From 84336bb749833f905c16198f714db609945451e4 Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Thu, 12 Mar 2020 12:52:26 +0100 Subject: [PATCH 1/8] Add files via upload --- your-project/README.md | 4 +- your-project/Untitled.ipynb | 2672 +++++++++++++++++++++++++++++ your-project/breed_info.csv | 151 ++ your-project/dog_intelligence.csv | 137 ++ 4 files changed, 2962 insertions(+), 2 deletions(-) create mode 100644 your-project/Untitled.ipynb create mode 100644 your-project/breed_info.csv create mode 100644 your-project/dog_intelligence.csv diff --git a/your-project/README.md b/your-project/README.md index 9563cd70..7192196a 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -1,7 +1,7 @@ Ironhack Logo -# Title of My Project -*[Your Name]* +# Size & Intelligence in Dogs +*[Santiago Mougán]* *[Your Cohort, Campus & Date]* diff --git a/your-project/Untitled.ipynb b/your-project/Untitled.ipynb new file mode 100644 index 00000000..b23c7be2 --- /dev/null +++ b/your-project/Untitled.ipynb @@ -0,0 +1,2672 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationobeyreps_lowerreps_upper
0Border CollieBrightest Dogs95%14
1PoodleBrightest Dogs95%14
2German ShepherdBrightest Dogs95%14
3Golden RetrieverBrightest Dogs95%14
4Doberman PinscherBrightest Dogs95%14
..................
131BorzoiLowest Degree of Working/Obedience IntelligenceNaN81100
132Chow ChowLowest Degree of Working/Obedience IntelligenceNaN81100
133BulldogLowest Degree of Working/Obedience IntelligenceNaN81100
134BasenjiLowest Degree of Working/Obedience IntelligenceNaN81100
135Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN81100
\n", + "

136 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification obey \\\n", + "0 Border Collie Brightest Dogs 95% \n", + "1 Poodle Brightest Dogs 95% \n", + "2 German Shepherd Brightest Dogs 95% \n", + "3 Golden Retriever Brightest Dogs 95% \n", + "4 Doberman Pinscher Brightest Dogs 95% \n", + ".. ... ... ... \n", + "131 Borzoi Lowest Degree of Working/Obedience Intelligence NaN \n", + "132 Chow Chow Lowest Degree of Working/Obedience Intelligence NaN \n", + "133 Bulldog Lowest Degree of Working/Obedience Intelligence NaN \n", + "134 Basenji Lowest Degree of Working/Obedience Intelligence NaN \n", + "135 Afghan Hound Lowest Degree of Working/Obedience Intelligence NaN \n", + "\n", + " reps_lower reps_upper \n", + "0 1 4 \n", + "1 1 4 \n", + "2 1 4 \n", + "3 1 4 \n", + "4 1 4 \n", + ".. ... ... \n", + "131 81 100 \n", + "132 81 100 \n", + "133 81 100 \n", + "134 81 100 \n", + "135 81 100 \n", + "\n", + "[136 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dog_int = pd.read_csv('dog_intelligence.csv')\n", + "dog_int" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Breedheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Akita262880120
1Anatolian Sheepdog2729100150
2Bernese Mountain Dog232785110
3Bloodhound242680120
4Borzoi262870100
..................
145Papillon811510
146Pomeranian121237
147Poodle Toy10101010
148Toy Fox Terrier101047
149Yorkshire Terrier8837
\n", + "

150 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Breed height_low_inches height_high_inches weight_low_lbs \\\n", + "0 Akita 26 28 80 \n", + "1 Anatolian Sheepdog 27 29 100 \n", + "2 Bernese Mountain Dog 23 27 85 \n", + "3 Bloodhound 24 26 80 \n", + "4 Borzoi 26 28 70 \n", + ".. ... ... ... ... \n", + "145 Papillon 8 11 5 \n", + "146 Pomeranian 12 12 3 \n", + "147 Poodle Toy 10 10 10 \n", + "148 Toy Fox Terrier 10 10 4 \n", + "149 Yorkshire Terrier 8 8 3 \n", + "\n", + " weight_high_lbs \n", + "0 120 \n", + "1 150 \n", + "2 110 \n", + "3 120 \n", + "4 100 \n", + ".. ... \n", + "145 10 \n", + "146 7 \n", + "147 10 \n", + "148 7 \n", + "149 7 \n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breed_info = pd.read_csv('breed_info.csv', encoding= 'unicode_escape')\n", + "breed_info" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "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", + " \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", + " \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", + " \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", + "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Border CollieBrightest Dogs95%1419214040
1Golden RetrieverBrightest Dogs95%1421245575
2Doberman PinscherBrightest Dogs95%14262860100
3Labrador RetrieverBrightest Dogs95%1421245580
4PapillonBrightest Dogs95%14811510
..............................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN81100242680120
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN81100262870100
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110019224555
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017172022
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110025275060
\n", + "

105 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " obey reps_lower reps_upper height_low_inches height_high_inches \\\n", + "0 95% 1 4 19 21 \n", + "1 95% 1 4 21 24 \n", + "2 95% 1 4 26 28 \n", + "3 95% 1 4 21 24 \n", + "4 95% 1 4 8 11 \n", + ".. ... ... ... ... ... \n", + "100 NaN 81 100 24 26 \n", + "101 NaN 81 100 26 28 \n", + "102 NaN 81 100 19 22 \n", + "103 NaN 81 100 17 17 \n", + "104 NaN 81 100 25 27 \n", + "\n", + " weight_low_lbs weight_high_lbs \n", + "0 40 40 \n", + "1 55 75 \n", + "2 60 100 \n", + "3 55 80 \n", + "4 5 10 \n", + ".. ... ... \n", + "100 80 120 \n", + "101 70 100 \n", + "102 45 55 \n", + "103 20 22 \n", + "104 50 60 \n", + "\n", + "[105 rows x 9 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breed_int = dog_int.merge(breed_info, left_on = 'Breed', right_on = 'Breed')\n", + "breed_int" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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", + "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
70Alaskan MalamuteAverage Working/Obedience Intelligence50%2640nananana
\n", + "
" + ], + "text/plain": [ + " Breed Classification obey reps_lower \\\n", + "70 Alaskan Malamute Average Working/Obedience Intelligence 50% 26 \n", + "\n", + " reps_upper height_low_inches height_high_inches weight_low_lbs \\\n", + "70 40 na na na \n", + "\n", + " weight_high_lbs \n", + "70 na " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breed_int.loc[breed_int.weight_high_lbs == 'na']" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "breed_int.replace({'height_high_inches':{'na':'25'}, 'height_low_inches':{'na':'25'}, 'weight_high_lbs':{'na':'82'}, 'weight_low_lbs':{'na':'82'}}, inplace= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "breed_int['height_low_inches'] = pd.to_numeric(breed_int['height_low_inches'])\n", + "breed_int['height_high_inches'] = pd.to_numeric(breed_int['height_high_inches'])\n", + "breed_int['weight_low_lbs'] = pd.to_numeric(breed_int['weight_low_lbs'])\n", + "breed_int['weight_high_lbs'] = pd.to_numeric(breed_int['weight_high_lbs'])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 105 entries, 0 to 104\n", + "Data columns (total 9 columns):\n", + "Breed 105 non-null object\n", + "Classification 105 non-null object\n", + "obey 96 non-null object\n", + "reps_lower 105 non-null int64\n", + "reps_upper 105 non-null int64\n", + "height_low_inches 105 non-null float64\n", + "height_high_inches 105 non-null float64\n", + "weight_low_lbs 105 non-null int64\n", + "weight_high_lbs 105 non-null int64\n", + "dtypes: float64(2), int64(4), object(3)\n", + "memory usage: 8.2+ KB\n" + ] + } + ], + "source": [ + "breed_int.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "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", + " \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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbsHeightWeight
0Border CollieBrightest Dogs95%1419.021.0404020.040.0
1Golden RetrieverBrightest Dogs95%1421.024.0557522.565.0
2Doberman PinscherBrightest Dogs95%1426.028.06010027.080.0
3Labrador RetrieverBrightest Dogs95%1421.024.0558022.567.5
4PapillonBrightest Dogs95%148.011.05109.57.5
....................................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN8110024.026.08012025.0100.0
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN8110026.028.07010027.085.0
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110019.022.0455520.550.0
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017.017.0202217.021.0
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110025.027.0506026.055.0
\n", + "

105 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " obey reps_lower reps_upper height_low_inches height_high_inches \\\n", + "0 95% 1 4 19.0 21.0 \n", + "1 95% 1 4 21.0 24.0 \n", + "2 95% 1 4 26.0 28.0 \n", + "3 95% 1 4 21.0 24.0 \n", + "4 95% 1 4 8.0 11.0 \n", + ".. ... ... ... ... ... \n", + "100 NaN 81 100 24.0 26.0 \n", + "101 NaN 81 100 26.0 28.0 \n", + "102 NaN 81 100 19.0 22.0 \n", + "103 NaN 81 100 17.0 17.0 \n", + "104 NaN 81 100 25.0 27.0 \n", + "\n", + " weight_low_lbs weight_high_lbs Height Weight \n", + "0 40 40 20.0 40.0 \n", + "1 55 75 22.5 65.0 \n", + "2 60 100 27.0 80.0 \n", + "3 55 80 22.5 67.5 \n", + "4 5 10 9.5 7.5 \n", + ".. ... ... ... ... \n", + "100 80 120 25.0 100.0 \n", + "101 70 100 27.0 85.0 \n", + "102 45 55 20.5 50.0 \n", + "103 20 22 17.0 21.0 \n", + "104 50 60 26.0 55.0 \n", + "\n", + "[105 rows x 11 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breed_int['Height'] = (0.5 * breed_int['height_low_inches']) + (0.5 * breed_int['height_high_inches'])\n", + "breed_int['Weight'] = (0.5 * breed_int['weight_low_lbs']) + (0.5 * breed_int['weight_high_lbs'])\n", + " \n", + "breed_int" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "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", + " \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", + " \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", + " \n", + "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs95%1420.040.0
1Golden RetrieverBrightest Dogs95%1422.565.0
2Doberman PinscherBrightest Dogs95%1427.080.0
3Labrador RetrieverBrightest Dogs95%1422.567.5
4PapillonBrightest Dogs95%149.57.5
........................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN8110025.0100.0
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN8110027.085.0
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110020.550.0
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017.021.0
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110026.055.0
\n", + "

105 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " obey reps_lower reps_upper Height Weight \n", + "0 95% 1 4 20.0 40.0 \n", + "1 95% 1 4 22.5 65.0 \n", + "2 95% 1 4 27.0 80.0 \n", + "3 95% 1 4 22.5 67.5 \n", + "4 95% 1 4 9.5 7.5 \n", + ".. ... ... ... ... ... \n", + "100 NaN 81 100 25.0 100.0 \n", + "101 NaN 81 100 27.0 85.0 \n", + "102 NaN 81 100 20.5 50.0 \n", + "103 NaN 81 100 17.0 21.0 \n", + "104 NaN 81 100 26.0 55.0 \n", + "\n", + "[105 rows x 7 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del breed_int['height_low_inches']\n", + "del breed_int['height_high_inches']\n", + "del breed_int['weight_low_lbs']\n", + "del breed_int['weight_high_lbs']\n", + "breed_int" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "scrolled": true + }, + "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", + " \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", + " \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", + " \n", + "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs951420.040.0
1Golden RetrieverBrightest Dogs951422.565.0
2Doberman PinscherBrightest Dogs951427.080.0
3Labrador RetrieverBrightest Dogs951422.567.5
4PapillonBrightest Dogs95149.57.5
........................
80Sealyham TerrierFair Working/Obedience Intelligence30418012.019.0
81PugFair Working/Obedience Intelligence30418010.518.0
82French BulldogFair Working/Obedience Intelligence30418011.522.5
83MalteseFair Working/Obedience Intelligence3041809.05.0
84Italian GreyhoundFair Working/Obedience Intelligence30418013.58.0
\n", + "

85 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification obey reps_lower \\\n", + "0 Border Collie Brightest Dogs 95 1 \n", + "1 Golden Retriever Brightest Dogs 95 1 \n", + "2 Doberman Pinscher Brightest Dogs 95 1 \n", + "3 Labrador Retriever Brightest Dogs 95 1 \n", + "4 Papillon Brightest Dogs 95 1 \n", + ".. ... ... ... ... \n", + "80 Sealyham Terrier Fair Working/Obedience Intelligence 30 41 \n", + "81 Pug Fair Working/Obedience Intelligence 30 41 \n", + "82 French Bulldog Fair Working/Obedience Intelligence 30 41 \n", + "83 Maltese Fair Working/Obedience Intelligence 30 41 \n", + "84 Italian Greyhound Fair Working/Obedience Intelligence 30 41 \n", + "\n", + " reps_upper Height Weight \n", + "0 4 20.0 40.0 \n", + "1 4 22.5 65.0 \n", + "2 4 27.0 80.0 \n", + "3 4 22.5 67.5 \n", + "4 4 9.5 7.5 \n", + ".. ... ... ... \n", + "80 80 12.0 19.0 \n", + "81 80 10.5 18.0 \n", + "82 80 11.5 22.5 \n", + "83 80 9.0 5.0 \n", + "84 80 13.5 8.0 \n", + "\n", + "[85 rows x 7 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breed_int.head(85)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "breed_int['obey'] = breed_int['obey'].fillna('15%')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "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", + " \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", + "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs951420.040.0
1Golden RetrieverBrightest Dogs951422.565.0
2Doberman PinscherBrightest Dogs951427.080.0
3Labrador RetrieverBrightest Dogs951422.567.5
4PapillonBrightest Dogs95149.57.5
\n", + "
" + ], + "text/plain": [ + " Breed Classification obey reps_lower reps_upper Height \\\n", + "0 Border Collie Brightest Dogs 95 1 4 20.0 \n", + "1 Golden Retriever Brightest Dogs 95 1 4 22.5 \n", + "2 Doberman Pinscher Brightest Dogs 95 1 4 27.0 \n", + "3 Labrador Retriever Brightest Dogs 95 1 4 22.5 \n", + "4 Papillon Brightest Dogs 95 1 4 9.5 \n", + "\n", + " Weight \n", + "0 40.0 \n", + "1 65.0 \n", + "2 80.0 \n", + "3 67.5 \n", + "4 7.5 " + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def percentage_sign_remover(string):\n", + " return int(re.sub('%', '', string))\n", + "\n", + "breed_int['obey'] = breed_int['obey'].apply(percentage_sign_remover)\n", + "breed_int.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 105 entries, 0 to 104\n", + "Data columns (total 7 columns):\n", + "Breed 105 non-null object\n", + "Classification 105 non-null object\n", + "obey 105 non-null int64\n", + "reps_lower 105 non-null int64\n", + "reps_upper 105 non-null int64\n", + "Height 105 non-null float64\n", + "Weight 105 non-null float64\n", + "dtypes: float64(2), int64(3), object(2)\n", + "memory usage: 6.6+ KB\n" + ] + } + ], + "source": [ + "breed_int.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationResponsivityHeightWeight
0Border CollieBrightest Dogs9520.040.0
1Golden RetrieverBrightest Dogs9522.565.0
2Doberman PinscherBrightest Dogs9527.080.0
3Labrador RetrieverBrightest Dogs9522.567.5
4PapillonBrightest Dogs959.57.5
..................
100BloodhoundLowest Degree of Working/Obedience Intelligence1525.0100.0
101BorzoiLowest Degree of Working/Obedience Intelligence1527.085.0
102Chow ChowLowest Degree of Working/Obedience Intelligence1520.550.0
103BasenjiLowest Degree of Working/Obedience Intelligence1517.021.0
104Afghan HoundLowest Degree of Working/Obedience Intelligence1526.055.0
\n", + "

105 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " Responsivity Height Weight \n", + "0 95 20.0 40.0 \n", + "1 95 22.5 65.0 \n", + "2 95 27.0 80.0 \n", + "3 95 22.5 67.5 \n", + "4 95 9.5 7.5 \n", + ".. ... ... ... \n", + "100 15 25.0 100.0 \n", + "101 15 27.0 85.0 \n", + "102 15 20.5 50.0 \n", + "103 15 17.0 21.0 \n", + "104 15 26.0 55.0 \n", + "\n", + "[105 rows x 5 columns]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint = breed_int.rename(columns={'obey': 'Responsivity'})\n", + "del brint['reps_lower']\n", + "del brint['reps_upper']\n", + "brint" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Breed Vizsla\n", + "Classification Excellent Working Dogs\n", + "Responsivity 85\n", + "Height 23.5\n", + "Weight 57\n", + "Name: 19, dtype: object" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Height.loc[19] = 23.5\n", + "brint.Weight.loc[19] = 57\n", + "brint.Height.loc[92] = 29.5\n", + "brint.loc[19]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Breed Saint Bernard\n", + "Classification Fair Working/Obedience Intelligence\n", + "Responsivity 30\n", + "Height 29.5\n", + "Weight 150\n", + "Name: 92, dtype: object" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.loc[92]" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(brint)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "brint.plot('Responsivity', y='Height')\n", + "brint.plot('Responsivity', y='Weight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "grouped = brint.groupby('Responsivity', as_index=False).mean()\n", + "\n", + "plt.plot(grouped['Height'], grouped['Responsivity'], c='b')\n", + "plt.plot(grouped['Weight'], grouped['Responsivity'], c='r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "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", + " \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", + "
BreedClassificationResponsivityHeightWeight
0Border CollieBrightest Dogs9520.040.0
1Golden RetrieverBrightest Dogs9522.565.0
2Doberman PinscherBrightest Dogs9527.080.0
3Labrador RetrieverBrightest Dogs9522.567.5
4PapillonBrightest Dogs959.57.5
5RottweilerBrightest Dogs9524.5100.0
6Australian Cattle DogBrightest Dogs9518.540.0
\n", + "
" + ], + "text/plain": [ + " Breed Classification Responsivity Height Weight\n", + "0 Border Collie Brightest Dogs 95 20.0 40.0\n", + "1 Golden Retriever Brightest Dogs 95 22.5 65.0\n", + "2 Doberman Pinscher Brightest Dogs 95 27.0 80.0\n", + "3 Labrador Retriever Brightest Dogs 95 22.5 67.5\n", + "4 Papillon Brightest Dogs 95 9.5 7.5\n", + "5 Rottweiler Brightest Dogs 95 24.5 100.0\n", + "6 Australian Cattle Dog Brightest Dogs 95 18.5 40.0" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.loc[brint.Responsivity == 95]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "#ml predictions on any given size" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([95, 85, 70, 50, 30, 15], dtype=int64)" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Responsivity.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Brightest Dogs', 'Excellent Working Dogs',\n", + " 'Above Average Working Dogs',\n", + " 'Average Working/Obedience Intelligence',\n", + " 'Fair Working/Obedience Intelligence',\n", + " 'Lowest Degree of Working/Obedience Intelligence '], dtype=object)" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Classification.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Border Collie', 'Golden Retriever', 'Doberman Pinscher',\n", + " 'Labrador Retriever', 'Papillon', 'Rottweiler',\n", + " 'Australian Cattle Dog', 'English Springer Spaniel', 'Schipperke',\n", + " 'Belgian Sheepdog', 'Keeshond', 'German Shorthaired Pointer',\n", + " 'Standard Schnauzer', 'Brittany', 'Weimaraner', 'Belgian Malinois',\n", + " 'Bernese Mountain Dog', 'Pomeranian', 'Irish Water Spaniel',\n", + " 'Vizsla', 'Cardigan Welsh Corgi', 'Chesapeake Bay Retriever',\n", + " 'Puli', 'Yorkshire Terrier', 'Giant Schnauzer',\n", + " 'Portuguese Water Dog', 'Border Terrier', 'Briard',\n", + " 'Welsh Springer Spaniel', 'Samoyed', 'Field Spaniel',\n", + " 'Newfoundland', 'Australian Terrier',\n", + " 'American Staffordshire Terrier', 'Gordon Setter',\n", + " 'Bearded Collie', 'Cairn Terrier', 'Kerry Blue Terrier',\n", + " 'Irish Setter', 'Norwegian Elkhound', 'Affenpinscher',\n", + " 'English Setter', 'Pharaoh Hound', 'Clumber Spaniel', 'Dalmatian',\n", + " 'Bedlington Terrier', 'Curly Coated Retriever', 'Irish Wolfhound',\n", + " 'Kuvasz', 'Australian Shepherd', 'Saluki', 'Finnish Spitz',\n", + " 'Pointer', 'Cavalier King Charles Spaniel',\n", + " 'German Wirehaired Pointer', 'American Water Spaniel',\n", + " 'Siberian Husky', 'Bichon Frise', 'Tibetan Spaniel',\n", + " 'English Foxhound', 'American Foxhound', 'Greyhound',\n", + " 'Wirehaired Pointing Griffon', 'West Highland White Terrier',\n", + " 'Scottish Deerhound', 'Boxer', 'Great Dane', 'Dachshund',\n", + " 'Shiba Inu', 'Staffordshire Bull Terrier', 'Alaskan Malamute',\n", + " 'Whippet', 'Chinese Shar Pei', 'Rhodesian Ridgeback',\n", + " 'Ibizan Hound', 'Welsh Terrier', 'Irish Terrier', 'Boston Terrier',\n", + " 'Akita', 'Skye Terrier', 'Sealyham Terrier', 'Pug',\n", + " 'French Bulldog', 'Maltese', 'Italian Greyhound',\n", + " 'Chinese Crested', 'Dandie Dinmont Terrier', 'Tibetan Terrier',\n", + " 'Japanese Chin', 'Lakeland Terrier', 'Great Pyrenees',\n", + " 'Scottish Terrier', 'Saint Bernard', 'Bull Terrier', 'Chihuahua',\n", + " 'Bullmastiff', 'Shih Tzu', 'Basset Hound', 'Mastiff', 'Beagle',\n", + " 'Bloodhound', 'Borzoi', 'Chow Chow', 'Basenji', 'Afghan Hound'],\n", + " dtype=object)" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Breed.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pre_ml_data = brint[['Classification','Height','Weight']].copy()\n", + "\n", + "sns.pairplot(data= pre_ml_data, hue='Classification')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [], + "source": [ + "# pre_ml_data.replace({'Classification':{'Brightest Dogs': 'Brightest', 'Excellent Working Dogs': 'Excellent',\n", + "# 'Above Average Working Dogs': 'Above_Average',\n", + "# 'Average Working/Obedience Intelligence': 'Average',\n", + "# 'Fair Working/Obedience Intelligence': 'Below_Average',\n", + "# 'Lowest Degree of Working/Obedience Intelligence ': 'Lowest'}}, inplace= True)\n", + "# pre_ml_data = pd.get_dummies(ml_data)\n", + "# \n", + "# pre_ml_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ClassificationHeightWeight
