diff --git a/your-code/ImportExportLab.ipynb b/your-code/ImportExportLab.ipynb new file mode 100644 index 0000000..2f79811 --- /dev/null +++ b/your-code/ImportExportLab.ipynb @@ -0,0 +1,13631 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import and Export Files \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 0 - Accessing our database\n", + "Create a connection to access the sakila database with the ip, user and password provided in class. Take a look at the data. Do some joins in order to have more elaborated tables in a dataframe (you can try first the query in the DBMS and then use it in your notebook)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import mysql.connector\n", + "from getpass import getpass" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "········\n" + ] + } + ], + "source": [ + "cnx = mysql.connector.connect(user = \"root\",\n", + " password = getpass(),\n", + " host = \"localhost\",\n", + " database = \"sakila\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnx.is_connected()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "cursor = cnx.cursor()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "query = (\"\"\"SELECT * FROM sakila.film;\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.execute(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "results = cursor.fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1,\n", + " 'ACADEMY DINOSAUR',\n", + " 'A Epic Drama of a Feminist And a Mad Scientist who must Battle a Teacher in The Canadian Rockies',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('0.99'),\n", + " 86,\n", + " Decimal('20.99'),\n", + " 'PG',\n", + " {'Behind the Scenes', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 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Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (964,\n", + " 'WATERFRONT DELIVERANCE',\n", + " 'A Unbelieveable Documentary of a Dentist And a Technical Writer who must Build a Womanizer in Nigeria',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 61,\n", + " Decimal('17.99'),\n", + " 'G',\n", + " {'Behind the Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (965,\n", + " 'WATERSHIP FRONTIER',\n", + " 'A Emotional Yarn of a Boat And a Crocodile who must Meet a Moose in Soviet Georgia',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('0.99'),\n", + " 112,\n", + " Decimal('28.99'),\n", + " 'G',\n", + " {'Commentaries'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (966,\n", + " 'WEDDING APOLLO',\n", + " 'A Action-Packed Tale of a Student And a Waitress who must Conquer a Lumberjack in An Abandoned Mine Shaft',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('0.99'),\n", + " 70,\n", + " Decimal('14.99'),\n", + " 'PG',\n", + " {'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (967,\n", + " 'WEEKEND PERSONAL',\n", + " 'A Fast-Paced Documentary of a Car And a Butler who must Find a Frisbee in A Jet Boat',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 5,\n", + " Decimal('2.99'),\n", + " 134,\n", + " Decimal('26.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Commentaries', 'Deleted Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (968,\n", + " 'WEREWOLF LOLA',\n", + " 'A Fanciful Story of a Man And a Sumo Wrestler who must Outrace a Student in A Monastery',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('4.99'),\n", + " 79,\n", + " Decimal('19.99'),\n", + " 'G',\n", + " {'Behind the Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (969,\n", + " 'WEST LION',\n", + " 'A Intrepid Drama of a Butler And a Lumberjack who must Challenge a Database Administrator in A Manhattan Penthouse',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 159,\n", + " Decimal('29.99'),\n", + " 'G',\n", + " {'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (970,\n", + " 'WESTWARD SEABISCUIT',\n", + " 'A Lacklusture Tale of a Butler And a Husband who must Face a Boy in Ancient China',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 7,\n", + " Decimal('0.99'),\n", + " 52,\n", + " Decimal('11.99'),\n", + " 'NC-17',\n", + " {'Commentaries', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (971,\n", + " 'WHALE BIKINI',\n", + " 'A Intrepid Story of a Pastry Chef And a Database Administrator who must Kill a Feminist in A MySQL Convention',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 109,\n", + " Decimal('11.99'),\n", + " 'PG-13',\n", + " {'Behind the Scenes', 'Commentaries', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (972,\n", + " 'WHISPERER GIANT',\n", + " 'A Intrepid Story of a Dentist And a Hunter who must Confront a Monkey in Ancient