCB5ORkatWqxZYtWypcV1BQwH/+8x/27t3L4cOHSU9PV75zdHQkKiqKuLg45s+fz1tvvaV8Fx8fT1hYGCdPniQsLIzLly8/gacX7iVquQqCIAjCc0ir1ZKSksKGDRvo0aNHtecdOHCAb775BgCVSoWlpSUZGRk0bdoUNzc3AFq3bk1KSkqF606fPo2DgwNNmzYFYPDgwaxYsQKArKwsRowYwblz55AkiaKiIuW6rl27YmlpCYCTkxOpqanY2dk9tucWqiYCOkEQBEF4DmyPS1PKX+UX6dgel0bv3r2ZOXMmkZGR3Lp166HaMzIyUn5WqVTKlGsZWa4+U9jcuXPp3Lkz27ZtIyUlBT8/v2rbLS4ufqh+CY9GTLkKgiAIwjNue1wac7aeJC0zHxmQZZiz9SS2bbvz7rvvotFoqr22a9eufPnll0Dpurk7d+480D0dHR25cOGCMnIXFhamfJeVlaVswli7du0jPZPweImAThAEQRCecYv3nSG/SPfHAVnm0uaPmDj0Vb766it69OhR7Vq1Tz/9lJkzZ6LRaNBqtbz66qvKd4MHD0ar1RIVFVXpOmNjY/773//SrVs3OnbsiI2NjTKV+sYbbzBnzhw6dOiATlfar5iYGEJDQx/jUwsPQ7rfkOqzxtPTU46JiXna3RAEQRCEGtV09m7K/t9almWur5+JmUtXLNx7cHFhT+Lj48nOzsbHx6fK683MzMjJyalw7Pr163h5eZGamlrtfXNycjAzM0OWZSZMmICDgwMzZ858XI/1tydJUqwsy541cS8xQicIgiAIzzjbWsbKzwWXEpH09DF376Ecd3Nzw93dna5du+Lh4YFGo2HHjspl08snFH755Zf5/fffcXNz49ChQ8THx9OuXTu0Wi19+vQhIyODlStXYmZmRt26ddm8eTNFRUX4+fnx5ptv0rZtW1q2bMmhQ4cAiIyM/NO0JsKTIwI6QRAEQXjGzfJvhbGBCoCi9FQM6zfH2EDFLP9WyjlqtZpt27Zx4sQJIiIimDFjxn03NuzcuZNmzZoRHx+Pj48Pw4cP5+OPPyYxMRGNRsO8efOYNm0anp6evPbaa/z+++/MmTMHKK1Mcfz4cZYtW8a8efMqtX2/tCbCkyF2uQqCIAjCMy7AvXQDwuJ9Z8gGzIz0WdBXoxyH0qnYt956i6ioKPT09EhLS+PGjRvUr1//T9vPysoiMzMTX19fAEaMGMGAAQOU7wcOHFjh/L59+wJVpzspa6+6tCbCkyFG6ARBEAThGVc+ZUn9Ji2onXelQjAHEBoaSnp6OrGxscTHx2NjY0NBQcFjub+pqWmFz2WpSapLS1KW1iQpKYnvv//+sfVDqJ4I6ARBEAThGXZvypIcK0cu3Mhk/NxFyjnR0dGkpqZSr149DAwMiIiIuO9mh3tZWlpSu3ZtZT3cunXrlNG6RyHSmtQ8EdAJgiAIwjPs3pQlkiRhFfAW3+3YQ7NmzXB2diYoKIgePXoQExODp6cnoaGhODo6PtR9vv76a2bNmoVWqyU+Pp533333kftcVVqTZ5EkScyYMUP5HBwcTFBQ0GNp+3+jks6SJClJAiVJekOSpOUP2LcgSZIeeEuxSFsiCIIgCM+w8ilLypOAiwt71nR3/lbUajUNGjQgOjoaa2trgoODycnJeWxBnSRJ54AbQCfAFogCPGVZzviT6/SBd4AcWZaDH+ReYoROEARBEJ5h5VOWPMhx4cHp6+szZswYli5dWum79PR0+vXrR5s2bWjTpg1HjhwBQKPRkJmZiSzLWFlZKXVyhw0bRnh4+L3N3AGuAcOBpUAQYCFJ0k+SJCX+7z8bA0iStFaSpE8kSYoAPi7fiCRJoyVJ2itJUrV/6SKgEwThuTRt2jSWLVumfPb392fUqFHK5xkzZvDJJ588cHtBQUEEB1f9i7C3t/cj9zMyMpKjR48+8vWCUD5lSZl7U5YIj27ChAmEhoaSlZVV4fiUKVOYNm0a0dHRbNmyRfn3pUOHDhw5coTk5GQcHByUdYc///wz7dq1q+oWU4EPgbqyLK8DPge+kWVZC4QCIeXObQm8KMuyMg8sSdJE4BUgQJbligV3yxFpSwRBeC55e3uzadMmpk6dSklJCTdv3qxQo/Lo0aMVAr6/4q8EZJGRkZiZmf2loFD4ZyufsuRqZj62tYyZ5d+q0i5X4dFYWFgwfPhwQkJCMDb+YwAsPDycU6dOKZ/v3LmjVOOIiorC3t6ecePGsWLFCtLS0qhTpw5mZmaV2pdl+aokSQeAXf871B7o+7+f1wGLyp2+SZbl8osOhwFXKA3m7pv7RYzQCYLwXOrQoYMSaCUnJ+Pi4oK5uTkZGRkUFhby66+/sm/fPtq0aYOLiwtjxoxRkqyGhITg5OSEVqtl0KBBSpunTp3Cz88PBwcHQkL++KW57B/pyMhI/Pz86N+/P46OjgwdOlRpc8+ePTg6OtKxY0cmT55Mr169SElJYfny5SxdulTJxp+amkrXrl3RarV07dqVS5cuARAYGMjkyZPx9vbGwcGBzZs318h7FJ4PAe4NOTK7CxcX9uTI7C4imPsLtsel0WHhAZrO3k1+kY7tcWlMnTqVVatWkZubq5xXUlLCsWPHiI+PJz4+nrS0NMzNzenUqROHDh3i0KFD+Pn5KVU0qiu7Vtbc//5UpfwSydx7vksCmgCN/uy5REAnCMJzydbWFn19fS5dusTRo0dp3749Xl5eHDt2jJiYGLRaLRMnTiQ6OpqkpCTy8/PZtav0F+SFCxcSFxdHYmIiy5f/seHs9OnT7Nu3j+PHjzNv3rwqk6HGxcWxbNkyTp06xYULFzhy5AgFBQX85z//Ye/evRw+fJj09HQAmjRpwtixY5k2bZqSjX/ixIkMHz6cxMREhg4dyuTJk5W2r127xuHDh9m1axezZ89+wm9QEP557k0BI8swZ+tJolLzee2111i1apVy7ssvv8znn3+ufI6PjwfAzs6Omzdvcu7cORwcHOjYsSPBwcF/FtCVdxQo+01yKHD4PufGAf8BdkqSZHu/RkVAJwjCc6tslK4soGvfvr3y2dvbm4iICLy8vNBoNBw4cIDk5GQAtFotQ4cOZf369ejr/7HypGfPnhgZGWFtbU29evW4ceNGpXu2bduWRo0aoaenh5ubGykpKZw+fRoHBweaNm0KwODBg6vt87FjxxgyZAhQuoj68OE//i0PCAhAT08PJyenKu8tCMJfc28KGID8Ih2L951hxowZ3Lx5UzkeEhKi/HLo5ORU4Zc/Ly8vWrZsCYCPjw9paWl07NjxQbsxGXhdkqRESqdUp9zvZFmWDwMzgd2SJFlXd55YQycIwnOlfMZ8KceKrB0/cvXMSVxcXLCzs2PJkiVYWFgwcuRIRo0aRUxMDHZ2dgQFBSnZ6nfv3k1UVBQ7d+7k/fffVwK9suz3UH0G/KrO+SvpnyRJqrLt5ymllCA8L65mVtxT0Hj6ZuW4jY0NeXl5ynfW1taEhYVV2c66deuUn729vSkpqW42tZQsy4Hlfk4ButzvnP99Dir38z5g3/3uIUboBEF4btw7XVJQpwU//vD/7d1/rNV1Hcfx56srjRtcY3TJQgxaQ5FxEVKYoH+I2ryZmhmNTEpHY65wKZUV2Za2OZe45qCYSYk0TWqlkTpTShxtQv5IBAx0hjlIN3J2r/eEA7n33R/nc+EAh8thXPh+P/B6bI5zvuf7+ZzX/YDnvu/3872fz2N0DxhEU1MTQ4cOpaOjg9WrVzNlyhSg+qFcqVR235PW09PDli1bmDZtGrfffjsdHR1UKpXDyjVmzBg2b968e0/L2m8CLS0tdHV17X4+depUli1bBlS3ajqEn+rN7DAdy0vA+AqdmWVj3+mSAcNGsmt7J28PGrn7WFtbG5VKhdbWVmbPnk1bWxujRo1i0qRJAHR3dzNz5kw6OzuJCObOncuQIUMOK1dzczOLFi2ivb2d1tZWJk+evPu1Sy+9lOnTp7N8+XIWLlzIggULmDVrFvPnz2fYsGEsWbLksN7bzBp340WnMe/B9Xt9jhwrS8B4pwgzy0aZV8yvVCoMHjyYiGDOnDmMHj2auXPnFprJzPZXe9vGkV4CRtLzEXHWEel8H75CZ2bZGD6kmX937L+uZhmmSxYvXszSpUvZuXMnEydO5Nprry06kpnVcfnEk4/JZV98hc7MstF7D92+0yW3XdF2TH5Am1nefIXOzKwOr5hvZlafCzozy8qxOl1iZnY4vGyJmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeYUEUVnaJik/wCvF52jD63AW0WHyIDHqTEep8Z4nBrjcWqMx6kxHqfGjIyIYUfjjbIq6MpO0nMRcVbROcrO49QYj1NjPE6N8Tg1xuPUGPiup08AAAZoSURBVI9T+XjK1czMzCxzLujMzMzMMueCrn/dXXSATHicGuNxaozHqTEep8Z4nBrjcSoZ30NnZmZmljlfoTMzMzPLnAu6fiRpvqRNktZJekjSkKIzlZWkL0h6SVKPJP+mVA1J7ZJelvSqpO8VnaesJN0jaZukDUVnKTNJp0haKWlj+n/u+qIzlZGkgZKekfRiGqdbis5UZpKaJL0g6ZGis1iVC7r+tQIYFxHjgVeAeQXnKbMNwBXAqqKDlImkJuBnwKeBscCVksYWm6q07gXaiw6RgV3AtyLidOBsYI7/TdW1Azg/Is4AJgDtks4uOFOZXQ9sLDqE7eGCrh9FxBMRsSs9XQOMKDJPmUXExoh4uegcJTQZeDUiNkfETmAZ8NmCM5VSRKwC3i46R9lFxJsR8ff0uIvqN+GTi01VPlFVSU8HpP98k3kdkkYAnwF+UXQW28MF3ZEzC3is6BCWnZOBLTXPt+JvvtZPJI0CJgJ/KzZJOaVpxLXANmBFRHic6rsT+A7QU3QQ2+OEogPkRtKfgY/UeemmiFiezrmJ6jTH/UczW9k0Mla2H9U55qsEdtgkDQZ+D9wQEe8UnaeMIqIbmJDuf35I0riI8D2aNSRdAmyLiOclnVd0HtvDBd0hiogL+3pd0tXAJcAFcZyvCXOwsbK6tgKn1DwfAbxRUBY7RkgaQLWYuz8iHiw6T9lFRIekp6jeo+mCbm/nAJdJuhgYCJwo6b6ImFlwruOep1z7kaR24LvAZRGxveg8lqVngdGSPi7p/cAXgT8WnMkyJknAL4GNEfGTovOUlaRhvSsTSGoGLgQ2FZuqfCJiXkSMiIhRVD+fnnQxVw4u6PrXT4EWYIWktZLuKjpQWUn6nKStwBTgUUmPF52pDNIv1VwHPE715vXfRsRLxaYqJ0kPAKuB0yRtlfTVojOV1DnAl4Hz0+fS2nR1xfb2UWClpHVUf7BaERFeksOy4Z0izMzMzDLnK3RmZmZmmXNBZ2ZmZpY5F3RmZmZmmXNBZ2ZmZpY5F3RmZmZmmXNBZ2ZHhaTummUz1koaJenpQ+zjBkkfOFIZy0TS5ZLGFp3DzPLgZUvM7KiQVImIwQ2c15S2YKr32r+AsyLirf7OVzaS7gUeiYjfFZ3FzMrPV+jMrDCSKunP8yStlPRrYL2kQZIelfSipA2SZkj6BjCc6uKvK+v0NUnS06nNM5JaJA2UtETSekkvSJqWzr1G0h8kPSzpNUnXSfpmOmeNpKHpvKck3Zn63SBpcjo+NLVfl84fn47fLOme1G5zytybb2bKtVbSzyU19Y6BpFtT7jWSTpI0FbgMmJ/O/8QR/Ysws+y5oDOzo6W5Zrr1oTqvTwZuioixVPfQfCMizoiIccCfImIB1X1tp0XEtNqGaZu03wDXR8QZVLdteheYAxARbcCVwFJJA1OzccCX0vveCmyPiIlUd5/4Sk33gyJiKvB14J507BbghYgYD3wf+FXN+WOAi1K/P5Q0QNLpwAzgnIiYAHQDV/X2D6xJuVcBsyPiaapbvt0YERMi4p8HG1wzO76dUHQAMztuvJuKmQN5JiJeS4/XA3dI+jHVace/HqTv04A3I+JZgIh4B0DSucDCdGyTpNeBU1OblRHRBXRJ6gQernnv8TV9P5Dar5J0Ytrv81zg8+n4k5I+JOmD6fxHI2IHsEPSNuAk4ALgTODZ6taqNAPb0vk7gd4tpp4HPnWQr9XMbD8u6MysLP7X+yAiXpF0JnAxcJukJyLiR320FVDvhmD10WZHzeOemuc97P3ZuG+/cYB+e8+r7bc79SVgaUTMq9PuvdhzM3Pv+WZmh8RTrmZWOpKGU50CvQ+4A/hkeqkLaKnTZBMwXNKk1L5F0glUpzCvSsdOBT4GvHyIcWak9ucCnRHRuU+/5wFv9V4VPIC/ANMlfTi1GSpp5EHe90Bfq5nZfvyToJmVURvVXwjoAd4DvpaO3w08JunN2vvoImKnpBnAQknNVO+fuxBYBNwlaT2wC7gmInakac9G/Tctr3IiMCsduxlYImkdsB24uq8OIuIfkn4APCHpfelrmgO83kezZcDi9IsV030fnZn1xcuWmJkdgKSngG9HxHNFZzEz64unXM3MzMwy5yt0ZmZmZpnzFTozMzOzzLmgMzMzM8ucCzozMzOzzLmgMzMzM8ucCzozMzOzzLmgMzMzM8vc/wHjHVE1dWn6zgAAAABJRU5ErkJggg==\n", 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" ] @@ -1766,41 +1452,24 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "state\n", - "Alaska 57.333333\n", - "Arizona 83.500000\n", - "California 81.416667\n", - "Colorado 90.714286\n", - "Connecticut 56.800000\n", - "Name: AdultWeekend, dtype: float64" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 8#\n", "#Calculate the average 'AdultWeekend' ticket price by state\n", - "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n", + "state_avg_price = ski_data.groupby(___)[___].___\n", "state_avg_price.head()" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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uBGZFxOOF5QuAsyNiUZ6/DjgtIhbXbD8PmAfQ2dl5wPz58/vto7e3l46OjgHjWLZ6XdMxz54+pemyzWh2383st7e3l/vXPdmy+oZqKP24rTonwYMb0/RoPiat0Mw5PJbV9nfx2BYNV38Pp9FwbOfMmbMkIrrqrStzaAgASR3AFcD7ikmgb3WdTfplpoi4CLgIoKurK7q7u/tt1NPTQ73lRSedfnVTMQOsPG7guoaq2X03s9+enh7OWbShZfUN1VD6cVudMnsT5yxLp+loPiat0Mw5PJbV9nfx2BYNV38Pp9F+bEv91JCkiaQk8K2IuLJOkVXAnoX5PYA1ZcZkZmZbKvNTQwK+BqyIiHMbFLsKOCF/euilwLqIWFtWTGZm1l+ZQ0OHAMcDyyTdkZf9M7AXQERcCCwEDgfuBf4AvKXEeMzMrI7SEkF+A7jeewDFMgG8s6wYzMxscP5msZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVVxTiSD/ctigy8zMbOwZ8O6jknYAdgSmSXoGm+8mujOwe8mxmZnZMBjsNtRvB95HetJfwuZE8DjwpRLjMjOzYTJgIoiI84DzJL07Ir44TDGZmdkwauqHaSLii5IOBmYUt4mIS0uKy8zMhklTiUDSN4DnAHcAT+bFATgRmJmNcc3+VGUXsG/+aUkzM2sjzX6P4C7gWWUGYmZmI6PZK4JpwN2SbgX+1LcwIl5bSlRmZjZsmk0EZ5YZhJmZjZxmPzV0Q9mBmJnZyGj2U0PrSZ8SAtgOmAhsiIidywrMzMyGR7NXBJOL85JeBxxYSkRmZjasturuoxHxA+CVA5WRdLGkhyTd1WB9t6R1ku7IjzO2JhYzM9s2zQ4Nvb4wO470vYLBvlNwCXA+A3/p7KaIOLKZGMzMrBzNfmroqML0JmAlMHegDSLiRkkztioqMzMbNirzy8I5ESyIiFl11nUDVwCrgDXAqRGxvEE984B5AJ2dnQfMnz+/X5ne3l46OjoGjGfZ6nVNxz57+pSmyzaj2X03s9/e3l7uX/fkoOWarW+ohtKP26pzEjy4MU2P5mPSCs2cw2NZbX8Xj23RcPX3cBoNx3bOnDlLIqKr3rqmEoGkPYAvAoeQhoQWAe+NiFWDbDeDxolgZ+CpiOiVdDhwXkTMHCyWrq6uWLx4cb/lPT09dHd3D7jtjNOvHqz6p608+4imyzaj2X03s9+enh5O+smGltU3VEPpx211yuxNnLMsXbiO5mPSCs2cw2NZbX8Xj23RcPX3cBoNx1ZSw0TQ7JvFXweuIv0uwXTgR3nZVouIxyOiN08vBCZKmrYtdZqZ2dA1mwh2jYivR8Sm/LgE2HVbdizpWZKUpw/MsTyyLXWamdnQNftm8cOS3gxcluePZZAnbUmXAd2kn7lcBXyM9EU0IuJC4GjgHZI2ARuBY3x3UzOz4ddsIngr6aOg/0J6j+Bm4C0DbRARxw6y/vxcp5mZjaBmE8EngBMj4n8AJO0CfJ6UIMzMbAxr9j2CF/QlAYCIeBR4UTkhmZnZcGo2EYyT9Iy+mXxF0OzVhJmZjWLNPpmfA9ws6XLSewRvBD5VWlRmZjZsmr376KWSFpNuNCfg9RFxd6mRmZnZsGh6eCc/8fvJ38yszWzVbajNzKx9OBGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcU5EZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVVcaYlA0sWSHpJ0V4P1kvQFSfdKWipp/7JiMTOzxsq8IrgEOHSA9YcBM/NjHnBBibGYmVkDpSWCiLgReHSAInOBSyO5BZgqabey4jEzs/oUEeVVLs0AFkTErDrrFgBnR8SiPH8dcFpELK5Tdh7pqoHOzs4D5s+f329fvb29dHR0DBjPstXrmo599vQpTZUbSp2t0jkJHtw47LsdEVVva7PnYasNx3k9XMe21X24NX3TqrZuS1vmzJmzJCK66q2bsNW1bjvVWVY3K0XERcBFAF1dXdHd3d2vTE9PD/WWF510+tVNB7fyuIHr2po6W+WU2Zs4Z9lIHrrhU/W2NnsettpwnNfDdWxb3Ydb0zetamtZ58NIfmpoFbBnYX4PYM0IxWJmVlkjmQiuAk7Inx56KbAuItaOYDxmZpVU2nWZpMuAbmCapFXAx4CJABFxIbAQOBy4F/gD8JayYjEzs8ZKSwQRcewg6wN4Z1n7NzOz5vibxWZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFVdqIpB0qKR7JN0r6fQ667slrZN0R36cUWY8ZmbW34SyKpY0HvgS8GpgFXCbpKsi4u6aojdFxJFlxWFmZgMr84rgQODeiLgvIp4A5gNzS9yfmZltBUVEORVLRwOHRsTb8vzxwEsi4l2FMt3AFaQrhjXAqRGxvE5d84B5AJ2dnQfMnz+/3/56e3vp6OgYMKZlq9c1Hf/s6VOaKjeUOlulcxI8uHHYdzsiqt7WZs/DVhuO83q4jm2r+3Br+qZVbd2WtsyZM2dJRHTVW1fa0BCgOstqs87twN4R0SvpcOAHwMx+G0VcBFwE0NXVFd3d3f0q7unpod7yopNOv7qZuAFYedzAdW1Nna1yyuxNnLOszEM3elS9rc2eh602HOf1cB3bVvfh1vRNq9pa1vlQ5tDQKmDPwvwepFf9T4uIxyOiN08vBCZKmlZiTGZmVqPMRHAbMFPSsyVtBxwDXFUsIOlZkpSnD8zxPFJiTGZmVqO067KI2CTpXcA1wHjg4ohYLunkvP5C4GjgHZI2ARuBY6KsNy3MzKyuUgfo8nDPwpplFxamzwfOLzMGMzMbmL9ZbGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYV50RgZlZxTgRmZhXnRGBmVnFOBGZmFedEYGZWcU4EZmYVV2oikHSopHsk3Svp9DrrJekLef1SSfuXGY+ZmfVXWiKQNB74EnAYsC9wrKR9a4odBszMj3nABWXFY2Zm9ZV5RXAgcG9E3BcRTwDzgbk1ZeYCl0ZyCzBV0m4lxmRmZjUUEeVULB0NHBoRb8vzxwMviYh3FcosAM6OiEV5/jrgtIhYXFPXPNIVA8DzgHvq7HIa8HDLGzI6ua3tqUpthWq1dzS0de+I2LXeigkl7lR1ltVmnWbKEBEXARcNuDNpcUR0NR/e2OW2tqcqtRWq1d7R3tYyh4ZWAXsW5vcA1mxFGTMzK1GZieA2YKakZ0vaDjgGuKqmzFXACfnTQy8F1kXE2hJjMjOzGqUNDUXEJknvAq4BxgMXR8RySSfn9RcCC4HDgXuBPwBv2YZdDjh01Gbc1vZUpbZCtdo7qtta2pvFZmY2NvibxWZmFedEYGZWcWM2EUgaL+lX+bsISNpF0rWS/jP/fcZIx9gKklZKWibpDkmL87J2betUSZdL+rWkFZIOauO2Pi8f077H45Le18btfb+k5ZLuknSZpB3auK3vze1cLul9edmobuuYTQTAe4EVhfnTgesiYiZwXZ5vF3MiYr/C55Dbta3nAT+JiL8CXkg6vm3Z1oi4Jx/T/YADSB+W+D5t2F5J04H3AF0RMYv04ZFjaM+2zgL+H+nOCi8EjpQ0k9He1ogYcw/S9w2uA14JLMjL7gF2y9O7AfeMdJwtautKYFrNsrZrK7AzcD/5Awzt3NY6bX8N8PN2bS8wHXgA2IX0ScUFuc3t2NY3AF8tzH8U+NBob+tYvSL4V1LnPlVY1hn5Owj571+MRGAlCOCnkpbkW21Ae7Z1H+D3wNfzkN9XJe1Ee7a11jHAZXm67dobEauBzwO/BdaSvi/0U9qwrcBdwCskPVPSjqSPx+/JKG/rmEsEko4EHoqIJSMdyzA5JCL2J92p9Z2SXjHSAZVkArA/cEFEvAjYwGi7fC5B/rLla4HvjXQsZcnj4XOBZwO7AztJevPIRlWOiFgBfBa4FvgJcCewaUSDasKYSwTAIcBrJa0k3dH0lZK+CTzYd+fS/PehkQuxdSJiTf77EGkM+UDas62rgFUR8cs8fzkpMbRjW4sOA26PiAfzfDu291XA/RHx+4j4M3AlcDDt2VYi4msRsX9EvAJ4FPhPRnlbx1wiiIh/iog9ImIG6ZL6PyLizaTbVZyYi50I/HCEQmwZSTtJmtw3TRpXvYs2bGtE/A54QNLz8qK/Be6mDdta41g2DwtBe7b3t8BLJe0oSaRju4L2bCuS/iL/3Qt4Pen4juq2julvFkvqBk6NiCMlPRP4LrAX6cR7Q0Q8OpLxbStJ+5CuAiANnXw7Ij7Vjm0FkLQf8FVgO+A+0i1HxtGGbQXIY8gPAPtExLq8rF2P7ceBN5GGSX4FvA3ooD3behPwTODPwAci4rrRflzHdCIwM7NtN+aGhszMrLWcCMzMKs6JwMys4pwIzMwqzonAzKzinAjakKSQ9I3C/ARJv++7U+swx9It6eBWldvKGC6RdPQgZU6SdH6efp2kfcuIpc5+u8s4LpJ6JPX7sfR8h9MdC/MLJU0doJ5B+65Qdqqkf9y6iLeo5yxJr9rWeqx5TgTtaQMwS9KkPP9qYPUIxdJN+hZpq8oNh9cBw5IIRsD7gKcTQUQcHhGPtajuqcA2JQJJ4yPijIj4WYtisiY4EbSvHwNH5Oktvr2av7F8saTb8g3e5ublMyTdJOn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" ] @@ -1834,91 +1503,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PC1PC2