0020.040.0
1022.565.0
2027.080.0
3022.567.5
409.57.5
5024.5100.0
6018.540.0
7120.050.0
8111.515.0
9124.067.5
10118.042.5
11123.565.0
12118.033.0
13119.035.0
14126.077.5
15124.062.5
16125.097.5
17112.05.0
18116.555.0
19123.557.0
\n", + "
" + ], + "text/plain": [ + " Classification Height Weight\n", + "0 0 20.0 40.0\n", + "1 0 22.5 65.0\n", + "2 0 27.0 80.0\n", + "3 0 22.5 67.5\n", + "4 0 9.5 7.5\n", + "5 0 24.5 100.0\n", + "6 0 18.5 40.0\n", + "7 1 20.0 50.0\n", + "8 1 11.5 15.0\n", + "9 1 24.0 67.5\n", + "10 1 18.0 42.5\n", + "11 1 23.5 65.0\n", + "12 1 18.0 33.0\n", + "13 1 19.0 35.0\n", + "14 1 26.0 77.5\n", + "15 1 24.0 62.5\n", + "16 1 25.0 97.5\n", + "17 1 12.0 5.0\n", + "18 1 16.5 55.0\n", + "19 1 23.5 57.0" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pre_ml_data.replace({'Classification':{'Brightest Dogs': 0, 'Excellent Working Dogs': 1,\n", + " 'Above Average Working Dogs': 2,\n", + " 'Average Working/Obedience Intelligence': 3,\n", + " 'Fair Working/Obedience Intelligence': 4,\n", + " 'Lowest Degree of Working/Obedience Intelligence ': 5}}, inplace= True)\n", + "\n", + "pre_ml_data.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [], + "source": [ + "ml_data = pre_ml_data[['Height','Weight']].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "model = KMeans(n_clusters=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "clusters = model.fit(ml_data)\n", + "ml_data['Kmeans_labels'] = clusters.fit_predict(ml_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
HeightWeightKmeans_labels
020.040.04
122.565.00
227.080.00
322.567.50
49.57.55
524.5100.03
618.540.04
720.050.04
811.515.05
924.067.50
1018.042.54
1123.565.00
1218.033.01
1319.035.01
1426.077.50
1524.062.50
1625.097.53
1712.05.05
1816.555.04
1923.557.04
\n", + "
" + ], + "text/plain": [ + " Height Weight Kmeans_labels\n", + "0 20.0 40.0 4\n", + "1 22.5 65.0 0\n", + "2 27.0 80.0 0\n", + "3 22.5 67.5 0\n", + "4 9.5 7.5 5\n", + "5 24.5 100.0 3\n", + "6 18.5 40.0 4\n", + "7 20.0 50.0 4\n", + "8 11.5 15.0 5\n", + "9 24.0 67.5 0\n", + "10 18.0 42.5 4\n", + "11 23.5 65.0 0\n", + "12 18.0 33.0 1\n", + "13 19.0 35.0 1\n", + "14 26.0 77.5 0\n", + "15 24.0 62.5 0\n", + "16 25.0 97.5 3\n", + "17 12.0 5.0 5\n", + "18 16.5 55.0 4\n", + "19 23.5 57.0 4" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ml_data.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [], + "source": [ + "ml_data['True_labels'] = pre_ml_data['Classification']" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(18, 8))\n", + "ax1.scatter(ml_data['Height'], ml_data['Weight'], c=ml_data['Kmeans_labels'])\n", + "ax2.scatter(ml_data['Height'], ml_data['Weight'], c=ml_data['True_labels'])\n", + "ax1.set_title('K-means clusters')\n", + "ax2.set_title('True clusters')\n", + "ax1.set_xlabel('Height (ins)')\n", + "ax2.set_xlabel('Height (ins)')\n", + "ax1.set_ylabel('Weight (lbs)')\n", + "ax2.set_ylabel('Weight (lbs)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4 27\n", + "0 23\n", + "5 21\n", + "1 21\n", + "3 10\n", + "2 3\n", + "Name: Kmeans_labels, dtype: int64" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ml_data.Kmeans_labels.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [], + "source": [ + "test_df = pd.DataFrame({'Height': [26, 30], 'Weight': [100, 120]})\n", + "test_df['Kmeans_labels'] = clusters.predict(test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "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", + "
HeightWeightKmeans_labels
0261003
1301203
\n", + "
" + ], + "text/plain": [ + " Height Weight Kmeans_labels\n", + "0 26 100 3\n", + "1 30 120 3" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def imaginary_dog \n", + " jumps = int(input\n", + " if 0 >= jumps >= -95:\n", + " coded_index.append(i+jumps)\n", + " #print(f'Encrypting the message <{message}> with <{jumps}> jumps.')\n", + " test_df = pd.DataFrame({'Height': [26, 30], 'Weight': [100, 120]})\n", + " test_df['Kmeans_labels'] = clusters.predict(test_df)" + ] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/breed_info.csv b/your-project/breed_info.csv new file mode 100644 index 00000000..53fd3092 --- /dev/null +++ b/your-project/breed_info.csv @@ -0,0 +1,151 @@ +Breed,height_low_inches,height_high_inches,weight_low_lbs,weight_high_lbs +Akita,26,28,80,120 +Anatolian Sheepdog,27,29,100,150 +Bernese Mountain Dog,23,27,85,110 +Bloodhound,24,26,80,120 +Borzoi,26,28,70,100 +Bullmastiff,25,27,100,130 +Great Dane,32,32,120,160 +Great Pyrenees,27,32,95,120 +Great Swiss Mountain Dog,23,28,130,150 +Irish Wolfhound,28,35,90,150 +Kuvasz,28,30,70,120 +Mastiff,27,30,175,190 +Neopolitan Mastiff,24,30,100,150 +Newfoundland,26,28,100,150 +Otter Hound,24,26,65,110 +Rottweiler,22,27,90,110 +Saint Bernard,25,28,110,190 +Afghan Hound,25,27,50,60 +Alaskan Malamute,na,na,na,na +American Foxhound,22,25,65,70 +Beauceron,24,27,100,120 +Belgian Malinois,22,26,60,65 +Belgian Sheepdog,22,26,60,75 +Belgian Tervuren,22,26,60,75 +Black And Tan Coonhound,23,27,50,75 +Black Russian Terrier,25,29,80,140 +Bouvier Des Flandres,23,27,75,95 +Boxer,21,25,65,70 +Briard,23,27,74,76 +Chesapeake Bay Retriever,21,26,55,75 +Clumber Spaniel,19,20,35,65 +Collie (Rough) & (Smooth),22,26,50,75 +Curly Coated Retriever,25,27,65,80 +Doberman Pinscher,26,28,60,100 +English Foxhound,22,25,65,70 +English Setter,23,27,45,80 +German Shepherd Dog,22,26,75,90 +German Shorthaired Pointer,20,27,50,80 +German Wirehaired Pointer,22,26,60,70 +Giant Schnauzer,25,28,70,75 +Golden Retriever,21,24,55,75 +Gordon Setter,23,27,45,80 +Greyhound,27,30,60,70 +Irish Setter,25,27,60,70 +Komondor,26.5,35.5,80,135 +Labrador Retriever,21,24,55,80 +Old English Sheepdog (Bobtail),20,24,60,65 +Poodle Standard,15,25,45,45 +Rhodesian Ridgeback,24,27,70,85 +Scottish Deerhound,28,32,75,110 +Spinone Italiano,23,28,65,80 +Tibetan Mastiff,24,26,140,170 +Weimaraner,25,27,70,85 +Airdale Terrier,22,24,45,45 +American Staffordshire Terrier,17,19,40,50 +American Water Spaniel,15,18,25,45 +Australian Cattle Dog,17,20,35,45 +Australian Shepherd,18,23,40,60 +Basset Hound,14,14,40,50 +Bearded Collie,20,22,40,60 +Border Collie,19,21,40,40 +Brittany,17,21,30,40 +Bull Dog,12,16,50,60 +Bull Terrier,21,22,50,70 +Canaan Dog,19,24,35,55 +Chinese Shar Pei,18,20,45,55 +Chow Chow,19,22,45,55 +Cocker Spaniel-American,15,16,22,28 +Cocker Spaniel-English,15,17,25,35 +Dalmatian,19,23,45,70 +English Springer Spaniel,20,20,45,55 +Field Spaniel,18,18,35,50 +Flat Coated Retriever,22,23,60,70 +Finnish Spitz,15,20,31,35 +Harrier,19,21,40,60 +Ibizan Hound,22,29,42,55 +Irish Terrier,18,19,25,27 +Irish Water Spaniel,10,23,45,65 +Keeshond,17,19,35,50 +Kerry Blue Terrier,17,21,30,45 +Norwegian Elkhound,19,20,40,60 +Nova Scotia Duck Tolling Retriever,17,21,35,50 +Petit Basset Griffon Vendeen,13,15,30,40 +Pharaoh Hound,21,25,45,55 +Plott Hound,20,24,45,55 +Pointer,21,24,44,66 +Polish Lowland Sheepdog,16,20,30,35 +Portuguese Water Dog,20,23,42,60 +Redbone Coonhound,21,27,50,70 +Saluki,23,28,35,70 +Samoyed,19,24,50,65 +Siberian Husky,20,23,40,60 +Soft-Coated Wheaten Terrier,18,20,35,45 +Staffordshire Bull Terrier,14,16,24,28 +Standard Schnauzer,17,19,33,33 +Sussex Spaniel,15,16,40,45 +Vizsla,48,66,22,25 +Welsh Springer Spaniel,16,19,35,45 +Wirehaired Pointing Griffon,20,24,45,60 +American Eskimo,9,19,25,30 +Australian Terrier,10,10,10,14 +Basenji,17,17,20,22 +Beagle,13,16,18,30 +Bedlington Terrier,15,16,18,23 +Bichon Frise,9.5,11.5,10,18 +Border Terrier,12,15,12,15 +Boston Terrier,14,15,15,25 +Brussels Griffon,7,8,6,12 +Cairn Terrier,9,10,14,14 +Cardigan Welsh Corgi,10,12,25,35 +Cavalier King Charles Spaniel,10,15,15,20 +Coton de Tulear,not found,not found,not found,not found +Dachshund,7,10,16,32 +Dandie Dinmont Terrier,18,24,8,11 +English Toy Spaniel,10,10,9,15 +Fox Terrier ‰ÛÒ Smooth,13,16,15,20 +Fox Terrier ‰ÛÒ Wirehair,13,16,15,20 +French Bulldog,11,12,17,28 +German Pinscher,16,19,25,35 +Glen Imaal Terrier,14,14,34,36 +Lakeland Terrier,13,14,15,17 +Manchester Terrier (Standard),15,16,17,18 +Poodle Miniature,10,15,20,20 +Pug,10,11,14,22 +Puli,16,17,29,33 +Schipperke,10,13,12,18 +Scottish Terrier,10,12,18,22 +Sealyham Terrier,12,12,18,20 +Shetland Sheepdog (Sheltie),13,16,14,20 +Shiba Inu,13,16,15,30 +Shih Tzu,8,11,9,16 +Silky Terrier,9,10,8,11 +Skye Terrier,10,10,25,25 +Tibetan Spaniel,10,10,9,15 +Tibetan Terrier,14,17,20,30 +Welsh Terrier,15,15,20,21 +West Highland White Terrier,11,11,13,15 +Whippet,18,22,27,30 +Affenpinscher,9,12,8,12 +Chihuahua,6,9,2,5 +Chinese Crested,11,13,5,12 +Italian Greyhound,12,15,6,10 +Japanese Chin,8,11,4,11 +Maltese,8,10,4,6 +Manchester Terrier (Toy),10,12,6,8 +Papillon,8,11,5,10 +Pomeranian,12,12,3,7 +Poodle Toy,10,10,10,10 +Toy Fox Terrier,10,10,4,7 +Yorkshire Terrier,8,8,3,7 \ No newline at end of file diff --git a/your-project/dog_intelligence.csv b/your-project/dog_intelligence.csv new file mode 100644 index 00000000..b84bffdd --- /dev/null +++ b/your-project/dog_intelligence.csv @@ -0,0 +1,137 @@ +Breed,Classification,obey,reps_lower,reps_upper +Border Collie,Brightest Dogs,95%,1,4 +Poodle,Brightest Dogs,95%,1,4 +German Shepherd,Brightest Dogs,95%,1,4 +Golden Retriever,Brightest Dogs,95%,1,4 +Doberman Pinscher,Brightest Dogs,95%,1,4 +Shetland Sheepdog,Brightest Dogs,95%,1,4 +Labrador Retriever,Brightest Dogs,95%,1,4 +Papillon,Brightest Dogs,95%,1,4 +Rottweiler,Brightest Dogs,95%,1,4 +Australian Cattle Dog,Brightest Dogs,95%,1,4 +Pembroke Welsh Corgi,Excellent Working Dogs,85%,5,15 +Miniature Schnauzer,Excellent Working Dogs,85%,5,15 +English Springer Spaniel,Excellent Working Dogs,85%,5,15 +Belgian Shepherd Dog (Tervuren),Excellent Working Dogs,85%,5,15 +Schipperke,Excellent Working Dogs,85%,5,15 +Belgian Sheepdog,Excellent Working Dogs,85%,5,15 +Collie,Excellent Working Dogs,85%,5,15 +Keeshond,Excellent Working Dogs,85%,5,15 +German Shorthaired Pointer,Excellent Working Dogs,85%,5,15 +Flat-Coated Retriever,Excellent Working Dogs,85%,5,15 +English Cocker Spaniel,Excellent Working Dogs,85%,5,15 +Standard Schnauzer,Excellent Working Dogs,85%,5,15 +Brittany,Excellent Working Dogs,85%,5,15 +Cocker Spaniel,Excellent Working Dogs,85%,5,15 +Weimaraner,Excellent Working Dogs,85%,5,15 +Belgian Malinois,Excellent Working Dogs,85%,5,15 +Bernese Mountain Dog,Excellent Working Dogs,85%,5,15 +Pomeranian,Excellent Working Dogs,85%,5,15 +Irish Water Spaniel,Excellent Working Dogs,85%,5,15 +Vizsla,Excellent Working Dogs,85%,5,15 +Cardigan Welsh Corgi,Excellent Working Dogs,85%,5,15 +Chesapeake Bay Retriever,Above Average Working Dogs,70%,16,25 +Puli,Above Average Working Dogs,70%,16,25 +Yorkshire Terrier,Above Average Working Dogs,70%,16,25 +Giant Schnauzer,Above Average Working Dogs,70%,16,25 +Portuguese Water Dog,Above Average Working Dogs,70%,16,25 +Airedale Terrier,Above Average Working Dogs,70%,16,25 +Bouvier des Flandres,Above Average Working Dogs,70%,16,25 +Border Terrier,Above Average Working Dogs,70%,16,25 +Briard,Above Average Working Dogs,70%,16,25 +Welsh Springer Spaniel,Above Average Working Dogs,70%,16,25 +Manchester Terrier,Above Average Working Dogs,70%,16,25 +Samoyed,Above Average Working Dogs,70%,16,25 +Field Spaniel,Above Average Working Dogs,70%,16,25 +Newfoundland,Above Average Working Dogs,70%,16,25 +Australian Terrier,Above Average Working Dogs,70%,16,25 +American Staffordshire Terrier,Above Average Working Dogs,70%,16,25 +Gordon Setter,Above Average Working Dogs,70%,16,25 +Bearded Collie,Above Average Working Dogs,70%,16,25 +Cairn Terrier,Above Average Working Dogs,70%,16,25 +Kerry Blue Terrier,Above Average Working Dogs,70%,16,25 +Irish Setter,Above Average Working Dogs,70%,16,25 +Norwegian Elkhound,Above Average Working Dogs,70%,16,25 +Affenpinscher,Above Average Working Dogs,70%,16,25 +Australian Silky Terrier,Above Average Working Dogs,70%,16,25 +Miniature Pinscher,Above Average Working Dogs,70%,16,25 +English Setter,Above Average Working Dogs,70%,16,25 +Pharaoh Hound,Above Average Working Dogs,70%,16,25 +Clumber Spaniel,Above Average Working Dogs,70%,16,25 +Norwich Terrier,Above Average Working Dogs,70%,16,25 +Dalmatian,Above Average Working Dogs,70%,16,25 +Soft-coated Wheaten Terrier,Average Working/Obedience Intelligence,50%,26,40 +Bedlington Terrier,Average Working/Obedience Intelligence,50%,26,40 +Fox Terrier (Smooth),Average Working/Obedience Intelligence,50%,26,40 +Curly Coated Retriever,Average Working/Obedience Intelligence,50%,26,40 +Irish Wolfhound,Average Working/Obedience Intelligence,50%,26,40 +Kuvasz,Average Working/Obedience Intelligence,50%,26,40 +Australian Shepherd,Average Working/Obedience Intelligence,50%,26,40 +Saluki,Average Working/Obedience Intelligence,50%,26,40 +Finnish Spitz,Average Working/Obedience Intelligence,50%,26,40 +Pointer,Average Working/Obedience Intelligence,50%,26,40 +Cavalier King Charles Spaniel,Average Working/Obedience Intelligence,50%,26,40 +German Wirehaired Pointer,Average Working/Obedience Intelligence,50%,26,40 +Black and Tan Coonhound,Average Working/Obedience Intelligence,50%,26,40 +American Water Spaniel,Average Working/Obedience Intelligence,50%,26,40 +Siberian Husky,Average Working/Obedience Intelligence,50%,26,40 +Bichon Frise,Average Working/Obedience Intelligence,50%,26,40 +King Charles Spaniel,Average Working/Obedience Intelligence,50%,26,40 +Tibetan Spaniel,Average Working/Obedience Intelligence,50%,26,40 +English Foxhound,Average Working/Obedience Intelligence,50%,26,40 +Otterhound,Average Working/Obedience Intelligence,50%,26,40 +Jack Russell terrier,Average Working/Obedience Intelligence,50%,26,40 +American Foxhound,Average Working/Obedience Intelligence,50%,26,40 +Greyhound,Average Working/Obedience Intelligence,50%,26,40 +Wirehaired Pointing Griffon,Average Working/Obedience Intelligence,50%,26,40 +West Highland White Terrier,Average Working/Obedience Intelligence,50%,26,40 +Havanese,Average Working/Obedience Intelligence,50%,26,40 +Scottish Deerhound,Average Working/Obedience Intelligence,50%,26,40 +Boxer,Average Working/Obedience Intelligence,50%,26,40 +Great Dane,Average Working/Obedience Intelligence,50%,26,40 +Dachshund,Average Working/Obedience Intelligence,50%,26,40 +Shiba Inu,Average Working/Obedience Intelligence,50%,26,40 +Staffordshire Bull Terrier,Average Working/Obedience Intelligence,50%,26,40 +Alaskan Malamute,Average Working/Obedience Intelligence,50%,26,40 +Whippet,Average Working/Obedience Intelligence,50%,26,40 +Chinese Shar Pei,Average Working/Obedience Intelligence,50%,26,40 +Wire Fox Terrier,Average Working/Obedience Intelligence,50%,26,40 +Rhodesian Ridgeback,Average Working/Obedience Intelligence,50%,26,40 +Ibizan Hound,Average Working/Obedience Intelligence,50%,26,40 +Welsh Terrier,Average Working/Obedience Intelligence,50%,26,40 +Irish Terrier,Average Working/Obedience Intelligence,50%,26,40 +Boston Terrier,Average Working/Obedience Intelligence,50%,26,40 +Akita,Average Working/Obedience Intelligence,50%,26,40 +Skye Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Norfolk Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Sealyham Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Pug,Fair Working/Obedience Intelligence,30%,41,80 +French Bulldog,Fair Working/Obedience Intelligence,30%,41,80 +Griffon Bruxellois,Fair Working/Obedience Intelligence,30%,41,80 +Maltese,Fair Working/Obedience Intelligence,30%,41,80 +Italian Greyhound,Fair Working/Obedience Intelligence,30%,41,80 +Chinese Crested,Fair Working/Obedience Intelligence,30%,41,80 +Dandie Dinmont Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Petit Basset Griffon VendÌ©en,Fair Working/Obedience Intelligence,30%,41,80 +Tibetan Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Japanese Chin,Fair Working/Obedience Intelligence,30%,41,80 +Lakeland Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Old English Sheepdog,Fair Working/Obedience Intelligence,30%,41,80 +Great Pyrenees,Fair Working/Obedience Intelligence,30%,41,80 +Scottish Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Saint Bernard,Fair Working/Obedience Intelligence,30%,41,80 +Bull Terrier,Fair Working/Obedience Intelligence,30%,41,80 +Chihuahua,Fair Working/Obedience Intelligence,30%,41,80 +Lhasa Apso,Fair Working/Obedience Intelligence,30%,41,80 +Bullmastiff,Fair Working/Obedience Intelligence,30%,41,80 +Shih Tzu,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Basset Hound,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Mastiff,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Beagle,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Pekingese,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Bloodhound,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Borzoi,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Chow Chow,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Bulldog,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Basenji,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 +Afghan Hound,Lowest Degree of Working/Obedience Intelligence ,n/a,81,100 \ No newline at end of file From a87abb77b8a5e18dc7f522426958a300b050aded Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 11:42:26 +0100 Subject: [PATCH 2/8] Add files via upload --- ...Dog_Intelligence_Size_ Relationships.ipynb | 3321 +++++++++++++++++ your-project/HW-I.PNG | Bin 0 -> 127073 bytes your-project/README.md | 38 +- 3 files changed, 3341 insertions(+), 18 deletions(-) create mode 100644 your-project/Dog_Intelligence_Size_ Relationships.ipynb create mode 100644 your-project/HW-I.PNG diff --git a/your-project/Dog_Intelligence_Size_ Relationships.ipynb b/your-project/Dog_Intelligence_Size_ Relationships.ipynb new file mode 100644 index 00000000..a4c0a481 --- /dev/null +++ b/your-project/Dog_Intelligence_Size_ Relationships.ipynb @@ -0,0 +1,3321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing all necessary libraries. Added in the first cell for clarity.\n", + "\n", + "import pandas as pd\n", + "import re\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationobeyreps_lowerreps_upper
0Border CollieBrightest Dogs95%14
1PoodleBrightest Dogs95%14
2German ShepherdBrightest Dogs95%14
3Golden RetrieverBrightest Dogs95%14
4Doberman PinscherBrightest Dogs95%14
..................
131BorzoiLowest Degree of Working/Obedience IntelligenceNaN81100
132Chow ChowLowest Degree of Working/Obedience IntelligenceNaN81100
133BulldogLowest Degree of Working/Obedience IntelligenceNaN81100
134BasenjiLowest Degree of Working/Obedience IntelligenceNaN81100
135Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN81100
\n", + "

136 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification obey \\\n", + "0 Border Collie Brightest Dogs 95% \n", + "1 Poodle Brightest Dogs 95% \n", + "2 German Shepherd Brightest Dogs 95% \n", + "3 Golden Retriever Brightest Dogs 95% \n", + "4 Doberman Pinscher Brightest Dogs 95% \n", + ".. ... ... ... \n", + "131 Borzoi Lowest Degree of Working/Obedience Intelligence NaN \n", + "132 Chow Chow Lowest Degree of Working/Obedience Intelligence NaN \n", + "133 Bulldog Lowest Degree of Working/Obedience Intelligence NaN \n", + "134 Basenji Lowest Degree of Working/Obedience Intelligence NaN \n", + "135 Afghan Hound Lowest Degree of Working/Obedience Intelligence NaN \n", + "\n", + " reps_lower reps_upper \n", + "0 1 4 \n", + "1 1 4 \n", + "2 1 4 \n", + "3 1 4 \n", + "4 1 4 \n", + ".. ... ... \n", + "131 81 100 \n", + "132 81 100 \n", + "133 81 100 \n", + "134 81 100 \n", + "135 81 100 \n", + "\n", + "[136 rows x 5 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking breed-intelligence dataset.\n", + "# Columns 'reps_lower' and 'reps_upper' are the minimum and maximum number of repetitions needed for the dog to fulfill a task.\n", + "\n", + "dog_int = pd.read_csv('dog_intelligence.csv')\n", + "dog_int" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Breedheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Akita262880120
1Anatolian Sheepdog2729100150
2Bernese Mountain Dog232785110
3Bloodhound242680120
4Borzoi262870100
..................