Japan',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 59,\n", + " Decimal('24.99'),\n", + " 'PG-13',\n", + " {'Deleted Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (973,\n", + " 'WIFE TURN',\n", + " 'A Awe-Inspiring Epistle of a Teacher And a Feminist who must Confront a Pioneer in Ancient Japan',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('4.99'),\n", + " 183,\n", + " Decimal('27.99'),\n", + " 'NC-17',\n", + " {'Behind the Scenes', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (974,\n", + " 'WILD APOLLO',\n", + " 'A Beautiful Story of a Monkey And a Sumo Wrestler who must Conquer a A Shark in A MySQL Convention',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('0.99'),\n", + " 181,\n", + " Decimal('24.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Commentaries', 'Deleted Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (975,\n", + " 'WILLOW TRACY',\n", + " 'A Brilliant Panorama of a Boat And a Astronaut who must Challenge a Teacher in A Manhattan Penthouse',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('2.99'),\n", + " 137,\n", + " Decimal('22.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Commentaries', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (976,\n", + " 'WIND PHANTOM',\n", + " 'A Touching Saga of a Madman And a Forensic Psychologist who must Build a Sumo Wrestler in An Abandoned Mine Shaft',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('0.99'),\n", + " 111,\n", + " Decimal('12.99'),\n", + " 'R',\n", + " {'Commentaries', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (977,\n", + " 'WINDOW SIDE',\n", + " 'A Astounding Character Study of a Womanizer And a Hunter who must Escape a Robot in A Monastery',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('2.99'),\n", + " 85,\n", + " Decimal('25.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (978,\n", + " 'WISDOM WORKER',\n", + " 'A Unbelieveable Saga of a Forensic Psychologist And a Student who must Face a Squirrel in The First Manned Space Station',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('0.99'),\n", + " 98,\n", + " Decimal('12.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (979,\n", + " 'WITCHES PANIC',\n", + " 'A Awe-Inspiring Drama of a Secret Agent And a Hunter who must Fight a Moose in Nigeria',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('4.99'),\n", + " 100,\n", + " Decimal('10.99'),\n", + " 'NC-17',\n", + " {'Behind the Scenes', 'Commentaries'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (980,\n", + " 'WIZARD COLDBLOODED',\n", + " 'A Lacklusture Display of a Robot And a Girl who must Defeat a Sumo Wrestler in A MySQL Convention',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 75,\n", + " Decimal('12.99'),\n", + " 'PG',\n", + " {'Behind the Scenes', 'Commentaries', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (981,\n", + " 'WOLVES DESIRE',\n", + " 'A Fast-Paced Drama of a Squirrel And a Robot who must Succumb a Technical Writer in A Manhattan Penthouse',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 7,\n", + " Decimal('0.99'),\n", + " 55,\n", + " Decimal('13.99'),\n", + " 'NC-17',\n", + " {'Behind the Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (982,\n", + " 'WOMEN DORADO',\n", + " 'A Insightful Documentary of a Waitress And a Butler who must Vanquish a Composer in Australia',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('0.99'),\n", + " 126,\n", + " Decimal('23.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (983,\n", + " 'WON DARES',\n", + " 'A Unbelieveable Documentary of a Teacher And a Monkey who must Defeat a Explorer in A U-Boat',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 7,\n", + " Decimal('2.99'),\n", + " 105,\n", + " Decimal('18.99'),\n", + " 'PG',\n", + " {'Behind the Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (984,\n", + " 'WONDERFUL DROP',\n", + " 'A Boring Panorama of a Woman And a Madman who must Overcome a Butler in A U-Boat',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('2.99'),\n", + " 126,\n", + " Decimal('20.99'),\n", + " 'NC-17',\n", + " {'Commentaries'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (985,\n", + " 'WONDERLAND CHRISTMAS',\n", + " 'A Awe-Inspiring Character Study of a Waitress And a Car who must Pursue a Mad Scientist in The First Manned Space Station',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 111,\n", + " Decimal('19.99'),\n", + " 'PG',\n", + " {'Commentaries'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (986,\n", + " 'WONKA SEA',\n", + " 'A Brilliant Saga of a Boat And a Mad Scientist who must Meet a Moose in Ancient