state
Alaska-1.336533-0.182208
Arizona-1.839049-0.387959
California3.537857-1.282509
Colorado4.402210-0.898855
Connecticut-0.9880271.020218
\n", - "
" - ], - "text/plain": [ - " PC1 PC2\n", - "state \n", - "Alaska -1.336533 -0.182208\n", - "Arizona -1.839049 -0.387959\n", - "California 3.537857 -1.282509\n", - "Colorado 4.402210 -0.898855\n", - "Connecticut -0.988027 1.020218" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 9#\n", "#Create a dataframe containing the values of the first two PCA components\n", "#Remember the first component was given by state_pca_x[:, 0],\n", "#and the second by state_pca_x[:, 1]\n", "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n", - "pca_df = pd.DataFrame({'PC1': state_pca_x[:,0], 'PC2': state_pca_x[:,1]}, index=state_summary_index)\n", + "pca_df = pd.DataFrame({'PC1': ___, 'PC2': ___}, index=__)\n", "pca_df.head()" ] }, @@ -1931,7 +1525,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1946,7 +1540,7 @@ "Name: AdultWeekend, dtype: float64" ] }, - "execution_count": 41, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1958,7 +1552,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -2024,7 +1618,7 @@ "Connecticut 56.800000" ] }, - "execution_count": 42, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -2043,96 +1637,14 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PC1PC2AdultWeekend
state
Alaska-1.336533-0.18220857.333333
Arizona-1.839049-0.38795983.500000
California3.537857-1.28250981.416667
Colorado4.402210-0.89885590.714286
Connecticut-0.9880271.02021856.800000
\n", - "
" - ], - "text/plain": [ - " PC1 PC2 AdultWeekend\n", - "state \n", - "Alaska -1.336533 -0.182208 57.333333\n", - "Arizona -1.839049 -0.387959 83.500000\n", - "California 3.537857 -1.282509 81.416667\n", - "Colorado 4.402210 -0.898855 90.714286\n", - "Connecticut -0.988027 1.020218 56.800000" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Code task 10#\n", "#Use pd.concat to concatenate `pca_df` and `state_avg_price` along axis 1\n", "# remember, pd.concat will align on index\n", - "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n", + "pca_df = ___([___, ___], axis=___)\n", "pca_df.head()" ] }, @@ -2145,7 +1657,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2174,13 +1686,6 @@ " AdultWeekend\n", " Quartile\n", " \n", - " \n", - " state\n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2224,7 +1729,6 @@ ], "text/plain": [ " PC1 PC2 AdultWeekend Quartile\n", - "state \n", "Alaska -1.336533 -0.182208 57.333333 (53.1, 60.4]\n", "Arizona -1.839049 -0.387959 83.500000 (78.4, 93.0]\n", "California 3.537857 -1.282509 81.416667 (78.4, 93.0]\n", @@ -2232,7 +1736,7 @@ "Connecticut -0.988027 1.020218 56.800000 (53.1, 60.4]" ] }, - "execution_count": 45, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2244,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2257,7 +1761,7 @@ "dtype: object" ] }, - "execution_count": 46, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2277,7 +1781,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2306,13 +1810,6 @@ " AdultWeekend\n", " Quartile\n", " \n", - " \n", - " state\n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2328,11 +1825,10 @@ ], "text/plain": [ " PC1 PC2 AdultWeekend Quartile\n", - "state \n", "Rhode Island -1.843646 0.761339 NaN NaN" ] }, - "execution_count": 47, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2357,20 +1853,20 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PC1 -1.843646\n", - "PC2 0.761339\n", - "AdultWeekend 64.124388\n", - "Quartile NA\n", + "PC1 -1.84365\n", + "PC2 0.761339\n", + "AdultWeekend 64.1244\n", + "Quartile NA\n", "Name: Rhode Island, dtype: object" ] }, - "execution_count": 48, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2453,22 +1949,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Code task 11#\n", "#Create a seaborn scatterplot by calling `sns.scatterplot`\n", @@ -2483,8 +1966,8 @@ "plt.subplots(figsize=(12, 10))\n", "# Note the argument below to make sure we get the colours in the ascending\n", "# order we intuitively expect!\n", - "sns.scatterplot(x=x, y=y, size='AdultWeekend', hue='Quartile', \n", - " hue_order=pca_df.Quartile.cat.categories, data=pca_df)\n", + "sns.___(x=___, y=___, size=___, hue=___, \n", + " hue_order=___, data=pca_df)\n", "#and we can still annotate with the state labels\n", "for s, x, y in zip(state, x, y):\n", " plt.annotate(s, (x, y)) \n", @@ -2897,11 +2380,11 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, - "source": [ - "***Since there does not seem to be any categorical patterns in the data especially from the features that contribute most to the variance of the data, we may consider treating all states the same in our analysis. Fortunately, we have been able to identify some key features in the data that can help predict the price of the resorts in our dataset.***" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -2912,7 +2395,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -3058,91 +2541,91 @@ " \n", " \n", " Runs\n", - " 76.0\n", - " 36.0\n", - " 13.0\n", - " 55.0\n", - " 65.0\n", + " 76\n", + " 36\n", + " 13\n", + " 55\n", + " 65\n", " \n", " \n", " TerrainParks\n", - " 2.0\n", - " 1.0\n", - " 1.0\n", - " 4.0\n", - " 2.0\n", + " 2\n", + " 1\n", + " 1\n", + " 4\n", + " 2\n", " \n", " \n", " LongestRun_mi\n", - " 1.0\n", - " 2.0\n", - " 1.0\n", - " 2.0\n", + " 1\n", + " 2\n", + " 1\n", + " 2\n", " 1.2\n", " \n", " \n", " SkiableTerrain_ac\n", - " 1610.0\n", - " 640.0\n", - " 30.0\n", - " 777.0\n", - " 800.0\n", + " 1610\n", + " 640\n", + " 30\n", + " 777\n", + " 800\n", " \n", " \n", " Snow Making_ac\n", - " 113.0\n", - " 60.0\n", - " 30.0\n", - " 104.0\n", - " 80.0\n", + " 113\n", + " 60\n", + " 30\n", + " 104\n", + " 80\n", " \n", " \n", " daysOpenLastYear\n", - " 150.0\n", - " 45.0\n", - " 150.0\n", - " 122.0\n", - " 115.0\n", + " 150\n", + " 45\n", + " 150\n", + " 122\n", + " 115\n", " \n", " \n", " yearsOpen\n", - " 60.0\n", - " 44.0\n", - " 36.0\n", - " 81.0\n", - " 49.0\n", + " 60\n", + " 44\n", + " 36\n", + " 81\n", + " 49\n", " \n", " \n", " averageSnowfall\n", - " 669.0\n", - " 350.0\n", - " 69.0\n", - " 260.0\n", - " 250.0\n", + " 669\n", + " 350\n", + " 69\n", + " 260\n", + " 250\n", " \n", " \n", " AdultWeekend\n", - " 85.0\n", - " 53.0\n", - " 34.0\n", - " 89.0\n", - " 78.0\n", + " 85\n", + " 53\n", + " 34\n", + " 89\n", + " 78\n", " \n", " \n", " projectedDaysOpen\n", - " 150.0\n", - " 90.0\n", - " 152.0\n", - " 122.0\n", - " 104.0\n", + " 150\n", + " 90\n", + " 152\n", + " 122\n", + " 104\n", " \n", " \n", " NightSkiing_ac\n", - " 550.0\n", + " 550\n", " NaN\n", - " 30.0\n", + " 30\n", " NaN\n", - " 80.0\n", + " 80\n", " \n", " \n", "\n", @@ -3164,17 +2647,17 @@ "double 0 4 0 \n", "surface 2 0 2 \n", "total_chairs 7 4 3 \n", - "Runs 76.0 36.0 13.0 \n", - "TerrainParks 2.0 1.0 1.0 \n", - "LongestRun_mi 1.0 2.0 1.0 \n", - "SkiableTerrain_ac 1610.0 640.0 30.0 \n", - "Snow Making_ac 113.0 60.0 30.0 \n", - "daysOpenLastYear 150.0 45.0 150.0 \n", - "yearsOpen 60.0 44.0 36.0 \n", - "averageSnowfall 669.0 350.0 69.0 \n", - "AdultWeekend 85.0 53.0 34.0 \n", - "projectedDaysOpen 150.0 90.0 152.0 \n", - "NightSkiing_ac 550.0 NaN 30.0 \n", + "Runs 76 36 13 \n", + "TerrainParks 2 1 1 \n", + "LongestRun_mi 1 2 1 \n", + "SkiableTerrain_ac 1610 640 30 \n", + "Snow Making_ac 113 60 30 \n", + "daysOpenLastYear 150 45 150 \n", + "yearsOpen 60 44 36 \n", + "averageSnowfall 669 350 69 \n", + "AdultWeekend 85 53 34 \n", + "projectedDaysOpen 150 90 152 \n", + "NightSkiing_ac 550 NaN 30 \n", "\n", " 3 4 \n", "Name Arizona Snowbowl Sunrise Park Resort \n", @@ -3191,20 +2674,20 @@ "double 1 1 \n", "surface 2 0 \n", "total_chairs 8 7 \n", - "Runs 55.0 65.0 \n", - "TerrainParks 4.0 2.0 \n", - "LongestRun_mi 2.0 1.2 \n", - "SkiableTerrain_ac 777.0 800.0 \n", - "Snow Making_ac 104.0 80.0 \n", - "daysOpenLastYear 122.0 115.0 \n", - "yearsOpen 81.0 49.0 \n", - "averageSnowfall 260.0 250.0 \n", - "AdultWeekend 89.0 78.0 \n", - "projectedDaysOpen 122.0 104.0 \n", - "NightSkiing_ac NaN 80.0 " + "Runs 55 65 \n", + "TerrainParks 4 2 \n", + "LongestRun_mi 2 1.2 \n", + "SkiableTerrain_ac 777 800 \n", + "Snow Making_ac 104 80 \n", + "daysOpenLastYear 122 115 \n", + "yearsOpen 81 49 \n", + "averageSnowfall 260 250 \n", + "AdultWeekend 89 78 \n", + "projectedDaysOpen 122 104 \n", + "NightSkiing_ac NaN 80 " ] }, - "execution_count": 52, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -3229,7 +2712,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3346,7 +2829,7 @@ "4 256.0 0.140242 90.203861 " ] }, - "execution_count": 53, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -3357,7 +2840,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -3503,91 +2986,91 @@ " \n", " \n", " Runs\n", - " 76.0\n", - " 36.0\n", - " 13.0\n", - " 55.0\n", - " 65.0\n", + " 76\n", + " 36\n", + " 13\n", + " 55\n", + " 65\n", " \n", " \n", " TerrainParks\n", - " 2.0\n", - " 1.0\n", - " 1.0\n", - " 4.0\n", - " 2.0\n", + " 2\n", + " 1\n", + " 1\n", + " 4\n", + " 2\n", " \n", " \n", " LongestRun_mi\n", - " 1.0\n", - " 2.0\n", - " 1.0\n", - " 2.0\n", + " 1\n", + " 2\n", + " 1\n", + " 2\n", " 1.2\n", " \n", " \n", " SkiableTerrain_ac\n", - " 1610.0\n", - " 640.0\n", - " 30.0\n", - " 777.0\n", - " 800.0\n", + " 1610\n", + " 640\n", + " 30\n", + " 777\n", + " 800\n", " \n", " \n", " Snow Making_ac\n", - " 113.0\n", - " 60.0\n", - " 30.0\n", - " 104.0\n", - " 80.0\n", + " 113\n", + " 60\n", + " 30\n", + " 104\n", + " 80\n", " \n", " \n", " daysOpenLastYear\n", - " 150.0\n", - " 45.0\n", - " 150.0\n", - " 122.0\n", - " 115.0\n", + " 150\n", + " 45\n", + " 150\n", + " 122\n", + " 115\n", " \n", " \n", " yearsOpen\n", - " 60.0\n", - " 44.0\n", - " 36.0\n", - " 81.0\n", - " 49.0\n", + " 60\n", + " 44\n", + " 36\n", + " 81\n", + " 49\n", " \n", " \n", " averageSnowfall\n", - " 669.0\n", - " 350.0\n", - " 69.0\n", - " 260.0\n", - " 250.0\n", + " 669\n", + " 350\n", + " 69\n", + " 260\n", + " 250\n", " \n", " \n", " AdultWeekend\n", - " 85.0\n", - " 53.0\n", - " 34.0\n", - " 89.0\n", - " 78.0\n", + " 85\n", + " 53\n", + " 34\n", + " 89\n", + " 78\n", " \n", " \n", " projectedDaysOpen\n", - " 150.0\n", - " 90.0\n", - " 152.0\n", - " 122.0\n", - " 104.0\n", + " 150\n", + " 90\n", + " 152\n", + " 122\n", + " 104\n", " \n", " \n", " NightSkiing_ac\n", - " 550.0\n", + " 550\n", " NaN\n", - " 30.0\n", + " 30\n", " NaN\n", - " 80.0\n", + " 80\n", " \n", " \n", " resorts_per_state\n", @@ -3599,43 +3082,43 @@ " \n", " \n", " state_total_skiable_area_ac\n", - " 2280.0\n", - " 2280.0\n", - " 2280.0\n", - " 1577.0\n", - " 1577.0\n", + " 2280\n", + " 2280\n", + " 2280\n", + " 1577\n", + " 1577\n", " \n", " \n", " state_total_days_open\n", - " 345.0\n", - " 345.0\n", - " 345.0\n", - " 237.0\n", - " 237.0\n", + " 345\n", + " 345\n", + " 345\n", + " 237\n", + " 237\n", " \n", " \n", " state_total_terrain_parks\n", - " 4.0\n", - " 4.0\n", - " 4.0\n", - " 6.0\n", - " 6.0\n", + " 4\n", + " 4\n", + " 4\n", + " 6\n", + " 6\n", " \n", " \n", " state_total_nightskiing_ac\n", - " 580.0\n", - " 580.0\n", - " 580.0\n", - " 80.0\n", - " 80.0\n", + " 580\n", + " 580\n", + " 580\n", + " 80\n", + " 80\n", " \n", " \n", " resorts_per_100kcapita\n", " 0.410091\n", " 0.410091\n", " 0.410091\n", - " 0.027477\n", - " 0.027477\n", + " 0.0274774\n", + " 0.0274774\n", " \n", " \n", " resorts_per_100ksq_mile\n", @@ -3665,22 +3148,22 @@ "double 0 4 \n", "surface 2 0 \n", "total_chairs 7 4 \n", - "Runs 76.0 36.0 \n", - "TerrainParks 2.0 1.0 \n", - "LongestRun_mi 1.0 2.0 \n", - "SkiableTerrain_ac 1610.0 640.0 \n", - "Snow Making_ac 113.0 60.0 \n", - "daysOpenLastYear 150.0 45.0 \n", - "yearsOpen 60.0 44.0 \n", - "averageSnowfall 669.0 350.0 \n", - "AdultWeekend 85.0 53.0 \n", - "projectedDaysOpen 150.0 90.0 \n", - "NightSkiing_ac 550.0 NaN \n", + "Runs 76 36 \n", + "TerrainParks 2 1 \n", + "LongestRun_mi 1 2 \n", + "SkiableTerrain_ac 1610 640 \n", + "Snow Making_ac 113 60 \n", + "daysOpenLastYear 150 45 \n", + "yearsOpen 60 44 \n", + "averageSnowfall 