145Papillon811510
146Pomeranian121237
147Poodle Toy10101010
148Toy Fox Terrier101047
149Yorkshire Terrier8837
\n", + "

150 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Breed height_low_inches height_high_inches weight_low_lbs \\\n", + "0 Akita 26 28 80 \n", + "1 Anatolian Sheepdog 27 29 100 \n", + "2 Bernese Mountain Dog 23 27 85 \n", + "3 Bloodhound 24 26 80 \n", + "4 Borzoi 26 28 70 \n", + ".. ... ... ... ... \n", + "145 Papillon 8 11 5 \n", + "146 Pomeranian 12 12 3 \n", + "147 Poodle Toy 10 10 10 \n", + "148 Toy Fox Terrier 10 10 4 \n", + "149 Yorkshire Terrier 8 8 3 \n", + "\n", + " weight_high_lbs \n", + "0 120 \n", + "1 150 \n", + "2 110 \n", + "3 120 \n", + "4 100 \n", + ".. ... \n", + "145 10 \n", + "146 7 \n", + "147 10 \n", + "148 7 \n", + "149 7 \n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking breed-height-weight dataset.\n", + "\n", + "breed_info = pd.read_csv('breed_info.csv', encoding= 'unicode_escape')\n", + "breed_info" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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", + " \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", + " \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", + " \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", + "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Border CollieBrightest Dogs95%1419214040
1Golden RetrieverBrightest Dogs95%1421245575
2Doberman PinscherBrightest Dogs95%14262860100
3Labrador RetrieverBrightest Dogs95%1421245580
4PapillonBrightest Dogs95%14811510
..............................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN81100242680120
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN81100262870100
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110019224555
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017172022
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110025275060
\n", + "

105 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " obey reps_lower reps_upper height_low_inches height_high_inches \\\n", + "0 95% 1 4 19 21 \n", + "1 95% 1 4 21 24 \n", + "2 95% 1 4 26 28 \n", + "3 95% 1 4 21 24 \n", + "4 95% 1 4 8 11 \n", + ".. ... ... ... ... ... \n", + "100 NaN 81 100 24 26 \n", + "101 NaN 81 100 26 28 \n", + "102 NaN 81 100 19 22 \n", + "103 NaN 81 100 17 17 \n", + "104 NaN 81 100 25 27 \n", + "\n", + " weight_low_lbs weight_high_lbs \n", + "0 40 40 \n", + "1 55 75 \n", + "2 60 100 \n", + "3 55 80 \n", + "4 5 10 \n", + ".. ... ... \n", + "100 80 120 \n", + "101 70 100 \n", + "102 45 55 \n", + "103 20 22 \n", + "104 50 60 \n", + "\n", + "[105 rows x 9 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Merging both datasets in order to manage all the info together.\n", + "\n", + "breed_int = dog_int.merge(breed_info, left_on = 'Breed', right_on = 'Breed')\n", + "breed_int" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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", + "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
70Alaskan MalamuteAverage Working/Obedience Intelligence50%2640nananana
\n", + "
" + ], + "text/plain": [ + " Breed Classification obey reps_lower \\\n", + "70 Alaskan Malamute Average Working/Obedience Intelligence 50% 26 \n", + "\n", + " reps_upper height_low_inches height_high_inches weight_low_lbs \\\n", + "70 40 na na na \n", + "\n", + " weight_high_lbs \n", + "70 na " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Looking for NaNs in weight and height (For some reason, the original dataset had 'na' string instead of NaN).\n", + "\n", + "breed_int.loc[breed_int.weight_high_lbs == 'na']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Data for the height and weight 'na' researched and filled. \n", + "# Since all the other values are strings, the replace is done with strings also, in order to change everything together later.\n", + "\n", + "breed_int.replace({'height_high_inches':{'na':'25'}, 'height_low_inches':{'na':'25'}, 'weight_high_lbs':{'na':'82'}, 'weight_low_lbs':{'na':'82'}}, inplace= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Transformation of strings into numerical values.\n", + "\n", + "breed_int['height_low_inches'] = pd.to_numeric(breed_int['height_low_inches'])\n", + "breed_int['height_high_inches'] = pd.to_numeric(breed_int['height_high_inches'])\n", + "breed_int['weight_low_lbs'] = pd.to_numeric(breed_int['weight_low_lbs'])\n", + "breed_int['weight_high_lbs'] = pd.to_numeric(breed_int['weight_high_lbs'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 105 entries, 0 to 104\n", + "Data columns (total 9 columns):\n", + "Breed 105 non-null object\n", + "Classification 105 non-null object\n", + "obey 96 non-null object\n", + "reps_lower 105 non-null int64\n", + "reps_upper 105 non-null int64\n", + "height_low_inches 105 non-null float64\n", + "height_high_inches 105 non-null float64\n", + "weight_low_lbs 105 non-null int64\n", + "weight_high_lbs 105 non-null int64\n", + "dtypes: float64(2), int64(4), object(3)\n", + "memory usage: 8.2+ KB\n" + ] + } + ], + "source": [ + "breed_int.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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", + " \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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbsHeightWeight
0Border CollieBrightest Dogs95%1419.021.0404020.040.0
1Golden RetrieverBrightest Dogs95%1421.024.0557522.565.0
2Doberman PinscherBrightest Dogs95%1426.028.06010027.080.0
3Labrador RetrieverBrightest Dogs95%1421.024.0558022.567.5
4PapillonBrightest Dogs95%148.011.05109.57.5
....................................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN8110024.026.08012025.0100.0
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN8110026.028.07010027.085.0
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110019.022.0455520.550.0
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017.017.0202217.021.0
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110025.027.0506026.055.0
\n", + "

105 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " obey reps_lower reps_upper height_low_inches height_high_inches \\\n", + "0 95% 1 4 19.0 21.0 \n", + "1 95% 1 4 21.0 24.0 \n", + "2 95% 1 4 26.0 28.0 \n", + "3 95% 1 4 21.0 24.0 \n", + "4 95% 1 4 8.0 11.0 \n", + ".. ... ... ... ... ... \n", + "100 NaN 81 100 24.0 26.0 \n", + "101 NaN 81 100 26.0 28.0 \n", + "102 NaN 81 100 19.0 22.0 \n", + "103 NaN 81 100 17.0 17.0 \n", + "104 NaN 81 100 25.0 27.0 \n", + "\n", + " weight_low_lbs weight_high_lbs Height Weight \n", + "0 40 40 20.0 40.0 \n", + "1 55 75 22.5 65.0 \n", + "2 60 100 27.0 80.0 \n", + "3 55 80 22.5 67.5 \n", + "4 5 10 9.5 7.5 \n", + ".. ... ... ... ... \n", + "100 80 120 25.0 100.0 \n", + "101 70 100 27.0 85.0 \n", + "102 45 55 20.5 50.0 \n", + "103 20 22 17.0 21.0 \n", + "104 50 60 26.0 55.0 \n", + "\n", + "[105 rows x 11 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Getting mean values of each height and weight minimum and maximum, to ease the analysis.\n", + "\n", + "breed_int['Height'] = (0.5 * breed_int['height_low_inches']) + (0.5 * breed_int['height_high_inches'])\n", + "breed_int['Weight'] = (0.5 * breed_int['weight_low_lbs']) + (0.5 * breed_int['weight_high_lbs'])\n", + " \n", + "breed_int" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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", + " \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", + " \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", + " \n", + "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs95%1420.040.0
1Golden RetrieverBrightest Dogs95%1422.565.0
2Doberman PinscherBrightest Dogs95%1427.080.0
3Labrador RetrieverBrightest Dogs95%1422.567.5
4PapillonBrightest Dogs95%149.57.5
........................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN8110025.0100.0
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN8110027.085.0
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110020.550.0
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017.021.0
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110026.055.0
\n", + "

105 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " obey reps_lower reps_upper Height Weight \n", + "0 95% 1 4 20.0 40.0 \n", + "1 95% 1 4 22.5 65.0 \n", + "2 95% 1 4 27.0 80.0 \n", + "3 95% 1 4 22.5 67.5 \n", + "4 95% 1 4 9.5 7.5 \n", + ".. ... ... ... ... ... \n", + "100 NaN 81 100 25.0 100.0 \n", + "101 NaN 81 100 27.0 85.0 \n", + "102 NaN 81 100 20.5 50.0 \n", + "103 NaN 81 100 17.0 21.0 \n", + "104 NaN 81 100 26.0 55.0 \n", + "\n", + "[105 rows x 7 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cleaning original height and weight columns, now the mean values can be used.\n", + "\n", + "del breed_int['height_low_inches']\n", + "del breed_int['height_high_inches']\n", + "del breed_int['weight_low_lbs']\n", + "del breed_int['weight_high_lbs']\n", + "breed_int" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
50SalukiAverage Working/Obedience Intelligence50264025.552.5
51Finnish SpitzAverage Working/Obedience Intelligence50264017.533.0
52PointerAverage Working/Obedience Intelligence50264022.555.0
53Cavalier King Charles SpanielAverage Working/Obedience Intelligence50264012.517.5
54German Wirehaired PointerAverage Working/Obedience Intelligence50264024.065.0
55American Water SpanielAverage Working/Obedience Intelligence50264016.535.0
56Siberian HuskyAverage Working/Obedience Intelligence50264021.550.0
57Bichon FriseAverage Working/Obedience Intelligence50264010.514.0
58Tibetan SpanielAverage Working/Obedience Intelligence50264010.012.0
59English FoxhoundAverage Working/Obedience Intelligence50264023.567.5
60American FoxhoundAverage Working/Obedience Intelligence50264023.567.5
61GreyhoundAverage Working/Obedience Intelligence50264028.565.0
62Wirehaired Pointing GriffonAverage Working/Obedience Intelligence50264022.052.5
63West Highland White TerrierAverage Working/Obedience Intelligence50264011.014.0
64Scottish DeerhoundAverage Working/Obedience Intelligence50264030.092.5
65BoxerAverage Working/Obedience Intelligence50264023.067.5
66Great DaneAverage Working/Obedience Intelligence50264032.0140.0
67DachshundAverage Working/Obedience Intelligence5026408.524.0
68Shiba InuAverage Working/Obedience Intelligence50264014.522.5
69Staffordshire Bull TerrierAverage Working/Obedience Intelligence50264015.026.0
70Alaskan MalamuteAverage Working/Obedience Intelligence50264025.082.0
71WhippetAverage Working/Obedience Intelligence50264020.028.5
72Chinese Shar PeiAverage Working/Obedience Intelligence50264019.050.0
73Rhodesian RidgebackAverage Working/Obedience Intelligence50264025.577.5
74Ibizan HoundAverage Working/Obedience Intelligence50264025.548.5
75Welsh TerrierAverage Working/Obedience Intelligence50264015.020.5
76Irish TerrierAverage Working/Obedience Intelligence50264018.526.0
77Boston TerrierAverage Working/Obedience Intelligence50264014.520.0
78AkitaAverage Working/Obedience Intelligence50264027.0100.0
79Skye TerrierFair Working/Obedience Intelligence30418010.025.0
80Sealyham TerrierFair Working/Obedience Intelligence30418012.019.0
81PugFair Working/Obedience Intelligence30418010.518.0
82French BulldogFair Working/Obedience Intelligence30418011.522.5
83MalteseFair Working/Obedience Intelligence3041809.05.0
84Italian GreyhoundFair Working/Obedience Intelligence30418013.58.0
85Chinese CrestedFair Working/Obedience Intelligence30418012.08.5
86Dandie Dinmont TerrierFair Working/Obedience Intelligence30418021.09.5
87Tibetan TerrierFair Working/Obedience Intelligence30418015.525.0
88Japanese ChinFair Working/Obedience Intelligence3041809.57.5
89Lakeland TerrierFair Working/Obedience Intelligence30418013.516.0
90Great PyreneesFair Working/Obedience Intelligence30418029.5107.5
91Scottish TerrierFair Working/Obedience Intelligence30418011.020.0
92Saint BernardFair Working/Obedience Intelligence30418026.5150.0
93Bull TerrierFair Working/Obedience Intelligence30418021.560.0
94ChihuahuaFair Working/Obedience Intelligence3041807.53.5
95BullmastiffFair Working/Obedience Intelligence30418026.0115.0
96Shih TzuLowest Degree of Working/Obedience Intelligence15811009.512.5
97Basset HoundLowest Degree of Working/Obedience Intelligence158110014.045.0
98MastiffLowest Degree of Working/Obedience Intelligence158110028.5182.5
99BeagleLowest Degree of Working/Obedience Intelligence158110014.524.0
100BloodhoundLowest Degree of Working/Obedience Intelligence158110025.0100.0
101BorzoiLowest Degree of Working/Obedience Intelligence158110027.085.0
102Chow ChowLowest Degree of Working/Obedience Intelligence158110020.550.0
103BasenjiLowest Degree of Working/Obedience Intelligence158110017.021.0
104Afghan HoundLowest Degree of Working/Obedience Intelligence158110026.055.0
\n", + "