India',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('2.99'),\n", + " 85,\n", + " Decimal('24.99'),\n", + " 'NC-17',\n", + " {'Commentaries', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (987,\n", + " 'WORDS HUNTER',\n", + " 'A Action-Packed Reflection of a Composer And a Mad Scientist who must Face a Pioneer in A MySQL Convention',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('2.99'),\n", + " 116,\n", + " Decimal('13.99'),\n", + " 'PG',\n", + " {'Commentaries', 'Deleted Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (988,\n", + " 'WORKER TARZAN',\n", + " 'A Action-Packed Yarn of a Secret Agent And a Technical Writer who must Battle a Sumo Wrestler in The First Manned Space Station',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 7,\n", + " Decimal('2.99'),\n", + " 139,\n", + " Decimal('26.99'),\n", + " 'R',\n", + " {'Behind the Scenes', 'Commentaries', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (989,\n", + " 'WORKING MICROCOSMOS',\n", + " 'A Stunning Epistle of a Dentist And a Dog who must Kill a Madman in Ancient China',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('4.99'),\n", + " 74,\n", + " Decimal('22.99'),\n", + " 'R',\n", + " {'Commentaries', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (990,\n", + " 'WORLD LEATHERNECKS',\n", + " 'A Unbelieveable Tale of a Pioneer And a Astronaut who must Overcome a Robot in An Abandoned Amusement Park',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('0.99'),\n", + " 171,\n", + " Decimal('13.99'),\n", + " 'PG-13',\n", + " {'Behind the Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (991,\n", + " 'WORST BANGER',\n", + " 'A Thrilling Drama of a Madman And a Dentist who must Conquer a Boy in The Outback',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('2.99'),\n", + " 185,\n", + " Decimal('26.99'),\n", + " 'PG',\n", + " {'Behind the Scenes', 'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (992,\n", + " 'WRATH MILE',\n", + " 'A Intrepid Reflection of a Technical Writer And a Hunter who must Defeat a Sumo Wrestler in A Monastery',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 5,\n", + " Decimal('0.99'),\n", + " 176,\n", + " Decimal('17.99'),\n", + " 'NC-17',\n", + " {'Commentaries', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (993,\n", + " 'WRONG BEHAVIOR',\n", + " 'A Emotional Saga of a Crocodile And a Sumo Wrestler who must Discover a Mad Cow in New Orleans',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('2.99'),\n", + " 178,\n", + " Decimal('10.99'),\n", + " 'PG-13',\n", + " {'Behind the Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (994,\n", + " 'WYOMING STORM',\n", + " 'A Awe-Inspiring Panorama of a Robot And a Boat who must Overcome a Feminist in A U-Boat',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('4.99'),\n", + " 100,\n", + " Decimal('29.99'),\n", + " 'PG-13',\n", + " {'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (995,\n", + " 'YENTL IDAHO',\n", + " 'A Amazing Display of a Robot And a Astronaut who must Fight a Womanizer in Berlin',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 5,\n", + " Decimal('4.99'),\n", + " 86,\n", + " Decimal('11.99'),\n", + " 'R',\n", + " {'Commentaries', 'Deleted Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (996,\n", + " 'YOUNG LANGUAGE',\n", + " 'A Unbelieveable Yarn of a Boat And a Database Administrator who must Meet a Boy in The First Manned Space Station',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('0.99'),\n", + " 183,\n", + " Decimal('9.99'),\n", + " 'G',\n", + " {'Behind the Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (997,\n", + " 'YOUTH KICK',\n", + " 'A Touching Drama of a Teacher And a Cat who must Challenge a Technical Writer in A U-Boat',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 4,\n", + " Decimal('0.99'),\n", + " 179,\n", + " Decimal('14.99'),\n", + " 'NC-17',\n", + " {'Behind the Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (998,\n", + " 'ZHIVAGO CORE',\n", + " 'A Fateful Yarn of a Composer And a Man who must Face a Boy in The Canadian Rockies',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 6,\n", + " Decimal('0.99'),\n", + " 105,\n", + " Decimal('10.99'),\n", + " 'NC-17',\n", + " {'Deleted Scenes'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (999,\n", + " 'ZOOLANDER FICTION',\n", + " 'A Fateful Reflection of a Waitress And a Boat who must Discover a Sumo Wrestler in Ancient China',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 5,\n", + " Decimal('2.99'),\n", + " 101,\n", + " Decimal('28.99'),\n", + " 'R',\n", + " {'Deleted Scenes', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42)),\n", + " (1000,\n", + " 'ZORRO ARK',\n", + " 'A Intrepid Panorama of a Mad Scientist And a Boy who must Redeem a Boy in A Monastery',\n", + " 2006,\n", + " 1,\n", + " None,\n", + " 3,\n", + " Decimal('4.99'),\n", + " 50,\n", + " Decimal('18.99'),\n", + " 'NC-17',\n", + " {'Behind the Scenes', 'Commentaries', 'Trailers'},\n", + " datetime.datetime(2006, 2, 15, 5, 3, 42))]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1 - Working with JSON files\n", + "Import the pandas library." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, load the data in nasa.json in the data folder and load it into a pandas dataframe. Name the dataframe nasa." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "nasa = pd.read_json('nasa.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have loaded the data, let's examine it using the head() function." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " :@computed_region_cbhk_fwbd :@computed_region_nnqa_25f4 fall \\\n", + "0 NaN NaN Fell \n", + "1 NaN NaN Fell \n", + "2 NaN NaN Fell \n", + "3 NaN NaN Fell \n", + "4 NaN NaN Fell \n", + "\n", + " geolocation id mass name \\\n", + "0 {'type': 'Point', 'coordinates': [6.08333, 50.... 1 21.0 Aachen \n", + "1 {'type': 'Point', 'coordinates': [10.23333, 56... 2 720.0 Aarhus \n", + "2 {'type': 'Point', 'coordinates': [-113, 54.216... 6 107000.0 Abee \n", + "3 {'type': 'Point', 'coordinates': [-99.9, 16.88... 10 1914.0 Acapulco \n", + "4 {'type': 'Point', 'coordinates': [-64.95, -33.... 370 780.0 Achiras \n", + "\n", + " nametype recclass reclat reclong year \n", + "0 Valid L5 50.77500 6.08333 1880-01-01T00:00:00.000 \n", + "1 Valid H6 56.18333 10.23333 1951-01-01T00:00:00.000 \n", + "2 Valid EH4 54.21667 -113.00000 1952-01-01T00:00:00.000 \n", + "3 Valid Acapulcoite 16.88333 -99.90000 1976-01-01T00:00:00.000 \n", + "4 Valid L6 -33.16667 -64.95000 1902-01-01T00:00:00.000 \n" + ] + } + ], + "source": [ + "print(nasa.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The value_counts() function is commonly used in pandas to find the frequency of every value in a column.\n", + "\n", + "In the cell below, use the value_counts() function to determine the frequency of all types of asteroid landings by applying the function to the fall column." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "landings = nasa['fall'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fell 996\n", + "Found 4\n", + "Name: fall, dtype: int64\n" + ] + } + ], + "source": [ + "print(landings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's save the dataframe as a json file again. Save the dataframe using the orient=records argument and name the file nasa-output.json. Remember to save the file inside the data folder." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "output_path = '/Users/danieldepaoli/Desktop/Labs/nasa-output.json'\n", + "nasa.to_json(output_path, orient='records')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 2 - Working with CSV and Other Separated Files\n", + "\n", + "Import the pandas library\n", + "\n", + "csv files are more commonly used as dataframes. In the cell below, load the file from the URL provided using the `read_csv()` function in pandas. Starting version 0.19 of pandas, you can load a csv file into a dataframe directly from a URL without having to load the file first like we did with the JSON URL. The dataset we will be using contains informtaions about NASA shuttles. \n", + "\n", + "In the cell below, we define the column names and the URL of the data. Following this cell, read the tst file to a variable called `shuttle`. Since the file does not contain the column names, you must add them yourself using the column names declared in `cols` using the `names` argument. Additionally, a tst file is space separated, make sure you pass ` sep=' '` to the function." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "#Your pandas import here:\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this code:\n", + "\n", + "cols = ['time', 'rad_flow', 'fpv_close', 'fpv_open', 'high', 'bypass', 'bpv_close', 'bpv_open', 'class']\n", + "tst_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/shuttle/shuttle.tst'" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n", + "shuttle = pd.read_csv(tst_url, sep=' ', names=cols)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's verify that this worked by looking at the `head()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " time rad_flow fpv_close fpv_open high bypass bpv_close bpv_open \\\n", + "55 0 81 0 -6 11 25 88 64 \n", + "56 0 96 0 52 -4 40 44 4 \n", + "50 -1 89 -7 50 0 39 40 2 \n", + "53 9 79 0 42 -2 25 37 12 \n", + "55 2 82 0 54 -6 26 28 2 \n", + "\n", + " class \n", + "55 4 \n", + "56 4 \n", + "50 1 \n", + "53 4 \n", + "55 1 \n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "print(shuttle.