669 350 \n", + "AdultWeekend 85 53 \n", + "projectedDaysOpen 150 90 \n", + "NightSkiing_ac 550 NaN \n", "resorts_per_state 3 3 \n", - "state_total_skiable_area_ac 2280.0 2280.0 \n", - "state_total_days_open 345.0 345.0 \n", - "state_total_terrain_parks 4.0 4.0 \n", - "state_total_nightskiing_ac 580.0 580.0 \n", + "state_total_skiable_area_ac 2280 2280 \n", + "state_total_days_open 345 345 \n", + "state_total_terrain_parks 4 4 \n", + "state_total_nightskiing_ac 580 580 \n", "resorts_per_100kcapita 0.410091 0.410091 \n", "resorts_per_100ksq_mile 0.450867 0.450867 \n", "\n", @@ -3699,23 +3182,23 @@ "double 0 1 \n", "surface 2 2 \n", "total_chairs 3 8 \n", - "Runs 13.0 55.0 \n", - "TerrainParks 1.0 4.0 \n", - "LongestRun_mi 1.0 2.0 \n", - "SkiableTerrain_ac 30.0 777.0 \n", - "Snow Making_ac 30.0 104.0 \n", - "daysOpenLastYear 150.0 122.0 \n", - "yearsOpen 36.0 81.0 \n", - "averageSnowfall 69.0 260.0 \n", - "AdultWeekend 34.0 89.0 \n", - "projectedDaysOpen 152.0 122.0 \n", - "NightSkiing_ac 30.0 NaN \n", + "Runs 13 55 \n", + "TerrainParks 1 4 \n", + "LongestRun_mi 1 2 \n", + "SkiableTerrain_ac 30 777 \n", + "Snow Making_ac 30 104 \n", + "daysOpenLastYear 150 122 \n", + "yearsOpen 36 81 \n", + "averageSnowfall 69 260 \n", + "AdultWeekend 34 89 \n", + "projectedDaysOpen 152 122 \n", + "NightSkiing_ac 30 NaN \n", "resorts_per_state 3 2 \n", - "state_total_skiable_area_ac 2280.0 1577.0 \n", - "state_total_days_open 345.0 237.0 \n", - "state_total_terrain_parks 4.0 6.0 \n", - "state_total_nightskiing_ac 580.0 80.0 \n", - "resorts_per_100kcapita 0.410091 0.027477 \n", + "state_total_skiable_area_ac 2280 1577 \n", + "state_total_days_open 345 237 \n", + "state_total_terrain_parks 4 6 \n", + "state_total_nightskiing_ac 580 80 \n", + "resorts_per_100kcapita 0.410091 0.0274774 \n", "resorts_per_100ksq_mile 0.450867 1.75454 \n", "\n", " 4 \n", @@ -3733,27 +3216,27 @@ "double 1 \n", "surface 0 \n", "total_chairs 7 \n", - "Runs 65.0 \n", - "TerrainParks 2.0 \n", + "Runs 65 \n", + "TerrainParks 2 \n", "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800.0 \n", - "Snow Making_ac 80.0 \n", - "daysOpenLastYear 115.0 \n", - "yearsOpen 49.0 \n", - "averageSnowfall 250.0 \n", - "AdultWeekend 78.0 \n", - "projectedDaysOpen 104.0 \n", - "NightSkiing_ac 80.0 \n", + "SkiableTerrain_ac 800 \n", + "Snow Making_ac 80 \n", + "daysOpenLastYear 115 \n", + "yearsOpen 49 \n", + "averageSnowfall 250 \n", + "AdultWeekend 78 \n", + "projectedDaysOpen 104 \n", + "NightSkiing_ac 80 \n", "resorts_per_state 2 \n", - "state_total_skiable_area_ac 1577.0 \n", - "state_total_days_open 237.0 \n", - "state_total_terrain_parks 6.0 \n", - "state_total_nightskiing_ac 80.0 \n", - "resorts_per_100kcapita 0.027477 \n", + "state_total_skiable_area_ac 1577 \n", + "state_total_days_open 237 \n", + "state_total_terrain_parks 6 \n", + "state_total_nightskiing_ac 80 \n", + "resorts_per_100kcapita 0.0274774 \n", "resorts_per_100ksq_mile 1.75454 " ] }, - "execution_count": 54, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -3781,7 +3264,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -3810,28 +3293,15 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Code task 12#\n", "#Show a seaborn heatmap of correlations in ski_data\n", "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n", "plt.subplots(figsize=(12,10))\n", - "sns.heatmap(ski_data.corr());" + "sns.___(ski_data.___);" ] }, { @@ -3863,7 +3333,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -3885,26 +3355,26 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Code task 13#\n", "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n", "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n", - "features = [i for i in ski_data.columns if i not in ['name', 'Region', 'state', 'AdultWeekend']]" + "features = [___ for ___ in ski_data.columns if ___ not in [___, ___, ___, ___]]" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": { @@ -3933,7 +3403,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -3945,12 +3415,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -4017,7 +3487,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -4163,91 +3633,91 @@ " \n", " \n", " Runs\n", - " 76.0\n", - " 36.0\n", - " 13.0\n", - " 55.0\n", - " 65.0\n", + " 76\n", + " 36\n", + " 13\n", + " 55\n", + " 65\n", " \n", " \n", " TerrainParks\n", - " 2.0\n", - " 1.0\n", - " 1.0\n", - " 4.0\n", - " 2.0\n", + " 2\n", + " 1\n", + " 1\n", + " 4\n", + " 2\n", " \n", " \n", " LongestRun_mi\n", - " 1.0\n", - " 2.0\n", - " 1.0\n", - " 2.0\n", + " 1\n", + " 2\n", + " 1\n", + " 2\n", " 1.2\n", " \n", " \n", " SkiableTerrain_ac\n", - " 1610.0\n", - " 640.0\n", - " 30.0\n", - " 777.0\n", - " 800.0\n", + " 1610\n", + " 640\n", + " 30\n", + " 777\n", + " 800\n", " \n", " \n", " Snow Making_ac\n", - " 113.0\n", - " 60.0\n", - " 30.0\n", - " 104.0\n", - " 80.0\n", + " 113\n", + " 60\n", + " 30\n", + " 104\n", + " 80\n", " \n", " \n", " daysOpenLastYear\n", - " 150.0\n", - " 45.0\n", - " 150.0\n", - " 122.0\n", - " 115.0\n", + " 150\n", + " 45\n", + " 150\n", + " 122\n", + " 115\n", " \n", " \n", " yearsOpen\n", - " 60.0\n", - " 44.0\n", - " 36.0\n", - " 81.0\n", - " 49.0\n", + " 60\n", + " 44\n", + " 36\n", + " 81\n", + " 49\n", " \n", " \n", " averageSnowfall\n", - " 669.0\n", - " 350.0\n", - " 69.0\n", - " 260.0\n", - " 250.0\n", + " 669\n", + " 350\n", + " 69\n", + " 260\n", + " 250\n", " \n", " \n", " AdultWeekend\n", - " 85.0\n", - " 53.0\n", - " 34.0\n", - " 89.0\n", - " 78.0\n", + " 85\n", + " 53\n", + " 34\n", + " 89\n", + " 78\n", " \n", " \n", " projectedDaysOpen\n", - " 150.0\n", - " 90.0\n", - " 152.0\n", - " 122.0\n", - " 104.0\n", + " 150\n", + " 90\n", + " 152\n", + " 122\n", + " 104\n", " \n", " \n", " NightSkiing_ac\n", - " 550.0\n", + " 550\n", " NaN\n", - " 30.0\n", + " 30\n", " NaN\n", - " 80.0\n", + " 80\n", " \n", " \n", " resorts_per_state\n", @@ -4262,8 +3732,8 @@ " 0.410091\n", " 0.410091\n", " 0.410091\n", - " 0.027477\n", - " 0.027477\n", + " 0.0274774\n", + " 0.0274774\n", " \n", " \n", " resorts_per_100ksq_mile\n", @@ -4277,7 +3747,7 @@ " resort_skiable_area_ac_state_ratio\n", " 0.70614\n", " 0.280702\n", - " 0.013158\n", + " 0.0131579\n", " 0.492708\n", " 0.507292\n", " \n", @@ -4301,13 +3771,13 @@ " resort_night_skiing_state_ratio\n", " 0.948276\n", " NaN\n", - " 0.051724\n", + " 0.0517241\n", " NaN\n", - " 1.0\n", + " 1\n", " \n", " \n", " total_chairs_runs_ratio\n", - " 0.092105\n", + " 0.0921053\n", " 0.111111\n", " 0.230769\n", " 0.145455\n", @@ -4315,7 +3785,7 @@ " \n", " \n", " total_chairs_skiable_ratio\n", - " 0.004348\n", + " 0.00434783\n", " 0.00625\n", " 0.1\n", " 0.010296\n", @@ -4323,18 +3793,18 @@ " \n", " \n", " fastQuads_runs_ratio\n", - " 0.026316\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.015385\n", + " 0.0263158\n", + " 0\n", + " 0\n", + " 0\n", + " 0.0153846\n", " \n", " \n", " fastQuads_skiable_ratio\n", - " 0.001242\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0.00124224\n", + " 0\n", + " 0\n", + " 0\n", " 0.00125\n", " \n", " \n", @@ -4357,17 +3827,17 @@ "double 0 4 \n", "surface 2 0 \n", "total_chairs 7 4 \n", - "Runs 76.0 36.0 \n", - "TerrainParks 2.0 1.0 \n", - "LongestRun_mi 1.0 2.0 \n", - "SkiableTerrain_ac 1610.0 640.0 \n", - "Snow Making_ac 113.0 60.0 \n", - "daysOpenLastYear 150.0 45.0 \n", - "yearsOpen 60.0 44.0 \n", - "averageSnowfall 669.0 350.0 \n", - "AdultWeekend 85.0 53.0 \n", - "projectedDaysOpen 150.0 90.0 \n", - "NightSkiing_ac 550.0 NaN \n", + "Runs 76 36 \n", + "TerrainParks 2 1 \n", + "LongestRun_mi 1 2 \n", + "SkiableTerrain_ac 1610 640 \n", + "Snow Making_ac 113 60 \n", + "daysOpenLastYear 150 45 \n", + "yearsOpen 60 44 \n", + "averageSnowfall 669 350 \n", + "AdultWeekend 85 53 \n", + "projectedDaysOpen 150 90 \n", + "NightSkiing_ac 550 NaN \n", "resorts_per_state 3 3 \n", "resorts_per_100kcapita 0.410091 0.410091 \n", "resorts_per_100ksq_mile 0.450867 0.450867 \n", @@ -4375,10 +3845,10 @@ "resort_days_open_state_ratio 0.434783 0.130435 \n", "resort_terrain_park_state_ratio 0.5 0.25 \n", "resort_night_skiing_state_ratio 0.948276 NaN \n", - "total_chairs_runs_ratio 0.092105 0.111111 \n", - "total_chairs_skiable_ratio 0.004348 0.00625 \n", - "fastQuads_runs_ratio 0.026316 0.0 \n", - "fastQuads_skiable_ratio 0.001242 0.0 \n", + "total_chairs_runs_ratio 0.0921053 0.111111 \n", + "total_chairs_skiable_ratio 0.00434783 0.00625 \n", + "fastQuads_runs_ratio 0.0263158 0 \n", + "fastQuads_skiable_ratio 0.00124224 0 \n", "\n", " 2 3 \\\n", "Name Hilltop Ski Area Arizona Snowbowl \n", @@ -4395,28 +3865,28 @@ "double 0 1 \n", "surface 2 2 \n", "total_chairs 3 8 \n", - "Runs 13.0 55.0 \n", - "TerrainParks 1.0 4.0 \n", - "LongestRun_mi 1.0 2.0 \n", - "SkiableTerrain_ac 30.0 777.0 \n", - "Snow Making_ac 30.0 104.0 \n", - "daysOpenLastYear 150.0 122.0 \n", - "yearsOpen 36.0 81.0 \n", - "averageSnowfall 69.0 260.0 \n", - "AdultWeekend 34.0 89.0 \n", - "projectedDaysOpen 152.0 122.0 \n", - "NightSkiing_ac 30.0 NaN \n", + "Runs 13 55 \n", + "TerrainParks 1 4 \n", + "LongestRun_mi 1 2 \n", + "SkiableTerrain_ac 30 777 \n", + "Snow Making_ac 30 104 \n", + "daysOpenLastYear 150 122 \n", + "yearsOpen 36 81 \n", + "averageSnowfall 69 260 \n", + "AdultWeekend 34 89 \n", + "projectedDaysOpen 152 122 \n", + "NightSkiing_ac 30 NaN \n", "resorts_per_state 3 2 \n", - "resorts_per_100kcapita 0.410091 0.027477 \n", + "resorts_per_100kcapita 0.410091 0.0274774 \n", "resorts_per_100ksq_mile 0.450867 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.013158 0.492708 \n", + "resort_skiable_area_ac_state_ratio 0.0131579 0.492708 \n", "resort_days_open_state_ratio 0.434783 0.514768 \n", "resort_terrain_park_state_ratio 0.25 0.666667 \n", - "resort_night_skiing_state_ratio 0.051724 NaN \n", + "resort_night_skiing_state_ratio 0.0517241 NaN \n", "total_chairs_runs_ratio 0.230769 0.145455 \n", "total_chairs_skiable_ratio 0.1 0.010296 \n", - "fastQuads_runs_ratio 0.0 0.0 \n", - "fastQuads_skiable_ratio 0.0 0.0 \n", + "fastQuads_runs_ratio 0 0 \n", + "fastQuads_skiable_ratio 0 0 \n", "\n", " 4 \n", "Name Sunrise Park Resort \n", @@ -4433,31 +3903,31 @@ "double 1 \n", "surface 0 \n", "total_chairs 7 \n", - "Runs 65.0 \n", - "TerrainParks 2.0 \n", + "Runs 65 \n", + "TerrainParks 2 \n", "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800.0 \n", - "Snow Making_ac 80.0 \n", - "daysOpenLastYear 115.0 \n", - "yearsOpen 49.0 \n", - "averageSnowfall 250.0 \n", - "AdultWeekend 78.0 \n", - "projectedDaysOpen 104.0 \n", - "NightSkiing_ac 80.0 \n", + "SkiableTerrain_ac 800 \n", + "Snow Making_ac 80 \n", + "daysOpenLastYear 115 \n", + "yearsOpen 49 \n", + "averageSnowfall 250 \n", + "AdultWeekend 78 \n", + "projectedDaysOpen 104 \n", + "NightSkiing_ac 80 \n", "resorts_per_state 2 \n", - "resorts_per_100kcapita 0.027477 \n", + "resorts_per_100kcapita 0.0274774 \n", "resorts_per_100ksq_mile 1.75454 \n", "resort_skiable_area_ac_state_ratio 0.507292 \n", "resort_days_open_state_ratio 0.485232 \n", "resort_terrain_park_state_ratio 0.333333 \n", - "resort_night_skiing_state_ratio 1.0 \n", + "resort_night_skiing_state_ratio 1 \n", "total_chairs_runs_ratio 0.107692 \n", "total_chairs_skiable_ratio 0.00875 \n", - "fastQuads_runs_ratio 0.015385 \n", + "fastQuads_runs_ratio 0.0153846 \n", "fastQuads_skiable_ratio 0.00125 " ] }, - "execution_count": 77, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -4468,30 +3938,15 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing file. \"../data\\ski_data_step3_features.csv\"\n" - ] - } - ], + "outputs": [], "source": [ "# Save the data \n", "\n", "datapath = '../data'\n", "save_file(ski_data, 'ski_data_step3_features.csv', datapath)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -4510,11 +3965,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", -<<<<<<< HEAD "version": "3.9.7" -======= - "version": "3.8.5" ->>>>>>> 73ddd980c804af3da7cb915c14ca85ca2c56a65a }, "toc": { "base_numbering": 1, From 82304688ccd3fe498e0113c8a5305a4af0dd21ec Mon Sep 17 00:00:00 2001 From: Thapelo Date: Tue, 5 Apr 2022 13:25:27 +0200 Subject: [PATCH 7/7] Commiting the completion of notebooks 4 and 5 --- Notebooks/04_preprocessing_and_training.ipynb | 715 ++++++++++++------ Notebooks/05_modeling.ipynb | 146 ++-- models/ski_resort_pricing_model.pkl | Bin 0 -> 825353 bytes 3 files changed, 558 insertions(+), 303 deletions(-) create mode 100644 models/ski_resort_pricing_model.pkl diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb index db4cee86e..4305f3eb5 100644 --- a/Notebooks/04_preprocessing_and_training.ipynb +++ b/Notebooks/04_preprocessing_and_training.