" + ], + "text/plain": [ + " Breed \\\n", + "50 Saluki \n", + "51 Finnish Spitz \n", + "52 Pointer \n", + "53 Cavalier King Charles Spaniel \n", + "54 German Wirehaired Pointer \n", + "55 American Water Spaniel \n", + "56 Siberian Husky \n", + "57 Bichon Frise \n", + "58 Tibetan Spaniel \n", + "59 English Foxhound \n", + "60 American Foxhound \n", + "61 Greyhound \n", + "62 Wirehaired Pointing Griffon \n", + "63 West Highland White Terrier \n", + "64 Scottish Deerhound \n", + "65 Boxer \n", + "66 Great Dane \n", + "67 Dachshund \n", + "68 Shiba Inu \n", + "69 Staffordshire Bull Terrier \n", + "70 Alaskan Malamute \n", + "71 Whippet \n", + "72 Chinese Shar Pei \n", + "73 Rhodesian Ridgeback \n", + "74 Ibizan Hound \n", + "75 Welsh Terrier \n", + "76 Irish Terrier \n", + "77 Boston Terrier \n", + "78 Akita \n", + "79 Skye Terrier \n", + "80 Sealyham Terrier \n", + "81 Pug \n", + "82 French Bulldog \n", + "83 Maltese \n", + "84 Italian Greyhound \n", + "85 Chinese Crested \n", + "86 Dandie Dinmont Terrier \n", + "87 Tibetan Terrier \n", + "88 Japanese Chin \n", + "89 Lakeland Terrier \n", + "90 Great Pyrenees \n", + "91 Scottish Terrier \n", + "92 Saint Bernard \n", + "93 Bull Terrier \n", + "94 Chihuahua \n", + "95 Bullmastiff \n", + "96 Shih Tzu \n", + "97 Basset Hound \n", + "98 Mastiff \n", + "99 Beagle \n", + "100 Bloodhound \n", + "101 Borzoi \n", + "102 Chow Chow \n", + "103 Basenji \n", + "104 Afghan Hound \n", + "\n", + " Classification obey reps_lower \\\n", + "50 Average Working/Obedience Intelligence 50 26 \n", + "51 Average Working/Obedience Intelligence 50 26 \n", + "52 Average Working/Obedience Intelligence 50 26 \n", + "53 Average Working/Obedience Intelligence 50 26 \n", + "54 Average Working/Obedience Intelligence 50 26 \n", + "55 Average Working/Obedience Intelligence 50 26 \n", + "56 Average Working/Obedience Intelligence 50 26 \n", + "57 Average Working/Obedience Intelligence 50 26 \n", + "58 Average Working/Obedience Intelligence 50 26 \n", + "59 Average Working/Obedience Intelligence 50 26 \n", + "60 Average Working/Obedience Intelligence 50 26 \n", + "61 Average Working/Obedience Intelligence 50 26 \n", + "62 Average Working/Obedience Intelligence 50 26 \n", + "63 Average Working/Obedience Intelligence 50 26 \n", + "64 Average Working/Obedience Intelligence 50 26 \n", + "65 Average Working/Obedience Intelligence 50 26 \n", + "66 Average Working/Obedience Intelligence 50 26 \n", + "67 Average Working/Obedience Intelligence 50 26 \n", + "68 Average Working/Obedience Intelligence 50 26 \n", + "69 Average Working/Obedience Intelligence 50 26 \n", + "70 Average Working/Obedience Intelligence 50 26 \n", + "71 Average Working/Obedience Intelligence 50 26 \n", + "72 Average Working/Obedience Intelligence 50 26 \n", + "73 Average Working/Obedience Intelligence 50 26 \n", + "74 Average Working/Obedience Intelligence 50 26 \n", + "75 Average Working/Obedience Intelligence 50 26 \n", + "76 Average Working/Obedience Intelligence 50 26 \n", + "77 Average Working/Obedience Intelligence 50 26 \n", + "78 Average Working/Obedience Intelligence 50 26 \n", + "79 Fair Working/Obedience Intelligence 30 41 \n", + "80 Fair Working/Obedience Intelligence 30 41 \n", + "81 Fair Working/Obedience Intelligence 30 41 \n", + "82 Fair Working/Obedience Intelligence 30 41 \n", + "83 Fair Working/Obedience Intelligence 30 41 \n", + "84 Fair Working/Obedience Intelligence 30 41 \n", + "85 Fair Working/Obedience Intelligence 30 41 \n", + "86 Fair Working/Obedience Intelligence 30 41 \n", + "87 Fair Working/Obedience Intelligence 30 41 \n", + "88 Fair Working/Obedience Intelligence 30 41 \n", + "89 Fair Working/Obedience Intelligence 30 41 \n", + "90 Fair Working/Obedience Intelligence 30 41 \n", + "91 Fair Working/Obedience Intelligence 30 41 \n", + "92 Fair Working/Obedience Intelligence 30 41 \n", + "93 Fair Working/Obedience Intelligence 30 41 \n", + "94 Fair Working/Obedience Intelligence 30 41 \n", + "95 Fair Working/Obedience Intelligence 30 41 \n", + "96 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "97 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "98 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "99 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "100 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "101 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "102 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "103 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "104 Lowest Degree of Working/Obedience Intelligence 15 81 \n", + "\n", + " reps_upper Height Weight \n", + "50 40 25.5 52.5 \n", + "51 40 17.5 33.0 \n", + "52 40 22.5 55.0 \n", + "53 40 12.5 17.5 \n", + "54 40 24.0 65.0 \n", + "55 40 16.5 35.0 \n", + "56 40 21.5 50.0 \n", + "57 40 10.5 14.0 \n", + "58 40 10.0 12.0 \n", + "59 40 23.5 67.5 \n", + "60 40 23.5 67.5 \n", + "61 40 28.5 65.0 \n", + "62 40 22.0 52.5 \n", + "63 40 11.0 14.0 \n", + "64 40 30.0 92.5 \n", + "65 40 23.0 67.5 \n", + "66 40 32.0 140.0 \n", + "67 40 8.5 24.0 \n", + "68 40 14.5 22.5 \n", + "69 40 15.0 26.0 \n", + "70 40 25.0 82.0 \n", + "71 40 20.0 28.5 \n", + "72 40 19.0 50.0 \n", + "73 40 25.5 77.5 \n", + "74 40 25.5 48.5 \n", + "75 40 15.0 20.5 \n", + "76 40 18.5 26.0 \n", + "77 40 14.5 20.0 \n", + "78 40 27.0 100.0 \n", + "79 80 10.0 25.0 \n", + "80 80 12.0 19.0 \n", + "81 80 10.5 18.0 \n", + "82 80 11.5 22.5 \n", + "83 80 9.0 5.0 \n", + "84 80 13.5 8.0 \n", + "85 80 12.0 8.5 \n", + "86 80 21.0 9.5 \n", + "87 80 15.5 25.0 \n", + "88 80 9.5 7.5 \n", + "89 80 13.5 16.0 \n", + "90 80 29.5 107.5 \n", + "91 80 11.0 20.0 \n", + "92 80 26.5 150.0 \n", + "93 80 21.5 60.0 \n", + "94 80 7.5 3.5 \n", + "95 80 26.0 115.0 \n", + "96 100 9.5 12.5 \n", + "97 100 14.0 45.0 \n", + "98 100 28.5 182.5 \n", + "99 100 14.5 24.0 \n", + "100 100 25.0 100.0 \n", + "101 100 27.0 85.0 \n", + "102 100 20.5 50.0 \n", + "103 100 17.0 21.0 \n", + "104 100 26.0 55.0 " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking last rows, where there were no values for 'obey'.\n", + "\n", + "breed_int.tail(55)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# With the info on 'Classification', 'reps_lower' and 'reps_upper', the NaNs on 'obey' lower rows can be filled.\n", + "\n", + "breed_int['obey'] = breed_int['obey'].fillna('15%')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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", + " \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", + "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs951420.040.0
1Golden RetrieverBrightest Dogs951422.565.0
2Doberman PinscherBrightest Dogs951427.080.0
3Labrador RetrieverBrightest Dogs951422.567.5
4PapillonBrightest Dogs95149.57.5
\n", + "
" + ], + "text/plain": [ + " Breed Classification obey reps_lower reps_upper Height \\\n", + "0 Border Collie Brightest Dogs 95 1 4 20.0 \n", + "1 Golden Retriever Brightest Dogs 95 1 4 22.5 \n", + "2 Doberman Pinscher Brightest Dogs 95 1 4 27.0 \n", + "3 Labrador Retriever Brightest Dogs 95 1 4 22.5 \n", + "4 Papillon Brightest Dogs 95 1 4 9.5 \n", + "\n", + " Weight \n", + "0 40.0 \n", + "1 65.0 \n", + "2 80.0 \n", + "3 67.5 \n", + "4 7.5 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Getting rid of the percentage sign in 'obey' values and transforming to integer.\n", + "\n", + "def percentage_sign_remover(string):\n", + " return int(re.sub('%', '', string))\n", + "\n", + "breed_int['obey'] = breed_int['obey'].apply(percentage_sign_remover)\n", + "breed_int.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 105 entries, 0 to 104\n", + "Data columns (total 7 columns):\n", + "Breed 105 non-null object\n", + "Classification 105 non-null object\n", + "obey 105 non-null int64\n", + "reps_lower 105 non-null int64\n", + "reps_upper 105 non-null int64\n", + "Height 105 non-null float64\n", + "Weight 105 non-null float64\n", + "dtypes: float64(2), int64(3), object(2)\n", + "memory usage: 6.6+ KB\n" + ] + } + ], + "source": [ + "breed_int.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BreedClassificationResponsivityHeightWeight
0Border CollieBrightest Dogs9520.040.0
1Golden RetrieverBrightest Dogs9522.565.0
2Doberman PinscherBrightest Dogs9527.080.0
3Labrador RetrieverBrightest Dogs9522.567.5
4PapillonBrightest Dogs959.57.5
..................
100BloodhoundLowest Degree of Working/Obedience Intelligence1525.0100.0
101BorzoiLowest Degree of Working/Obedience Intelligence1527.085.0
102Chow ChowLowest Degree of Working/Obedience Intelligence1520.550.0
103BasenjiLowest Degree of Working/Obedience Intelligence1517.021.0
104Afghan HoundLowest Degree of Working/Obedience Intelligence1526.055.0
\n", + "

105 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Breed Classification \\\n", + "0 Border Collie Brightest Dogs \n", + "1 Golden Retriever Brightest Dogs \n", + "2 Doberman Pinscher Brightest Dogs \n", + "3 Labrador Retriever Brightest Dogs \n", + "4 Papillon Brightest Dogs \n", + ".. ... ... \n", + "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", + "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", + "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", + "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", + "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", + "\n", + " Responsivity Height Weight \n", + "0 95 20.0 40.0 \n", + "1 95 22.5 65.0 \n", + "2 95 27.0 80.0 \n", + "3 95 22.5 67.5 \n", + "4 95 9.5 7.5 \n", + ".. ... ... ... \n", + "100 15 25.0 100.0 \n", + "101 15 27.0 85.0 \n", + "102 15 20.5 50.0 \n", + "103 15 17.0 21.0 \n", + "104 15 26.0 55.0 \n", + "\n", + "[105 rows x 5 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now the intelligence classification is complete, reps info is not needed anymore.\n", + "# Obey column is called 'Resposivity' to better reflect the measurement as the result of dogs responding to tasks.\n", + "\n", + "brint = breed_int.rename(columns={'obey': 'Responsivity'})\n", + "del brint['reps_lower']\n", + "del brint['reps_upper']\n", + "brint" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\yago\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:205: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " self._setitem_with_indexer(indexer, value)\n" + ] + }, + { + "data": { + "text/plain": [ + "Breed Vizsla\n", + "Classification Excellent Working Dogs\n", + "Responsivity 85\n", + "Height 23.5\n", + "Weight 57\n", + "Name: 19, dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Some mistakes were found in 'Height' and 'Weight' (e.g. weight value in the place of the height value, or just incorrect values) and fixed.\n", + "\n", + "brint.Height.loc[19] = 23.5\n", + "brint.Weight.loc[19] = 57\n", + "brint.Height.loc[92] = 29.5\n", + "\n", + "# Checking Vizsla\n", + "brint.loc[19]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Breed Saint Bernard\n", + "Classification Fair Working/Obedience Intelligence\n", + "Responsivity 30\n", + "Height 29.5\n", + "Weight 150\n", + "Name: 92, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking Saint Bernard\n", + "brint.loc[92]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting data to look for possible relationships.\n", + "\n", + "sns.pairplot(brint)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEJCAYAAACE39xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAZMklEQVR4nO3df7RdZX3n8feXJJIA0SAJFIwQltIqWo02pHSAKv5AoA7qkIqIlrHOZNaMWUSqVmZNl9rOzBKnFRjGGV1YGNAqtBUFOloVGSgVU8cEQviRIj+MGBLhAiqBJOTXd/549oXD5d57ft9zsu/7tdZZ99k/nuc+Z99zPmfvZ++zb2QmkqS93z6D7oAkqTcMdEmqCQNdkmrCQJekmjDQJakmDHRJqomZU/nL5s+fn4sWLZrKXylJe701a9Y8mpkLmq03pYG+aNEiVq9ePZW/UpL2ehHx01bWc8hFkmrCQJekmjDQJakmpnQMXZIa7dy5k40bN7J9+/ZBd2UozJ49m4ULFzJr1qyO6hvokgZm48aNzJ07l0WLFhERg+7OQGUmjz32GBs3buTII4/sqA2HXCQNzPbt2znooIOmfZgDRAQHHXRQV0crBrqkgTLMn9XttjDQJU1rBxxwwHOmL7/8clasWDFpneuuu47zzz9/0nVuuukm3v72t4+77KKLLmLr1q3tdbQFjqFLe6FF530TgA3n/96Ae9Jbo8+rV/q1fU477TROO+20jutfdNFFvO9972O//fbrYa/cQ5ekCY2MjHD66adzzDHHcMwxx3DLLbcAz92Lv//++zn22GM55phj+MQnPvGcPf4nn3ySZcuW8YpXvIKzzjqLzOTiiy9m06ZNnHjiiZx44ok97a976JKmtW3btrF48eJnph9//PFn9r5XrlzJueeey/HHH8+DDz7I2972NtavX/+c+itXrmTlypWceeaZfOELX3jOsttuu4277rqLww47jOOOO45bbrmFc845hwsuuIAbb7yR+fPn9/S5GOiqhUXnfbN2ww+aGnPmzGHt2rXPTF9++eXP3HPqe9/7Hnffffczy5544gm2bNnynPqrVq3immuuAeC9730vH/3oR59ZtnTpUhYuXAjA4sWL2bBhA8cff3zfnouBLkkT2LNnD6tWrWLOnDkd1d93332fKc+YMYNdu3b1qmvjcgxdkiZw0kkn8bnPfe6Z6cY9+VHHHnssV199NQBXXXVVS+3OnTv3eXv6vWCgS9IELr74YlavXs1rXvMajj766OeNkUO5YuWCCy5g6dKlbN68mRe96EVN212+fDmnnHJKz0+KRmb2tMHJLFmyJL0fuvphuo2h1+WyxfXr1/PKV75y0N3oytatW5kzZw4RwVVXXcWVV17Jtdde23F7422TiFiTmUua1XUMXZK6sGbNGlasWEFmMm/ePC677LKB9aVpoEfEbOBmYN9q/a9l5icj4kjgKuDFwK3A+zNzRz87K0nD5oQTTuD2228fdDeA1sbQnwbelJmvBRYDJ0fEscBngAsz8yjgF8AH+9dNSVIzTQM9iyeryVnVI4E3AV+r5l8BvLMvPZRUa1N5Hm/YdbstWrrKJSJmRMRa4BHgeuB+4JeZOXpR5UbgJV31RNK0M3v2bB577DFDnWfvhz579uyO22jppGhm7gYWR8Q84BvAeKelx/2LRMRyYDnA4Ycf3mE3pemp7lfvLFy4kI0bNzIyMjLorgyF0f9Y1Km2rnLJzF9GxE3AscC8iJhZ7aUvBDZNUOcS4BIoly123FNJtTNr1qyO/zuPnq/pkEtELKj2zImIOcBbgPXAjcCyarWzgc4vvJQkda2VMfRDgRsjYh3wI+D6zPw/wMeBP4qI+4CDgEv7101J6s5E91pfdN43e34f9kFpOuSSmeuA140z/wFgaT86JUlqn/dykaSaMNBVO3U5fJbaZaBLUk0Y6JJUEwa6JNWEgS4NQJ0uldOzBv13NdAlqSYMdEmqCQNdkmrCQJekmjDQJakmDHRJqgkDXZJqwkCXpJoYmkAf9AX505HbW73m+7g1/dpGQxPokqTuGOiSVBMGuiTVhIE+5ByTlNQqA12SasJAl6SaMNCbcMijfVOxzfy7aKpN9eutk9e4gS5JNWGgS1JNNA30iHhpRNwYEesj4q6IWFnN/1REPBQRa6vHqf3vrtQ5h2la07iN3F57l5ktrLML+Ehm3hoRc4E1EXF9tezCzPyL/nVPktSqpoGemZuBzVV5S0SsB17S745JktrT1hh6RCwCXgf8sJq1IiLWRcRlEXHgBHWWR8TqiFg9MjLSVWeHzTAdjvarL/0apnD4Y+q5zQdrKrZ9y4EeEQcAVwMfzswngM8DLwMWU/bgPztevcy8JDOXZOaSBQsW9KDLkqTxtBToETGLEuZfycyvA2Tmw5m5OzP3AF8Elvavm5KkZlq5yiWAS4H1mXlBw/xDG1Z7F3Bn77snSWpVK3voxwHvB9405hLF/xYRd0TEOuBE4Nx+dnSUY4CSNL5WrnL5PhDjLPpW77sjSeqU3xSVpJqofaD3+lIth3ymFy/1096k9oEuSdOFgS5JNTGtA30qDqUnO2T3UL7+xt7oyr+5+mlaB7ok1YmBLkk1YaBrKDgU0ZxDNmrGQJekmjDQJakmBhroEx1CemipqebrTXXgHrok1YSBLkk1YaBLUk0Y6NKAec5IvWKgS1JNGOiSVBMG+l6kk0NzD+f7w+1aT3v739VAl6SaMNAlqSYMdLVsEIeje/shsPrD18X4DHRJqgkDXZJqommgR8RLI+LGiFgfEXdFxMpq/osj4vqIuLf6eWD/uyu1x0NzDbtevkZb2UPfBXwkM18JHAt8KCKOBs4DbsjMo4AbqmlJ0oA0DfTM3JyZt1blLcB64CXAO4ArqtWuAN7Zr05Kkppraww9IhYBrwN+CBySmZuhhD5wcK87J0lqXcuBHhEHAFcDH87MJ9qotzwiVkfE6pGRkb6NZzpWKrXO90s9tRToETGLEuZfycyvV7MfjohDq+WHAo+MVzczL8nMJZm5ZMGCBb3osyRpHK1c5RLApcD6zLygYdF1wNlV+Wzg2t53T5LUqlb20I8D3g+8KSLWVo9TgfOBt0bEvcBbq2npOTq9oZik9s1stkJmfh+ICRa/ubfdkSR1ym+KSlJNGOjj8AqA9rm99l6+3uvDQJekmjDQJakm9upA9zBRGgyHaYbTXh3okqRnGeiSVBMGuiTVxLQL9InG/abDmGCrz3E6bAupjqZdoEtSXRnoklQTBnoNOWTSnNtIdWSgS1JNGOiSVBMGuiQNiW6HAg10SaoJA12SasJA7wP/7Vo9eWWMhp2BLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNNA30iLgsIh6JiDsb5n0qIh6KiLXV49T+dlPyskGNz9fFs1rZQ78cOHmc+Rdm5uLq8a3edkuS1K6mgZ6ZNwOPT0FfJEld6GYMfUVErKuGZA6caKWIWB4RqyNi9cjISBe/TlK7ejkU4bDG8Os00D8PvAxYDGwGPjvRipl5SWYuycwlCxYs6PDXSZKa6SjQM/PhzNydmXuALwJLe9stSVK7Ogr0iDi0YfJdwJ0TrStpak310IhXmYxvENtlZrMVIuJK4I3A/IjYCHwSeGNELAYS2AD8uz72UZLUgqaBnplnjjP70j70RZLUBb8pKkldGKYhJwNdkmrCQJekmjDQJakmDPS92LCM20n90u1rfLq9Rwx0SaoJA12SasJAl6SaMNAlqSYMdEmqCQNdGmO6XRmh+jDQJakmDHRJqgkDvUeG6QY90jDyS0L9Z6BLUk0Y6JJUEwa6JNVELQPd8Wxp7+H7tXdqGeiSNB0Z6JJUEwb6FPPQUs34Gum/ug7zGOiSVBMGuiTVRNNAj4jLIuKRiLizYd6LI+L6iLi3+nlgf7upOqjrYW5dn1c/uc36o5U99MuBk8fMOw+4ITOPAm6opiVJA9Q00DPzZuDxMbPfAVxRla8A3tnjfkmS2tTpGPohmbkZoPp58EQrRsTyiFgdEatHRkY6/HXqNw9/e8dt+Sy3xdTq+0nRzLwkM5dk5pIFCxb0+9dJ0rTVaaA/HBGHAlQ/H+ldlyRJneg00K8Dzq7KZwPX9qY7kqROtXLZ4pXAKuA3ImJjRHwQOB94a0TcC7y1mu4bx+GGk38XDZupuhxyWF/7M5utkJlnTrDozT3uiySpC35TVJJqwkCXNDT8Bml3DHRJqgkDXZJqwkCX9nIOU2iUgS5JNWGgS1JNGOhDyENoSZ0w0CWpJgx0SaoJA12SasJAl6Rx7I3nsgx0SaoJA12SasJAl6SaMNAlqSYMdEmqCQNdkmrCQJekmjDQJakmDHRJqgkDXZJqwkCXpJqY2U3liNgAbAF2A7syc0kvOiVJal9XgV45MTMf7UE7kqQuOOQiSTXRbaAn8N2IWBMRy3vRIUlSZ7odcjkuMzdFxMHA9RHxz5l5c+MKVdAvBzj88MOJLn+hJGl8Xe2hZ+am6ucjwDeApeOsc0lmLsnMJQsWLOjm10mSJtFxoEfE/hExd7QMnATc2auOSZLa082QyyHANyJitJ2vZua3e9IrSVLbOg70zHwAeG0P+yJJ6oKXLUpSTRjoklQTBrok1YSBLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNGOiSVBMGuiTVhIEuSTVhoEtSTRjoklQTBrok1YSBLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNGOiSVBMGuiTVhIEuSTXRVaBHxMkRcU9E3BcR5/WqU5Kk9nUc6BExA/ifwCnA0cCZEXF0rzomSWpPN3voS4H7MvOBzNwBXAW8ozfdkiS1KzKzs4oRy4CTM/PfVNPvB347M1eMWW85sLya/A3gHmA+8Gg1r5vydK8/TH3xuQxnX3wu9dgWR2TmAprJzI4ewO8Df9kw/X7gf7RYd3UvytO9/jD1xecynH3xudRnW7Ty6GbIZSPw0obphcCmLtqTJHWhm0D/EXBURBwZES8A3gNc15tuSZLaNbPTipm5KyJWAN8BZgCXZeZdLVa/pEfl6V5/mPrSbf1h6ku39YepL93WH6a+DLr+oPvSVMcnRSVJw8VvikpSTRjoklQTBrok1YSBLkk10fFVLq2IiJMz89tV+V8BvwccQ/m26EGUb0HdCVwPHF8t+yXwM+A3Jyn/HBipyncBTwEHV239HfBmyq0JRijXy79ukvLdwIuAl/S5/v3V83st5aqg7cAeICgfrLOrn3uqzddOeTuwu4u2Bl1/mPric+murT1AAluBncALgKeBxyiv+6jaObBq4+lq/XbK3bbVSf3HKN+zuRa4NDN3Moza+RZSuw/g1obyo8B/AY4A/onyAjkCOJeywUaXPUUJ/FbLNwI7gDcAFwLbgMur6a3AfU3Ka6q+9Lv+r6pt8K5qvYeA/1w9HgU+TvmA+jFwbxvlj1f1O21r0PWHqS8+l+7aWkvZ6TqVsqO1Cfgg8CXKjs3V1WNjNe+DwE+ADW2Uu22rk/pfAlYBxwKfB24BLgXWATcDXxmn/LfA/63KXwU+0EKdsfW/0lD/kJYydwoDfWtDee2Y6T2N6wFr2yg/0xblE3cPMKthvXVNymuBbVNQf3tDOYDtDc95R0P5HuCeVsvj1G+rrUHXH6a++FwG1pcft1rutq1O6o++fxvKu2m+A9rtTus/NZTPBa4Z/f2TPfo65AIcHBF/RAmwFzSUXwjMbJiOxvWqeh+ZpDwLmBcRpwP7AjOqIZ2ofu+/jIigDCm9sFpvovK+wD5TUH8XMD8iDsnMhyNiZ0T8WVVnS0T8MfDNhu3xqhbLb6/qd9rWoOsPU198Lt21NQf4ZUT8WkN5BrAMeDoi3k0Zmnk6In4f+Ho1PXo77lbK3bbVSf1lwO6I2IdyDysy80+q5UHZofvpmPJsyo7qT4ELI+KzLdQZW39bQ/2zaUFfv1gUEZ9smHwD5XYBW4H9KYdlfwscALyPclizFXgrMK9aNlF5WdXGP1BeOL9NGXrZDzgNuIbyifiGat7fT1KeA7wR+Faf67+QMq7+FPBrVf9/RXlDzKR8YO1DGbejmt9qeTvlA6PTtgZdf5j64nPpvK3Rn/sAT1LGqfenvOZ/QTnPBfAIZdx6f8p7Hsr7pJVyt211Uv8pyvt8G+Vc2KuBP66e62eqNj47pvwhyv2tzqvW+wvgI03qjK3/ZGa+BiAi1o2WJ9PXPfTM/NOIeDll3PgOyknQeZROf4cyPPEE8DcNy+6gvFheM0n5B8DhlED8MOU+7H9AOUG5gLLxDgX+O+WfbxwySfn6qi9n9bn+zmr+UcB6ylHG1Zn5s4g4B/hGJ2WAvbn+ZG2NfT1FxAmUD8U7KG+uScuZ+d126/Sy/jD1ZQqfyyLKa/4fKRc+vIryfn0IeDfwAPBl4PSGZS9qs9xtW53UP4kS0CPA45T39Fbgh9V6B4wpX0355z9zq+mft1BnbP3DAaojnrW0opVxmU4fwDnAd4E/qTbez4Cbqie1rpr3COXkyeiyHdW8ycq7KCcz7qVc/fIIZczpZ8CWatmtVfmpJuX7KXsT/a6/u+r3PZQTLDuq5/2PlDfEzzss/3vKh2KnbQ26/mRtbQDmV6+lnwC3AZ+k7C0+NE75vmq7fpJy4urxFur0sv4w9WUQz+Wxar2HKDtbo8serOqMvj82VMsforx/t7dR7ratTuo/SHmff4Jy4vI/jcm5D7RT7qTO2PoTZm6fA/0OYEZVvhO4qSr/OiXgZlA+hbY1LLsH2NKk/M/VH2Mm5RNz9KTkTMoh4IyqvJPywTFZ+eEprn8S5Wx3At8G/rx6gY1QhqS2UK4iaLX8D5QPik7bGnT9Vtr6NmUIa1G1nbcCd49TXtPwt9ifcgS4oEmdXtYfpr4M4rlsoxyl7kd5f99VlZ+ghOMM4MWU1/68hvXubKPcbVud1H8CuLN6jnOoLm5oyLkH2yl3Umds/YkeU/HFotFhndGToVDepAHMzMwHxyzb1VBnovKeqjyXcp1oRLmF7/xq+fxq2T6Uk5aTlfejnNSckvqZ+V3K+Nh24H9RDuPmAodRLgnbSXmRtVreVJU7bWvQ9Sdr64WUw/cvU96AqyPioGob7xqnnAARMXpd8UTr9av+MPVlEM8lgaczcyvlA3hHVb6fcoJwd2Y+DmRm/rJhvV1tlLttq5P69wMvi4h1lGGRV0fEtuqxB3jpOOVtE5UnqTO2/sKIWBcRd1CGa5rr8x76Sspe6SWUPdmfVeV7q+l1lJOhOxqWjVDCrll5B/BT4D9S9hKeqObdTdlT2EI5lNrZpLyJcqjY7/o7KUNMX6QcuWxu2E5rG8q3AXNaLY9Tv622Bl2/SVs/rbbZT6rt92BDecc45Z9U5Qcoh9JZtTFZnV7WH6a+DOK5bAfuqv52PwJur8rzKEOP+1HGiJ8C9quWrQZua6PcbVud1J9XPc/FlBOiuyjj4/+CchT52Djl4yh7+aeMKU9WZ2z9hymXLS4CNrWUuf0M9GpjvIpyVcorGsuTLWuxfBhwWMMG/7fAqdX0K6rppS2Wp6L+6dV6y6r5v96wjTou7+31J2trktfUfsCRrZY7qdPL+sPUlz4/l3kTlOcDrx9brqYPA36zjXK3bXVSfz7lssbjG8vVskuB68eWq+n7G9a7v1mdcep/dbzyZA/vhy5JNeHNuSSpJgx0SaoJA11DJSJ2R8TaiLgzIv4uIuYNuk/jiYhvTda3iDgsIr5WlRdHxKlT1ztNV46ha6hExJOZeUBVvoJyg6T/OuBudSUi/jWwJDNXDLovqjf30DXMVlHuMw9ARHwsIn5UXZv7p9W8/SPimxFxe7VXf0Y1f0NEfCYi/l/1eHk1/4iIuKFq44aIGP169eURcXFE/CAiHoiIZdX8QyPi5oajhhMa2p9f/Y7/0NDHT0XERyJiUbX+C4A/A86o2jgjIu6NiAXV+vtExH0RMfodBqljBrqGUpS73L0ZuK6aPolyH5yllOuBfysifhc4mXKN7msz89WUb5WOeiIzlwKfAy6q5n0O+FKWGx19Bbi4Yf1DKZemvR04v5r3XuA7mbmY8s9Jxt5T4yrgjIbpd1NuIAdAZu6gfGX8rzNzcWb+NfBXlHv/ALyFcr32o61uG2kiBrqGzZyIWEv5gsWLKTc/g3LLhJMoX0S6lXJ9/1GUL2m9pdpTPiEzf9XQ1pUNP3+nKv8O5R8GQPkG6vEN61+TmXsy826e/Wbej4APRMSnKNclb2nsbGbeRrmt82ER8VrgF1m+/TyZyyg3kwP4Q+B/N1lfaomBrmGzrdobPoJyd80PVfMD+HS1l7s4M1+emZdm5o+B36IE+6cj4hMNbeUEZSaY/3RDOQAy82bgdyk3afpyRPwBz/c1yhfGzqDssU8qy50kH46IN1Fu/fz3zepIrTDQNZSqPe1zgI9GxCzKLYr/MCJGT5i+JCIOjojDKP+x6q8otyd+fUMzZzT8XFWVfwC8pyqfBXx/sn5ExBHAI5n5Rco3+V4/zmpXVW0uo4T7WFso97hp9JeUoZe/yczdk/VBalW//2OR1LHMvC0ibgfek5lfjohXAqsiAsoti98HvBz48+rGRjspt+AdtW9E/JCy43JmNe8c4LKI+BjlnkAfaNKNNwIfi4id1e983h56Zt4VEXOBhzJz8zht3AicVw0lfboaR7+OMtTicIt6xssWVUsRsYFyqeBQnmyMiCXAhZl5wqD7ovpwD12aYhFxHuVI4qxm60rtcA9dkmrCk6KSVBMGuiTVhIEuSTVhoEtSTRjoklQTBrok1cT/Bw9TyWt5vsonAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "brint.plot('Responsivity', y='Height')\n", + "brint.plot('Responsivity', y='Weight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Grouping by 'Responsivity' for further analysis, since height and weight are obviously related.\n", + "\n", + "grouped = brint.groupby('Responsivity', as_index=False).mean()\n", + "\n", + "plt.plot(grouped['Height'], grouped['Responsivity'], c='b')\n", + "plt.plot(grouped['Weight'], grouped['Responsivity'], c='r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "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", + " \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", + "
BreedClassificationResponsivityHeightWeight
0Border CollieBrightest Dogs9520.040.0
1Golden RetrieverBrightest Dogs9522.565.0
2Doberman PinscherBrightest Dogs9527.080.0
3Labrador RetrieverBrightest Dogs9522.567.5
4PapillonBrightest Dogs959.57.5
5RottweilerBrightest Dogs9524.5100.0
6Australian Cattle DogBrightest Dogs9518.540.0
\n", + "
" + ], + "text/plain": [ + " Breed Classification Responsivity Height Weight\n", + "0 Border Collie Brightest Dogs 95 20.0 40.0\n", + "1 Golden Retriever Brightest Dogs 95 22.5 65.0\n", + "2 Doberman Pinscher Brightest Dogs 95 27.0 80.0\n", + "3 Labrador Retriever Brightest Dogs 95 22.5 67.5\n", + "4 Papillon Brightest Dogs 95 9.5 7.5\n", + "5 Rottweiler Brightest Dogs 95 24.5 100.0\n", + "6 Australian Cattle Dog Brightest Dogs 95 18.5 40.0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Each Responsivity category contains (correct) outlyiers, which harden to find a correlation, if it exist.\n", + "\n", + "brint.loc[brint.Responsivity == 95]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([95, 85, 70, 50, 30, 15], dtype=int64)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Responsivity.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Brightest Dogs', 'Excellent Working Dogs',\n", + " 'Above Average Working Dogs',\n", + " 'Average Working/Obedience Intelligence',\n", + " 'Fair Working/Obedience Intelligence',\n", + " 'Lowest Degree of Working/Obedience Intelligence '], dtype=object)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Classification.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Border Collie', 'Golden Retriever', 'Doberman Pinscher',\n", + " 'Labrador Retriever', 'Papillon', 'Rottweiler',\n", + " 'Australian Cattle Dog', 'English Springer Spaniel', 'Schipperke',\n", + " 'Belgian Sheepdog', 'Keeshond', 'German Shorthaired Pointer',\n", + " 'Standard Schnauzer', 'Brittany', 'Weimaraner', 'Belgian Malinois',\n", + " 'Bernese Mountain Dog', 'Pomeranian', 'Irish Water Spaniel',\n", + " 'Vizsla', 'Cardigan Welsh Corgi', 'Chesapeake Bay Retriever',\n", + " 'Puli', 'Yorkshire Terrier', 'Giant Schnauzer',\n", + " 'Portuguese Water Dog', 'Border Terrier', 'Briard',\n", + " 'Welsh Springer Spaniel', 'Samoyed', 'Field Spaniel',\n", + " 'Newfoundland', 'Australian Terrier',\n", + " 'American Staffordshire Terrier', 'Gordon Setter',\n", + " 'Bearded Collie', 'Cairn Terrier', 'Kerry Blue Terrier',\n", + " 'Irish Setter', 'Norwegian Elkhound', 'Affenpinscher',\n", + " 'English Setter', 'Pharaoh Hound', 'Clumber Spaniel', 'Dalmatian',\n", + " 'Bedlington Terrier', 'Curly Coated Retriever', 'Irish Wolfhound',\n", + " 'Kuvasz', 'Australian Shepherd', 'Saluki', 'Finnish Spitz',\n", + " 'Pointer', 'Cavalier King Charles Spaniel',\n", + " 'German Wirehaired Pointer', 'American Water Spaniel',\n", + " 'Siberian Husky', 'Bichon Frise', 'Tibetan Spaniel',\n", + " 'English Foxhound', 'American Foxhound', 'Greyhound',\n", + " 'Wirehaired Pointing Griffon', 'West Highland White Terrier',\n", + " 'Scottish Deerhound', 'Boxer', 'Great Dane', 'Dachshund',\n", + " 'Shiba Inu', 'Staffordshire Bull Terrier', 'Alaskan Malamute',\n", + " 'Whippet', 'Chinese Shar Pei', 'Rhodesian Ridgeback',\n", + " 'Ibizan Hound', 'Welsh Terrier', 'Irish Terrier', 'Boston Terrier',\n", + " 'Akita', 'Skye Terrier', 'Sealyham Terrier', 'Pug',\n", + " 'French Bulldog', 'Maltese', 'Italian Greyhound',\n", + " 'Chinese Crested', 'Dandie Dinmont Terrier', 'Tibetan Terrier',\n", + " 'Japanese Chin', 'Lakeland Terrier', 'Great Pyrenees',\n", + " 'Scottish Terrier', 'Saint Bernard', 'Bull Terrier', 'Chihuahua',\n", + " 'Bullmastiff', 'Shih Tzu', 'Basset Hound', 'Mastiff', 'Beagle',\n", + " 'Bloodhound', 'Borzoi', 'Chow Chow', 'Basenji', 'Afghan Hound'],\n", + " dtype=object)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brint.Breed.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Simplifying to a chart with only the necessary info.\n", + "pre_ml_data = brint[['Classification','Height','Weight']].copy()\n", + "\n", + "# Plotting relationship with Responsivity \n", + "sns.pairplot(data= pre_ml_data, hue='Classification')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# pre_ml_data.replace({'Classification':{'Brightest Dogs': 'Brightest', 'Excellent Working Dogs': 'Excellent',\n", + "# 'Above Average Working Dogs': 'Above_Average',\n", + "# 'Average Working/Obedience Intelligence': 'Average',\n", + "# 'Fair Working/Obedience Intelligence': 'Below_Average',\n", + "# 'Lowest Degree of Working/Obedience Intelligence ': 'Lowest'}}, inplace= True)\n", + "# pre_ml_data = pd.get_dummies(ml_data)\n", + "# \n", + "# pre_ml_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ClassificationHeightWeight
0020.040.0
1022.565.0
2027.080.0
3022.567.5
409.57.5
5024.5100.0
6018.540.0
7120.050.0
8111.515.0
9124.067.5
10118.042.5
11123.565.0
12118.033.0
13119.035.0
14126.077.5
15124.062.5
16125.097.5
17112.05.0
18116.555.0
19123.557.0
\n", + "
" + ], + "text/plain": [ + " Classification Height Weight\n", + "0 0 20.0 40.0\n", + "1 0 22.5 65.0\n", + "2 0 27.0 80.0\n", + "3 0 22.5 67.5\n", + "4 0 9.5 7.5\n", + "5 0 24.5 100.0\n", + "6 0 18.5 40.0\n", + "7 1 20.0 50.0\n", + "8 1 11.5 15.0\n", + "9 1 24.0 67.5\n", + "10 1 18.0 42.5\n", + "11 1 23.5 65.0\n", + "12 1 18.0 33.0\n", + "13 1 19.0 35.0\n", + "14 1 26.0 77.5\n", + "15 1 24.0 62.5\n", + "16 1 25.0 97.5\n", + "17 1 12.0 5.0\n", + "18 1 16.5 55.0\n", + "19 1 23.5 57.0" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transforming responsivity categories to numerical for ML work\n", + "\n", + "pre_ml_data.replace({'Classification':{'Brightest Dogs': 0, 'Excellent Working Dogs': 1,\n", + " 'Above Average Working Dogs': 2,\n", + " 'Average Working/Obedience Intelligence': 3,\n", + " 'Fair Working/Obedience Intelligence': 4,\n", + " 'Lowest Degree of Working/Obedience Intelligence ': 5}}, inplace= True)\n", + "\n", + "pre_ml_data.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
HeightWeightKmeans_labels
020.040.04
122.565.02
227.080.02
322.567.52
49.57.55
524.5100.01
618.540.04
720.050.04
811.515.05
924.067.52
1018.042.54
1123.565.02
1218.033.00
1319.035.00
1426.077.52
1524.062.52
1625.097.51
1712.05.05
1816.555.04
1923.557.04
\n", + "
" + ], + "text/plain": [ + " Height Weight Kmeans_labels\n", + "0 20.0 40.0 4\n", + "1 22.5 65.0 2\n", + "2 27.0 80.0 2\n", + "3 22.5 67.5 2\n", + "4 9.5 7.5 5\n", + "5 24.5 100.0 1\n", + "6 18.5 40.0 4\n", + "7 20.0 50.0 4\n", + "8 11.5 15.0 5\n", + "9 24.0 67.5 2\n", + "10 18.0 42.5 4\n", + "11 23.5 65.0 2\n", + "12 18.0 33.0 0\n", + "13 19.0 35.0 0\n", + "14 26.0 77.5 2\n", + "15 24.0 62.5 2\n", + "16 25.0 97.5 1\n", + "17 12.0 5.0 5\n", + "18 16.5 55.0 4\n", + "19 23.5 57.0 4" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Creating six clusters for Height and Weight values.\n", + "\n", + "ml_data = pre_ml_data[['Height','Weight']].copy()\n", + "\n", + "model = KMeans(n_clusters=6)\n", + "clusters = model.fit(ml_data)\n", + "ml_data['Kmeans_labels'] = clusters.fit_predict(ml_data)\n", + "\n", + "ml_data.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Clustering and plotting, taking into account the intelligente classification parameter.\n", + "\n", + "ml_data['True_labels'] = pre_ml_data['Classification']\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(18, 8))\n", + "ax1.scatter(ml_data['Height'], ml_data['Weight'], c=ml_data['Kmeans_labels'])\n", + "ax2.scatter(ml_data['Height'], ml_data['Weight'], c=ml_data['True_labels'])\n", + "ax1.set_title('K-means clusters')\n", + "ax2.set_title('True clusters')\n", + "ax1.set_xlabel('Height (ins)')\n", + "ax2.set_xlabel('Height (ins)')\n", + "ax1.set_ylabel('Weight (lbs)')\n", + "ax2.set_ylabel('Weight (lbs)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4 27\n", + "2 23\n", + "5 21\n", + "0 21\n", + "1 10\n", + "3 3\n", + "Name: Kmeans_labels, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking content on each cluster.\n", + "ml_data.Kmeans_labels.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# Checking cluster assignment of new data.\n", + "\n", + "test_df = pd.DataFrame({'Height': [30], 'Weight': [40]})\n", + "test_df['Kmeans_labels'] = clusters.predict(test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\yago\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_df.get_value(0, 'Kmeans_labels')" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a function that allows the user to input new data, which will be assigned to its correspondent responsivity cluster.\n", + "# The program can now provide the intelligence level of any dog, given height and weight.\n", + "\n", + "def imaginary_dog():\n", + " height = int(input('\\nInsert dog´s height in inches:'))\n", + " weight = int(input('Insert dog´s weight in pounds:'))\n", + " \n", + " test_df = pd.DataFrame({'Height': [height], 'Weight': [weight]})\n", + " test_df['Kmeans_labels'] = clusters.predict(test_df)\n", + " \n", + " dog_iq = test_df.get_value(0, 'Kmeans_labels')\n", + " if dog_iq == 0:\n", + " print('\\nThis dog would have a lot of nice things, intelligence is not that important.')\n", + " if dog_iq == 1:\n", + " print('\\nThis dog would have a fair level of intelligence.')\n", + " if dog_iq == 2:\n", + " print('\\nThis dog would have an average level of intelligence.')\n", + " if dog_iq == 3:\n", + " print('\\nThis dog would have an above average intelligence.')\n", + " if dog_iq == 4:\n", + " print('\\nThis dog would have a very high level of intelligence.')\n", + " if dog_iq == 5:\n", + " print('\\nThis would be one of the brightest dogs ever!')" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Insert dog´s height in inches:17\n", + "Insert dog´s weight in pounds:20\n", + "\n", + "This dog would have a lot of nice things, intelligence is not that important.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\yago\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: FutureWarning: get_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", + " \n" + ] + } + ], + "source": [ + "imaginary_dog()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/HW-I.PNG b/your-project/HW-I.PNG new file mode 100644 index 0000000000000000000000000000000000000000..d1f10d0ff7b9a661de30b66d24d7dee2e7592825 GIT binary patch literal 127073 zcmd43WmJ`a)Geyg-CY|&P`W$Zq$nxf9nvKo(%mf$N=tWlcb9Z`ciac?|K4-XIQRQy zpon`9o?ost*PL?^AT2411dk8@>eVYGu}?x@U%i6pe)Z}N1}rr2KXdb}D!`Z5HeW>r zUKI`!ZUf&y8iFOjuU?geA>3(00pG(}ep0r1^$Ppl)1TL3U&)VNy?XpDCIpsq(Ac~8 zu~*xiEJC=@9Ok_Vb{-;f6%FIFY;tZS=e9z(>3{k#E$MjryGJJ|0r=cbnId*ZLVp=}`=Sq|BQ_i??2r0-o+fEZ*p=ND4V%4+E)B1L zGds2?1*Zs`nweQ0%yY`g%LkZ~!mgM!|KHw3LqlVEwk0E{pb+@~{iaXha4U2Fvs(So zrtA%w z0BS_~ZztMKKYH>60dJ+HpvWF(=kYCUkvT&Q(RR^+m(95K_kn^t;nA^1mt9?b3=M$O z3e-q=o@77Lr*K0w^tBp@x&?!;gv#^qhy70Vy(R+AkFaqDI?G^B9{zZAjrb@*!T7D6 zRtHsrPHrxSk-0wM1oDKZ~*5XO~bY9MplrTWU2)0mYuoy!=xos5F*u z1g%}b6Vfr1teZY+>3ERttgAh3U>f%iKEBb!gm7FCf(R8A10%!7$mdhk&hGcT1StgK z@HjIxHh0au1k`dGtCTg;G>QSuc)OTahU{9VI+2BKIJo59HUtVHydz^?9?^0vJw55R z6I7UwyN1y;8lHJ=ek1D#E$28)qG4HQ1KF87}$Lit4ZCjg6 zM!g*T;}8&VPCkV<@d{>Qbmt$oFc+!O$OJa?R?