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make life easier for us, let's turn this dataframe into a comma separated file by saving it using the `to_csv()` function. Save `shuttle` into the file `shuttle.csv` and ensure the file is comma separated and that we are not saving the index column." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n", + "shuttle.to_csv(\"shuttle.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3 - Working with Excel Files\n", + "\n", + "We can also use pandas to convert excel spreadsheets to dataframes. Let's use the `read_excel()` function. In this case, `astronauts.xls` is in the same folder that contains this notebook. Read this file into a variable called `astronaut`. \n", + "\n", + "Note: Make sure to install the `xlrd` library if it is not yet installed." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting xlrd\n", + " Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.5/96.5 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: xlrd\n", + "Successfully installed xlrd-2.0.1\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "!pip install xlrd" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Year Group Status Birth Date Birth Place Gender \\\n", + "0 Joseph M. Acaba 2004.0 19.0 Active 1967-05-17 Inglewood, CA Male \n", + "1 Loren W. Acton NaN NaN Retired 1936-03-07 Lewiston, MT Male \n", + "2 James C. Adamson 1984.0 10.0 Retired 1946-03-03 Warsaw, NY Male \n", + "3 Thomas D. Akers 1987.0 12.0 Retired 1951-05-20 St. Louis, MO Male \n", + "4 Buzz Aldrin 1963.0 3.0 Retired 1930-01-20 Montclair, NJ Male \n", + "\n", + " Alma Mater Undergraduate Major \\\n", + "0 University of California-Santa Barbara; Univer... Geology \n", + "1 Montana State University; University of Colorado Engineering Physics \n", + "2 US Military Academy; Princeton University Engineering \n", + "3 University of Missouri-Rolla Applied Mathematics \n", + "4 US Military Academy; MIT Mechanical Engineering \n", + "\n", + " Graduate Major Military Rank Military Branch Space Flights \\\n", + "0 Geology NaN NaN 2 \n", + "1 Solar Physics NaN NaN 1 \n", + "2 Aerospace Engineering Colonel US Army (Retired) 2 \n", + "3 Applied Mathematics Colonel US Air Force (Retired) 4 \n", + "4 Astronautics Colonel US Air Force (Retired) 2 \n", + "\n", + " Space Flight (hr) Space Walks Space Walks (hr) \\\n", + "0 3307 2 13.0 \n", + "1 190 0 0.0 \n", + "2 334 0 0.0 \n", + "3 814 4 29.0 \n", + "4 289 2 8.0 \n", + "\n", + " Missions Death Date Death Mission \n", + "0 STS-119 (Discovery), ISS-31/32 (Soyuz) NaT NaN \n", + "1 STS 51-F (Challenger) NaT NaN \n", + "2 STS-28 (Columbia), STS-43 (Atlantis) NaT NaN \n", + "3 STS-41 (Discovery), STS-49 (Endeavor), STS-61 ... NaT NaN \n", + "4 Gemini 12, Apollo 11 NaT NaN \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "astronaut = pd.read_excel(\"astronauts.xls\")\n", + "\n", + "print(astronaut.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `head()` function to inspect the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Year Group Status Birth Date Birth Place Gender \\\n", + "0 Joseph M. Acaba 2004.0 19.0 Active 1967-05-17 Inglewood, CA Male \n", + "1 Loren W. Acton NaN NaN Retired 1936-03-07 Lewiston, MT Male \n", + "2 James C. Adamson 1984.0 10.0 Retired 1946-03-03 Warsaw, NY Male \n", + "3 Thomas D. Akers 1987.0 12.0 Retired 1951-05-20 St. Louis, MO Male \n", + "4 Buzz Aldrin 1963.0 3.0 Retired 1930-01-20 Montclair, NJ Male \n", + "\n", + " Alma Mater Undergraduate Major \\\n", + "0 University of California-Santa Barbara; Univer... Geology \n", + "1 Montana State University; University of Colorado Engineering Physics \n", + "2 US Military Academy; Princeton University Engineering \n", + "3 University of Missouri-Rolla Applied Mathematics \n", + "4 US Military Academy; MIT Mechanical Engineering \n", + "\n", + " Graduate Major Military Rank Military Branch Space Flights \\\n", + "0 Geology NaN NaN 2 \n", + "1 Solar Physics NaN NaN 1 \n", + "2 Aerospace Engineering Colonel US Army (Retired) 2 \n", + "3 Applied Mathematics Colonel US Air Force (Retired) 4 \n", + "4 Astronautics Colonel US Air Force (Retired) 2 \n", + "\n", + " Space Flight (hr) Space Walks Space Walks (hr) \\\n", + "0 3307 2 13.0 \n", + "1 190 0 0.0 \n", + "2 334 0 0.0 \n", + "3 814 4 29.0 \n", + "4 289 2 8.0 \n", + "\n", + " Missions Death Date Death Mission \n", + "0 STS-119 (Discovery), ISS-31/32 (Soyuz) NaT NaN \n", + "1 STS 51-F (Challenger) NaT NaN \n", + "2 STS-28 (Columbia), STS-43 (Atlantis) NaT NaN \n", + "3 STS-41 (Discovery), STS-49 (Endeavor), STS-61 ... NaT NaN \n", + "4 Gemini 12, Apollo 11 NaT NaN \n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "print(astronaut.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `value_counts()` function to find the most popular undergraduate major among all astronauts." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Name Year Group Status Birth Date Birth Place Gender Alma Mater Undergraduate Major Graduate Major Military Rank Military Branch Space Flights Space Flight (hr) Space Walks Space Walks (hr) Missions Death Date Death Mission \n", + "David M. Brown 1996.0 16.0 Deceased 1956-04-16 Arlington, VA Male College of William & Mary; Eastern Virginia Medical School Biology Medicine Captain US Navy 1 382 0 0.0 STS-107 (Columbia) 2003-02-01 STS-107 (Columbia) 1\n", + "Ellison S. Onizuka 1978.0 8.0 Deceased 1946-06-24 Kealakekua, HI Male University of Colorado Aerospace Engineering Aerospace Engineering Lieutenant Colonel US Air Force 2 73 0 0.0 STS 51-C (Discovery), STS 51-L (Challenger) 1986-01-28 STS 51-L (Challenger) 1\n", + "Laurel B. Clark 1996.0 16.0 Deceased 1961-03-10 Ames, IA Female University of Wisconsin-Madison Zoology Medicine Captain US Navy 1 382 0 0.0 STS-107 (Columbia) 2003-02-01 STS-107 (Columbia) 1\n", + "Michael J. Smith 1980.0 9.0 Deceased 1945-04-30 Beaufort, NC Male US Naval Academy; US Naval Postgraduate School Naval Sciences Aeronautical Engineering Captain US Navy 1 0 0 0.0 STS 51-L (Challenger) 1986-01-28 STS 51-L (Challenger) 1\n", + "Michael P. Anderson 1995.0 15.0 Deceased 1959-12-25 Plattsburgh, NY Male University of Washington; Creighton University Physics & Astronomy Physics Lieutenant Colonel US Air Force 2 594 0 0.0 STS-89 (Endeavor), STS-107 (Columbia) 2003-02-01 STS-107 (Columbia) 1\n", + "Rick D. Husband 1995.0 15.0 Deceased 1957-07-12 Amarillo, TX Male Texas Tech University; California State University Mechanical Engineering Mechanical Engineering Colonel US Air Force 2 617 0 0.0 STS-96 (Discovery), STS-107 (Columbia) 2003-02-01 STS-107 (Columbia) 1\n", + "William C. McCool 1996.0 16.0 Deceased 1961-09-23 San Diego, CA Male US Naval Academy; University of Maryland; US Naval Postgraduate School Naval Sciences Computer Science; Aeronautical Engineering Commander US Navy 1 382 0 0.0 STS-107 (Columbia) 2003-02-01 STS-107 (Columbia) 1\n", + "dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "\n", + "astronaut.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Due to all the commas present in the cells of this file, let's save it as a tab separated csv file. In the cell below, save `astronaut` as a tab separated file using the `to_csv` function. Call the file `astronaut.csv` and remember to remove the index column." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n", + "astronaut.to_csv('astronaut.csv', sep='\\t', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bonus Challenge - Fertility Dataset\n", + "\n", + "Visit the following [URL](https://archive.ics.uci.edu/ml/datasets/Fertility) and retrieve the dataset as well as the column headers. Determine the correct separator and read the file into a variable called `fertility`. Examine the dataframe using the `head()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'fertility_Diagnosis.txt'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[42], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Your code here:\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m fertility \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfertility_Diagnosis.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(fertility\u001b[38;5;241m.\u001b[39mhead())\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950\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 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1734\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles 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Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m 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"nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb deleted file mode 100644 index 999b648..0000000 --- a/your-code/main.ipynb +++ /dev/null @@ -1,303 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import and Export Files \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 0 - Accessing our database\n", - "Create a connection to access the sakila database with the ip, user and password provided in class. Take a look at the data. Do some joins in order to have more elaborated tables in a dataframe (you can try first the query in the DBMS and then use it in your notebook)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 1 - Working with JSON files\n", - "Import the pandas library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, load the data in nasa.json in the data folder and load it into a pandas dataframe. Name the dataframe nasa." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have loaded the data, let's examine it using the head() function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### The value_counts() function is commonly used in pandas to find the frequency of every value in a column.\n", - "\n", - "In the cell below, use the value_counts() function to determine the frequency of all types of asteroid landings by applying the function to the fall column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's save the dataframe as a json file again. Save the dataframe using the orient=records argument and name the file nasa-output.json. Remember to save the file inside the data folder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 2 - Working with CSV and Other Separated Files\n", - "\n", - "Import the pandas library\n", - "\n", - "csv files are more commonly used as dataframes. In the cell below, load the file from the URL provided using the `read_csv()` function in pandas. Starting version 0.19 of pandas, you can load a csv file into a dataframe directly from a URL without having to load the file first like we did with the JSON URL. The dataset we will be using contains informtaions about NASA shuttles. \n", - "\n", - "In the cell below, we define the column names and the URL of the data. Following this cell, read the tst file to a variable called `shuttle`. Since the file does not contain the column names, you must add them yourself using the column names declared in `cols` using the `names` argument. Additionally, a tst file is space separated, make sure you pass ` sep=' '` to the function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#Your pandas import here:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Run this code:\n", - "\n", - "cols = ['time', 'rad_flow', 'fpv_close', 'fpv_open', 'high', 'bypass', 'bpv_close', 'bpv_open', 'class']\n", - "tst_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/shuttle/shuttle.tst'" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's verify that this worked by looking at the `head()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make life easier for us, let's turn this dataframe into a comma separated file by saving it using the `to_csv()` function. Save `shuttle` into the file `shuttle.csv` and ensure the file is comma separated and that we are not saving the index column." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 3 - Working with Excel Files\n", - "\n", - "We can also use pandas to convert excel spreadsheets to dataframes. Let's use the `read_excel()` function. In this case, `astronauts.xls` is in the same folder that contains this notebook. Read this file into a variable called `astronaut`. \n", - "\n", - "Note: Make sure to install the `xlrd` library if it is not yet installed." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `head()` function to inspect the dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `value_counts()` function to find the most popular undergraduate major among all astronauts." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Due to all the commas present in the cells of this file, let's save it as a tab separated csv file. In the cell below, save `astronaut` as a tab separated file using the `to_csv` function. Call the file `astronaut.csv` and remember to remove the index column." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bonus Challenge - Fertility Dataset\n", - "\n", - "Visit the following [URL](https://archive.ics.uci.edu/ml/datasets/Fertility) and retrieve the dataset as well as the column headers. Determine the correct separator and read the file into a variable called `fertility`. Examine the dataframe using the `head()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - } - ], - "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": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/your-code/shuttle.csv b/your-code/shuttle.csv new file mode 100644 index 0000000..b780dbd --- /dev/null +++ b/your-code/shuttle.csv @@ -0,0 +1,14501 @@ +time,rad_flow,fpv_close,fpv_open,high,bypass,bpv_close,bpv_open,class +0,81,0,-6,11,25,88,64,4 +0,96,0,52,-4,40,44,4,4 +-1,89,-7,50,0,39,40,2,1 +9,79,0,42,-2,25,37,12,4 +2,82,0,54,-6,26,28,2,1 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