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -575,7 +575,7 @@ "fastQuads_skiable_ratio 0.00125 " ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -601,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -826,7 +826,7 @@ "fastQuads_skiable_ratio 0.001" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -837,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -846,7 +846,7 @@ "(277, 36)" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -857,7 +857,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -866,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -875,7 +875,7 @@ "(276, 36)" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -907,16 +907,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(193.89999999999998, 83.1)" + "(193.2, 82.8)" ] }, - "execution_count": 6, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -927,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -938,16 +938,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((193, 35), (84, 35))" + "((193, 35), (83, 35))" ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -958,16 +958,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((193,), (84,))" + "((193,), (83,))" ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -978,16 +978,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((193, 32), (84, 32))" + "((193, 32), (83, 32))" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1006,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1047,7 +1047,7 @@ "dtype: object" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1060,7 +1060,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1101,7 +1101,7 @@ "dtype: object" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1135,16 +1135,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "63.961398963730566" + "63.811088082901556" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1165,16 +1165,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[63.96139896]])" + "array([[63.81108808]])" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1243,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1274,16 +1274,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([63.96139896, 63.96139896, 63.96139896, 63.96139896, 63.96139896])" + "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1302,16 +1302,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([63.96139896, 63.96139896, 63.96139896, 63.96139896, 63.96139896])" + "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1330,7 +1330,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1339,7 +1339,7 @@ "0.0" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1364,16 +1364,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-0.0015324079131544543" + "-0.0031235200417913944" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1415,7 +1415,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1437,16 +1437,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "17.73548283175387" + "17.92346371714677" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1457,16 +1457,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "19.58960461386627" + "19.136142081278486" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1500,7 +1500,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": { "scrolled": true }, @@ -1524,16 +1524,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "558.7754824988588" + "614.1334096969046" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1544,16 +1544,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "704.8367793503885" + "581.4365441953483" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1571,16 +1571,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([23.63843232, 26.54876229])" + "array([24.78171523, 24.11299534])" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1612,16 +1612,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.0, -0.0015324079131544543)" + "(0.0, -0.0031235200417913944)" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1639,16 +1639,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(17.735482831753874, 19.58960461386627)" + "(17.92346371714677, 19.136142081278486)" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1666,16 +1666,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(558.775482498859, 704.8367793503884)" + "(614.1334096969046, 581.4365441953483)" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1707,16 +1707,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.0, -1.1067688684101517e+31)" + "(0.0, -3.041041349306602e+30)" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1729,16 +1729,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(-0.0015324079131544543, -3.490182681275203e+30)" + "(-0.0031235200417913944, 0.0)" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1751,16 +1751,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.0, -1.1067688684101517e+31)" + "(0.0, -3.041041349306602e+30)" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1773,16 +1773,24 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":15: RuntimeWarning: divide by zero encountered in double_scalars\n", + " R2 = 1.0 - sum_sq_res / sum_sq_tot\n" + ] + }, { "data": { "text/plain": [ - "(-0.0015324079131544543, -3.490182681275203e+30)" + "(-0.0031235200417913944, -inf)" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1854,36 +1862,36 @@ { "data": { "text/plain": [ - "summit_elev 2250.000000\n", - "vertical_drop 800.000000\n", + "summit_elev 2215.000000\n", + "vertical_drop 750.000000\n", "base_elev 1300.000000\n", "trams 0.000000\n", "fastSixes 0.000000\n", "fastQuads 0.000000\n", - "quad 0.000000\n", + "quad 1.000000\n", "triple 1.000000\n", "double 1.000000\n", "surface 2.000000\n", "total_chairs 7.000000\n", - "Runs 30.000000\n", + "Runs 28.000000\n", "TerrainParks 2.000000\n", "LongestRun_mi 1.000000\n", - "SkiableTerrain_ac 178.000000\n", - "Snow Making_ac 100.000000\n", - "daysOpenLastYear 110.000000\n", - "yearsOpen 58.000000\n", - "averageSnowfall 125.000000\n", + "SkiableTerrain_ac 170.000000\n", + "Snow Making_ac 96.500000\n", + "daysOpenLastYear 109.000000\n", + "yearsOpen 57.000000\n", + "averageSnowfall 120.000000\n", "projectedDaysOpen 115.000000\n", "NightSkiing_ac 70.000000\n", "resorts_per_state 15.000000\n", "resorts_per_100kcapita 0.248243\n", "resorts_per_100ksq_mile 22.902162\n", - "resort_skiable_area_ac_state_ratio 0.051687\n", - "resort_days_open_state_ratio 0.071821\n", + "resort_skiable_area_ac_state_ratio 0.051458\n", + "resort_days_open_state_ratio 0.071225\n", "resort_terrain_park_state_ratio 0.069444\n", "resort_night_skiing_state_ratio 0.077081\n", "total_chairs_runs_ratio 0.200000\n", - "total_chairs_skiable_ratio 0.040000\n", + "total_chairs_skiable_ratio 0.040323\n", "fastQuads_runs_ratio 0.000000\n", "fastQuads_skiable_ratio 0.000000\n", "dtype: float64" @@ -2001,7 +2009,7 @@ { "data": { "text/plain": [ - "(0.8144386003347039, 0.7588675340576477)" + "(0.8177988515690603, 0.7209725843435146)" ] }, "execution_count": 42, @@ -2030,7 +2038,7 @@ { "data": { "text/plain": [ - "(8.168087397673386, 10.045694095532316)" + "(8.547850301825427, 9.407020118581316)" ] }, "execution_count": 43, @@ -2062,7 +2070,7 @@ { "data": { "text/plain": [ - "(103.68716063113943, 169.69898262779168)" + "(111.8958125365848, 161.7315645119226)" ] }, "execution_count": 44, @@ -2107,38 +2115,38 @@ { "data": { "text/plain": [ - "summit_elev 4103.155440\n", - "vertical_drop 1085.886010\n", - "base_elev 2999.854922\n", - "trams 0.098446\n", - "fastSixes 0.056995\n", - "fastQuads 0.740933\n", - "quad 0.937824\n", - "triple 1.445596\n", - "double 1.792746\n", - "surface 2.590674\n", - "total_chairs 7.663212\n", - "Runs 43.366492\n", - "TerrainParks 2.444444\n", - "LongestRun_mi 1.339267\n", - "SkiableTerrain_ac 480.272251\n", - "Snow Making_ac 132.935673\n", - "daysOpenLastYear 111.777778\n", - "yearsOpen 56.948187\n", - "averageSnowfall 165.951872\n", - "projectedDaysOpen 116.766467\n", - "NightSkiing_ac 91.564103\n", - "resorts_per_state 16.424870\n", - "resorts_per_100kcapita 0.442261\n", - "resorts_per_100ksq_mile 42.539036\n", - "resort_skiable_area_ac_state_ratio 0.096123\n", - "resort_days_open_state_ratio 0.121879\n", - "resort_terrain_park_state_ratio 0.113350\n", - "resort_night_skiing_state_ratio 0.163529\n", - "total_chairs_runs_ratio 0.260567\n", - "total_chairs_skiable_ratio 0.068081\n", - "fastQuads_runs_ratio 0.011186\n", - "fastQuads_skiable_ratio 0.001760\n", + "summit_elev 4074.554404\n", + "vertical_drop 1043.196891\n", + "base_elev 3020.512953\n", + "trams 0.103627\n", + "fastSixes 0.072539\n", + "fastQuads 0.673575\n", + "quad 1.010363\n", + "triple 1.440415\n", + "double 1.813472\n", + "surface 2.497409\n", + "total_chairs 7.611399\n", + "Runs 41.188482\n", + "TerrainParks 2.434783\n", + "LongestRun_mi 1.293122\n", + "SkiableTerrain_ac 448.785340\n", + "Snow Making_ac 129.601190\n", + "daysOpenLastYear 110.100629\n", + "yearsOpen 56.559585\n", + "averageSnowfall 162.310160\n", + "projectedDaysOpen 115.920245\n", + "NightSkiing_ac 86.384615\n", + "resorts_per_state 16.264249\n", + "resorts_per_100kcapita 0.424802\n", + "resorts_per_100ksq_mile 40.957785\n", + "resort_skiable_area_ac_state_ratio 0.097205\n", + "resort_days_open_state_ratio 0.126014\n", + "resort_terrain_park_state_ratio 0.116022\n", + "resort_night_skiing_state_ratio 0.155024\n", + "total_chairs_runs_ratio 0.271441\n", + "total_chairs_skiable_ratio 0.070483\n", + "fastQuads_runs_ratio 0.010401\n", + "fastQuads_skiable_ratio 0.001633\n", "dtype: float64" ] }, @@ -2246,7 +2254,7 @@ { "data": { "text/plain": [ - "(0.8153961204004764, 0.7516735399853209)" + "(0.8170154093990025, 0.716381471695996)" ] }, "execution_count": 50, @@ -2266,7 +2274,7 @@ { "data": { "text/plain": [ - "(8.171846911392267, 10.145495313823073)" + "(8.536884040670977, 9.416375625789273)" ] }, "execution_count": 51, @@ -2286,7 +2294,7 @@ { "data": { "text/plain": [ - "(103.1521218943851, 174.7618159146046)" + "(112.37695054778276, 164.3926930952436)" ] }, "execution_count": 52, @@ -2465,7 +2473,7 @@ { "data": { "text/plain": [ - "(0.8144386003347039, 0.7588675340576477)" + "(0.8177988515690603, 0.7209725843435146)" ] }, "execution_count": 58, @@ -2492,7 +2500,7 @@ { "data": { "text/plain": [ - "(0.8144386003347039, 0.7588675340576477)" + "(0.8177988515690603, 0.7209725843435146)" ] }, "execution_count": 59, @@ -2512,7 +2520,7 @@ { "data": { "text/plain": [ - "(8.168087397673386, 10.045694095532316)" + "(8.547850301825427, 9.407020118581316)" ] }, "execution_count": 60, @@ -2533,16 +2541,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.168087397673386, 10.045694095532316)" + "(8.547850301825427, 9.407020118581316)" ] }, - "execution_count": 62, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2553,16 +2561,16 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(103.68716063113943, 169.69898262779168)" + "(111.8958125365848, 161.7315645119226)" ] }, - "execution_count": 63, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -2580,16 +2588,16 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(103.68716063113943, 169.69898262779168)" + "(111.8958125365848, 161.7315645119226)" ] }, - "execution_count": 64, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -2636,7 +2644,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2660,7 +2668,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -2669,11 +2677,11 @@ "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", " ('standardscaler', StandardScaler()),\n", " ('selectkbest',\n", - " SelectKBest(score_func=)),\n", + " SelectKBest(score_func=)),\n", " ('linearregression', LinearRegression())])" ] }, - "execution_count": 68, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -2691,7 +2699,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -2701,16 +2709,16 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.76754436691681, 0.6523454604324264)" + "(0.7674914326052744, 0.6259877354190833)" ] }, - "execution_count": 70, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -2721,16 +2729,16 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9.223528160840567, 11.49927307517182)" + "(9.501495079727484, 11.201830190332059)" ] }, - "execution_count": 71, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -2755,7 +2763,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -2778,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -2788,11 +2796,11 @@ " ('standardscaler', StandardScaler()),\n", " ('selectkbest',\n", " SelectKBest(k=15,\n", - " score_func=)),\n", + " score_func=)),\n", " ('linearregression', LinearRegression())])" ] }, - "execution_count": 73, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -2810,7 +2818,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -2820,16 +2828,16 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.7719217266659402, 0.6354911802705958)" + "(0.7924096060483825, 0.6376199973170797)" ] }, - "execution_count": 75, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -2840,16 +2848,16 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9.087070639150939, 11.799373665384277)" + "(9.211767769307116, 10.488246867294354)" ] }, - "execution_count": 76, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -2876,7 +2884,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -2885,7 +2893,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -2894,7 +2902,7 @@ "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])" ] }, - "execution_count": 74, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -2913,16 +2921,16 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.6327128053007867, 0.09502487849877672)" + "(0.6327128053007864, 0.09502487849877704)" ] }, - "execution_count": 75, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -2938,9 +2946,16 @@ "These results highlight that assessing model performance in inherently open to variability. You'll get different results depending on the quirks of which points are in which fold. An advantage of this is that you can also obtain an estimate of the variability, or uncertainty, in your performance estimate." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*****NOT SURE WHAT THE BELOW IS ABOUT*****" + ] + }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -2949,7 +2964,7 @@ "array([0.44, 0.82])" ] }, - "execution_count": 76, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -2984,14 +2999,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['memory', 'steps', 'verbose', 'simpleimputer', 'standardscaler', 'selectkbest', 'linearregression', 'simpleimputer__add_indicator', 'simpleimputer__copy', 'simpleimputer__fill_value', 'simpleimputer__missing_values', 'simpleimputer__strategy', 'simpleimputer__verbose', 'standardscaler__copy', 'standardscaler__with_mean', 'standardscaler__with_std', 'selectkbest__k', 'selectkbest__score_func', 'linearregression__copy_X', 'linearregression__fit_intercept', 'linearregression__n_jobs', 'linearregression__normalize', 'linearregression__positive'])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 18#\n", "#Call `pipe`'s `get_params()` method to get a dict of available parameters and print their names\n", "#using dict's `keys()` method\n", - "pipe.___.keys()" + "pipe.get_params().keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SimpleImputer(strategy='median')" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.get_params()['simpleimputer']" ] }, { @@ -3003,7 +3049,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -3011,6 +3057,57 @@ "grid_params = {'selectkbest__k': k}" ] }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'selectkbest__k': [1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 11,\n", + " 12,\n", + " 13,\n", + " 14,\n", + " 15,\n", + " 16,\n", + " 17,\n", + " 18,\n", + " 19,\n", + " 20,\n", + " 21,\n", + " 22,\n", + " 23,\n", + " 24,\n", + " 25,\n", + " 26,\n", + " 27,\n", + " 28,\n", + " 29,\n", + " 30,\n", + " 31,\n", + " 32]}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid_params" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -3021,7 +3118,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -3030,7 +3127,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -3041,7 +3138,7 @@ " SimpleImputer(strategy='median')),\n", " ('standardscaler', StandardScaler()),\n", " ('selectkbest',\n", - " SelectKBest(score_func=)),\n", + " SelectKBest(score_func=)),\n", " ('linearregression',\n", " LinearRegression())]),\n", " n_jobs=-1,\n", @@ -3051,7 +3148,7 @@ " 30, ...]})" ] }, - "execution_count": 80, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -3062,7 +3159,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -3073,24 +3170,68 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_selectkbest__k', 'params', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split3_test_score', 'split4_test_score', 'mean_test_score', 'std_test_score', 'rank_test_score'])" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr_grid_cv.cv_results_.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'selectkbest__k': 8}" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 19#\n", "#Print the `best_params_` attribute of `lr_grid_cv`\n", - "lr_grid_cv.___" + "lr_grid_cv.best_params_" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 20#\n", "#Assign the value of k from the above dict of `best_params_` and assign it to `best_k`\n", - "___ = lr_grid_cv.___['selectkbest__k']\n", + "best_k = lr_grid_cv.best_params_['selectkbest__k']\n", "plt.subplots(figsize=(10, 5))\n", "plt.errorbar(cv_k, score_mean, yerr=score_std)\n", "plt.axvline(x=best_k, c='r', ls='--', alpha=.5)\n", @@ -3115,7 +3256,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -3131,9 +3272,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "vertical_drop 10.767857\n", + "Snow Making_ac 6.290074\n", + "total_chairs 5.794156\n", + "fastQuads 5.745626\n", + "Runs 5.370555\n", + "LongestRun_mi 0.181814\n", + "trams -4.142024\n", + "SkiableTerrain_ac -5.249780\n", + "dtype: float64" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 21#\n", "#Get the linear model coefficients from the `coef_` attribute and store in `coefs`,\n", @@ -3142,7 +3302,7 @@ "#sorting the values in descending order\n", "coefs = lr_grid_cv.best_estimator_.named_steps.linearregression.coef_\n", "features = X_train.columns[selected]\n", - "pd.Series(___, index=___).___(ascending=___)" + "pd.Series(coefs, index=features).sort_values(ascending=False)" ] }, { @@ -3177,7 +3337,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -3187,9 +3347,9 @@ "#StandardScaler(),\n", "#and then RandomForestRegressor() with a random state of 47\n", "RF_pipe = make_pipeline(\n", - " ___(strategy=___),\n", - " ___,\n", - " ___(random_state=___)\n", + " SimpleImputer(strategy='median'),\n", + " StandardScaler(),\n", + " RandomForestRegressor(random_state=47)\n", ")" ] }, @@ -3202,7 +3362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -3210,12 +3370,12 @@ "#Call `cross_validate` to estimate the pipeline's performance.\n", "#Pass it the random forest pipe object, `X_train` and `y_train`,\n", "#and get it to use 5-fold cross-validation\n", - "rf_default_cv_results = cross_validate(___, ___, ___, cv=___)" + "rf_default_cv_results = cross_validate(RF_pipe, X_train, y_train, cv=5)" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -3224,7 +3384,7 @@ "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])" ] }, - "execution_count": 88, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -3236,7 +3396,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -3245,7 +3405,7 @@ "(0.6975826707112506, 0.07090742940774528)" ] }, - "execution_count": 89, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -3270,7 +3430,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -3300,7 +3460,7 @@ " 'simpleimputer__strategy': ['mean', 'median']}" ] }, - "execution_count": 90, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -3317,37 +3477,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "#Code task 24#\n", "#Call `GridSearchCV` with the random forest pipeline, passing in the above `grid_params`\n", "#dict for parameters to evaluate, 5-fold cross-validation, and all available CPU cores (if desired)\n", - "rf_grid_cv = GridSearchCV(___, param_grid=___, cv=___, n_jobs=-1)" + "rf_grid_cv = GridSearchCV(RF_pipe, param_grid=grid_params, cv=5, n_jobs=-1)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5,\n", + " estimator=Pipeline(steps=[('simpleimputer',\n", + " SimpleImputer(strategy='median')),\n", + " ('standardscaler', StandardScaler()),\n", + " ('randomforestregressor',\n", + " RandomForestRegressor(random_state=47))]),\n", + " n_jobs=-1,\n", + " param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n", + " 26, 33, 42, 54,\n", + " 69, 88, 112,\n", + " 143, 183, 233,\n", + " 297, 379, 483,\n", + " 615, 784,\n", + " 1000],\n", + " 'simpleimputer__strategy': ['mean', 'median'],\n", + " 'standardscaler': [StandardScaler(), None]})" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 25#\n", "#Now call the `GridSearchCV`'s `fit()` method with `X_train` and `y_train` as arguments\n", "#to actually start the grid search. This may take a minute or two.\n", - "rf_grid_cv.___(___, ___)" + "rf_grid_cv.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'randomforestregressor__n_estimators': 69,\n", + " 'simpleimputer__strategy': 'median',\n", + " 'standardscaler': None}" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 26#\n", "#Print the best params (`best_params_` attribute) from the grid search\n", - "rf_grid_cv.___" + "rf_grid_cv.best_params_" ] }, { @@ -3359,7 +3558,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -3368,7 +3567,7 @@ "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])" ] }, - "execution_count": 94, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -3381,7 +3580,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -3390,7 +3589,7 @@ "(0.7097384501425082, 0.06451341966873386)" ] }, - "execution_count": 95, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -3408,9 +3607,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 27#\n", "#Plot a barplot of the random forest's feature importances,\n", @@ -3419,8 +3631,8 @@ "#create a pandas Series object of the feature importances, with the index given by the\n", "#training data column names, sorting the values in descending order\n", "plt.subplots(figsize=(10, 5))\n", - "imps = rf_grid_cv.best_estimator_.named_steps.randomforestregressor.___\n", - "rf_feat_imps = pd.Series(___, index=X_train.columns).sort_values(ascending=False)\n", + "imps = rf_grid_cv.best_estimator_.named_steps.randomforestregressor.feature_importances_\n", + "rf_feat_imps = pd.Series(imps, index=X_train.columns).sort_values(ascending=False)\n", "rf_feat_imps.plot(kind='bar')\n", "plt.xlabel('features')\n", "plt.ylabel('importance')\n", @@ -3470,7 +3682,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -3481,16 +3693,16 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(10.499032338015297, 1.6220608976799646)" + "(10.499032338015292, 1.622060897679965)" ] }, - "execution_count": 98, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -3503,7 +3715,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -3512,7 +3724,7 @@ "11.793465668669327" ] }, - "execution_count": 99, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -3530,7 +3742,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -3540,7 +3752,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -3549,7 +3761,7 @@ "(9.644639167595688, 1.3528565172191818)" ] }, - "execution_count": 101, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -3562,7 +3774,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -3571,7 +3783,7 @@ "9.537730050637332" ] }, - "execution_count": 102, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -3610,7 +3822,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -3624,12 +3836,12 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 110, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -3664,7 +3876,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -3679,19 +3891,28 @@ "#and the current datetime (`datetime.datetime.now()`) to the `build_datetime` attribute\n", "#Let's call this model version '1.0'\n", "best_model = rf_grid_cv.best_estimator_\n", - "best_model.version = ___\n", - "best_model.pandas_version = ___\n", - "best_model.numpy_version = ___\n", - "best_model.sklearn_version = ___\n", + "best_model.version = pd.