*}@;eZGEHLth`tCE<}RA+q~m#s$d z+691pMrL63$sLgqM@G~tZF1*=WZan1T|R9oXQ6BD2*OU^;&;EYXAsk<_l{F7OVLXa ze%bpgk(cAJm@?=gQ=L7zQt%yKAy>GSBaiSkXGpPBK_~PdBSJO_Omk{MlSTh6L>=IM z-kBx*Oln{2h85Y}WsFY<@L1EMYo{^k|D4}dS9@g^Q+H`e>LwcQeidODtt`bvFfaUa zrQ55#pEu*-g2fI6{Gk8(0Ia`88BiwsE_wTan@F2A&X78OknIv z*h%JszjyuE64z-I^t{{GYOChUZBjzjtK7X3bnTT>k$AfN?P@Ra;Gq(e_59>ql3Gr4 zwbYfXo_XiG-I$`=N`K!WX3jft5YJ+gSAW7TV32%+o4k7wnW8(DC*8?(&nv3%+au$1 z9I<)*95SxwB9%svKP>Xl$c8pUT$&1DRlH5FPe!oLKt1*(FFThKPdltJAT6zyx20fQ z1Qy9|I8^sJHesVbhpUq+AANuN=`5WF15Ca9)5Y#%aRxC3c$#+XWL!d4DNlQYhX3ZY z7g!nbgji9&FDns%+yj$+VuD4DR0F0 zFz)hFa6VrW9lqi;s00b2{k^?4oFS}MVf>)HJSrA;cHNKFTDq5EAtC7v4ap?38X6B9 z4^o^>js+(1V^QCzbw>>gGK7LM75l$fzzXb^;+)33_ z{5EqXlXjE$Uc_p%TBIci6=T(bnxU=09;&y#vMPOm%Gt+VvzF+$0K!{p)4Tdq((#w{ zWfK8K+r8QfrDAQ(6dV$wZEW1j&vf13a?P~2wV~PD?YHlPcc#`u6!kH>pm0tih{zzP`s^Bq!>n%etRn`p45tk&24R7sh7FoAa{aVXp!Xv4L|0|4_AV;=yq@u zo(xil{0}zmF_kTxHP9yGnU<|g32{31u$vgC1W!+$Wc&BjQ*~HtQBjyf;mOfa&tJdD zB~$s3n31_;mw&@(@;Bh^rs3}YoWpLhfCvErF*350pl7$-U@}D)A0O{;?wY!=pyBQ7dvanL zTcguL?0kE%4;(TKB4HOK{5n6-EfdLY2!`b_Ia;GKlFqsV#huS>SR$o%stgagi}lDb zE4dT7PGj=}N5{jD4l3Shldya3o$ORM&&I~aE!dUAR!Bz>CmxWnjtY(mI3K2l-rwtD z<jMq#9~AB7SP4eD-|b>-3@2GnE)q0zNK3?1r`;Lt9mo6Keit1U=7$ieuV| z?V^kRVm2rsU^%j%N)XtfwdPmQ%*--2J1mFXDQIh>6DHM!>`s25zpM~^Kk+PMq&)nL z@mtN{howh-(i^2T!@5FsyUI?dulKB`SFerqLEIVG4f|t};t6^$+e86u(@=AkN*Qlg zLBEQa7@U)nlTy9I@gh|THq1H5-E|sy6@X$I8V$1i$YDICXlEX;)tS+frur#0%2t&p zYpQE-Fba60a)2=VnL9os!%&Xc*Q;9L8JNq=6l$7b~{0KKT-qGy|Zh zyZ7D%>pLp$$mHbk8PLKgF$Va;6(0kc?+@0{VTbKBLDe zt?ouxw4Nzonx?dqu@(m!`cP8x(RKgNFx_vDr&r6wnW(D(H1cnVLBJ5j?GnyPadB~% zBKw)T3;3^gCh}ckqGDpOB$K&(aOgGat2P3}n5ios2m#o~r|y;MPh;NU^o+c`TXva* zFf0ABRuh1%4RL5+Q}IXQ$?=I8#oDu-SC)uy?Fn^WD`7hh$#( zxkfmyP%8H9zX{#l-9k<<}ZneKpUT!u)4qbuECca69Yv_Ntq(ycCrSSYJGoq zZNoQA{izzHx9N^d-mRH>Q%LHdKdxKhL@ufN`v^-)#*-lkV>LmGIxQ*E*m9B!E8qKc z{;P@19OL`{hR*;_AtV%XS0hyU{{5P7O+k@W=No~w6Vtx3wQ6n9Pvc! z5SHI9OYU~ROE+xDDRO3s4*kDVi=YyopP&DfnP3cwcpOk~FAq1VQ&?D8BT`ZZFW<7W zv$u72LW_nHE_>AsCULgC`-ya}qr5c)puSz%(RrAl>CXjnt|5IF+eg@XoRxBS$ZH8& zm$&+?OL>A^3b30AB5|2Hxr%50?JnMs*BEH+_Qo!Ei!BSQ?PmyOw-mLt!v?0oqw@Jz zkh?o`moodrfv64S9G@xbd+wNODqK(zq`*d}8N1op%8$ZQ{B@~78aD*OWDR)`(?|hj z0@8ub*q<5r9F*4HmW2K?g`WXd4-q}Q&v>hSQ6GE>Viq$uFr;xiSX;|pAs#WzpmuLso)xmuT~ zy?YbJYvW+YQKD9lJ0z5WDxQ(*u&(9PYr=!Y*BMh0KpYVt2Bcd^Fm}z6on1OVg|DGc z!d2<$HXRO5u$-Khj@Aznb_jkAR3p3oe$3s-cVA>m?D>u5D`brD{K@?g+v~7Mdy0|_ zJrnj$JXBJ5vRbY79PEg>Q)5)ihz;1PgCwd_h%e4x&tSVv%78DpcXq>x@ckVWVcI!V z$a?yqVwAt(q6;PToX$N;yxjNHH!m3UTpOaYko&i8h!gp*`;!j%V?TjTei9Sb49f{d zBGT)Df9p=-8zUoCtnKq5w?57xOYdiZ#l&*kmi6UQP;7OyUuEWJc+Vx=F>wxtz+;eu+yk~&Y_*){il{u+DZPsf;T^A zZ&6^e1Uq(Df7DXcwIjrge+^Lg`y~)!Lsk}}B1xsBxtzwbzZ>JBrtznb(*t`u*0Ao1 z8Nj0VdM5-hV;Ir(cuM0u-XZj|n+gSm()spMF)r|TUt*r7tPaJj;>D9AY?igCGar|r zfBPTUT{7a6Im}stGuw4(DOOo)0|)k1G5V8-DwLQt_L5>9Un*8jlI%77z2UG9zEuY! zpICpaWfY%E%R9GK$i3kpAKkAZTWA5-uusF?JM7X@!H=6?5e||*OXa`g1|Gc9_c>Qt zjCJVk5a<&Xb;Cv^4J-?{Z;U}1x|u@Wz~{=vK`wXNX%O9iXtz+4FY05Wkc$Q$Ff(iCuD9F|#H~#p1p)dNoRD57Nb}Qt;0>$Yp)kMdztVWDpOB ziHwq|aYn&ev4_0&BXJZ-2%snEJr4yjd*eXWh3Lm6d@R$Q4yR|^+f_ws)fvm1`S>&n zrcQvo0LonLN@rV)3P(;{JVMmuf8qMuzZF5TGvnjpgG)a8dY6Uj+jsG;I4~cRx|Iap zj)yxed5GALmssrN4hUUsPb>igGX`t6q!h}$)0glBLVy#9#WGmAO#v~-1BIJdt1GjI z#q?Ysb9V!;3kYYIb0hK&fG-85iSx27(U>~64xenXUqVwh!0G^0kvnfEW!CTl|C`^< z0(Vrf2S7QfVNNRv93x3F*6PF9fuWZqPRIBNl)ntJnVGnMibVbX&>GXLCB^FTS(>07 ziWzv-DfO`Q!MlGszL%R`qOI%=kjOWHs8JeXq|P@;(52`~`8@gwk9I5b-1}37urvRfrq0TMy=$-u&F#cx53HKCNhdtkqqB~V+)_qM^S`mP$hc_rQ*}{ zJ2}$YU-D1epz~$!$|-wdCxXK)=1tYOiY;<*U?T)$DsUp4#o^y_GO>Ad9*|gb}0ES6-3Kk%HVh^`6(LWZ!8BMi^jZizA_ zwCF&j7P@+9Wh2^%J!k_1ST~d}rA(ZKyM*5MB1Gk02;nJ;p&TUi)xEsyybT3amoXj` z#!$-UuG?Y0n?o|+kCtF=fSFW--b=8SQ3#3vV{CpaWk4{-yj7B0(Hy;45e0C=?)%eBf1?F zwVHxfMB7^$JC)%5g}LVZ@F0q=|3ybAc965|bk6`%`Rh6R04JfLh zu8Lau>muS551B)|bcYDX?UDZ5F&CvKD@j+%gX%0n_V+N1CAr0MPW|gw`wgw8Lkx55 zgeC=k*zD}lX7gt)M7{s-iB8>jcf)AneDM8#u&edcthcON&iEfc^^fmzD(;_xxTM)* z4eU}*l&i-SQcQaFC=a>8vQn$dWk=AtTQE*Tbe=i$A$f=#Rg{eiVva&ENOm;#gAg$>DRZhWA z{m@11HO)Qu<;a4VLZ8YOwtuj4gZy7;@e0_3s*~Ge^KV1SMg-d?w!hVZqaCgtjB{*!)PF8w;KZ_*@^?dj!UBN_ z!yr~ZlxfeJX_$XJz76^d z|F+wFSD}VFBbXK7Io-KjB8rToZuRC)5nwbBl^+%? z4Jb&)6he|s?jY*2(eSl(Ph3%x_Bt*QH4CcQch>jDy@ICl4ujN4t z`G{h#^?i|1f;NxbM7wVJaC;d>uR(G38_bq21FO=Isaaj2;`6Ex%fx#vTu_`F;4x1tH?;|f-Z-Q7%CK% zb^#Or1}*-L0Ep&RJ#kbmT{V422O>Vg&z$k;H?(Ymg< z(`jhYu}}^(OiwB3x=iuU#ibad))bsg5Lu_R>g(X5+FUJe;u3z8fz_B>oI;hAS)UF= zB~ZvkCCV@~qV>F+k>Ntqp1d|H-0my}jRGO=zqS2GL<4JU5+&W?6C3bH(TND>ulI>U zdo=6Yj9>6Fzt3Bz%yfQV?fcW4Y(g|nlke$Mp$#L^3v0NBy)3kzmaP#gfHu{<`8fh+ zd*BB?z~yk*!v1zI5a_rd%Bi!$03djV%vX4cQtPdY(@rB-&aB!Uj|jez%%-8Wc1&nz z(eORR7UnLs&?3=BxH9gMBh>bq^ztuPGe$bB&td=cMftnl4WAl@`0M|b2FLp%OO`@o zoefl9jZKemvXn%G5Ok(!*&Xa@0dOzt)ukvXN1tgdL~X^W|A8G1NX(-L106T8Cr$O< zM}A|QGVRN)4H$bo(V2uyR?gS!Fvw}vi+n6RZl1^{CbrH_>2qB$>gESqVIi$b5^z~u zCLYTgz>rZe);vYwp9BTBjpQJ#yrOH#wfIQ35&O3r z2#a-~GW4y8MvZX<&pnD;Z^NKPY(0vu0!q%w0pvfN+d0k`n6&u#Tefy0e=AIK=F4!f z<|>bS#`m*2aX-*^>M3F{aDi@+58EIeb1X1(J>&td{>zD~XMbrgp9-E4&0*&j!@p2{ z>IGD_1r~)(O(Y*ad?+p{@yN~n05Is+n2jd=$FcuE+Q~5=&vn1YYlpEEeI3A;qYjA`iQ-2AD?~*{i&^6Y1 zBdaSIk4xN~bHv&w7lsU- zs=K>8X<1n^r*bJTAn-Or#s=(h4SY(nx$uySMxLxO+ela~d(D1)R)Jp##os}ENTzgf z2O|li=I2%l(;C>vKOI+am*;;d&!-)MxL5%FauCWuWgg0=wJfSFa?X!@`1L#ydfIQM z3@^2uFX0JE{tBh}zBL|=9Fdm|Sm;zi99PH`mbx6k+?t9MK`f#Ky+*pgs^E znh8s(L*c{QNGB%tQ0iC7#p>o%;XwTL%AwyDs+M>SWFh^Dr-WKv(r?K}vV zjIJkf-hQdQkJ_>~P+0C;vJ!X_k+y{|(*Zy>UN*g(_uAf97cPgM{R;FY_wBz;CNp+c zgypuk(wLe_u(Vrp5aET)EKzIA%7!%}{d@xn%N4NGM3f&EWeXP30czHut>xJ?LfoXg#gJ*amWVsQoeDdPtf2nhH6&{{3AS9!v((t=U zFE6ip@t*-q7#jfraM97xOXIn+($dmP%6hc4v^hCB&6ATCWnc0h5rLYo<=t=WIz+OP ziD@X40t@YsUFanE>Rz8|3SLAzyyapnKK$&fC7EjUi)+Fzk;{Dt;#|16J%pswO0x4z!Io{;B#k2iEt6s(O0#l^*~_Qp`F1quoZI#OKkBQo_7SCy9o zS70qO0p&uep`pRZ)N~DJlLqjOI0mrw31Dad4Qe3KgR?@(>CFXo{622`Z91{rBA`?c`>r$$fx= z^dw<6Y0Q|=Q&2!VL*?o+LpHK2g&lbxX^gM0wECHrxOS>JJt_MB%3)T2_f@yx9Rga* zQN0bEk3X@?eC$Z~dawL;>N&>U7Pkg@cdp?~Y_SASY+clQ>~2(w46p|q^U!&L&7 znjy6kr3NR~N{hwN(o&`onRe5*2P18Cd0)+Fwc`1cHFVk_g0_g(dibI5ZJ3-~THjw+l}H@=<yY24(vM9R5GVwIf@ zM~OTTW3ervwH5adc)Ahm=BB3{KXzM&95~NY*QXh2VKuDi zEDonjLFfTnUbv%6R&}dtH*J`2*kf~poqUIEc)XQ^nGY6Vq{qbCly0-3v*Na`pbEyt>ZFyNc1r5+PH!$*NR46Z4siO?qs;6ryC(HUh_2aRyuxj?4 z932CQm&ekG*XR+boe%Kvy;OwWJP4jcxs|E}2{vYv;+VmnjyJPgo2C*&Pmyb0i7Jy0 zYT|!0pF-QQgpAu)(2na90rDks(Y$SYTtz*a-eIs)qpD-cSB{k1wG2SNYBRl|W2DzA zsyVEHxkkzmKXH&k>71QqoSgR@nZzzlsPF?~>iRmW4cDsqGspS6{l zL50El4r=R*q(D^|3jXh3G9|i{pWQTrxhC80xDY*l1{T-aL>LAGE`z>fpG->!Hxj&- z*SK4|WG{v+9dPjyRDzg=nHephHXkpwKxlkbmzO`!>WUX*;x_Qj%A%-uIu9)JDJ?I@ z{Cfkif9}dkB30?msDWs&uQY3?j>^udk`U^V+Vtp^f_n242)Tblf^OCkOFP1*H)>$Aavxx6AMCL?8L}g!N;R?wJ)F&zKmFNR(w}U<`vIv8 z8Kt+(8xnA`HjUdUDmpwm=kHz1(cK{?$gY7^#H5ZIwd%JQiHoWDo2-TjTUhwmC=^y; zU_bHwVtqoN)y&R>(a5Nfg$$qZ*b1t7R-f~We^$-(6&mg&&PU%iDA5T- zq#fp-u-|2U`==U@1|q*)YudmYsP3eWgpyLpsc7voR8L`8c4@&eJx-YM7U24px1Zwg zy^#h@KzKKA%oAiRAUO-tpeRPL0F3qXPzjmdROE0tev2E!h6r8HO8oe;y0Jx4u4;|! z?Hqzt%FX-nXu*UG7GqX<84JUJ(f&{4avNzC3{AY;{c~?bgKKW`jx7u6Z-J@7DG0vVkGEOi;iv zAJo0?|KXdLs}3t*0DJO*)ido3vtBtm7YA6tz?I@=W&H?X6u+?7DsRMe4X`qbj#>Ar zp8iP379WIkwqt9*`fjqJVIOzLw9UwwpmW-|zK{;_>?}RK0S68$#(#)b^yd-dyQViq z%zauo^8RHCV#WTsA59J!I&%$EpWQo9&49yY(!Rg_a-qTbs_5~&X$4SmH^=g4@F}oO z8!)JU8!+|XgrE>|G_Q6?kX74kBDc1+Io6y%*=_W^XI+O)8&}~n6|thq ztF^u3Os^+8_*S7*gg0*#nPj))jWT2)AJsWw)k%8#GOqN$sSx;vq%`v-NG zB~yz~J``}>1xt;N+y6@n00^8JH>Rk5czAHJu1HTOU5wzu(AL&=`TSj48g+;%@#oJI zt@UYBCRXmt{n1@1K)LK2@NDL$szq?Xs;qG}>bgoO`9sH$K4`U+;!3W;t? zIP`wPCKscZpCJb|GC!5w4;UGHj5n%KV7eA*0Sd<93MZOog8=d&mjVn z%|b*;0DGiMI}MzwtxjxY2ipFQ-QqZN zC-A6mM>aV3OQ%SNCsl9Y*N=FTa8MIe)Px6e8Wr+UV{ibjYp}waE{GNH&&^E^pVb>I zZu|6|CGhxSLr_ZdN5q(DfVl{gYJOm*Vdf$0*`7%L1XSyv^juDQ5enp z7d3$%rcYYlR&m+3nU$twCg}V^F%9eR_2PTgUk(eRVH+q$qpF^We#}QGU(xiG9$h&N z(6Uex73nR9w8=te?|hjLoR_2Ug1kpXAUD{SV{Itx2-=tH&8YI`V1yQv<9EYB2t{bi z?if)RxWq&s-R+TX@-rBZREXQQ3vuZN1>>?|0K#*GoZ3%B?1P5J--2JkcFUZ*QlFZY z3@Hz}v0-e?70d?|QqPgX(%l^h0XwV}kqs(!4a- z7Dow(otPj1ppmeM2|1cHr4hEf{D8#6O(hf^`otbMdlIgJ;j@OWskdI2md-=$b?s<0 zGAWSQ$X7c*KeE5-WiCZ;J3E`M41WFh!Og|`+z)V5KOO27=YOn)bva4}Sv|!YZdbcy z`=Hh7Z@JK?3W?@6gqY0@tn}W^`q$eRJAoPx0xv%{rX2<%ba+f`5#o z0)mbpUm9v1BzPeUC&;stIE9pKgYSvPfDbBMru)VAd&O~&nb6r0^44dnx8{fLQBQH3 zr99S6M#gYPUT?O7qUoG(FR!zV{XTxJnmS{AjE~Geh8q@epRGA#>M3Y&GO`6++_*(& zv5|4tV{;bmO28!whj%BbG5~i-EHyev1Kn z5r;q_R4XC2(h(&`rzX!ceIO(Uzv4w=5X5h9`YMdrv&2A3ILph zk;?-iFBlyC&%TcOL?${B;Quf5X-PUUbGoeW`i5*S#37FgR&hFkYF!Ei8 z-C~xhmZisJg`6J{luVvb6ei-54mj|0*-HEn(%+D)@K!IL8x{*AME14ZKw= zR)>!I1W`|C?D5T(bJ45+qRtnKCDRMwauFI0pX_)v&Za-jsOtG9b!$$o3%;v+?GvjO z_vsSsMTLySMF#IgQD(1S@w+=nZdB`%=C#Jowxz7NPXnlTkUla3U&$w>l9aCJ(YyMFgD1!}RDLkAcxr`9E*MiL z`vzea5Hp*7JFpAChRXe>^l7dC>12~`9H@s14d0*2)Z0}DYfvd$3$Ww>Xk$-wYcX>f z#Z>II68kBhf10bs(*;~i1Ti1eN*a4+0}}g+B1O%oi~NtCig*GvCtCCouiWoxG^}}t znOkqkAL%?|iY)22m||xST3J^y9CB^2lY1W_muh~m0<~;z{C+rE zFgFA9=ilTJBEsO_`&&olb8b%>Smr*evh7LT{r!IjW~J__CEz**($U%)exha{wv6t_ z-&06P{B;m}9x>}RQ4UrHsPc8=Y(o9~tQ0%K02VJUft!2RPImL;R5fOePTi*4oq-+f z3D+E83rAEpe)J%$%RItfn`YiSE$yN)y-WSrvi@q4i>AeF5#cD*?&C)TMsqGhof>OM z+qN$eii>17AN*_jFKN0lWHGjg_JtkvVZ~QiWN`O`xM(Zb+p2$JVU4UFhTQ-nMdLT- zA>1dc^D5w6vD!kNB}`@*ouIWt`DZ@Agak@2@lc^3V*CXa=q<5)H&v!B{bNRHa#HA`9^lGDKL&=~C`wL=Ni;^prll%igUdZIz z!TZU@NL_3?@aN&<8V@8%YO96slr$PWAaZsRAn_FoeITUQzy?+1$(}_$ZG>|8jET2? z#9SS|_mn!~PWi;h$s||y!V&CQ7iAhdIc7*?BtVUdJ% z-6tE|HlM?7tgV+055o=)%tvS+egAeI=yvtXH{yT|qH=e?)S{ZpzM9KEb}-Wf6e*-u zGiDjZ>44I51EFbbSkENykC~>?zpE^39tfv>+&3*L%f~88`VBrHZyk8}^&W1?-dl8A za)hMCmFlct%C_>#5m3AyDp|C`lAL*b{Vuf+wx_R!`B1Gu&M^d_SOE--aZlc^w&9Ge z2OYIm@}Pkj9g{ZJ0rf{k#V9fc!&?bn!kYdHweVq@!xru^J;doIM_O7WB7^Je_kraV zJg@jYt3DF)%Oy`nRRVW>Xqq?wV2pj*qRPV`7%~X3B~y zDzK6{FRwHp9n+j4zk%hCCi{PsnWC4H83O=!&a*$Gu^J=yb&QK;+}|>XU$ChtD8X*A z3VVLdXX>s#R(RVYKQlTJd`; z*5HMo4|O>WyjQak7yaZ`wna(0!30DCW{R6z#;@`|3&jkLwcGpRRuk!hPxQB@0ro7F zNkp_taN6`6C78{X0^v9P8GR%3%-)RuIqz&5Ye3AeiHV6tEG*~|5fLpvRfqQt3)n*#~i^T@-0bpX^e>M^- zw_zM&DRW!Di5wi74VmYk4D*+EPU#^ySGds@O?5xU@?V6L%a_N=Z!?hug>kX!{ zvGJidKu8s;)x6Qv)ciLiKPOR{m@#B_aOlk0j>8bsf(4CasIuI8IVp7kG#!29a%Qr} z*^)8e$c^ka@wTr;0)3&6dIw)0Nkqntdz$niiV&x|pH#T7()N9lCE{1l+A+SQa@Eje z^SPy@J(!p{v1vohum*wZtjRz(bR9ZDAh20$l0ZjBL80;WXUj8#>C_vbVMsGF17_-(~#~#ai6HWl9Py zCB0VX_oYqiJ0&cN14F9?D_*vm0zc(ywrE$4<#!XoCx3#`2($-5E6dc-yxhRH*eD?0Jt1yZ$%gw8GYP~ z143S$h2WzBC`ThB*gt>%+`X9^8^ied^XI28Usid$m}N9HLU(qIHH-&BQ}bO=012my z6#H5&e8#eHr~U#ZR&hv3Ux8ZZ@8_j-rUv?&#vBYXuT%wtztfAbKIBkI7qbD>S^Q+; z$L#f$JSbtP;Lt~HM^=O?REg=X1D^Bw>c!8gS@bxhm%WsKLP=4#xmv#%Q7?YLu&PHf1 zdJR6v1cypRzUZM~SQi_xTOuF`0J1>_(D)F<2sB9LPFRTAoX!*dEKh?{u68hJt2oKn zUCxSzwVd-XK;1c1dhAvx)!;bbI+)`&zP%^R&hdl(_+D^}93d>?h;H%^?mV>=KY zqi(W9gwoM`f%3~Rtt2PrO_o#Ta7s!FMji&=%2_^^Xh|t|3z|a_sX5fnE{cb*8rhv) zjH3&_c9fz|g&AN*wH58kq+r5Xpb}FH4SAAd$5dsC9nr(ao2d}tJb5_5O>-~wICK98 zaHzk3bDJNE)!mhqTCFWy7zzIdM4iFPOqn4p3JT84RQpR+X(t*y=dq82c9o{kF)4wr z79Fb=@&u$a>j)`d!0`{iLhx|X>TYR@x;?Nxy;+5$*#CgRrP61qA`CHVXRsoCN#b3) z$(dAg1LbGx-sMUn-+i@tN-FZT(J{wpw+ps!1I;G(nyPz`wV6DoL1bFP>SFe(nIKF7 zS~GQWj*s?9XOse3fqwnQ8&FYsui603ibIz+F$2N6{oOL+%UZ%}71YdCXE`~gL(Hj% zeU_It%>49R1cg31taR5#lvxj8>k zye0ef)6nOUrj;ZpI2^xze8{&!@6A^Yg$MnMyox3`()HKV%MC<05^i3;8_0xX48Fg- zDwKNef5DD;85Yy=2%ImYT^!Dx^UyDcPUx7HsC%nE1WLp8cA)>ZjW0ld=CqEKR_ z8#JdffD$d1G*4 zhg&`HfG^-jJ;I8(?$j`kMUasuJYS*-)@&l1Y zFtS9P6BFp4MO7aSY#f^YEAHaDkqKgE)ORP>`8`-b<)#095nGv};X|rVZz@fxfcUW=@YuA z_^>+JwQ}6W&xjT%^O-PJS`YowriKgz%!OYDVebH|$TiB2uWGs|){ukiTHP;p-jbeFB{IC^ZkKLZ#M;Naj0 zxorI}iFdcRTY7rn=;-Jy4h+Enxy83y@K~J(lVZGmBrPc%Ez3y7q}uWMs4Jr|C(1B_ z*9XB?s)D7xCN87NMCnX@fs$K}Q-Q@b$bh}`!wJe}%?DV&v4>hzFu>9r_R=!x>EH*TUN<+l zYRly}VPn}+x{nX{O0~8hDotlvoA}%x?hRw}$kTd8WD)5Lhaf&Qu55;cK9k<9)Y{nz2Fe|y<2s`LGObgBJi&wg`5<@G74 zu$K9Qx0TbhU3gPO;g)XR@_ED@In*mkyUuP`r+3jUwDX9Zx^GgkqGeD zcu|T#Jg66ICLt$3AN);_^MoL9m~S{V!s;gZ*|qO^#k{B%9uye3w7S~XHaABAfNz(M zsKi8q$DbYj_cYVTsc)iIFD~p-eE6I%XmAdJroff{1ZD;{HW71ky13Pc2R8>NCu=mr zOA>B7M8JRYo|d+wyL(gpBb^-Byl8jztLY`rYcNI4dog1HGq9i1>xcZDe)H9_g6nb( zA56D7=lhtw)`rYC=2rBU!#A27W=-h3<*BPUobGBBmOl!IG{sAh$3(*y$jDP>{F3-% zkAtmNY9Rc>>wHl|oMA^AhxkQ!`TW*0xogO?iKh|md%QUGZnz~m3Odbh5vG5wle#3s z#-zPvId|b0lK^gQCa7$WmfFg|ot(M4I`4pws$i-gYO`>@-s^r2qtK;a<=l? zo2OrCW{SMv;`%-S`vbLTO2hrQR+l@xuC6YRY(IsTiIGurbMp_sz(9C9%|=9^QF45I z{Al8M(d(Cqg$DOK6G;X zK_+S{G->L)ivWG1uR7{f_if^$fpHFk?loVr!*@J_eYZBpIQ8j*e1A>NtJT5^-rzg& zj-V1$jUroAEn2T>Rm$*+2NXSXYiW#_rbe`4|@QDA>)bXmD zw%u(h1P51sPot-R2KYB4tcAWOns$3HIK9+*Et+jbUV&(5_b2MEY2TB%ENtHm!zml3 z`S_YwZsS*GLq42(o2QXfD8k~;o%|Q<_Ryb%Kia|6NFzu?7Z?;&Q(TI$49J*#?Yp{B zg@&KA6Cle@jAM}uMw3!g!(4AKz><>4fIBKYI=VdwhyF_CgPn_$6W5`;+tJc%P3Ch> z8ac4RZRiY(dK^v<&n>m23qPw8iM6l@f&lHQqX^eRX0X(@D+IOkVXmlsJZ8CirMAzi zwVEw|m>G2Kl?3V<hHIXMYEF8alP!nUDDrVYv-z#VTwf>$DK{zkq08 z;OP|R`hFe8Px-Xz+Kws080`K~)QHdd+96c4aRJZKcC)u0ownKlPtP^P7&{n*E6|MF*P$JUcPJ6wkB-*+=Gvy)Pv7NrK;64~S~VQ;MOzQms^g2ozT-(rD=3YY4U&mJPo-Q}ZAJ+F^59(B|!sDXX-eUdCy z^OeYb_KZdPYPt9Ui}&)d>|Gmj|502iGKaHr*lg#X@*K=qeR$_@7pG;by(fCUX#B!^ z`9ka#bT2XqK`!q~&HczSYg9Idhk!-sWXq*tr1=Yxm$nr7kee$QjhPb=3cYi6i**9sDQFv`qw)gafu*H19Da9j@O@gDk=Fx9VM?-L;j`=geCYxEz0>CA zW}Rh)4ru57$v}VqJBguhGtPNecLMKtuue0C%Phzn7t~Tt+$~B4Ho(6%R0PA;-weM? zF&vdo4?B_+yGeQu`?~easl;v8ifD{@FmBhwBBpKPl-NN&aY7(`<(dQG;lkaiDHyb{ z8GJjjm!M9GVsL_85|SPiza}V_u=b%-n>%E|u%*NOIJ}m$3aQ52wp4K%J=sn#LUTHF z%nH|(OIRW}3PX*1 zMo3U59NXk{Mou$s?CckqQDyJ-^M+D4M*@cO@W{1o&#-!$5PMF2(-+{*pB9lmkntIz z4dzN!!2V{%(Zo8ivBy(C@9ZyC#Y%uJudA!u83f$V-iG0cDixCoW+uv}rZg?RT4j1& zfZ|T6d5A30uw2E75aY(>hkinH6Z#Krj#GKK&%*9bL~G`);zx0&hP&sYP4hfjdfp?K{~&u6wDVeDIVQ6B42rjNQtkkS27@+Qp4O~ zPft{+eChnnt17*Kq&>!h#O>Z$Adu&FhCv`^e+0eYnFTTtNwLLy(XKcu+jh771He6BP9ACtV6#gKCpo}nS?;mX^*oi`g(6W~I+ zH{!d{qq3j-sO-^oZ4zQvZ&ThX~ z8cF5t$@@_8@e7AZ@FCX|?TlLf#)g6W15n^wJwARmQk)nRV@KRYlhfX2(p!*T`56bz zcxjt;bBFQtFTmUOJE} z;v~~BOPB01!OT$|CNE#LUVIOS+4YPUf!4eg`2`$H66>g-v}pbR+*}4q41JJshh#>- zb7o)BPfZU9v;ZO!KvsP7ijJa;n3%Zn-JO2v0dw?Xo?QCc)!}^Ob3)(gpfi;c;gEBH zVC7aI;>sO9Rub5u2I1_0A$kT}p#gLr0%8|i@C?7u!?5+E5(;7p&nfI6akOPW3dH<; zP);d*0wK9rbkL_KbLr{%7kPn?f7|)b4tx<#TXLY|U*x3gwd%gQ45)LlRtpo%s=PbY zK`#deiX?Ew!Pcgf%1ZT;=6ebnn_cgw1z}F%8V|?;4bOO`Nzx7OHkPnM%d+cqH{3a`L zO!bKL$5SEf8Uh3vg(aoM#YnGTzixC*x`e9$9?N;tuU~Ih80_(*=hM;%#}bb_>TD<(ON_$1Na*N7j^7z&0`P@Cs1!E?(RM-sR~Tbs<7S6j8x={fL19uoNg3?mR8#51NTp+Q2>&rlCEVa5RMy{{A4z`T)9`eejAFXdu2l zqrSrB(hK(E48y5y=WB&b6XrQ_hynTBV2nGM30);&x(b14tFS1W)A7AjY^0j2KqZ`y z-}CLB+q2Le%A|?0_u9mI3lGuwc{?@qfrQb&Dk&sPue=0=ZhpD*l|e#G6MEr{KsO@e z?YY^fjhao;Vm5>{-FiUG`mXF3f;481{10k>OHR9+?D>qwCdkJux6HTXe~(h<=@3-n z<4_!L@!q(QQzYU_Z{gt==oLEqH?L|I7GRYs$`)F=+8C@ z!?TDwdZgeIw!V`2Do7bzkWI1ax{!un3$|=m-pP@V?}`-I1s`2cJv42XpT6xx+3d^h&xGv z_3cPY)A$Ba@D1u(k}lSWeci^ZD-O`=DRJxetx^eoyLwrElpJtgu9JhWIhO8V;#)i` zHSr=F%$TFX4}^RcCP#4lyWZX8Ea~5vEO`5$(bvMS=D360>ydBWicR0H+TLqRYG0A! za}u<7s3%2~hWG4eVvSXZ4zj-#v6N6b#+)0Apt3F%O$z(PKQ{{=`7E|lgon;-RzWcP zw&j;IC&mGt+PrCzKqcW=^QDk_3FNW#bpKP~f332BaAr!#+3D%)Q1D0q6KyCK==AjT zrLO`n6@fRiov8$-L!4hqpKz|1V?aW)gnBm!I{F0Y81B+e+ zfEZv)e(30=R9g9)QoEJ_eF`J=pTEb)9Xa9;iHt<+RZ2ZI-lrHhr@`6}Y8Vp3vZ!}p z=%u}N>4OA3BsPhdly%Q^JYy;oL1aCUjte#3BW^MYc(g03qfln{nh?7`_AQZWI%o_9 z4?bG@lsBT0ARh`}&BgZ4mBJ|?kC41uPg5ZVC_2I+kC%jP>gxlZ=W$n2yLrun^^o8a~C*qYh;e#E=a z%0}t+nOQ2TwKgnL4Sz;{QP6;ZMA?WwycXo5zg;PW67}6${|CDpLVmvx!-0r0Hv+wH z_@eoDngX9?Qm`v@tPirBki(+lb=}Xo5Jrf6+GZh`8ZA+bIKw*jo^WOanqKh|7ZRnd z_F$**zIwE2$Z)Zu{mek;yFDcq@Ssn6#LwpGaM6>Oi@qG@OVu0Q?y_j9ON^Vw2Y5)A zdrQELg&|2&*7bpJw6{A^FIkJ%v#5v1{J_@T4Fp{ zS!s131_)*6v9e}NpaC`saq-N`%FnhJ3;G!_XA>J3mA z{Tf4E^)f|=B@=A!x{n}_VYi$+#U6G%^dB!UF1;-8ESleAnz4B;wLe9o{~Nbcn60@93Gu`L=i!ZhFADt(QhP(~up+amN*CtD;WwD=V=7tG?bX(ujt z;K5Vzo836m-AS2+$~nhdskWoej4Sdw_#y?_l+v*IxXcUxF~aw{34khU5f1fdFUM&y z^qHZ=|3YAaBUwqZ2<4LFXY22WV`*}_a~xx?0x_KN_nRqVB>Vlfwg|f>0Pn1rnAnR7 z0N|wEn&z4VuCi6&oI7ctd_Nho_Q?wT;|S%4z9yO^ZPgpA)i;ukICAV3niw0VM=m&yh3Fx zb`uKmT}DoQd-Ays@jed(P(Y{{lZ{k$(N}FKy5IZE$}=FZCJ4T5SX5kvHX};&9P5G@ zZLrbin8y)q8cwU?PqN^JHS8UVFRhG2 zgp*^QR1F^RhHtO4^sX&Fr}JEjhXIA$yUo83=M5Lo3|U-Qu-qLdXJ|u1L*oM4>gink z0s_M>;o$_l&@wVIV`DbqzTjST{pcMF!gnDuumEX^xc?`%?`h~7`8ClyePIZ-by6nN zT0)4q0)-qmh+*VL@cAGbXH~k;(l!=;B?EZxMmL*5)3Y`d55@ioTO?*_Q< zCTsx(%TZ_T6aR4^UeC<=k;{xwE|@u5+hNt!%9;=TeU#=#aog3Ys)_atgg2?WPK^zz ztFpOBSRkUazc@3qR1|O>M0oKrwGL#eI_RJrZ4JPDKbWKa|_U*~DK%#dM& z$-6PD%G!3MLVAS6p~d(2YVqaVwm!T6tLa1n$3RI*>EQ)Q1iHJs<8r-W0vSNx_xj9E zOvHHbgMdT8$HNy^pUL2(w$*W^$_Mm|6w;PRl&qhnDzkGl?sJg3+1=Z}kI8vh_p-{RwyJH)A{Hn znP}5Fnq9H|KuB#Da|!~9b5FNowT-n`ZG~mEmdf?8Z|j2P-D+`6L7Z*2&q_BXs@}Hj z=4_wY^~?<3UznlV_yU(gA%2pfyDD@-tdz|Q)k{kNNLZ2$49C#WzK`!A%owPzDNKl_ z!16%_p7@W3Dtao%LF}pZGx1HxCF=j(3ONH=SQgq60lPg6#wrdLmNlxe%W7&a$>W@TXxG3#Yq5|>LyC%c_88&5-5GK>*Z82lZO&hshv_M+P>cXVpKFGUhPd)qx|&*7V1+Z(jRfV6#B4_vV!xcJRA{N{ zSs`p3xfJ8~G$t?x@edm8(Un45)@;2A*WTU^W@H?Js>{xX#KFN~Vq$6qy1lDMM+`j9 zM`b90D##cTaGAcSf(k_7;Lvf69iGd;J)yUkJEw|m2~&*NQ`+7P4%`?}Z##xZp`pP8aznlB70%3YcIAO>6Z{;7BtM~|v1go>U-ZpPDMx^thh>eI6!4*FzIKqe974}#( zAU*9rHQv^2Ciq70iS3vawZiwsM$}c}-1*nENkl}^+3+G%+XbZ2>lK#*`N#KIF&nAk zqBb2JK9{gP+IR)$XA0{M4IPvmpQh%9Ix_{ZB6ZsF` zC9)LKpOU(QkCsEaA9yNzAoWJC@Vh6UzFo{|3P*>-TJ!U-fa#$+zM?VlUE)`}AhjN> zXyvE#AO%){e%L<6fZP3V$xJycgJH|M4x>x!J(A*;D_|!I9TH+CUJ50MAmNCiD&^Im z{VW=e&CvaMH65oD%$V_ZQc?*_GI-_SGgzpWi(m&cCqYUd-9E zTOR-ye3&ICJC(SUZqYt29TwTGN-J|V8%0+5e~LcQiJyR@jt?j`040iR)Fn`_Ei)Rz zeX5I#!(yz-kciC?)>s1 z0nv8i?6j^fSYZ>+BB!EDWVoDRPspbG4A$8=TAotGSZJ)e1e+74Z?hcG*~DHGdB}b{ z-S%nNBj++TYbAKhnaQm~Cz!);l3;ZW<;eVT1U;c53Jl!?BDI%DsnS4m)ku zLNLnBx>^&9VbCVF*8$CYKhXTfY-qCAX}GrM3w8>6sOCcmQ;>P}w;h_tk_YMZu@;Pv zgClHX!}<#d@BvlKm6MDNd`mb1t8(J87ay}g;8c=UEp@K7aF`vfu%S}2PqlpmdADqPjAT8(t)S9Oc`bWPpr&vb{3bTdM|sQPhdCKDiJ1xYH8nGhLlfZX z*o4!PlGzWV@`n6!s)koGQ!3wck@=KoHm0s5VS+&LVVmOidvhLj=uM@5FV{awVFG(n_w@8k?e3;LLnRR1e_#Dt~tu0@IA8M;j}ABeQR zf`G^wMo=2f^h${}%r)Y)PVDY+?V}}L%DC)~@rYTS;!`UG zSUruzSU)mG&8G++SC@6*1Y}>+Sne(#fB8p(BLu9xvX}#zaP&uB5D-hkc zJZ}5uNMnVRG%~1T%lnFxsIk*oit`|<+Ox7TN+t5~<7$pIgr~IeNv96FlKMjstF$`U z$p-XZmf-ty(>@hEJl3D{nE}H>TR?bpEUGEY0FcdI0B5#xN8YQqq?XS1pjk#YNGT5C zp{o7-nJW0rWR48ieU8tBf_~C?=%1%HGc&W^N34q@AY8+g^S@br_DM)M%J@@R^a2a? z10BGN3)_Um#3oq4aagEcgA~u8>AZcFsM9l|xYYpWRK5iF)!yNdWL03tSIP>>NlTz?2rV>&Ld@lw zEG6gA7&kCGgAU^1in}ZB)phJGL4~q}?{aH+<8kyXO64J) z{)@BlnoO0^2&)P|_8bmu@**Pwnj~8W!Nr~LY~Mm>&i{r_O-nNgg%P(bL<||fxHZkI zUg8NsuGIfk0=4rE%CFutkU8ovJu#qVB^jP&pVR1ffia1E?aq9;A-sWs!BU+Y#muQq z>@7ntpp1s8q5t0WjwIbcQ+>wsPVaR5es?S|H-*KvWkR^%NdSZY36^l$zV&KGmg{)I z=cFik#l`}8Li?bHCz!m}%n}l-e2=N_5iR`4XYE8^FeQ@1yWA@U_}Wpx*G3PjpSN0g zkT~E}4{8Xn5x!nQ-62{Grh+H6ot)Iuv;BN=fVv#zOVXPq>YYzJ=nY`)c2Wgi9qKnM zcUF=S2^WUq@O=6-TA1NUp{iQBv!6lB4m(TE3`6VTn>59Tfbmv`Mw|d98d{6?)z3H~ zby%ixxxa{b$WzpkXfnmZ1`hY|nlVc?|>`bh^X^MlOzocefi= zl67T`JTOUTE3Zvfu`>yl5yuKoW&T?3E-N@y_fryUM0^bXcwX`J{Sc~}0i5a_K`W*cYH zw?c;Y7oa%V;Kb|SyQ!m=ThX&{n2YRr`!h{8`c-T-l#SgX^GyoAW^^)C5wP^0I|TDJ zfWG&PLys!NpwX6U^Mq8|uV$Qvw~t)0a!{polm$!6b2~#KO^*=w3*osH(PtWm-^9@+ zAe@~1OK+kQc=<`?B)@7Kj=kC7Nl!>>NIR=wEb&-xzy;ap^ zKkhr)7Nf)snj6D9^27SgD(PB_Yq34u@vwpfS)}gUX|5jWbcO$M1M`QWCE`6jr*4hq zYsLZ>tR*;UCJ#O2MP9}XXG)avkIqb=BT!zJNymOI)O`XfthmK#`K8^KJI^!9ZHvv( zbKN0x$E-#>z|71+;Y<LW(?|xndK4M^#BY(&$q__|ItJC52_!4Q2uA3fTrf~@Q{jz#`(n* zI8$wox3Rgo{GY!lO>gvi^v*H9c!~>kAyF4`dojvw8KPeHHEU)*;)hv76RT(m--!~- zx>}q#35Qm@X{>Ap^`OBHi3l3;jtIaecKL| z<)cUa6C1NtJRiqbf0PlKItN1uKS2rZXm#&V&M^tvVpPAiZ{J$N{G3gB9?RERWB772 zyAcu*h{=70e?j&F*g}Y~kz-<^PpF%oj0y$6f`SNm6&iP@9j?GNzY28u1aUw44dnUW zmW zuq;gdGfK|ia;Q89iQQ3_YD!~W88fz?&JZVP^LoGId|Me4ACxAwre3B4srp^f)>6s> zRm;1}H-C6Q#L$9=W}Q@_;HPJQR!{Dx3W`1jkV47;5`pB5*A&ud%!W;FJpUbNFlO2+ z#B2BuIVJF-NUyEs%K!DN8Mb;$3V?%1OG_Uw2%sO_)b#@zu2oOwn4GGUaZ{H-NCUsg zA2fL%3UAy$V$@_&^Wz;}T}P5s!G{@hwBGgFLt1EfBdm%DJg$*vOAml$Wgr;sHY|G% zm_Qr;&^#>OZSfp1?N4Y&=(a)y)szg(JDw;yZ>*aQa4LKmrji|+w7i8lPb z=ZSzY)w?OH7oq4ykD6i_Eswo?6MjX+xuuxJyodQqv4&*ljs}}x7PdrF9i>K>-nzy@ z^p_7=)FeAfQ4zHIqeQJqGw5qkyr}RsAN~Ykn<9z);{94NpW!X&3h;(y4P_xv@g&~i%`ZD4@(W& z$=Vh}pWhAJY)?o=wc*6sOw?SMb`LH|;rJBJZiGzU=>&ync=BWe7W(5yrlr}ypJT*7 z3ZNy~-k4QGbPxD)=3aik#suu1*gHR#KmE;h~PhP`_-kFeH(5`%|RRQG-m=BWP~^2=j=)IpXtCdXb+>9QU(NAi;= zX+0%zks;i`KJsdv8^rstL*bAt_PYk2Jo3aF3Oa0ItiDqd8Th+jxbn#g!A!v#s}9VV z3}<~OQ|~3}hV?`$mRZw3B;)!M05@=OF5cH`5@(YDd*62@MI(4x3gPg4&*9jkDa#r)R1Cq;) zxUL2XwvXNluNy%Y5~;&`fvKYmPFe2B+huP3z`e$n!)DA^Fjk`{ua0)q+Rwr;M6Qp% z_eE;LOejm3YjKGCLyO7LoK~q)dGV3=o;oUXLQD56Jk_H_R@Yg^NPnMAHAz?q@#=cS zzS%bU8Wf~bx%?X&qX2Ld z{Qdma0MfiljRk@GAs`e%SOjkuUmf#d6^U=uo1F&iTR8gs)4*K1CdlqKjqes$D3 zD}r(kGokL-0zLp~lpyx~2wsC_Ik+hf&vMFu@?es1HK!Z_9|O}$xErn^tzmVP)FXcp z!Fvkycxxc0RI2z;$|US%PE*cqmI7v+Wvlm0{0y@qR8*x9;3>@5`#aINRl?)d-# zpX1Lw(%F`?Z^%2Q{RJWt2@ovoCGG3&Y6(;S5^uV@pNFMWnSq1RFipwgCUvm8t^ z2zBegh9y74;|dydn`qKiJDN%yK>lMO+L1pjU14pP8tMI`PhDJohiC;`L(+Fmp3>Y}1U z($u%${Jk(*7iuK{G6)?MT~)OB|2^o|xf5w0$cyEC}Cy3v$SX zkf~uEap0}2Wn}#KhO_BIQF}BS0ds9_hHVfPO!+QJ3HP>PYn0LNKTGAc_1oRAp$fe$$Y23diDW zi0Wq*F)#S?Sa9!rwnnKI%Es>z^B?!U(h+YM6Be($qYvO~sTV!u^jZvR^R;{euV_*@ z)qMaBPN{|>@}8e;FDHx8g(tt|TosOz17j8&cN+3ONwWZuulxHk&5iptkJA|3gId-3 z31nFUEh9qwy<an15n-d@xVI&4b09QW0Ur7)K$N(M+voP9r+sjq8 z9l>wJR>x`tHn-*tROIx!s(}^BeNOv_OSA(v&b6REy$8K2td@d)(XLKhUo4|uE+>2~ zz&&(ur%L9`la*tA=Ma2T54;zI_?@LE%j2tiVcp7Q`TeFMx*FtgYyAq?+F=g~)-kyg znHtc>J-a}91`!hR+96pjvfaR=CsM7kzW z>ZYc88{FVuQzEsk9q2ZGRu*zHjoL{hZzc;34Gjnn-$vh_{VJ!Zh*_XqvQ@HrJJ{L! zO8i$r-HkmNSr$VL4D8VoOiiUoejT^g{7eRzhp_0&EI=y6PaPh{KHfjbN7)F6Tc1$3evD|YHYl*TH851+(a)29tNkaAgBhRrEw%dU?F`ekx_c1kAIbmpZ4N>0C<4+9p;QB@13!RKo^4iw52 zIuP{}p~ZSNo6Z71WMr@LNAN37K1X?nc|pfXIqMeqkfup~=@3!F02n zm;r&j+29fNudr3%jB*_^wc@aEs8ZsFOJigzS!E~Wd2&o-wM%D)eDwXXD7Y61X{AM? zGv4>ns()?{SGibYwS@o5|7W`UU0{h#@(3Ws!(1wWK0hBzNeTfllwABRZ~vE=zSftR zrb9%YlsZtB(;=iLu)pwMmCIw$0k~iQveMbS$nS{>AK+2c1@@!#4_Zm!fbV}zYf(q< zqR<_2g5(BJ1c2v2_nwWA5#Sr*d?XeB{!9YNZPEWzs5q3z%c9V4c9-B}u@NhAAddL! z^XqIu$D1^KiLV7B?Hg=UDvUkc(VC-E&3EDvnlT$0BqgCaQA;z_y2v=xb}F!5X7}$Q zu&xPooM|O*oQ76%Z*B^t%H#VHSB-hSC7_c_2Thr)caQzN67fk6H6o2ogKE{MSDNkk z--jKGH5Nely}gr)YGEq$$^wXPBM5*9R0+17Eb)*zedK*9^;A{WPM@Sil6RcDVUBp%Ujhb zo7+2?h;2u#@K!NzLI_6;>kwH#!V|7m{_Had&rcV3(Ix<^P9^oTRa_kIDhjTBSOqH4 ziF2JBkpV8o{dAy5x5%z=+RlDQDfm&->qb0z=k&!^50sC5mUba5U9IsZ%M`H9^0!sD znK>4|nxFs$7;FNnH~9%60CeRA(=;B*Ib9&`6wPhx$F78nD^c$) zPN34*A$3bQwA(z}cbcmNH-;uh&=3b2ggUr{Ykw_hZ9U@l5-U(LL`2nAQb zPK9rVoFw}oBgv?V%1EPDvqV|O#Ca{+unY9ftrb%qq~XXKM|Qy+#>2>Msz*%Edy;Mm z>ecMB@Ugf+g1&w;sBzo@{(&mM>F{fs`xdk4V z^nZbry=3I=X4fasrsXV5Gn2l=tNg_|zuo{~TA)6Ksul0M^W{tT&CZUo+#2PdZMs={ z8G>YRGgYLn5%RqK{E&XL{8aOd2n@*D@UB(KRZ&ukyT&8pvi#l?Nh)EvJxt(ENL%xS zzZDG*M+nQ}%fc)PvAGS(8yqzS{L@X0tsbcC)OEdMjR?nhyt>YoyzR;*LVMkay*%!3 zexAV{UlRH6P^~5IQXf>z1!!XJ>*QL!ZSMd5yFfL8CV#EEkQz(sX00}xJS2NlhmrI( zB{4o3rjFHe;5CW*f&Q*2LreSs(KjM5x z!>a8wuHFIt*z_2~pG^XaJJoNUvNK+5;_L>nvaay=Xhgm6FBR>h-Gc7C_%7HuGm60= zV0gugtEWMNbCu#6W7o5J=ZpF3f9EU($Qif6vkV|L)YrEI5Lh4(2PFGmKn&yR>KYLh z)edl!si$&tbNOa2OzrWa*K1@-1buG-)fkZX!j<@z*i)ozMk%1oe+N`iWoQo>I-8TR zC8VenR%o)@HSJHWN@NUAh>5%o#E(b(+4=d+q&=`|NhedW-Cu%JU4^Ehq8Ce*T_8Oo z@he7^<$K6+;2%g<{aK@oN`b=!0ovHMjEV9c(kBHXR}`{YWTf$5mXATI1`vmuwZ;$V z9IB5YpNt%Sn6LSM=0uLc#s%+>K{ygFKsEgKU&$f~4!cv^6MYIAR2Rltu+wuz%j#Ii z#=-5eJO=yC@rxT&tnz0VKVOZm$X1@1XWZBCDS z1)LG6;esTmMqMQ$NGla}GM-*3{TF>xG#>aPyP!i|Kt7=M6zhw7ABa^K}IRH+x z=jc^b5b14BEuF9Mjc=I(ZJuk%ukAAt0bo$U@bC{m^2}jAU@ih!U~v~w#(MSp(T`)E z^$%(oo!PB&pAeDcj!&0qal9jZokdF}g^5$?!;-ztF@ShIlu zEK38Ghhfx!G9MHy(ZIOc5@@9*puZHHz02>pnImdBSA$tn1L!~Is(Kr3Z~3{6Rx_{h z+axCWQ85)0i_k+ezvW^TPFxKm2`35U z;!wJBrrd+uRw`J|bHM7`JqkduSS?BS1(dP|{?nN(4VW2mPB2Wp?`rKR%G+7wqf$Kg zQs^c?8(9rMQAr^nc0iEa-D7thZvEjFXK4oiEq>mx0l=!rTaw3grI+%q*|=HOz5fi4b)5n{z`$)ixI**%{^t1R zj5b|k0!dVP8`-S%2@~(V>`HYr)|zIj{N8RDiHwawssL0vM1zj|Ek>ryT=oKrAMQ7@xty zyRWYZW}y^+Rt7}6w`SEC=j4TLZr6Mc479vYxg=a><@aLe7_c0-J4=q0^8%|2So&qf;4Z`v2F1?JBFIffGmXvpgif{SHtlLHFhvNZ z6c9)^nMLvy3+(2%FM%z20g{@bVjmIIywOSSvrXp+~ z6X&yNgPE{=>N88At-@dFUGQKdtk-hz?kiF3dFl9!_Xj1J(qUWHV!S03$k`-#>z~mO z(lN0pP6{{Q<|{DcKyqOIR=iJuDZGH`7}CiE|FSE!{n#Aty@mMcv|i*%Qiv_pfAV)= z1I&P_Yl86~a)F5ez!@N5+%|p@y91J#<|--;TQRpAsNQ*rMo2tbagy zhCf&Wuv8xl7SkSJ&~|u)RTh49eAn!iH2Eo?j~mImV}7OD97@alfq?KHYrV1C6rXcB zXn*T1Sl1f&BZaZ$$N`MKT2U8a@MW!0B{CN7_0m3nTA;smw+h2*AOYG{M8r4-Z7&^u z_J+=7Z-`8J9k>taPaPNvql;)s-4U8&-v4t&uD4STPpW6$a$&5ES{A zkz}J2;!^T-+s2MXCznb_O9cVCS1*co9FUtL=4igCIDk?Ymn20dxY#pZwc;N{nV=)g zaNAPY2qub;hpm%5(GU>V00X6D&#VD$0Ir|2OYOVgNtA!jh1?6o2{;!T>Z6|1_P#(5 zzJ(N+x|c|8CBlbp*Of{|YvO719_ENwX5DX)K~j4bVE`f-wdTN~aj6oY12nx}cm%zq z-#BN40|eyTW=g@qLWJ~D!SZ0gA2~jCNT00fFf8?uhA2#WEtVudc= zhwFO0B}N|$Ah54npN=YH&WCcY?-rReaW&y+-7Ax>*Jwx7+R(l^()LRoC&>S*-7V^! z#r3TmKJSD53ef!M-A&v41b}1jpT96ohv2)=_N^rqa=W`91?xw}$;W(jlM8t!K+}kt z)iS8yU1DlDWQ1uUUcex7Q;@1w5DZW54z}twLzPuO_#W>~&9VR~Y-Bj)MFx_TA<s79eX!u56UE0J*cS*(+^F_?DE@rC?%O3U>#=pE*f=%9o-IL|u32c&dXdBvgYVL47rFKWrH> z0Zg!Bw&umt&9i?!f*7}&KRd#u(*&hdog3hAnu29O95nek(tQ;lAL-C*eJD7flhW_}?$+8yy}(KMywJWZS^MS16VvB>t#=tTM;TS(%H zNT>-HVzdy1jgObQ6hogk9PtI4IW0pCmou*9UI-PgJpH!(+yG;^7|W%51z$a5;o}#} z+)f~dV?Zn&5*qQif!cU|>~%QSbQ0kbR6S>!Hc8rye=Zv##xzTSm)HtSa=hbxJElJS z{b<6h(*Iq?p(3Ojgzt%yZ}BJ$f`O54W)0}mMZ>#4LYvdB7tG&QJ-A}Az>T1zv2bq7 z3ZKLbUIFDdSSe^ZtagHB2l8Ie`!^xJl2$lq+P-aL5DU^=&C_Q&zRmJpRb|N$Mv(jz z@LsP{!<09B@r3oxta8GB{(Q3$xth}r4uIzf#&we}0 z&#gu4A0r&|p?Sr`Zm0%=FjrdO9CASNvD4iGUnJ<_PP{5(pa*kux?su@BI10!v-t{% z65AibYET5cnA9z$(hlF37wj3Te6i>xkM2_W(f~ajVz{dJ^-?@SW>HhY#4I4&`+l|B z1)k1AOUOv_6%XE?*fU&byv`D!-rUx4YuE2cz{cs`X=U=`bsj^-2;-5$`JBq>?|5osM7-L*N}F6 zdG(t%t;{B81Jsc@IxcoNe2&$?{26?-`il%X5p*|0 zycv>%mL?zf(K8g>tmk_Jq5G_%I1&DUX5>YtE?DhXk&+z6oPx&h)?GHkR!;Zna3ze-cZct*b^F$i;p=QQ{8Z75`c~XoM4nljc5q)!x<^$Q zcIe&E34zhwi1kQ|qV45G-U|#tnh8Ch8eJHd1 z{ISzc)y6E!w#yk7v?D#xp%4Q3tY|6aUi@ZX!icG5|8>Q@yK8;b;V1o70ma(J2((zu z=O#t@G#bv80&84kM2x6bs! z1P+1pmH>+a852)HU>MH@#x^l86VVgTZ`#zEC4FGFPW$l}l1%*Ih_iB$0O{rez9=^W z`M{(`d)pd>6MNV@O_D#gkp#DwG^UQOHyQwbn(X)kz3YY8kEpup--AMw%Jlfqx$9!( z;mSKv{cUE7>)w>d+qdp^I=>^jL9AL=8Vx{kb>_DMI_q-DYIP<`WL07~H2+A1&*6-1UZ!|W@SAL!N z#A`RuF8be78pZSfEvKdJe(Mk)5S#hdNJvpVB7as5ZfMBt>_?LVj2%mrSmOTn;PEN^ zro*BHY$DKOFiJ&VBkG3K>axcVf~?RSllM~I;c6uD`Y%(G^^C5q3SFp8;d$j_W_;Rj z#4*U(GpI69f%wmL?{Wk39|5(~O!TF*Z757gfV+JkqPg)2@SXa-OSk@PSQ7}$JeC|{ z=!81%nK3U#&LgW9Iz0de>#6u26bX`iI~{R!vlJ{$=lAx>&IMN?sZi`9k~QVfQ3TsL71~n?9piw7;C7#PiHGMcs(!R)bi8NFH&K4$yggYHmh8|7+if6FWps~ zw#gj}oX(pa$Qc2{e;47^pXUwmm;oG&K`SONxvQ1jFys~-jUM@pzHU)| zNa{z~#>TtsZ0-=jFI~U2eLItz4U+Sm8#!m!(ntr79pRz|b2VLHwHsh-cwx%TU>l6Z zOXDZdh82K&vJgBpBESWuu4qg-ob=?Ay44~)bi}~2$X83&(nkWX0HzJ~fpF2aGXX8+ z5E1jYzJ*xS%=AFj<49D-*?3YCju;q=h=M}yg49xae3@Trq`Ik6g9f-}VOqR@J|ra0 z&+c>5He&Ici@Mt3{kbF{ng0ZIHmVWozg!~Mv|$^zVPTj@L{-(`_uvHie$X(B7x+HhG`(NJ(U0RcLNmJs z#~BAjKhio%Rn>??r}KQfrt^d^pUhHOq8U?J3LVp>nf+T`P5AKd1_AV#u0rum;9fu% z-)I{hs}UTnk5%$J`Uh5FC`7Kh)!~>kHo3Gx>=3o^L>$JH!T!saTsw*^?DCX?3UZk*5?*?hk0r4`C1VuN~JU|200Vl+hbNU+^KpxMVEp!5(0RW`T=ee<}I3Qjkg=V9l5AsHr{SIo76d z=MDe&kpU*M{<%_&?!#55`ZmQ_*7yfHrBCNq<4(|~7cJHc$C3D&>kzjjYDm9qlWY_{ z!Qy;XYQNr6td-<*Cry{jsVq}$SBNl>t?n1F`u$O%v$D1d^KY1?= zAICEOE$}cNeyM=2w;ZS$%n1BOd)M{-noeDy6xC-|6~#y??2y#;2x-43j<4Y0K?HZ} z7bUo;<|E367}r-fBqgQpURwtz6VWjE7FcuBybX`V-HYJbl~<47rp1X5^0lQQZl=3;WmGkS># zRR*oSoGj`*gsh^=R-qmMWDxj|7cA)a^8QA9(P^+&e5olk9#f!*l(AORi3P%~Fxd&r znhvgU=zF@W)hlmJ`TAPCYA>*H@g3;wS;eO_HF@QBXP7thC%7mmD=O~hwhpb*T{Hk>#1 zD|NO4(Wvp34m&f4sS)~A-@<3taRVJ_GZp2GI_i&M_&z;pMWwY?NI##_ObULYJ+7lv zbTy(GK#!1x>zmdh>o@UZn;Z!_Sisho!;K|=3YsgVXx`gB{6A#9bySqy_Xj$NFfS@# z(P@AR0@5X+C>_#WA~|$7C@RujLwC>6Idp?G4Bb5g3^jDzhxh&d*1C7yzks!dfqBk3 z`|SORz5kZg+ZXH!z&^O!l0-Jx4@Eo;EJe`YimA4ttPB5B6J9^MZM&`W6yUD_EFI{T zOvYNZ;(B9e{VkcCJ%xy|iviE+3o)|Qc}_JPQAT63;oNewYVwWk3@;z8{^IC}y++7u zFI2@shIz3r>lrD*56_RCq&fErfC}o#Z4-2)&)>3vCV+MLlSShAv#TsB!gFK4$z4(* z&Zt~!(y}{l=ZTQUyo>9LUed6)X=lsNcr=PSy@Ra2a5emT%8Pcv)BN+8ig{JP9{Xk zl^;1;n);@9udF$GuW7$g_{h{G?Rc7-ixQg2So@eZu!kXtlCECSm&OayVc<&`MS%Tq3s33Q@W|e^h+a2@+7g0ptXx1 z+v`6bD;u@X)!|p~P#M@;5-;oS08K?jBY^-W@nPNm^2QV+GFw=H^tQPaIc=B_o;cLo z@EQ;v+~=QZsGhlVFYh%|M&9eRAM~%~zSR9ANhr11a5C?KX>gJJhylL4gA=)TRXj-V z*YJn=p)H<w+kwoXy1?PHQ;GmybOB z>LNltL<`;?au_|0Wyzn?&(g!-GZ|2PYQf#%WQwMF^TXIV3!eI4sOmS_CL$zCefLBx>{}En&^tcn=q~<>G@X5s zW_GaX3`%K91cQ)7a4w@ zJl1sl1>95YLzi*fF9_KwiI`5_)$J}4n-_A=dW;$y*}kMJbSZ^%RjZRF&(>>KM@5ck z^&FX!1o$S|zxu3htvj53|CJN}5ZwTqPEJ%|buA&5u|m!kxojYcAG&`hfUaC?Vj@)8 zBF<7N@rHqrm7YeEd9E(@|MkJKv$zK^-Fhy=*1Jw z2kI#&`F%7`{y<4V!Dw2EKrB;H<6018p3JRMsEbPLVkZ}>xsyAzNY3Zp!DYuf;+83n z4%$?-wqmc>^#8HE_#(%g*Naq0i|%uXV(}*I*@`!kNCyVyw#=2s*5!Ncw4fAi5GF23 zMNVRjbHv@|;fPAR;8?OV@v9(`vGyFoO1Epvxf*-^;%r$TwTOSZHNplL_~7LmVW`#! z+`5X5@M9QX+IZASZT3UN2!Uj0WN)ncsY>axZBiTK}LuP555 zlo)&S%nTt9~XQo^bn8)GpLtjx1{QPMY>LnNo>FG5t zm}|^Jsa)vHdA}|)&%S52lUrN4wnGiQ89jk&RJ%(WtZtG~6)t4dZ;j?f>?U{WCRR_h z%UX`9!le`%7R(SHaw>kC^1uTl4QmNO=c|_YT1=%S*Nr6=q!XH$e~2@m=5b%9d)rel zP%%*#*ktCq-%6=hj37PQf%TcnFU;lj9OaVa`+Dx4YbNu1Xl#Vh4y_rPRv03M z+OgE*fuNURHc@F2AroenL43T!PG)_Yy1F9zJ>`^J19_Oy#nTc;!JhNTo^S3Jt&2eW zyDa5g`K`qkzv+4xI2C07>~w#n&ikrvAVsL~g+ypg=|t2+aiRy$0hN>ER#X=-L=Fu| z!UQ}|OO7!Mn1eQ#pU*I!bgYVWfL_LKjZja?35!xS{rPN8g1bFlHs@9525qF84hUF7}1#A-pLEWpskR}3#-nk}~nSd3%^03O9$pi^(XZNhjtes@5e zjm5$_r?u~n9)=5TVBnj)f4*`mr_JrYE6EKspHn1hRI8^8H0y}NSV*KyRGzg}b7Lo@ zDQUcJ)^%FIa1#s4IkjIIznM%kr2?&{<=XBzQQZr?VlOr>vf$dytj=KEN)#6Z2Am3i zn;l$cuaUB>cn8awE%|})jhdv9 z5o&Wg9ambeRQ>R$7_f`q>(??-U)RbjV*NVi2W*=7U>9Q#b#RYi@L2QsF%uT=}8 zfdPJ%)^h^bof&m*i!s|T=P&P4htUdYMZHo$uJG5#dW>cFx%LFF1Ox<_n`Se}#O66{3=DWYddHg}JD38Tm`2B~$i$Yr z(LhM?rlUU$Utju%-iW=?c?WtC3br^qSW6Z>&fL_*U3HLvTg)YiQ6g<)Ot912_)`*9 zsW-Uf7e?2;zffF70n;kXIpQK$X*t=w=1wWqm7Jh%s{2iFDJPC9T8BN09BXzt)r?_K z0|mFHD%FWHeBDay2Rj%eMY~WI;9+CWqencR*Q6z0O+;~0?)t#|vR%~1ESPM6^`QWswG{SXaZsBO- zX@KwF)ov^#&XHlyvkqaN$htELzbZU_uf(~SKOHx5z^rXn zOiR!zm8YLr#{re0WIfN-R|=dJlvcfSv4Hb-UVJ=xrNy{3Fhf!u7$iG)z|-v75lBK5 z?bl_X$&$t;EUX8#P8=#V>PliD6Z;4MktmH(cRah1$k_t)xJkY`Qb0Ati+pWhW%?y;&8@MhF(NIp?W|QJw)6TogcRBx>PeV`c z-iLBQf-|OL6=x0>3Z$m&E%wDe7Z2=LRjql**sUEvKKdDGXt6cg*nrOO zYQh;6522i+@;lonUF_vdvU}2%z-gKU;n=s|D!?2i4IM4IitUA==K6+iNo^>XvZ0WKxZk3B zj*jjoSxuF7xGgWqhcI()AVBX$AC^F-%GMg#$v1ecXKz!ZEq^-iFZ;_KbKebrlv0F0 z5Ve`9k^|-+yBG$NvY=pHLJ4-1;eiZQKzhKWnDO(1=*0T%#40Y|Ixo)P^P*bi99hM+ z-Wcpb9~-mjK+Rd}%4vvORMuUVdhYY=-7Van(%<-gY#$L8WBNx}Dz zpygzVhsP4AEqZ?=)yKIrp2N7e=JMwH0s|PVnWK813ODTeCJDM&gS%UnFM%}t#Q(5m z8SMMv9O%6~=NID#X25l#yBU4#he@1>nu8k6dj*f1UnPk>TZ&&9I#v$UFV}lfSFN~m z@0SWXj&7{l>{;>I>uTq<>o~QzQ=O>H0&_>%PIQ=GSl%YlITLCZl4!xRRs5!y5~B5w zDD7xj0wox9Ubq?IF8HuY(~*iWIDm*^qud?TgB2iEZMmLODN|sr+*Of?g8m&!gC25M z4ePL=ICQlovaP1MEwY@QEaAUme2_yn_st~N%Ctm`$|7wR>_xhB{@w>@&CkK)?5%Si z56I)wzIF;fcDzsvWKC(@otJ5Jwg3g^T8@oG7;(ciUtmx3YXolgD(hldGrn_sBQL#$ zDohTZ7blg7`Jsz#1+~iECiaE!yKgVmdm8PJXDZB90`sa#YpYUfWxqeCs5^c4Z>mU@!MFb5>q zg1ol$Li&xKOzN;C8JpwnDeJ#vNxZNZM2j?Wb|WvXnn`8&++NKFQ*cs$TB-dx;|$yw zI_K?OrzJpJNMHzaT!A`#SJZR1#?CsE6RsFfK~*yOm-zUz^;~(o z_gH8fl?BzPi3z%>J$PtFqI)vOs)5Ajwd$ueW_88XkH0E7y9x!uZ&J0c4<{%|KrLT< z$x&t$qtnH&yfc@cUd_7t;u<2Ua>1(+EQy8UQNrh3?!o1JF2UBYrDw|?