__version__\n", + "best_model.pandas_version = pd.__version__\n", + "best_model.numpy_version = np.__version__\n", + "best_model.sklearn_version = sklearn_version\n", "best_model.X_columns = [col for col in X_train.columns]\n", - "best_model.build_datetime = ___" + "best_model.build_datetime = datetime.datetime.now()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory ../models was created.\n", + "Writing file. \"../models\\ski_resort_pricing_model.pkl\"\n" + ] + } + ], "source": [ "# save the model\n", "\n", @@ -3723,7 +3944,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -3737,7 +3958,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.5" }, "toc": { "base_numbering": 1, diff --git a/Notebooks/05_modeling.ipynb b/Notebooks/05_modeling.ipynb index 4e4008174..d1a8aa621 100644 --- a/Notebooks/05_modeling.ipynb +++ b/Notebooks/05_modeling.ipynb @@ -89,7 +89,15 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected model version doesn't match version loaded\n" + ] + } + ], "source": [ "# This isn't exactly production-grade, but a quick check for development\n", "# These checks can save some head-scratching in development when moving from\n", @@ -222,11 +230,11 @@ " \n", " \n", " Runs\n", - " 105\n", + " 105.0\n", " \n", " \n", " TerrainParks\n", - " 4\n", + " 4.0\n", " \n", " \n", " LongestRun_mi\n", @@ -234,35 +242,35 @@ " \n", " \n", " SkiableTerrain_ac\n", - " 3000\n", + " 3000.0\n", " \n", " \n", " Snow Making_ac\n", - " 600\n", + " 600.0\n", " \n", " \n", " daysOpenLastYear\n", - " 123\n", + " 123.0\n", " \n", " \n", " yearsOpen\n", - " 72\n", + " 72.0\n", " \n", " \n", " averageSnowfall\n", - " 333\n", + " 333.0\n", " \n", " \n", " AdultWeekend\n", - " 81\n", + " 81.0\n", " \n", " \n", " projectedDaysOpen\n", - " 123\n", + " 123.0\n", " \n", " \n", " NightSkiing_ac\n", - " 600\n", + " 600.0\n", " \n", " \n", " resorts_per_state\n", @@ -270,11 +278,11 @@ " \n", " \n", " resorts_per_100kcapita\n", - " 1.12278\n", + " 1.122778\n", " \n", " \n", " resorts_per_100ksq_mile\n", - " 8.16104\n", + " 8.161045\n", " \n", " \n", " resort_skiable_area_ac_state_ratio\n", @@ -298,11 +306,11 @@ " \n", " \n", " total_chairs_skiable_ratio\n", - " 0.00466667\n", + " 0.004667\n", " \n", " \n", " fastQuads_runs_ratio\n", - " 0.0285714\n", + " 0.028571\n", " \n", " \n", " fastQuads_skiable_ratio\n", @@ -328,27 +336,27 @@ "double 0\n", "surface 3\n", "total_chairs 14\n", - "Runs 105\n", - "TerrainParks 4\n", + "Runs 105.0\n", + "TerrainParks 4.0\n", "LongestRun_mi 3.3\n", - "SkiableTerrain_ac 3000\n", - "Snow Making_ac 600\n", - "daysOpenLastYear 123\n", - "yearsOpen 72\n", - "averageSnowfall 333\n", - "AdultWeekend 81\n", - "projectedDaysOpen 123\n", - "NightSkiing_ac 600\n", + "SkiableTerrain_ac 3000.0\n", + "Snow Making_ac 600.0\n", + "daysOpenLastYear 123.0\n", + "yearsOpen 72.0\n", + "averageSnowfall 333.0\n", + "AdultWeekend 81.0\n", + "projectedDaysOpen 123.0\n", + "NightSkiing_ac 600.0\n", "resorts_per_state 12\n", - "resorts_per_100kcapita 1.12278\n", - "resorts_per_100ksq_mile 8.16104\n", + "resorts_per_100kcapita 1.122778\n", + "resorts_per_100ksq_mile 8.161045\n", "resort_skiable_area_ac_state_ratio 0.140121\n", "resort_days_open_state_ratio 0.129338\n", "resort_terrain_park_state_ratio 0.148148\n", "resort_night_skiing_state_ratio 0.84507\n", "total_chairs_runs_ratio 0.133333\n", - "total_chairs_skiable_ratio 0.00466667\n", - "fastQuads_runs_ratio 0.0285714\n", + "total_chairs_skiable_ratio 0.004667\n", + "fastQuads_runs_ratio 0.028571\n", "fastQuads_skiable_ratio 0.001" ] }, @@ -586,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -621,7 +629,7 @@ " ski_x = ski_data.loc[ski_data.state == state, feat_name]\n", " ski_x = ski_x[np.isfinite(ski_x)]\n", " plt.hist(ski_x, bins=30)\n", - " plt.___(x=big_mountain[feat_name].___, c=___, ls=___, alpha=0.8, label=___)\n", + " plt.axvline(x=big_mountain[feat_name].values, c='r', ls='--', alpha=0.8, label='Big Mountain')\n", " plt.xlabel(description)\n", " plt.ylabel('frequency')\n", " plt.title(description + ' distribution for resorts in market share')\n", @@ -649,7 +657,7 @@ "outputs": [ { "data": { - "image/png": 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xFi5cyMyZM1mwYEGnQ1ExevRoxo4dyzLLLNP0PiZekiQNAzNnzmSllVZi3LhxRESnwxnxMpN58+Yxc+ZMNtpoo6b3s6tRkqRhYMGCBay++uomXUNERLD66qsvdgukiZckScOESdfQsiSvh4mXpMYmTqwekka0UaNGMWHCBDbbbDO22GIL/vznPwPw4IMPsu+++y5WWTvttBMbbLABtfeM3nvvvVlxxRUHNWaAadOmPRdrf84//3xOOOGEQT9+Lcd4SWrskEM6HYGkIWD55Zfn+uuvB+Ciiy7imGOO4Y9//CPrrrsu55577mKXN2bMGP70pz8xadIk5s+fz6xZswY7ZKBKvFZccUW23Xbbfrfbc8892XPPPVsSQw9bvCRJ0mJ77LHHWHXVVQGYMWMGm266KQBPPvkkb3/72xk/fjz7778/W2+9dd27zxxwwAGcddZZAJx33nnss88+z63LTD7xiU+w6aab8prXvIazzz4bqJKoPfbY47ntDj/8cKZOnQpUd7o57rjj2GKLLXjNa17DbbfdxowZMzj55JP5+te/zoQJE7jiiiv49a9/zdZbb83mm2/OrrvuyuzZswGYOnUqh5cLRh988MEceeSRbLvttrzkJS9ZosSyL7Z4SWrsyCOr55NO6mwckjrqqaeeYsKECSxYsIBZs2Zx2WWXvWCb73znO6y66qrceOON3HzzzUyYMKFuebvssgvve9/7ePbZZznrrLM49dRTOf7444EqEbv++uu54YYbeOihh3jta1/LDjvs0DDGNdZYg+uuu47vfOc7fOUrX+G0007jsMMOY8UVV+Soo44C4JFHHuEvf/kLEcFpp53GiSeeyFe/+tUXlDVr1iymT5/Obbfdxp577rnY3al9MfFSy4yb8tsBlzHjhN0HIRINmNcNkoaeQw994bI3vAH226/6zPb8w1TrrW+tHvPnv/CiyKee2vCQtV2NV155Je9617u4+eabF9lm+vTpfPjDHwZg0003Zfz48XXLGzVqFJMmTeLss8/mqaeeYty4cYuUc+CBBzJq1CjWXnttdtxxR66++mpWXnnlfmPsaTXbcsstOe+88/rcZubMmey///7MmjWLp59+uu7lIPbee2+WWmopNtlkk+daxQaqpV2NETEjIm6KiOsj4pqybLWIuCQi7izPq7YyBkmSNPi22WYbHnroIebOnbvI8trB8s044IADOOKII3j729/eVDlLL700//nPf56b7305h+WWWw6okrpnnnmmzzKOOOIIDj/8cG666SZOOeWUupeE6Cmrv3gWVztavHbOzIdq5qcAl2bmCRExpcx/sg1xSJLUPfproRo9uv/1Y8Y01cLVn9tuu41nn32W1VdfnSeffPK55ZMmTeKcc85h55135pZbbuGmm27qt5ztt9+eY445hgMPPHCR5TvssAOnnHIKkydP5uGHH+byyy/ny1/+MgsXLuSWW27h3//+NwsWLODSSy9l0qRJ/R5jpZVW4rHHHntu/tFHH2W99dYD4PTTT1/cqg9IJ7oa9wJ2KtOnA9Mw8ZIkacjrGeMFVQvQ6aefzqhRoxbZ5oMf/CCTJ09m/PjxbL755owfP55VVlmlbpkR8dzYq1pve9vbuPLKK9lss82ICE488URe/OIXAzw3eP/lL385m2++ecO43/rWt7Lvvvvyq1/9im9961t89rOfZb/99mO99dbjda97Hffcc8/inIYBicFqOuuz8Ih7gEeABE7JzFMjYn5mjqnZ5pHM7Le7cauttsp6v4jQ0OUYry5yxhnV8zvf2dk4pBHs1ltv5VWvelWnw2jo2WefZeHChYwePZp//OMf7LLLLtxxxx0su+yynQ6tJfp6XSLi2szcqq/tW93itV1mPhgRawGXRMRtze4YEYcChwJssMEGrYpPUjNMuCQ16cknn2TnnXdm4cKFZCbf/e53uzbpWhItTbwy88HyPCcifgFMBGZHxDqZOSsi1gHm1Nn3VOBUqFq8WhmnJEkaHCuttFLd63aphb9qjIgVImKlnmlgN+Bm4HxgctlsMvCrVsUgaZAcemjfP12XJC2WVrZ4rQ38otxAcmngJ5l5YURcDZwTEe8F7gP2a2EMkiR1jcz0RtlDyJKMk29Z4pWZdwOb9bF8HrBLq44rSVI3Gj16NPPmzWP11Vc3+RoCMpN58+YxevToxdrPK9dLkjQMjB07lpkzZ77ggqXqnNGjRzN27NjF2sfES5KkYWCZZZape2sbDR8mXpIae8MbOh2BJHUFEy9Jje3nb2AkaTC09CbZkrrEggXVQ5I0ILZ4SWrsyCOr5wHeVFeSRjpbvCRJktrExEuSJKlNTLwkSZLaxMRLkiSpTRxcL6mxt7610xFIUlcw8ZLUmImXJA0KuxolNTZ/fvWQJA2ILV6SGjv66OrZ63hJ0oDY4iVJktQmJl6SJEltYuIlSZLUJiZekiRJbeLgekmN7btvpyOQpK5g4iWpsd1263QEktQV7GqU1Njs2dVDkjQgtnhJauwzn6mevY6XJA2ILV6SJEltYuIlSZLUJiZekiRJbWLiJUmS1CYOrpfU2EEHdToCSeoKJl6SGtthh05HIEldwa5GSY3de2/1kCQNiC1ekhr74herZ6/jJUkDYouXJElSm5h4SZIktYmJlyRJUpuYeEmSJLWJg+slNXbIIZ2OQJK6gomXpMYmTux0BJLUFexqlNTYHXdUD0nSgNjiJamxr3ylevY6XpI0ILZ4SZIktYmJlyRJUpuYeEmSJLWJiZckSVKbtHxwfUSMAq4BHsjMPSJiNeBsYBwwA3h7Zj7S6jgkDcDhh3c6AknqCu1o8fowcGvN/BTg0sx8OXBpmZc0lI0fXz0kSQPS0sQrIsYCuwOn1SzeCzi9TJ8O7N3KGCQNghtvrB6SpAFpdYvXN4Cjgf/ULFs7M2cBlOe1WhyDpIH69rerhyRpQFqWeEXEHsCczLx2Cfc/NCKuiYhr5s6dO8jRSZIktV8rW7y2A/aMiBnAWcDrI+JMYHZErANQnuf0tXNmnpqZW2XmVmuuuWYLw5QkSWqPliVemXlMZo7NzHHAAcBlmXkQcD4wuWw2GfhVq2KQJEkaSjpxHa8TgDdExJ3AG8q8JElS12vLTbIzcxowrUzPA3Zpx3ElDZKjjup0BJLUFdqSeEka5jbeuNMRSFJX8JZBkhq76qrqIUkaEFu8JDV2WrkG8sSJnY1DkoY5W7wkSZLaxMRLkiSpTUy8JEmS2sTES5IkqU0cXC+psWOP7XQEktQVTLwkNbbhhp2OQJK6gl2Nkhq7/PLqIUkaEFu81PXGTfntgMuYccLugxDJMHbmmdXzDjt0Ng5JGuZs8ZIkSWoTEy9JkqQ2MfGSJElqExMvSZKkNnFwvaTGjj++0xFIUlcw8ZLU2NprdzoCSeoKdjVKauzii6uHJGlAbPGS1Ni551bPu+3W2TgkaZizxUuSJKlNTLwkSZLaxMRLkiSpTUy8JEmS2sTB9ZIaO/HETkcgSV3BxEtSY2PGdDoCSeoKdjVKauzXv64ekqQBMfGS1JiJlyQNChMvSZKkNjHxkiRJahMTL0mSpDYx8ZIkSWoTLychqbGTTup0BJLUFUy8JDU2enSnI5CkrmBXo6TGfvaz6iFJGhATL0mNXXJJ9ZAkDYiJlyRJUpuYeEmSJLWJiZckSVKbmHhJkiS1ScPLSUTENcAPgZ9k5iOtD0nSkHPqqZ2OQJK6QjMtXgcA6wJXR8RZEfHGiIgWxyVJktR1GiZemXlXZh4LbAz8BPgBcF9EfC4iVmt1gJKGgDPOqB6SpAFpaoxXRIwHvgp8Gfg5sC/wGHBZP/uMjoirIuKGiPh7RHyuLF8tIi6JiDvL86oDr4aklrriiuohSRqQZsZ4XQvMB74PTMnMf5dVf42I7frZ9d/A6zPzXxGxDDA9Ii4A9gEuzcwTImIKMAX45IBqIUmSNAw0c6/G/TLz7r5WZOY+9XbKzAT+VWaXKY8E9gJ2KstPB6Zh4iVJkkaAZroaD4mIMT0zEbFqRHyhmcIjYlREXA/MAS7JzL8Ca2fmLIDyvNYSxC1JkjTsNJN4vTkz5/fMlEtKvKWZwjPz2cycAIwFJkbEps0GFhGHRsQ1EXHN3Llzm91NUiuMHl09JEkD0kxX46iIWK5nbFdELA8stzgHycz5ETENeBMwOyLWycxZEbEOVWtYX/ucCpwKsNVWW+XiHE/SIDvppE5HIEldoZkWrzOBSyPivRHxHuASqrFZ/YqINXu6KEuytitwG3A+MLlsNhn41ZIELkmSNNw0bPHKzBMj4iZgFyCA4zPzoibKXgc4PSJGUSV452TmbyLiSuCciHgvcB+w35KHL6ktTjutej7kkM7GIUnDXDNdjWTmBcAFi1NwZt4IbN7H8nlUSZyk4eKqq6pnEy9JGpCGXY0RsU+52OmjEfFYRDweEY+1IzhJkqRu0kyL14nAWzPz1lYHI0mS1M2aGVw/26RLkiRp4Jpp8bomIs4Gfkl1GyAAMvO8lkUlaWgZM6bxNpKkhppJvFYGngR2q1mWgImXNFKceGKnI5CkrtDM5STe3Y5AJEmSul0zv2rcOCIujYiby/z4iPh060OTNGR8+9vVQ5I0IM0Mrv8ecAywEJ67PtcBrQxK0hBz443VQ5I0IM0kXi/KzKt6LXumFcFIkiR1s2YSr4ci4qVUA+qJiH2BWS2NSpIkqQs186vGDwGnAq+MiAeAe4CDWhqVJElSF2rmV413A7tGxArAUpn5eOvDkjSkrL12pyOQpK7QMPGKiP/uNQ9AZn6+RTFJGmqOP77TEUhSV2imq/GJmunRwB6AtxCSJElaTM10NX61dj4ivgKc37KIJA09Xy1fAx//eGfjkKRhrpkWr95eBLxksAORNITdfnunI5CkrtDMGK+bKJeSAEYBawKO75IkSVpMzbR47VEz/QwwOzO9gKokSdJiaibx6n35iJV7ftkIkJkPD2pEkiRJXaqZxOs6YH3gESCAMcB9ZV3ieC+p+224YacjkKSu0EzidSFwfmb+DiAi3gzsmpn+vEkaKY49ttMRSFJXaOZeja/tSboAMvMCYMfWhSRJktSdmmnxeigiPg2cSdW1eBAwr6VRSRpavvjF6tmWL0kakGZavA6kuoTEL8pjzbJM0khx773VQ5I0IM1cuf5h4MMRsWJm/qsNMUmSJHWlhi1eEbFtRNwC3FLmN4uI77Q8MkmSpC7TTFfj14E3UsZ1ZeYNwA6tDEqSJKkbNXWvxsy8v/aiqcCzrQlH0pD0ild0OgJJ6grNJF73R8S2QEbEssCRwK2tDUvSkPJxL9snSYOhma7Gw4APAesBM4EJZV6SJEmLod8Wr4gYBXwjM9/RpngkDUWf+Uz1fPzxnY1Dkoa5fhOvzHw2ItaMiGUz8+l2BSVpiJk9u9MRSFJXaGaM1wzgTxFxPvBEz8LM/FqrgpIkSepGdcd4RcQZZXJ/4Ddl25VqHpIkSVoM/bV4bRkRGwL3Ad9qUzySJEldq7/E62TgQmAj4Jqa5UF1s+yXtDAuaUgZN+W3A9p/xgm7D1IkHTJ+fKcjkKSuUDfxysyTgJMi4ruZ+YE2xiRpqDn88E5HIEldoeF1vEy6JEmSBkczF1CVNNIdfXT1kCQNSFP3apQ0ws2f3+kIJKkrmHhJbTDQwfkwOAP0lzSOL111HwCfmvLb4f9DAUnqoJZ1NUbE+hHxh4i4NSL+HhEfLstXi4hLIuLO8rxqq2KQJEkaSlo5xusZ4OOZ+SrgdcCHImITYApwaWa+HLi0zEuSJHW9lnU1ZuYsYFaZfjwibgXWA/YCdiqbnQ5MAz7ZqjgkDdwN62zc6RAkqSu0ZYxXRIwDNgf+CqxdkjIyc1ZErNWOGCQtubM3e2OnQ5CkrtDyxCsiVgR+DnwkMx+LiGb3OxQ4FGCDDTZoXYDq02AMBpckSYtq6XW8ImIZqqTrx5l5Xlk8OyLWKevXAeb0tW9mnpqZW2XmVmuuuWYrw5TUwHG/P4Xjfn9Kp8OQpGGvlb9qDOD7wK2Z+bWaVecDk8v0ZOBXrYpB0uBY7pmnWe6ZpzsdhiQNe63satwOeCdwU0RcX5Z9CjgBOCci3gvcB+zXwhgkSZKGjFb+qnE6UG9A1y6tOq4kSdJQ5b0aJUmS2k7silgAABFXSURBVMRbBklq6Oqxr+50CJLUFUy8JDX0i01f3+kQJKkr2NUoSZLUJiZekhr60oXf4ksXfqvTYUjSsGdXozRMeDcBSRr+bPGSJElqExMvSZKkNjHxkiRJahPHeElqaPq4zTsdgiR1BRMvSQ397pWTOh2CJHUFuxolNbTcM0+z3DNPdzoMSRr2TLwkNXTc70/huN+f0ukwJGnYM/GSJElqExMvSZKkNjHxkiRJahMTL0mSpDbxchKSGrr0ZRM7HYIkdQUTL0kNXfqyrTsdgiR1BbsaJTW08oJ/sfKCf3U6DEka9ky8JDU0ZdoPmTLth50OQ5KGPRMvSZKkNjHxkiRJahMTL0mSpDYx8ZIkSWoTLychqaELXrFdp0OQpK5g4iWpoSs22mJQyxs35bcDLmPGCbsPQiSS1F52NUpqaI0nHmGNJx7pdBiSNOyZeElq6GNXnMnHrjiz02FI0rBn4iVJktQmJl6SJElt4uB6DWmDMQhbkqShwhYvSZKkNrHFS1JDv3z1zp0OQZK6gomXpIauWn/TTocgSV3BrkZJDa336BzWe3ROp8OQpGHPxEtSQx+68mw+dOXZnQ5DkoY9Ey9JkqQ2MfGSJElqExMvSZKkNjHxkiRJahMvJ9GFvNq7BtvZ43d7btr3lyQtuZa1eEXEDyJiTkTcXLNstYi4JCLuLM+rtur4kgbPDeu+ghvWfUWnw5CkYa+VXY1TgTf1WjYFuDQzXw5cWuYlDXEbPfwAGz38QKfDkKRhr2WJV2ZeDjzca/FewOll+nRg71YdX9Lged9V5/G+q87rdBiSNOy1e3D92pk5C6A8r9Xm40uSJHXMkP1VY0QcGhHXRMQ1c+fO7XQ4kiRJA9buxGt2RKwDUJ7r3vwtM0/NzK0yc6s111yzbQFKkiS1SrsTr/OByWV6MvCrNh9fkiSpY1p2Ha+I+CmwE7BGRMwEjgNOAM6JiPcC9wH7ter4kgbPj7bYo9MhSFJXaFnilZkH1lm1S6uOKak1bltro06HIEldYcgOrpc0dLxyzj28cs49nQ5DkoY9Ey9JDb3rut/wrut+0+kwJGnYM/GSJElqExMvSZKkNjHxkiRJahMTL0mSpDZp2eUkJHWP703cp9MhSFJXMPGS1NA9q63X6RAkqSvY1Sipoc0evJ3NHry902FI0rBni5ekhva/8WIAblj3FR2ORJKGN1u8JEmS2sTES5IkqU1MvCRJktrExEuSJKlNHFwvqaH/22b/TocgSV3BxGuIGTflt50OQXqBB1ZZq9MhtMRgfN5mnLD7IEQiaaSwq1FSQxPvv5mJ99/c6TAkadizxUtSQ3v//Q8AXLX+ph2ORJKGN1u8JEmS2sTES5IkqU3sapQ0LPlDlEX5QwFpeLDFS5IkqU1s8ZLU0Ne2P6jTIUhSVzDxktTQQyus2ukQJKkr2NUoqaHt77mO7e+5rtNhSNKwZ4tX4cBUqb433/4nAK7YaIsORyJJw5stXpIkSW1i4iVJktQmJl6SJEltYuIlSZLUJg6ul9TQCTu9u9MhSFJXMPGS1NBjo1fsdAiS1BXsapTU0C53/ZVd7vprp8OQpGHPxEtSQ7vcdRW73HVVp8OQpGHPxEuSJKlNTLwkSZLaxMH1g2gwbjskScOVt14bmnxdhhZbvCRJktrEFi9JDX1u1/d3OgRJ6gomXpIa+vfSy3Y6BEnqCnY1SmroLbdN5y23Te90GJI07NniJamhSTP+BsDvXjmpw5EMPf6oZnANlfPpYPLB52tb6UiLV0S8KSJuj4i7ImJKJ2KQJElqt7YnXhExCvg/4M3AJsCBEbFJu+OQJElqt060eE0E7srMuzPzaeAsYK8OxCFJktRWnUi81gPur5mfWZZJkiR1tcjM9h4wYj/gjZl5SJl/JzAxM4/otd2hwKFl9hXA7W0NtL41gIc6HUQHjNR6g3UfiXUfqfWGkVv3kVpvsO6tqPuGmblmXys68avGmcD6NfNjgQd7b5SZpwKntiuoZkXENZm5VafjaLeRWm+w7iOx7iO13jBy6z5S6w3Wvd1170RX49XAyyNio4hYFjgAOL8DcUiSJLVV21u8MvOZiDgcuAgYBfwgM//e7jgkSZLarSMXUM3M3wG/68SxB8GQ6/5sk5Fab7DuI9FIrTeM3LqP1HqDdW+rtg+ulyRJGqm8V6MkSVKbmHjVERHrR8QfIuLWiPh7RHy4LF8tIi6JiDvL86qdjrUVImJURPwtIn5T5kdKvcdExLkRcVt57bcZQXX/aHmv3xwRP42I0d1a94j4QUTMiYiba5bVrWtEHFNucXZ7RLyxM1EPXJ16f7m832+MiF9ExJiadV1Rb+i77jXrjoqIjIg1apZ1fd0j4ohSv79HxIk1y7ui7nXe7xMi4i8RcX1EXBMRE2vWtaXeJl71PQN8PDNfBbwO+FC5tdEU4NLMfDlwaZnvRh8Gbq2ZHyn1/iZwYWa+EtiM6hx0fd0jYj3gSGCrzNyU6ocvB9C9dZ8KvKnXsj7rWj73BwCvLvt8p9z6bDiaygvrfQmwaWaOB+4AjoGuqzf0XXciYn3gDcB9Ncu6vu4RsTPVXWPGZ+arga+U5d1U96m88DU/EfhcZk4A/rvMt7XeJl51ZOaszLyuTD9O9Qd4Pao36ulls9OBvTsTYetExFhgd+C0msUjod4rAzsA3wfIzKczcz4joO7F0sDyEbE08CKq6+t1Zd0z83Lg4V6L69V1L+CszPx3Zt4D3EV167Nhp696Z+bFmflMmf0L1bUVoYvqDXVfc4CvA0cDtQOeR0LdPwCckJn/LtvMKcu7pu516p3AymV6FZ6/jmjb6m3i1YSIGAdsDvwVWDszZ0GVnAFrdS6ylvkG1RfRf2qWjYR6vwSYC/ywdLOeFhErMALqnpkPUP3Hex8wC3g0My9mBNS9Rr26jqTbnL0HuKBMd329I2JP4IHMvKHXqq6vO7AxsH1E/DUi/hgRry3Lu73uHwG+HBH3U33nHVOWt63eJl4NRMSKwM+Bj2TmY52Op9UiYg9gTmZe2+lYOmBpYAvgu5m5OfAE3dO11q8ynmkvYCNgXWCFiDios1ENGdHHsq77OXhEHEs1xOLHPYv62Kxr6h0RLwKOpepuesHqPpZ1Td2LpYFVqYbSfAI4JyKC7q/7B4CPZub6wEcpPRy0sd4mXv2IiGWokq4fZ+Z5ZfHsiFinrF8HmFNv/2FqO2DPiJgBnAW8PiLOpPvrDdV/ODMz869l/lyqRGwk1H1X4J7MnJuZC4HzgG0ZGXXvUa+uTd3mbDiLiMnAHsA78vlrDHV7vV9K9Y/GDeX7bixwXUS8mO6vO1R1PC8rV1H1cKxB99d9MtX3G8DPeL47sW31NvGqo2T+3wduzcyv1aw6n+qFozz/qt2xtVJmHpOZYzNzHNVAw8sy8yC6vN4AmflP4P6IeEVZtAtwCyOg7lRdjK+LiBeV9/4uVOMaR0Lde9Sr6/nAARGxXERsBLwcuKoD8bVERLwJ+CSwZ2Y+WbOqq+udmTdl5lqZOa58380EtijfA11d9+KXwOsBImJjYFmqm0V3e90fBHYs068H7izT7at3Zvro4wFMompmvBG4vjzeAqxO9YunO8vzap2OtYXnYCfgN2V6RNQbmABcU173X1I1xY+Uun8OuA24GTgDWK5b6w78lGos20KqP7jv7a+uVF1S/wBuB97c6fgHud53UY1t6fmeO7nb6l2v7r3WzwDWGCl1p0q0ziyf9+uA13db3evUexJwLXAD1bjtLdtdb69cL0mS1CZ2NUqSJLWJiZckSVKbmHhJkiS1iYmXJElSm5h4SZIktYmJl9SlIuJtEZER8cp+tpkWEVs1KOezEXFUmT44ItYd7FjrHPfgiPh2C8qdERFr9LH8U73m/9ygnIbnrmbbcRHx/xYv0j7LOa3czHeg5awTEb/ptezgcnu02mVnRcTLB3o8Sc8z8ZK614HAdKoL4Q6Wg6luKdSNFkm8MnPbQSx7HDCgxCsiRmXmIZl5yyDE8zHgez3lRsQpwJeAC8udKnp8l+q+rZIGiYmX1IXKPUa3o7pg4AE1y5cvrRg3RsTZwPI16/5VM71vREztVea+wFbAjyPi+oio3XetiLi2TG9WWto2KPP/KFfEXzMifh4RV5fHdmX9ChHxg7LsbxGxVx/12T0iroyINSJitzJ9XUT8rNS1pyXrc2X5TT0tfRGxekRcXMo+hT7uyRYRJwDLl3r9uI/zcXQp84aybe2+S0XE6RHxhZLEfLnU5caIeH/Z7ASqGxJfHxEf7bX/ThFxeUT8IiJuiYiTI2Kpnhgi4vMR8Vdgm9pWtoh4U6nrDRFxabPnsvgv4MIy/Uaq2+d8HngT8L81210B7BoRS9cpR9JiMvGSutPewIWZeQfwcERsUZZ/AHgyM8cDXwS2bLbAzDyX6qr+78jMCZn5VM26OcDoiFgZ2L5st31EbEh10/UngW8CX8/M11L94T+t7H4s1a2pXgvsDHw5IlboKTsi3kZ1s/K3lEWfBnbNzC3KcT5WE+ZDZfl3gaPKsuOA6Vnd+Px8YIM+6jYFeKrU6x216yLizVTnc+vM3Aw4sWb10lQ3lb4jMz9Nleg+WuryWuB95fYjU4ArSvlf7+P0TgQ+DryGKgnapyxfAbg5M7fOzOk1Ma1J1WL1XyWm/Zo5l2XfjYBHMvPfZdHTVHdoWK6ci5tqzst/qK5sv1kfMUtaAv4XI3WnA4FvlOmzyvx1wA7ASQCZeWNE3DiIx/wzVSvbDlTdVm+ial26oqzfFdgk4rkGp5UjYiVgN6obs/ckSqN5PjnamaqVbbfMfCwi9gA2Af5UylkWuLImhp6b317L88nLDj3TmfnbiHhkMeu1K/DDkjySmQ/XrDsFOCczv1jmdwPGl9ZBgFWo7vn2dINjXJWZdwNExE+pbmtyLvAs8PM+tn8dcHlm3tMrpnrn8taafdcB5vbMZObvI+JnwEeB90TEyZn53Zrt51B1L1/boA6SmmDiJXWZiFid6uavm0ZEAqOAjIiesTr17hNWu3z0Ehz6CqrWrg2pbjD9yVJmzyDupYBtalvKSrxB1XJze6/lWwN3Ay8BNqZq3Qrgksw8sE4MPa04z7Lo99tA7o0W/ez/Z2DniPhqZi4o2x6RmRctUkDETg2O0bv8nvkFmfnsYsTU57ns5Sl6vb6ZeUJE/JPqfJ8UETdnZk/CPLrsI2kQ2NUodZ99gR9l5oaZOS4z1wfuoWpFuRx4B0BEbAqMr9lvdkS8qowveludsh8HVqqz7nLgIODO0kX1MFX34J/K+ouBw3s2jogJZfIi4IiSgBERm9eUeS9Va9WPIuLVwF+A7SLiZWXbF0XExv2ejUXr/GaqbrW+LIyIZfpYfjFVS9CLShmr1az7PvA74GdlHNRFwAd6yomIjUtXX3/nDWBiRGxUzv3+VD+K6M+VwI6l27A2pv7OZY87qAb7U7bZsGecHNXNsh/sFevGwN8bxCOpSSZeUvc5EPhFr2U/p/pV3XeBFUsX49HAVTXbTKFqnboMmFWn7KnAydFrcD1AZs4ok5eX5+nA/Mzs6do7EtiqDDq/BTisLD8eWAa4MSJuLvO15d5OlTj9DFiZ6peVPy11+AtQ93IZxeeAHSLiOqquuPvqbHdqieHHvY5/IdXYsGsi4nqeHzvWs/5rVN24Z1CNW7sFuK7U5RSqlrcbgWfKQPhFBtcXV1INwL+ZKknu/fotIjPnAocC50XEDcDZZVW/57Ls+wTwj57klarr8QKqX3VeQNW1eBFARKxNNfat3vtB0mKKzIG0wEuSBqJ0Qx6VmXu08ZhvA7YsPwjoWXYwMK0mgaYkiY9l5vfbFZvU7RzjJUkjTGb+oowFrHU9ML/XsvlULXmSBoktXpIkSW3iGC9JkqQ2MfGSJElqExMvSZKkNjHxkiRJahMTL0mSpDYx8ZIkSWqT/w/NTD/Qd4a+5wAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -671,7 +679,7 @@ "outputs": [ { "data": { - "image/png": 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nvwWS9ibdRtOWuvtAGZycNeZw0lNuz0bEPysNcCbpy/9fpJutjyDV2BxMoUYhIp4gnfj+RnqKr+57XSJiKunk81SuXt2ozqi3ki6zPFvoFumeo4rPk3bSKTmuy8mXySLiKtKlr0skzSX9Mtq7RjzPkhK0/5Z0ZKPT1VG5UfQhUu1Oda1LtZ+RviznSDq2xvDPkU6UU0n3/yz2QtaIuJH0a26ipG1JtTDTgLtz/H8j3bOyJOv+ItKX6CukG2wPbWc5io4FHiY98fUKaV2ukk/0B5BObC+RfsV9m/rH6PmkJLHyGov3gJMlPUd6IOVa0j2RzwBI2pD04+LqJYi1roh4MiLuqzP4aFLNy1Okff0iUlLaiF8BI5XeJXgGaX+5lpQsPEOqeWw3Ec4xTiE9GXsX6UtgS9Llxnp+DNxHqpV8mLSPLtWLZSO94uQK0n2gdffziLiW9JDKzaT98uY2it2UtL++QVqms/MPEGj/WKnnAtL9SP8kXXI/Jsf1Gunhp/GkmqA3SU+3VVReBvuypAdqlHtuLnsSqZZ1Hmm/6AjNKPtXpAeFbpD0OunG8MqPnQ+Qzp1zSbVDt/J+0j6KdP/XTOAq4IR8zqmner2tQroVYSbpfLAzab2XZimOm+rpzyN9392s9Pqput9DpP39UeCfkmYvVthSrp98qfkY0o+fV0m3YUxsZ7K29oHlrvJUg5k1QOnt+zMi4nstEMtPSa9R+WVV/wkRcURVv9OBJyPi7OUY4kot1/JtFhGHlR2Lma1YWu/Fa2bWkIho78b94rjfamYstihJ65EucX2u7FjMbMXjy5pmnUx1rZktX5K+RLr0em1ETCo7HjNb8fiyppmZmVkLcc2ZmZmZWQtxcmZmZmbWQjrVAwG9evWKgQMHlh2GmZmZraieyX9AMWBA2+N1gPvvv392RCz2LzWdKjkbOHAg991X79VLZmZmZu0488z0OWZM02clqeZftnWq5MzMzMxsmSyHpKw9vufMzMzMrIU4OTMzMzOrOO641JTIlzXNrCnmz5/PjBkzmDdvXtmhWNatWzf69evHqquuWnYoZq1rzpyyI3ByZmbNMWPGDHr06MHAgQORVHY4K72I4OWXX2bGjBkMGjSo7HDMrA2+rGlmTTFv3jzWX399J2YtQhLrr7++azLNVgBOzsysaZyYtRZvD7MVQ9OSM0kbS/q7pMckPSrpazXGkaQzJE2TNFnSNoVhe0l6PA87vllxmlnn1aVLF4YMGcJWW23FNttsw5133gnAzJkzGTly5BKVtcsuu9C/f3+K/0d84IEH0r179w6NGeCWW25ZGGtbJk6cyMknn9zh8zdbqQ0blpoSNfOeswXAtyLiAUk9gPsl3RgRUwrj7A1smpvtgN8A20nqApwF7AHMAO6VNLFqWjOzNq2xxho8+OCDAFx//fV85zvf4dZbb2WjjTbi8ssvX+LyevbsyR133MHw4cOZM2cOs2bN6uiQgZScde/enR122KHN8fbff3/233//psRgttI68siyI2hezVlEzIqIB3L768BjQN+q0Q4Azo/kbqCnpA2BYcC0iHgqIt4FLsnjmpktlblz57LuuusCMH36dLbYYgsA3nrrLT772c8yePBgDj74YLbbbru6/zRyyCGHcMkllwBw5ZVX8pnPfGbhsIjg29/+NltssQVbbrkll156KZASrX333XfheGPGjGHChAlA+leTE044gW222YYtt9ySqVOnMn36dMaOHcsvfvELhgwZwm233caf//xntttuO7beemt23313XnjhBQAmTJjAmPzCzCOOOIJjjjmGHXbYgU022WSpkk8zaw3L5WlNSQOBrYF/VA3qCzxX6J6R+9Xqv13zIjSzzujtt99myJAhzJs3j1mzZnHzzTcvNs7ZZ5/Nuuuuy+TJk3nkkUcYMmRI3fJ22203vvSlL/Hee+9xySWXMG7cOE466SQgJWsPPvggDz30ELNnz+bf/u3fGDFiRLsx9urViwceeICzzz6b0047jfHjx3PUUUfRvXt3jj32WABeffVV7r77biQxfvx4TjnlFE4//fTFypo1axa33347U6dOZf/991/iS7dmBhxzTPo844zSQmh6ciapO3AF8PWImFs9uMYk0Ub/WuWPBkYD9O/ffxkibczA4/+6zGVMP/lTHRBJ59Aq67NV4ujURo9evN8ee8BBB8G8ee+fEIv22y81c+Ys/lLIcePanWXxsuZdd93F5z//eR555JFFxrn99tv52tfSLbFbbLEFgwcPrltely5dGD58OJdeeilvv/02AwcOXKScUaNG0aVLF/r06cPOO+/Mvffey9prr91mjJXat2233ZYrr7yy5jgzZszg4IMPZtasWbz77rt1X4Vx4IEHssoqq7D55psvrF0zsyXUAk80N/VpTUmrkhKzP0RErbPODGDjQnc/YGYb/RcTEeMiYmhEDO3de7E/djczA2D77bdn9uzZvPTSS4v0L97g34hDDjmEo48+ms9+9rMNldO1a1f+9a9/LeyufpXF6quvDqTEb8GCBTXLOProoxkzZgwPP/ww55xzTt3XYVTKaiseM2t9Tas5U3pm+3fAYxHx8zqjTQTGSLqEdNnytYiYJeklYFNJg4DngUOA/2hWrGa2HLRV09WtW9vDe/ZsqKasLVOnTuW9995j/fXX56233lrYf/jw4Vx22WXsuuuuTJkyhYcffrjNcnbaaSe+853vMGrUqEX6jxgxgnPOOYfDDz+cV155hUmTJnHqqacyf/58pkyZwjvvvMO8efO46aabGD58eJvz6NGjB3Pnvn+h4bXXXqNv33TL7nnnnbeki25mK5hmXtbcEfgc8LCkB3O//wH6A0TEWOAaYB9gGvAW8IU8bIGkMcD1QBfg3Ih4tImxmlknVLnnDFJN0nnnnUeXLl0WGecrX/kKhx9+OIMHD2brrbdm8ODBrLPOOnXLlLTwXrCiT3/609x1111stdVWSOKUU07hAx/4AMDCBw423XRTtt5663bj3m+//Rg5ciR/+tOf+PWvf82JJ57IQQcdRN++ffn4xz/O008/vSSrwcxWMOpMVd9Dhw6Nek9ZdRTfm9SxWmV9tkocncljjz3GRz/60bLDaNd7773H/Pnz6datG08++SS77bYbTzzxBKuttlrZoTXFirJdzEpzwQXp83Ofa/qsJN0fEUOr+/u/Nc1spfbWW2+x6667Mn/+fCKC3/zmN502MTOzBiyHpKw9Ts7MbKXWo0ePuu81MzMrg/9b08zMzKxi9Ojar/5ZjpycmVnTdKZ7WjsDbw+zFYOTMzNrim7duvHyyy87IWgREcHLL79Mt27dyg7FzNrhe87MrCn69evHjBkzFnvpq5WnW7du9OvXr+wwzKwdTs7MrClWXXXVun8zZGZm9Tk5MzMzM6vYY4+yI3ByZmZmZrbQQQeVHYEfCDAzMzNbaN681JTINWdmZmZmFccckz7HjSstBNecmZmZmbUQJ2dmZmZmLcTJmZmZmVkLcXJmZmZm1kL8QICZmZlZxX77lR2BkzMzMzOzhVogOfNlTTMzM7OKOXNSUyLXnJmZmZlVHHdc+vR7zszMzMwMnJyZmZmZtRQnZ2ZmZmYtxMmZmZmZWQtp2gMBks4F9gVejIgtagz/NnBoIY6PAr0j4hVJ04HXgfeABRExtFlxmpmZmS00cmTZETT1ac0JwJnA+bUGRsSpwKkAkvYDvhERrxRG2TUiZjcxPjMzM7NF7bln2RE077JmREwCXml3xGQUcHGzYjEzMzNryAsvpKZEpd9zJmlNYC/gikLvAG6QdL+k0eVEZmZmZiud738/NSVqhZfQ7gfcUXVJc8eImClpA+BGSVNzTdxicvI2GqB///7Nj9bMzMysiUqvOQMOoeqSZkTMzJ8vAlcBw+pNHBHjImJoRAzt3bt3UwM1MzMza7ZSkzNJ6wA7A38q9FtLUo9KO7An8Eg5EZqZmZktX818lcbFwC5AL0kzgBOAVQEiYmwe7dPADRHxZmHSPsBVkirxXRQR1zUrTjMzM7NW0rTkLCJGNTDOBNIrN4r9ngK2ak5UZmZmZm047LCyI2iJBwLMzMzMWsOIEWVH0BIPBJiZmZm1hmeeSU2JXHNmZmZmVvGTn6TPceNKC8E1Z2ZmZmYtxMmZmZmZWQtxcmZmZmbWQpycmZmZmbUQPxBgZmZmVnHkkWVH4OTMzMzMbKFhdf/Oe7nxZU0zMzOziieeSE2JXHNmZmZmVnHaaenT7zkzMzMzM3ByZmZmZtZSnJyZmZmZtRAnZ2ZmZmYtxA8EmJmZmVWMGVN2BE7OzMzMzBYaPLjsCHxZ08zMzGyhyZNTUyLXnJmZmZlVnHlm+vR7zszMzMwMnJyZmZmZtRQnZ2ZmZmYtxMmZmZmZWQvxAwFmZmZmFcceW3YEzas5k3SupBclPVJn+C6SXpP0YG5+UBi2l6THJU2TdHyzYjQzMzNbxGabpaZEzbysOQHYq51xbouIIbn5EYCkLsBZwN7A5sAoSZs3MU4zMzOz5J57UlOipl3WjIhJkgYuxaTDgGkR8RSApEuAA4ApHRedmZmZWQ3jx6fPYcNKC6HsBwK2l/SQpGslfSz36ws8VxhnRu5nZmZm1umV+UDAA8CAiHhD0j7A1cCmgGqMG/UKkTQaGA3Qv3//ZsRpZmZmttyUVnMWEXMj4o3cfg2wqqRepJqyjQuj9gNmtlHOuIgYGhFDe/fu3dSYzczMzJqttORM0gckKbcPy7G8DNwLbCppkKTVgEOAiWXFaWZmZrY8Ne2ypqSLgV2AXpJmACcAqwJExFhgJPBfkhYAbwOHREQACySNAa4HugDnRsSjzYrTzMzMbKHvfrfsCJr6tOaodoafCZxZZ9g1wDXNiMvMzMysrgEDyo6g9Kc1zczMzFrHpEmpKZH/vsnMzMys4sIL0+eIEaWF4JozMzMzsxbi5MzMzMyshTg5MzMzM2shTs7MzMzMWogfCDAzMzOrOOmksiNwcmZmZma2UJ8+ZUfgy5pmZmZmC91wQ2pK5JozMzMzs4rLL0+fe+5ZWgiuOTMzMzNrIU7OzMzMzFqIkzMzMzOzFuLkzMzMzKyF+IEAMzMzs4pTTik7AidnZmZmZgv17Fl2BL6saWZmZrbQn/+cmhI5OTMzMzOrcHJmZmZmZkVOzszMzMxaiJMzMzMzsxbi5MzMzMyshfhVGmZmZmYVZ5xRdgTNqzmTdK6kFyU9Umf4oZIm5+ZOSVsVhk2X9LCkByXd16wYzczMzBbRrVtqStTMy5oTgL3aGP40sHNEDAZOAsZVDd81IoZExNAmxWdmZma2qD/+MTUlalpyFhGTgFfaGH5nRLyaO+8G+jUrFjMzM7OG3HhjakrUKg8EfBG4ttAdwA2S7pc0uqSYzMzMzJa70h8IkLQrKTkbXui9Y0TMlLQBcKOkqbkmrtb0o4HRAP379296vGZmZmbNVGrNmaTBwHjggIh4udI/ImbmzxeBq4Bh9cqIiHERMTQihvbu3bvZIZuZmZk1VWnJmaT+wJXA5yLiiUL/tST1qLQDewI1n/g0MzMz62zavayZX2Xxe+Ciwg387ZJ0MbAL0EvSDOAEYFWAiBgL/ABYHzhbEsCC/GRmH+Cq3K9rnu91S7BMZmZmZktnXPXLI5a/Ru45OwT4AnBvIVG7ISKirYkiYlQ7w48EjqzR/ylgq8WnMDMzM+v82r2sGRHTIuK7wGbARcC5wLOSfihpvWYHaGZmZrbcXHBBakrU0D1n+cb904FTgSuAkcBc4ObmhWZmZma2nN12W2pK1Mg9Z/cDc4DfAcdHxDt50D8k7djM4MzMzMxWNo3cc3ZQvg9sMRHxmQ6Ox8zMzGyl1shlzSMl9ax0SFpX0o+bGJOZmZnZSquR5GzviJhT6civ09ineSGZmZmZlaRbt9SUqJHLml0krV6510zSGsDqzQ3LzMzMrARnnFF2BA0lZxcCN0n6PekPyf8TOK+pUZmZmZmtpNpNziLiFEkPA7sBAk6KiOubHpmZmZnZ8jZ+fPo8crH35C83jdScERHXAtc2ORYzMzOzct1zT/osMTlr94EASZ+R9H+SXpM0V9LrkuYuj+DMzMzMVjaN1JydAuwXEY81OxgzMzOzlV0jr9J4wYmZmZmZ2UwLDEkAABldSURBVPLRSM3ZfZIuBa4GKn/dRERc2bSozMzMzMrQs2f74zRZI8nZ2sBbwJ6FfgE4OTMzM7PO5ZRTyo6goVdpfGF5BGJmZmZmjT2tuZmkmyQ9krsHS/pe80MzMzMzW87OPDM1JWrkgYDfAt8B5gNExGTgkGYGZWZmZlaKyZNTU6JGkrM1I+Keqn4LmhGMmZmZ2cqukeRstqQPkh4CQNJIYFZTozIzMzNbSTXytOZXgXHARyQ9DzwNHNbUqMzMzMxWUo08rfkUsLuktYBVIuL15odlZmZmVoI+fcqOoP3kTNIPqroBiIgfNSkmMzMzs3KcdFLZETR0WfPNQns3YF/Af+dkZmZm1gTtPhAQEacXmp8AuwB925tO0rmSXqy8H63GcEk6Q9I0SZMlbVMYtpekx/Ow45dgeczMzMyW3umnp6ZEjTytWW1NYJMGxpsA7NXG8L2BTXMzGvgNgKQuwFl5+ObAKEmbL0WcZmZmZkvm8cdTU6JG7jl7mPwaDaAL0Bto936ziJgkaWAboxwAnB8RAdwtqaekDYGBwLT8IAKSLsnjTmlvnmZmZmYrukbuOdu30L4AeCEiOuIltH2B5wrdM3K/Wv23q1eIpNGkmjf69+/fAWGtGAYe/9dlLmP6yZ/qgEjK1xHrwqzVdaZjvjMti7WeZd2/fnrPs/zHsHLziUYua75eaN4G1pa0XqVZhnmrRr9oo39NETEuIoZGxNDevXsvQzhmZmZm5Wuk5uwBYGPgVVLi1BN4Ng8LGrv/rJYZudyKfsBMYLU6/c3MzMya6vm1N4ABA0qNoZGas+uA/SKiV0SsT7rMeWVEDIqIpU3MACYCn89PbX4ceC0iZgH3AptKGiRpNdKfrE9chvmYmZmZNeSsHQ6G73631BgaqTn7t4g4qtIREddKavcNbZIuJr12o5ekGcAJwKq5jLHANcA+wDTgLeALedgCSWOA60kPIJwbEY8uyUKZmZmZragaSc5mS/oecCHpMuZhwMvtTRQRo9oZHqT/7aw17BpS8mZmZma23Hz1zkvhJw+WWnvWyGXNUaTXZ1yVm965n5mZmVmn0nfui/DMM6XG0Mgfn78CfE1S94h4YznEZGZmZrbSarfmTNIOkqaQXwIraStJZzc9MjMzM7OVUCOXNX8BfJJ8n1lEPASMaGZQZmZmZiurhv5bMyKeq+r1XhNiMTMzMyvVU+v1hQ9/uNQYGknOnpO0AxCSVpN0LPBYk+MyMzMzW+7GD/sMfOtbpcbQSHJ2FOmVF31Jb/UfQp1XYJiZmZnZsmkzOZPUBfhlRBwaEX0iYoOIOCwi2n3PmZmZmdmK5puTLoDvf7/UGNpMziLiPaB3/hslMzMzs06t11tz4IUXSo2hkX8ImA7cIWki8GalZ0T8vFlBmZmZma2s6tacSbogtx4M/CWP26PQmJmZmVkHa6vmbFtJA4BngV8vp3jMzMzMVmptJWdjgeuAQcB9hf4i/QH6Jk2My8zMzGy5m9p7IAzeotQY6l7WjIgzIuKjwO8jYpNCMyginJiZmZlZp3P+tvvBmDGlxtDue84i4r+WRyBmZmZm1uDfN5mZmZmtDI7/+7lw3HGlxuDkzMzMzCxb+503Yc6cUmNwcmZmZmbWQpycmZmZmbUQJ2dmZmZmLcTJmZmZmVn20IabwbBhpcbg5MzMzMwsu3SrT8KRR5Yag5MzMzMzsxbS1ORM0l6SHpc0TdLxNYZ/W9KDuXlE0nuS1svDpkt6OA+7b/HSzczMzDrWCX87B445ptQY2vpvzWUiqQtwFrAHMAO4V9LEiJhSGSciTgVOzePvB3wjIl4pFLNrRMxuVoxmZmZmRasveBfmzSs1hmbWnA0DpkXEUxHxLnAJcEAb448CLm5iPGZmZmYtr5nJWV/guUL3jNxvMZLWBPYCrij0DuAGSfdLGt20KM3MzMxaSNMuawKq0S/qjLsfcEfVJc0dI2KmpA2AGyVNjYhJi80kJW6jAfr377+sMZuZmZmVqpk1ZzOAjQvd/YCZdcY9hKpLmhExM3++CFxFuky6mIgYFxFDI2Jo7969lzloMzMzW3nd2+9jsNNOpcbQzOTsXmBTSYMkrUZKwCZWjyRpHWBn4E+FfmtJ6lFpB/YEHmlirGZmZmZctcUn4HOfKzWGpl3WjIgFksYA1wNdgHMj4lFJR+XhY/OonwZuiIg3C5P3Aa6SVInxooi4rlmxmpmZmbWKZt5zRkRcA1xT1W9sVfcEYEJVv6eArZoZm5mZmVm1n173a3jlTzBuXGkx+B8CzMzMzFqIkzMzMzOzFuLkzMzMzKyFODkzMzMzayFOzszMzMyy2wduDXvsUWoMTX1a08zMzGxFcs1HhsNBnyo1BtecmZmZmWWrL3gX5s0rNQYnZ2ZmZmbZCX87B445ptQYnJyZmZmZtRAnZ2ZmZmYtxMmZmZmZWQtxcmZmZmbWQpycmZmZmWU3fWgY7LdfqTH4PWdmZmZm2U0f2g7283vOzMzMzFrC2vPegDlzSo3ByZmZmZlZdvwtv4fjjis1BidnZmZmZi3EyZmZmZlZC3FyZmZmZtZCnJyZmZmZtRAnZ2ZmZmbZtR/eEUaOLDUGJ2dmZmZm2W2DtoE99yw1BidnZmZmZlmvN1+FF14oNYamJmeS9pL0uKRpko6vMXwXSa9JejA3P2h0WjMzM7OO9s3bLoTvf7/UGJr2902SugBnAXsAM4B7JU2MiClVo94WEfsu5bRmZmZmnUoza86GAdMi4qmIeBe4BDhgOUxrZmZmtsJqZnLWF3iu0D0j96u2vaSHJF0r6WNLOK2ZmZlZp9K0y5qAavSLqu4HgAER8YakfYCrgU0bnDbNRBoNjAbo37//0kdrZmZm1gKaWXM2A9i40N0PmFkcISLmRsQbuf0aYFVJvRqZtlDGuIgYGhFDe/fu3ZHxm5mZ2Urm6o/tCocdVmoMzUzO7gU2lTRI0mrAIcDE4giSPiBJuX1YjuflRqY1MzMz62j3bLwFjBhRagxNu6wZEQskjQGuB7oA50bEo5KOysPHAiOB/5K0AHgbOCQiAqg5bbNiNTMzMwPo+9qL8MwzMGBAaTE0856zyqXKa6r6jS20nwmc2ei0ZmZmZs301bsuhZ/cBePGlRaD/yHAzMzMrIU4OTMzMzNrIU7OzMzMzFqIkzMzMzOzFuLkzMzMzCy7dPCecOSRpcbg5MzMzMwse2ijD8OwYaXG4OTMzMzMLBv0yvPwxBOlxuDkzMzMzCz70j1XwmmnlRqDkzMzMzOzFuLkzMzMzKyFODkzMzMzayFOzszMzMxaiJMzMzMzs+z8bfaFMWNKjcHJmZmZmVk2dYNBMHhwqTE4OTMzMzPLPvLi0zB5cqkxODkzMzMzyz7/wF/gzDNLjcHJmZmZmVkLcXJmZmZm1kKcnJmZmZm1ECdnZmZmZi3EyZmZmZlZ9tthn4Fjjy01BidnZmZmZtnT6/WFzTYrNQYnZ2ZmZmbZVjMfh3vuKTUGJ2dmZmZm2cGTb4Dx40uNoanJmaS9JD0uaZqk42sMP1TS5NzcKWmrwrDpkh6W9KCk+5oZp5mZmVmr6NqsgiV1Ac4C9gBmAPdKmhgRUwqjPQ3sHBGvStobGAdsVxi+a0TMblaMZmZmZq2mmTVnw4BpEfFURLwLXAIcUBwhIu6MiFdz591AvybGY2ZmZtbympmc9QWeK3TPyP3q+SJwbaE7gBsk3S9pdL2JJI2WdJ+k+1566aVlCtjMzMysbE27rAmoRr+oOaK0Kyk5G17ovWNEzJS0AXCjpKkRMWmxAiPGkS6HMnTo0Jrlm5mZmTXirO0P5j+O/0SpMTSz5mwGsHGhux8ws3okSYOB8cABEfFypX9EzMyfLwJXkS6TmpmZmTXN8+tsAAMGlBpDM5Oze4FNJQ2StBpwCDCxOIKk/sCVwOci4olC/7Uk9ai0A3sCjzQxVjMzMzOGPfcITFrsQt1y1bTLmhGxQNIY4HqgC3BuRDwq6ag8fCzwA2B94GxJAAsiYijQB7gq9+sKXBQR1zUrVjMzMzOAAx/9O1z4JIwYUVoMzbznjIi4Brimqt/YQvuRwJE1pnsK2Kq6v5mZmVln538IMDMzM2shTs7MzMzMWoiTMzMzM7MW4uTMzMzMLPv5TofBSSeVGoOTMzMzM7Ns9lrrQp8+pcbg5MzMzMws2+npB+CGG0qNwcmZmZmZWbb343fA5ZeXGoOTMzMzM7MW4uTMzMzMrIU4OTMzMzNrIU7OzMzMzFqIkzMzMzOz7ORdvgCnnFJqDE7OzMzMzLK53bpDz56lxuDkzMzMzCzbbdo/4M9/LjUGJ2dmZmZm2W7T7nFyZmZmZmbvc3JmZmZm1kKcnJmZmZm1ECdnZmZmZi3EyZmZmZlZ9sPdvwxnnFFqDE7OzMzMzLJ3uq4G3bqVGoOTMzMzM7Nsn6m3wx//WGoMTs7MzMzMsuHT/xduvLHUGJqanEnaS9LjkqZJOr7GcEk6Iw+fLGmbRqc1MzMz64yalpxJ6gKcBewNbA6MkrR51Wh7A5vmZjTwmyWY1szMzKzTaWbN2TBgWkQ8FRHvApcAB1SNcwBwfiR3Az0lbdjgtGZmZmadTjOTs77Ac4XuGblfI+M0Mq2ZmZlZp9O1iWWrRr9ocJxGpk0FSKNJl0QB3pD0eMMRLh+9gNnFHvp/JUVSpVXi6AR6AbO9Pju1xY7jFVFn2kc7eFk6xfa1NjW8jQ8FDn0I+O1vmxpQNqBWz2YmZzOAjQvd/YCZDY6zWgPTAhAR44Bxyxpss0i6LyKGlh2HNY+3cefnbdy5eft2fivaNm7mZc17gU0lDZK0GnAIMLFqnInA5/NTmx8HXouIWQ1Oa2ZmZtbpNK3mLCIWSBoDXA90Ac6NiEclHZWHjwWuAfYBpgFvAV9oa9pmxWpmZmbWKpp5WZOIuIaUgBX7jS20B/DVRqddQbXsJVfrMN7GnZ+3cefm7dv5rVDbWCk/MjMzM7NW4L9vMjMzM2shTs46mKQukv5X0l9y93qSbpT0f/lz3bJjtKUnabqkhyU9KOm+3M/buBOR1FPS5ZKmSnpM0vbexp2HpA/n47fSzJX0dW/jzkPSNyQ9KukRSRdL6raibV8nZx3va8Bjhe7jgZsiYlPgptxtK7ZdI2JI4bFsb+PO5VfAdRHxEWAr0vHsbdxJRMTj+fgdAmxLehjtKryNOwVJfYFjgKERsQXpocJDWMG2r5OzDiSpH/ApYHyh9wHAebn9PODA5R2XNZ23cSchaW1gBPA7gIh4NyLm4G3cWe0GPBkRz+Bt3Jl0BdaQ1BVYk/Se1BVq+zo561i/BI4D/lXo1ye/u438uUEZgVmHCeAGSffnf6cAb+POZBPgJeD3+faE8ZLWwtu4szoEuDi3ext3AhHxPHAa8Cwwi/T+1BtYwbavk7MOImlf4MWIuL/sWKypdoyIbYC9ga9KGlF2QNahugLbAL+JiK2BN2nxyx+2dPILzvcH/lh2LNZx8r1kBwCDgI2AtSQdVm5US87JWcfZEdhf0nTgEuATki4EXpC0IUD+fLG8EG1ZRcTM/Pki6T6VYXgbdyYzgBkR8Y/cfTkpWfM27nz2Bh6IiBdyt7dx57A78HREvBQR84ErgR1Ywbavk7MOEhHfiYh+ETGQVFV+c0QcRvrbqcPzaIcDfyopRFtGktaS1KPSDuwJPIK3cacREf8EnpP04dxrN2AK3sad0Sjev6QJ3sadxbPAxyWtKUmkY/gxVrDt65fQNoGkXYBjI2JfSesDlwH9STvNQRHxSpnx2dKRtAmptgzS5a+LIuIn3sadi6QhpId6VgOeIv2t3Cp4G3caktYEngM2iYjXcj8fx52EpB8CBwMLgP8FjgS6swJtXydnZmZmZi3ElzXNzMzMWoiTMzMzM7MW4uTMzMzMrIU4OTMzMzNrIU7OzMzMzFqIkzOz5UDSpyWFpI+0Mc4tkobWG57HOVHSsbn9CEkbdXSsdeZ7hKQzm1DudEm9avT/n6ruO9spp911Vxh3oKT/WLJIa5YzXtLmHVDOhpL+UtXvCEkDq/pdImnTpSg/JF1Q6O4q6aXqeS5BeT0lfWVppm02SW+UHYNZR3ByZrZ8jAJuJ72guKMcQfp7ks5okeQsInbowLIHAsuUnEnqEhFHRsSUDojnm8BvK+VKOgf4KXBd/peRit+Q/rt3Sb0JbCFpjdy9B/D8MsTbE2jJ5Myss3ByZtZkkrqT/t7rixSSM0lr5NqQyZIuBdYoDHuj0D5S0oSqMkcCQ4E/SHqw8MWLpA0k3Z/bt8o1J/1z95P5zdm9JV0h6d7c7JiHryXp3NzvfyUdUGN5PiXpLkm9JO2Z2x+Q9Me8rJUasR/m/g9XagwlrS/phlz2OYBqlH8ysEZerj/UWB/H5TIfyuMWp11F0nmSfpwTnVPzskyW9OU82snATrn8b1RNv4ukSZKukjRF0lhJq1RikPQjSf8Ati/W1knaKy/rQ5JuanRdZv8OXJfbPwl8EPgRsBfw/wrj3QbsLqlrnXLaci3wqdy+yJvxJa0n6eq8ju6WNDj3PzHHf4ukpyQdU1h/H8zr71RJ3SXdVNjWB+TpB0p6TNJvJT2at/saediX8np5KO+Ha1YHvBRxFae9oLi+Jf1B0v5Lsd7MyhERbty4aWIDHAb8LrffCWyT278JnJvbB5PeZj00d79RmH4kMCG3n0j69wmAWyrj15jno8DawBjgXuBQYABwVx5+ETA8t/cHHsvtPwUOy+09gSeAtUi1dGcCnyYlCesCvYBJwFp5/P8GfpDbpwNH5/avAONz+xmFcT4FBNCrRvxv1Oom/R/incCauXu9wrr4OCnp+G7uNxr4Xm5fHbiP9GfIuwB/qbPedgHmAZsAXYAbgZF5WACfLYx7CylB7k162/ygqphqrsuq+Q0C7i907w7cD3wNGFgjvhuBbZdw/3uDtH9dDnQDHiyuA+DXwAm5/RPAg4V97c687noBLwOrkmoeHymU3xVYO7f3AqaRku6BpH16SB52WWF9rF+Y/seVfaUq7iWKq2o/2Rm4OrevAzwNdC37XODGTaPN0vwCM7MlMwr4ZW6/JHc/AIwgJStExGRJkztwnneSautGkJKEvUhfmLfl4bsDm0sLK67WVvrf0D2B/ZXvayN9mffP7buSkpE9I2KupH2BzYE7cjmrAXcVYrgyf94PfCa3j6i0R8RfJb26hMu1O/D7iHgrl1H8+5VzgMsi4ie5e09gcK5lhPQlvSnwbjvzuCcingKQdDEwnJTYvAdcUWP8jwOTIuLpqpjqrcvHCtNuCLxU6YiIv0n6I/AN4D8ljY2I3xTGf5F0Kfv+dpZhEXn/Gkja966pGjycVHtHRNycazfXycP+GhHvAO9IehHoU6N4AT+VNAL4F9C3MN7TEfFgbr+flLBBusz6Y1LS2h24vka5SxrXjMLy3irpLEkbkPa3KyJiQf01ZNZanJyZNZHS//V9gvRlFKTamJBUuXeo3v+nFft3W4pZ3wbsRKot+xOpViuAyk3gqwDbR8TbVfEK+PeIeLyq/3ak/5ncBNiMVAsl4MaIGFUnhnfy53sseq5Zlv+MUxvT3wnsKun0iJiXxz06Ihb54lf679u2VJdf6Z4XEe8tQUw112WVt6navhFxsqR/ktb3GZIeiYhKUt0tT/P+TNK2OSd3/iAiJtaZ10TgNFKt2fpVcVarLM87hX7V27HiUFLt4bYRMV/S9MIyVU9fufw+ATgwIh6SdESOqdqyxnVBju0Q4D9rDDdrWb7nzKy5RgLnR8SAiBgYERuTLrEMJ10SPBRA0hakS08VL0j6aL7f6dN1yn4d6FFn2CTS5dT/i4h/Aa8A+wB35OE3kC55kuc/JLdeDxydkzQkbV0o8xlSLcT5kj4G3A3sKOlDedw1JW3W5tpYdJn3Jl0erWW+pFVr9L+BVKO0Zi5jvcKw35Fqhf6Y78u6HvivSjmSNpO0Fm2vN4BhkgbldX8w6UGOttwF7CxpUFVMba3Liid4vzYJSQOU79sjXSqdWRXrZqRL1gtFxD8iYkhu6iVmAOcCP4qIh6v6F7fJLsDsiJjbRjnV628d4MWcmO1K+kHQnh7ArLxtDq0zzpLGVW0C8HWAiHi07VHNWouTM7PmGgVcVdXvCtLTgr8BuufLmccB9xTGOZ5Uy3UzMKtO2ROAsap6IAAgIqbn1kn583ZgTkRULiMeAwzNN1tPAY7K/U8i3Vc0WdIjubtY7uOkL8w/ku5pOwK4OC/D3UDdV4VkPwRGSHqAdNnv2Trjjcsx/KFq/teRaoDuk/QgcGzV8J+TLhlfAIwHpgAP5GU5h1TDMhlYkG9GX+SBgOwu0k3vj5AS6ertt4iIeIl0f9uVkh4CLs2D2lyXedo3gScrCS7pMue1pKdVryVdxrweQFIf4O2IqLc/tCkiZkTEr2oMOpG8L5CW+/B2ynmZdCn7EUmnAn/I099H2jemNhDO94F/kO6hqzf+EsVVI84XSJeQf78k05m1AkUsyxUGM7POI9fQHBsR+y7HeX6adEnwe4V+RwC3FJJsciI5NyJ+t7xiW5Hl2tWHSQ/gvFZ2PGZLwjVnZmYlioirSE+3Fj0IzKnqNwc4b3nEtKKTtDupRu7XTsxsReSaMzMzM7MW4pozMzMzsxbi5MzMzMyshTg5MzMzM2shTs7MzMzMWoiTMzMzM7MW4uTMzMzMrIX8f3hgqFucMJLwAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -772,7 +780,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -844,7 +852,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -1078,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1103,7 +1111,7 @@ " \n", " bm2 = X_bm.copy()\n", " for f, d in zip(features, deltas):\n", - " bm2[___] += ___\n", + " bm2[f] += d\n", " return model.predict(bm2).item() - model.predict(X_bm).item()" ] }, @@ -1182,9 +1190,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Code task 3#\n", "#Create two plots, side by side, for the predicted ticket price change (delta) for each\n", @@ -1193,12 +1214,12 @@ "#There are two things to do here:\n", "#1 - use a list comprehension to create a list of the number of runs closed from `runs_delta`\n", "#2 - use a list comprehension to create a list of predicted revenue changes from `price_deltas`\n", - "runs_closed = [-1 * ___ for ___ in runs_delta] #1\n", + "runs_closed = [-1 * i for i in runs_delta] #1\n", "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", "fig.subplots_adjust(wspace=0.5)\n", "ax[0].plot(runs_closed, price_deltas, 'o-')\n", "ax[0].set(xlabel='Runs closed', ylabel='Change ($)', title='Ticket price')\n", - "revenue_deltas = [5 * expected_visitors * ___ for ___ in ___] #2\n", + "revenue_deltas = [5 * expected_visitors * i for i in price_deltas] #2\n", "ax[1].plot(runs_closed, revenue_deltas, 'o-')\n", "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue');" ] @@ -1226,14 +1247,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "#Code task 4#\n", "#Call `predict_increase` with a list of the features 'Runs', 'vertical_drop', and 'total_chairs'\n", "#and associated deltas of 1, 150, and 1\n", - "ticket2_increase = ___(['Runs', ___, ___], [1, ___, ___])\n", + "ticket2_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs'], [1, 150, 1])\n", "revenue2_increase = 5 * expected_visitors * ticket2_increase" ] }, @@ -1272,13 +1293,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "#Code task 5#\n", "#Repeat scenario 2 conditions, but add an increase of 2 to `Snow Making_ac`\n", - "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', ___], [1, 150, 1, ___])\n", + "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', 'Snow Making_ac'], [1, 150, 1, 2])\n", "revenue3_increase = 5 * expected_visitors * ticket3_increase" ] }, @@ -1324,13 +1345,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Code task 6#\n", "#Predict the increase from adding 0.2 miles to `LongestRun_mi` and 4 to `Snow Making_ac`\n", - "predict_increase([___, ___], [___, ___])" + "predict_increase(['LongestRun_mi', 'Snow Making_ac'], [0.2, 4])" ] }, { @@ -1358,7 +1390,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**A: 1** Your answer here" + "**A: 1** Your answer here\n", + "\n", + "Now that the preprocessing for the data is complete and the random forest regressor model has been chosen, the task here has been to come up with a few scenarios of how the 8 key features could be manipulated to cause a change in the predicted ticket price. As expected, changing the most significant features had the biggest effect on the model's predicted price. It can also be noted that there needed to be a significant change made to the feature in order to see a real change in predicted price. For example, increasing the Snow Making_ac feature by just 4 acres alone (1% of a change) isn't enough to really affect ticket price. Vertical drop does seem to be quite sensitive to change with a change of 150 (6%) leading to a change of ticket price by approximately $1.99 (2.5% increase in price)." ] }, { @@ -1399,7 +1433,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.5" }, "toc": { "base_numbering": 1, diff --git a/models/ski_resort_pricing_model.pkl b/models/ski_resort_pricing_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..4712143f5dd1142da106a7ea8e68fd200dfc093d GIT binary patch literal 825353 zcmd3P349er^8bJwk;@3-5K*3p2#7J<$Th`%1VTc%1SAQ`3onw393YDNTrbdwsPVpn zqN{k}jrVvfqOz-au5Mgc*VSG3?W(J;?(Y9v-PO~tpYQ{I=f6I$rl-4WdaA3dkC~~i z7jJdNCr7sM7#h-RSC&VMYAQ3U%c`U0WtGv!hO}IBdU@l3vm4K7?BCG3wk}#-+jufg zJJyy}RF_9_sIQCGG&W?I0(44d!SbS76w)zYk1Qc_S>SyEP9R998gxS=7fwyvhAF1liEV?&#YXh~U7Wn)9T(z5b$eQje- zL)+ESn&nlvUAEWIs<^6pZR7feLrbGYb@esTg36+bXe|mXXl&?OSzl4THnX^@CYo7M 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