$S}s8z`{?M zYL(ZZp5k+r(bj<#?t&IeLYR=gQO3H3)lJW|u*r|(V7kJ&Hh+t83^(_FkiuA@>jxH! zX?^vRm|3YyHHm{mk>FD^gZ95VbG#Hszy&n++?UZir9}hH_FH`anrOOS zKO&C(+Dy9nAcnW7sVEcQa%y!vj5r|b^o-Xo!UYtEfN51qs}`y`p`l({2;nimcTshE zEgKXzW#$uP`kqT~N5%-O=Ziw{mvIc=&i?dR3E`c+=A#{{4L#yJwd6=>eWmSp84n2; z+tS2Y&1#>SjDYh3O&`O9C{56Ug~c8e$tx~l z-olGFp6mKd4n6eIV?`ZpvRqk#R635ax-8hIuCROs%6A2su>0N3j0OZx%QCq(G~1GdJe)UL zz?TO?dLL_El_%lH8R;aJw%&=Tp^IWn@%6>#DQ3ymjf-Byj0_D3`g{5pg#uGZL>ry|gA@XGCTdUU4h!O6d~b1e zf49z8vGx-=s9!9MEzGxzu^BH>dQ{1*rNt?HsUqmm1PdV%LC)eEi`_0qQ!{ST1ov7X zH0{Umi}4C`^$@G6ZDGF7HTN$;YP%N*MWfk)iH7H9S9W{Du+Ml`BelNsRMBFU_|Zc} zDOQzP7k@zH3ZuZhW^Tm1XaCMSvBcjCx?zY0FYV&9Y{c)UY(A{^^%{vjv8{CafKS+reimYT-fG|H>;Df84WVMc*Ai8#gd(q zRabKhq?i)~y|V5NLw)W4e!*pM|L!tv|M&a*+BOYqzTt9}8Vi4+Uq#6czAcc6J`UnE zFApW_?&PMh5q*DiO55zMBYNB{AiU?1?EM~$xKk?1W&78OTv_s@i_XV!126<`&;!KZ zgG&yAW{qgRhDp`L^FM)s_Pg`ZaZaN(e>@CTfnwNcM59iQQTg;}7MFwg6RmAcQ%S;y z>&vrBgO)odqO0$5EkFt2x7N(pLf!ApYS$Hy-hwztvx9OUa~Gb(f}%SiCh-30L|XwM zFWuH?5DStBR>$-jtHPy+oBfUj<<8%x@*qxfoW0I}$BwfWyeS>OJuqE+ldqlrSf>+b zbfThwvU}L!wzAuE9*f41-aIb&tZnHR3 zFxliQj=hxYJ0}{-7NW`532CpiE-eq@L8eg->O&b>#}yKS5+`yP{Z}Un$ip|rN-?N>hpm|C{|W@ zBe`^iTs};&F`|wM%c&D(b4ROhpbuj7%w`n}t=1srz(T>1GmmSm{ z|KLkUR9I#8RGG2E-n~DM$&ebIroOKxaHE=;StmRP-Hn5(b?Y4MSi4vKPAElK={@dq z>=FFL*A9O}Y`*Mv3M3WvjsavK{N*>q1~MrnuI07lkQk{p<_0{9w62e|y+8i8<31#$ zZ@N03VNKOrO|}0*+iMI2vs|F4ciTHi!wtSUu5Q{Axn2=Tx*#Gp)KR+Jewhea>THX> zjA2r>7rb{qhs#-LiWBa(bAM5=>NBETzL>mvcG#yyHiouPS;u((c_ z7oH(ODTO~jL{%EKn_2B3Ki>}oZa;mW^kv#^1&H%Rb;6aUbvLHOV9gQPerS1-(AGvI4 z5!vRV&sJX1liD8Mc}ij0(b2*0di3yKNm1)X$q$zab`xp1Fx;bru2H>sO ze+Yf=R_5_;skIGCO3SgW(hW)k6A~NKDurbLOIAv2qie6yHsTDQM{MSK+}OFl6RtYc z0VRc*?fO1L>0l+ei}XafnZ4kh487*6p-BRJ9n90p+;KVLs)V8j>%5x>5~=Z>1w(n& z`&$M{0 zvxvfI^P{}ZHy!>s><*Gp9Z?iRm zg?^r^GnXE6Zs0)PHW#fK7qb}g77f&p;nuf*&_P`PgJxOmB@d-3p28+n)&%cN@>5n@ z`JUS0B=|(JvaaZaJ^FRJ|L$8D<=Z(8e@&0zwIurk)SK&mNc?+xqq8-4=QhUcFZCMA z;!Q!^zp0kf-^(&CV!KyyV(o#tXR8PGOT+In>o!0_LdXbb($fjmEw0mc20{)u2Sgfg zPWO;e(-rjcPjHE6VvUOuxO}xs9 z6cSO2@Saj_&=>AUO{hXHLY%`9h%P=ka~@qki~fl{_rr>`Lq{trKG_-c_(V~eNWl&9 zf`#W8X1es-2T+>Q19w`nh`awHedFy7D1Co7*W@#GLC%1q&*U%AsR>S5W(7h+z8By1IHVegthy@HNshMi9oWH5@5sU(p8 z8q2p7?%4R7j(etu3{9OM51jl;NvAD%yNV!6k9ID_9`UOf7ElXfH(&eE`2w0peY!$_xcCI z{HBo)9PqCzUTcU(bDhQcWD&DM`5nPr&dkm&0|U{!Pcgl*x`H>@0>5ttXRZzI()S*S zEBhhO0rY-!^zoOTX&vX%W6W^}WuGZ>WBGRo_9@61Y%v@A-Qv3ckV*yxv^1U5zYY;B z*Z29$ZxDXi-o?MX=&`&i=(E@xY)ZdiM1wDSg--8&@lIOc=QF}iIZ+Nn&-sTF=MK?+ zHKlKHyM!_AVkvx*afH3EMQml0+6PS!WT{1kHcCXF#3CMso_aP)cKK%~d4|=5I(A2q zZM+6g*fsQgyO;VPye%)6p!-cvex1FrR3YPz1Kb%p4k_mu5TSk?N^JSQ09&qZ_NmR? zLa`ywyAYe;U3gRc+bdDM-X4)L+HozD2w5Eup6m@nTu;)8u7ppD6L~KLP{P?VCHR>S z#$rnc#_OD;^xWBOg?+{($AfmCa)e3>ob#ONs?J>JvAKH{-SJx({WH&9QKY~N?xwEM z8nhD8e1Yn>Q(Ju_XZj}HWwBlW%T;fSgJo6w!YNRRY#}75a}#1OR+UR2Inj9u;_xe- z;Z>fMA49+hUhirf<-4nEXIxjt)*3&e+MpZBZrgCIgAnfwB*+Nrd&vpvpG`CvsUL3( zXcWc6UcG$H{ce- zwCfZPFNhW4(#9&~>$yx%F1}C4FlSArdZu~R-lRLkT+0v;-ZOKwYye#fou98PW*Rz5 zcs=6Qsm)-ln=6v2Fw6PgubpqXE!r?_Tjp&xrq5!e<&WTr4D}8{7m6P)9DP3dc>Yb2 z)d{2e$-$ks0eB{TxZ&?BryL0jIo16jY(uRBqaTD zzem40416jozAfst(@RJ*>!!sCwj9cfE#yMFB=8uteq`(E*u~F6 zC#33!zRZy)X_{agK2s@DWevG_Ud@Au(*+CP@&K^qE#r1C_urF&*WFXC>~!3%dNN>ivb&O!IQLtm>y9Y(gKq!ol#%scXs$}5 z4G=Zi1jeaJB_=*>JWbx3#rsBX=jIe6pm%O;Wgf2JVB~X;;76GIQBZ*D%BHh5FGTO` zXCXFs*yzY`3>YBIExU6EB*X`bONWJI`EgVPn!`9~d{nM+Y_{SWr^cHUR6DRV6uv9N zAHv^@JIpAIOm1pYsyH+&9Sq#x|(khGvR$!Loq`2NrOgIl&;LD=u>U<0jW9Pjs=E`yl`i*VCJDWmbFtv%X}RIH6EIVF zcI9tj)}UpfR~p`EbCvFGvzIO&dnt;Z_ZE9~uPLj%@+N0(xPuGmDjZIKng@nNcyw^7 z)rxyz701XMrfm!K?o+3J4*7x=pBv`|4W+|^D*WVnTr0Z!|CO;s-eRBl|IhKb132$k&3d|%t<`6zysFJkrlWS96k2>eZAaOQQ_GdwP**V8 z@xFXbzsU29i~7Lg<5;z|p8S5d`JrJYHni(sG?d~(X8L?(=-p{v*U+RmigHwiJF|YX zUfl1^=hEOCpv7m2UfrA&}&>(*BhS`%7AydFNf>6N{oQstp)27$!89tHjR@KWwn5 zWG+ZeY{>LpFdaQkf^T*6-l$6yG!$5c9RBWi-zHwPdyTmlYMx^*iW# zJJ>dyW{&GM4};o2@KhMT@X2~3)MxQ|q45NV1B{L!b56`wX#2 z|AEp)gy&S-q&L_VI&u#481@D~@A=_*HchfK>H6vLw+*snQ}tx$t5Ckf54FLm;wP^L zQ0^myCn-jHYwadFyOOd9w3=0{(Jku^eAu@UIQHb&EFAtbwSobf`W%KAF6Lj7NuZ-H zg*a<#|Bf@yeH{slf*{jqv!{&B18lCzt927_mw!N@lFE zciF?=Z#5L{WQlQ`Ll{z3{_lztGN+*TS5%-3hwIm7%iRw(vo{bGDjH^D_$!ciMKw4J z?gsNv50@!MaWM%<&-q+`z}4naNrs8!$p%$(3EfZy4o2x}-)~HpX}Za+>hvsuNYb!{3jkEsTS(TBpP6dIxRBs5os5K%bc!NfVlW$S) ze-}kT?Dl$!y1SY$$+@y-W2Zwaj!y$vFH)BhwH?THExViDlb3?b2{UFbyfB;p?C^N6|{0d=I zl==d(r-ZV{bq05Xz|XtiPEfhlPq=D(90}o^5`N4IW0Q}EaYR{*uh$wUwqZLJB8J6E zkv22+5p-$YIII7aYO-^fq7IPVJNgxCp+>nMp_tmuScQ1m!62X?UyfR_RzA?R88uT4 z7X+ZD+>RhJG@u-)y9!sI(M7l_-V))z38_W~zR5!-U;yRP=6HF!|6sHRptXr;1hN1j z!zQ3wFgLwHC@Cp90ty8hVNVT@lU-Hd$7x=lrwjYk=+WFT0hirvR4&km-LX%V=W(hR({vEE#)+6cc^;aZ>n5i+D7Y5gMZQU=+CKzd&fl&LMoZ;GGLMY= ze)mpg<{c1w%iy}Nj%K7_Kh|k_Nb1cR|6ptf;82tU7h;5*@T@fA+(%u-t1o8g;E|u> z){#{9WPJpuN;}9NMZ>@_?~Hk4+I5Cg8Mp9jwIC?@v)iY?7A)k5xrVBpW0sk;{-eu0 zx5M@GKK<*AL6IAs1+SCjz^89A;Em_f?Ck7X6=o5@$oWW8j*3tk!5n--N73o`zhsXttcC|G>->u-LGlVMlJ{fD4*P#T?)|gl* zHUAM(+oo3F96&d)JJkyXj$0!ZQ)St)Z#9=u1berF9cARd5%RI`1M1va`SGf?t~n6Z z1nP5}v)RpWPJz0^-A*hJOIr`-2~sIH!{B2 zE+3rzcE_p0Wl=MJR&M2j z6Q~BPp8Ch_E-1COT&CrYokplPjwtNWoaV1-BB)I5V5OVn(A$zyjeC7n8TVLUnn zotvJ259JeX8LA%9|{6IS}C6F(*H7Fdi`qqn;_-hsX|{LvXqu~G!9A;+!W7)W+FISC|T zp2dnDrU-d(IiXYt$=`_rQX}N%a3&y~dA(6ZXfTg(8*SMFnA6fMAO#RRWQ0DqGcL^P<8EZRSQ_6)CH)xWDTj$$;X<}wSWzTMX<%&dWPT~jGFM%X7QbXF6xDhrJLv#yNV?8c%9}rKlM?LL*L$1jfDa1Yrst< zFF9GhG91=YI&AS|PC8qQYh)(558eMAQ=peRt2K)iEZ&-90rk8PyTj2{FmJ@2OB4QH z`>IfW)K{r^tnd>97WPGpQ=obrrrHJn@B2H6 zZ>$$>IDTp8fA@Cckv36cyia2J;W$>&mj&S1 z0bP^bP?vx~+G(OqqarY*P^H1$>4dBVuL#GIeiXh#BjSzdjbpj%~?19QA^JIzjMInNqrBSm~J9Gel#(VP%HFRW_AekaxW=}MStt-bLEX@mJFBS6}# z5K{4|)Y`Aja^g>H0P9(g2}t!+BpT^rfMR+E|2%q80dz)-GXY}eQFYUmoWA#2HYGrD z-!jDXL~7!`!6l!f$PPYGj?sPT`_=+z-_}7%_KG$vS{1e%a6Ioe9h!~&8QV)G{a=t7u!250uMa#(Y1148$L)0sc1Z* zTL{`F*1NjKHPeayAt94_gIrGaxjqvX9Z2BJEms*# z;Ow$G7|)a-2G?5t{zXCpd4trPO)J$i>R;C?D$wyab_e7$178VM%4p-cy4lQ;d5U6U zzaQAarZqfDg^dh5BW0ji)VKrsPtU6@oVKlrM{e^tVbe z0nX}R=D%kZ^bP+lugyzzdy0$}r*RWwrFJfR|8i0g7P$Ky4OXN8)I_RJLe5$AeU>vJ z;b~`pDV%ACym&rP{ML=aDu_xrYwL7mMI!CgE z1;QVN`l^(x>6js3LiD*C9H^|c%S1QAXb41A4Z@ATkR;r)2hWy(3a93%nXh5~TgtV~ znB^6xNvN=8)2EOe%kfBEQ^zZ{z;Z6rU%tI@dhR_qRG@CCei5*ONY#_wIhtUcq~_OZ zIy!;hRfr=2EX8&C`2%|A?aATVM=SRpY#ZX>9sj^NyE<8LvF>>PimyvR&zFYJbca*z z>-Aitmj`%Wuf`adi<-*{h!Ur3b+e_yObLC7ZNC!Yub2K@F$v)&g}8qGnt|(9G`2b= z1`&6ENUscDr4luH!iM^~Jy|+XKsa{iZ_zM*V=4U;u$xiY_jl3B0r;)q{sK;@O+Y2= zXuPJyBYd&sPnX1y`W|#P#=c|)B$ONte{K(7D?#@qWANeZj=%h5MRzrHTl2aZSKWcaA>vG4{@9{uHK-)-85N zWaQ5^se8hS0?~c-5n?ve9p4$;408i0>4fBP3*^4Q?OAAEp1jo;f1=%K(YC!M;B)jR z5~k+_|Lz`P3@o4V+uIT~WR0B9JAbLjqpL5(gxRzY4^?FwR{s2z>F~O(=+`~UZ^*_w zR-*bXKk#Vs?C6XLJ6Z`qV1Q7%yn|~b zX&$%2zWpwX#e^1fZ=BMl|983;7yXG&_)RwM_zjF%@g_AEsM11l_;#sAMa2li%eTp? z&BC17`;Uxe=^ADlbL>wpzN7*w3epC9v(Fpw=%MoBf_*%!yp9!9Hw{tA6fzwJ2x(rFRP)I%BHZF z##36WN4|e|$3!{OE`}xO_`_EPoh|W^z7+=9;BSk3<-UKRMxvyJtiHj59Q&Z3#CN`3 z%Ry@la1U$f91INC1P&CEvhL@7&F^rcS_G@u_2B=#c}su6-Cd(lK0r=p2BJ9ae?Xs0 z{NzEmEaevkS;|GF0d9?CyZb(qh4}zpv*i|ZTfv)x64dKnE=Ui@9P&DT#W3vXcqtV! z?v7W$(A>{W^2NDR3InWiAmY{g&^d<^#!BK&H zw=_cF?Agkue|B-lbFMdj)0p^+uOO1oWZn%NqujWD{2{$^j)=jrY~(A2kH zjqU$KtptGGZBo=rv%Z6@h4A9~UVeMqT|9uAXj} z3BVz5Eb235-5kk36!6)$1O+RYZ4@+U37a%9R_PXbH_0hu3Rbw%)+Ix1_&+_-az=LN zoujaaVJJNm6_!=?Q(NJ6k!Gn}MUJj|2d`Ncx5(4c0%C{POUde$s3;!`&44C|L~>lk zt93dpnna1xVW;tzbqVL~T*wlQa#~aD2bvS2F{ianm^)mGxSE#Vw8`__Y%#H50!>Fb zeI>?%Qd7I{!8^JvoLpI9LtMN$BLlfO8m<$@#dz2pK!r0aYdRjv!Kc*Q3XjyAQYi~J zh(+=tJ&`EIstWDY_k|KJ#i>Hd5>-}6Kx=_}g{omq;menKrGP%j=d80|PlBa1k%Km~ zHc&$K02ZR$xvVPxf6M(f_%;G&ZoT)KbIAS;=g;cnwXRneHvToz1f~F{hzDdpJ~1vT z1160BeM*m;r(qIME*--}gz}v?AI+(itMk28Y@1MqPr&stJiA}x%$)xc&1VH&mReKF zR4z0J?zQ`4$zQK6%(u!mPClYU^Te9#ZT)0`(q!Q*f~`hAdu90$bHo}U|8WqavOhba ze?qoot9X*=SV~LDYiU!==yedPI%6}^!Ca_ea|u+=3l62nJ2;RiZZTsx#4$Kd9`MCKsg} zeeHagY%}cZo5Z@9v(LZ`4ArpI-aOMYC6sDo)_E)&EPXA|>|=f_RQbrEm}xJ7$LA=b zYRy~04zo6dP|zE%8MzsP>fcm{a-HgHQ6Aon&=g z0C-Z1eFv);W!*d67;BGZO(Vu{$Yui@t8ZP`j^&-yqoxnV2+{iqj81HMmAcXvK5+}w zFTvVT>L--hB+32|yFMJKZe~O0&BNkr5&U!Md?-q-3gLdGBYssFGqI?p%2i~|5)x{W zMIfhTVbK=~9Z%0CJ)$l$7JG$iuA`}oi9VPWEyF46i7xfk1JFwAam(%ws1ZFyAG_^) z#_K=F$+hdVh2<96`0qAcN{`_mTd&99gNupxrGC!haJSZier{RvFALey zG4}B0*4E1wFdd&OxE04170SnF`v@HF^&UX}ZHJ2OM3{Y4Z?q zUJBSVC#`(_%4w8p>F>H56&6sd>YZ@q&qB;9stbXtT=bw0u|TilCH?i$$-zU5gIqd` z^;dKj^4DP%i{-?Xdxt07H|5p1Hv>6SsW&nXP1kX;mwN@Vs=cs$?4g;(wkdRaTaeI7 zQI!S|-2|!4aiz%#hFH%?KbhvKULl~(yyG;VE9b#&Ij1dB4Jph>MsG%?tYOU;J~DC9 zS4$uHh#6lrzyg6#Zk(b!EA9q) zD!J3_s^4)1c{mK-N*GLEfk#j3Sc3{4_N}^N1G9=+lV~cXO2hBpSG2{a`S+s%W8k5s zEA6*`^PFA##W=Z|$zmvBt%gIeHg~MiVtfdq?7-aD>1mM7t!d+2(+8BHVQF`R_t=hb z-RW~t03oSp_Z4Bqvtu3lx8Z*~H9Y<6oxb0}OG;!X)t!x+fa#)nE?ez`s5q>LeP8T{ z6Pe!`(12^6$fit&Q2t?*qj%k%tz``r_ByXgE;>s&0BvbBmg?ZPcn<0#cfStfedC{; zqEt6Kkb?TYNuzyx(CZM)ry(ILEZMgKhOog_^`d{-gO0bq$?=VJmIdDzSo>y*G3pAf zl>*OpHHK(3hMX>@~;(0HyC6@h*zodWM_5Vc&#QNDP zZRT~DdiAvOSivlLav%Zs-*JN+znTDZ`Ksk+ZAcki#Q_$Z=NHA78AqF>^l>q?{=U zpGLxqP}sygwM-t1xteTzE}Nq7ER>KT2j>Ttw1wAlncQibvfY@}7{59Uotag^o|au75NUvk&s7TPPsl@%`PEaZGlE15U{^kalY z%Z;YZr8~|k)iQG2y|q8@+*AKj_b3Oz&#$|MU-Ay#2Z7y8Y(wx~jh#6EJV{H*w?PSwoecKk z-M_WHdPEJwuaai3SItbN&n}*;CtgPIci`VBmlLDWU zcrDax7#VgEUVeaN{2QEOf#BS+jqPJ=+NP&~#EJxxF~goOc;>PRvld;_k*Zu1H>zB} zPu(0n5&4I@)-Uwr&c?!oDc$Pu2Ay{r+Z~_#s{_UG`GM?@wf0-+Bz~-1DE02?<+wU9 zSC0)Hlr)9?yi5HZaz|pG`kYB$p5*#v+&%Au6yd&;h=cVD6*`}TgaaR_zW(~nQSE!- z!IRPh#9`F|9*Pb-UP7ZwXPJ}(wG2SUMHa;oJwRgG%zpor#@~do* zx*S=3XFKYT!=tnH%(iJfRnqr4Pq^n3t)Rblrs3v*D4c~i7VJ+^*!O6cez6E z?l<1^))VarByEDHp5v;2)cJ?PQarGPwA6taE=r)9W4hn`>oJ7PIy$|v`UL`vmGx4_ zq&NDg5zPioc`_9ufsX(XOLw1^KO@_n&|FhX9{;NQZnvw3d__<8%5B16@$|EgKbvhZ zUISq0fGu#kHG4Hrf7OAP`rdN!TVc0F+GG;$tywfkNsSip-wXk+k4>IC1lX$}$1mV9 zJg3K~3$mTlaxYx<_sGFgO_{8Jcw1Whz=C~>c^pDP{@9sU9-n$SpSWl;=05N7TQLvd zZXF|o^=)7NHFY*2a{lZc@r#O=mca78$?hl+kE7iv=q2(_BaEu3C-TX3$8elJB*z!o zd}wNWdZeDkGFUnZd!bl$eDS83eLAj%6Wa%Yjw>Nywx z)_uMc6*EWnA8*9*;?`Mn`&*g?<)(uc00c5!ZIcT4J$*1hQX0>yhuECgzI+)Cg`yax z_FnS29b{QAG!|!Nz0|F-{Q$tF$V$k>$j9FjSuR)SPAAhaa7=`wRH`SQ!tx&L$}JfK zm`qO^s`>cd?~A~?pG=e=`3n5Qpa+hLkKfcIB-uSDU>JG97VQB9YuMnapMxiWuNISC z3rzqI6nz`z|9!MFHd&I+COhyLg2^VS|Osg&F`7Sf>ZR%B9Cl{y{7XUWDoa6?5-+jQhI$mz7P^?k< ze8&<=09v2x2W+hBfVBCR`al7GOlO~-4FH680(^krp4;6+!Xf&fX*oLkmKM=--(ujl znpOdP$_*~EDepA_8{w(4+K|MfW@vbL8qn!laIgJ&EdWe{_W-DBbbG3N^y9UgAwfs0 zp#7?3M|(RTv#mJbKMDcw~G70mEl_@gmo!M!;$4{*XEGli z{+uSn8z?On!W)$^^9`eowfdr5^-Z{&04pXZnCNt;yXXFnGE9e`*KW8_oy}=$@Qwl3Fs?Ja%)JO9MHYcr2Xcq58W zE!9`gd*OO2JGgOmf`LKI6>+o@8c;`Ere@nxu=pmjF=SGzFnbL zWvOZUEStK<`{e)O?5)F^eB1c{dmC&bElNnuA_M_Jx`qK#f}$`bq$H$4x`u=T5(0uW zDk-BBWP~`nLqJeErKAQ(%lFzRp6~PgzW@FH@$fhlc5nCY>$=YO`F_97r9mxE(u`9x zF#us$)50A~jh!yh&{1qqscu*u{azBOlhAM@tyOkhhmOuT!Lb&nF%m@iiweOZ=2=?? zDd?_s#EpjAP&K>PTxvngr`r`D*AI_C`rITnl$Rz7Ay4r1LLEBTF@tB{W|N=(!0Efkwk1 zP_<{e80pd1zJq>fzDAdpHqDpRzCU2x%3{;?Zy_5s@#Cp~Aw;H-2{NfM>}- z`tbmMVJd3`sD0Wo4#phr78!oiO%a77SbNLPq*r+#yeDf$-&{i-0_FSS{STQo<>UTG zhReg{gP^y&s%c9ha1N90%iq2>S)ckS(d+HYwg=#F}ft8m*r5r*N4R&X66ADk^9tt)MA~H(c zYf4KCt|%%$=r;)CMyHNm8a3a@!UxX0Fu=p^2qqGRqv4sK9)D}AFhW>Dr-*QZF(vXn zPGf)`tR`DVa=6#!B2#WO$6)?AW)UP!NgB&qL{yYN8jY~)%h7V5!wF5Hnub2vEuozz z>w>q@kq5Az8Ai0t2%%(Oa>)}WOHA?qvimbxs2lHU^x3Pd-KH34gxQSMQgB*;Ce;&! zTyY~AN(ht>K6$Lhx9a2L6M8_GD+4VD$(PW~K%t@j?c2A7itv^{WelqF9zyi(S5}6= zfV?{G>;UT&aIfttDEXS+M_hQlN1-phq!b6#xnYKagf@p*};U>cCopOZpE-d&?2_w8Re9-8nE(dW>Lgo_Vb%yK$l7&6Qvlwa)CZ2N`KkXlR*Lrh) z^|QDtv7+OsXz-u?nWLS2o33gCVC#hQ*DfCwcC$-Oy)W3M(tTM+K3HGFkJT6)urEny zYjG9--Ut&dBtP1b|32Djt{d;4Sj`$T*zX=8Cw7juSL>BXN0m$Vok`{rD{XL?Ed5X> zHS7{3FmdfW`0QmfkkQ3cCRCI)jNHcgntr{F`1Yg9+f4O03D+UBFlgl6xg6hldVHeVf}(}~ zqHMhB>SAP8sttok#0Bn~Aa~Vt_Lm_(578Di2s8OSPwkiEFvJxuYJ#&69`gKNY(f{p z&$1Nv<96!aSvww%7Dc8Lgxn3_S^mG<%?3VG0A`Bi+49;JVn888DX41`xrHB;geBPyVWb+J6_=6}6$+>28w-H?-%V2gp4;#%^P%Sr){ zUQa7An|C3;jXl-aXGDVd(#oZllxeTt94%;^P@pqMFzaI>_XW=Ub7W1k9IfO5935IA z$H51{eM&ZK{2&OdkLZ-45f>n8ZN!YJd}Afbs>JWrRu=t(K$;+vbu*OB1XYic4@%o? zp@RnPLML!(-8lFW=liHloqadd>;sb0|Zkn=Q;Cv6WdrT4!Zk2ah2^d3s0_I|b{Nra!Mpht|;@i4A?G zr+0aQXg`dfL*7lK@Vtxp9edt`b!SlSHY$#mnA5!@Q~SQd%d`vDj12*4o+(t!kCTz& z{eHBfgC>(r7q#@)STZa=!1GD389>Id-jQ@a!=^5}GVs@} zb}P3i5M5R6g2Q;;xRoI1zh_b>JVH}Y^Jr5XD;d5bREMvk`~>Mptiz)rZV)+{Xc6#% z_=s!?58OnW4B2|%mAe`#i7<0Omib)lGu5T((@|182Fun}vkKy8j zw}k|AAgSGquJ0nhZo?AG221(Jr$JBUD1)*SqS8e&spj`3&OiL6-6>5> zRi$1`3}xbB#wK^n9GlHn0Kd}$j#H%vSMPM04YwqI7_CZKN6#hqJxePcEw@XgP4yuk zR)H(-uR=$Du#zdGC=)u*T|*6wIZovknxgFPB~{`@xXM`9B2T!4^K9GfNcJJ!00&Oc z=AP#(1{279AjGs2@jT!Zv8AY-(h5X5*d(z=?Lk?uB5H~S;bFE?lrLaOw$&#O3iY8@ zeU2XnVYGOhkTIjpr2*^S+EjMjS*|}PgoRpXg5?;s*7ki`xs#teab>{0N{r!K_$VF) zeBB$toMRKS4;&aH!o$_~l?x59H~8H}X3j2#d2nxXnUf~RAN`^vB8dI6Yq$0^&O+0_ z&eO4qEim{jzl%#%rQ<<*phQu2H{{{P8drC00%xZ!orAWo>M$Gh@2aQq$r9D|-*r@@ z5=Cz(;7WlU?fZQ1tl;6ZP$cPAWo9GNfsiZ41`A^PpA+kbkYGJs4E?CspW@{-?yb7Q#Jjt`zLww-phl__Z@4B!uQd~8Bc;Wf&fV6!bwC-p zD|GHQJr;ag#&b6+ai?d`EE_X&ub=oLEH>y225OLnaB zdmigyMa={AxOXz%T*Bzwi`BrOGhC#|VOK{lpfl_y09lz_^9TpQ5k4aY_hUPn41d`# zkGzL*upcDX3CkfIQ=7#(2QBffSlb^~)U1?h=|Paw>cz+$xX>`Rq@=}69+^u%ivCkP zfxn$}n@oy$HBePGJ@i80$`T$Pq)q#DbUXHelxSDf}} zeZ>G>my#Yu+zQt-hXZY-)P{s6GBciRq1@A5~^=;FQPbfhN@h9Atf6 zYJe1SXL+dXTbYd>fQbk#|9ThhR|?ExzFXh)=rGq7fHRED9P;}8%k$yb2enKUBT&V? z0WX>7I9W$FRgT`y)?jm;@2QT(L!^m{3T@zt_!@<7EmZIy047T4HR=J@B@6pTwWj0i14vlz>TZlmcS@4XRgZYG+Vca>{Ku$kG3Mv0>bl7EQF#n%TSpE7B|SD@s5Z zDY4HP8Q2;gbY1=}eqrp^-8;@PcUH~it+FyRrQG#-K3u$;n*lCOw*7SR4CNKg*CI;f z((m*Ys|n&;hw+-z($1M=&oGe0q@enH?B(|bV=z>x0V`v6R(Qko)Ksa*y6N1&lIvnW zKT3!|=^|fPN$>&a_h~C?=(T7^y5f-nCfnh*-pnh7Yl`)i+`kG}4KsjDx3p-}@SIUd zXfa~%xEo{hxzH;u`#2cFAT`>>{GszMGklp?o7BLf_s9m&rE+6-KGepb(0ec&vD5*x`$`o^G9~ zE02*e1w?7Ur)7*o< z4fcV>+kCwO0wxV!VmLmu=c{0#7se>gV?0Nn@kR*LZbxG@%M2^fwK@2 zLI{Ef{~LI$I5B%7*zZ!$?lBa@2daxxbPElB1W-C5-0`85<}fvq(jJskem&$wyjCIQ zT1i-p1BQMFW`*OP;4$!o_{Ke^Y4t3o#(|=X5S@5lS`lK2_D9j4osMpz}u|4|Ia+|@ffXfrAqhc?W9`rX(!$q|v0xvhVNZ|D1Yr5Ojt+$i!FH?kX!u>&sgM{N$W>@1wIJ2cj>pWB2mzgGZ^E z+~g-ax8ym=wjnm^^2hYer{?tTu6(U{U)@=Yk3o8T!38i zDcBW4Fr0v?FSYJVImM7+)^h|rZ8A4uBaWSoSE)su>E>Y+2Zxctr;fcAvy2?(3*Uj! zVcr00O4o-_N~Um*i2zsH@6dP0Gf3x>mUYg;JU}mU9t3~1)!OjEZqEQmGl`CbjiTAq zW)>{NP>Zk?%6VXrk5!XfDT0-zdg7W?Mp`THvYP+efIXDp`U|U}np|zVOBuN!wwa}k(-;-K?>N)K zs29~wxh``1S`8n<$>bgnonSFhVyi8)6Wp2Qn@5jGPeZxdeh!C{w1pOfMEa?i(5e-)PI~aG_@t~<#a)r|&!b9D&dG=nqrfHEH$L2` zTU33Wo6Coi#w#_7Qtv=I*y$ab6u2Bg24M@K?Z03xrHeaEejx6)nDM>z@2!2WYE(RL zYIk0~u=(`O#QIk#w3PSnUvw0^hf4hd49N2^6$6yu36P@q$F1lfXi<~_vwby-gpj93pFP@KM zEvkndK%$sNgy=vY3MN`OM2|3sD5D`asRK(iDjI&P}n9T zu05$s#>n6af1l!?b^19v8jRUJIz2APe(`wt2bQfcflI{^kGS0JCaA1TdjrbaAm)rX zHq7t%dr-typMo$JUvdZD%+(c0Y1OM+D!gQrM-3%P%*jAM5Q-)(Os=u_F-;dZsl}PC zgdLRrZ1!orI;TR1A`z_3XnzBvNZvIPJ59?BeR1XpA-7L?5^4SgnYhVsW&yDkJqN1K zA#^kQm&OSZ)!Hq*-fx>!fT4WX{`VI=@)R_oFKIu2Q1Wi*ibJ+;BwOL5b4csXhOaWk zmz5;p88mWCU7Isoau6xCUlxQ6QsN_r3U&iO*)+dr0$_mnJ34r+#6Kzjv0=E1pb;I| zS5*%4h19Anb#yJbQ@R>BT6Jz+ZQ}^U;B5%0rz2>KAakRIYivHA zj`fr)0fHG42A=3bawp1gDR@cPICP6`jUwn+c)R3U!*$roC#-V`9AV+dc^Q4|W|n(N zb_Dd`&+?_Cnb9m1H9ApPp@hiN!v%C*A9Y$5{!}pTf3iiz#ZJHln$y&t$Y%+&u8z=#)$qkEvDYdFsm0 z0~5l@;@Bl^joI5T_uOE`Fi~?XnZh)Qk>Y7ZU;b1MM;~v`K=I*XBGNqUq3eET7e^9d ztO_5$>J~#%y*Yx|-@Qizh$lFP_}gvZs0MaeVz`Jc7v*<&oq-CgU=wt1;&int%bE%+ zF?6r-23vw9CPnqwV(bQn)moDjAUI@n^I5D|6^5&e{M-uOPn#P^QYF9XlS zi?If~)c7@dZCEbs9PYRcuB48UtHt8uS#+&~Y2!zB_q&2w(8YKuV%!Ec+bOjXyw`Hf ziJpLVQQ5O*-)M~b!k}}#S%R6t)K0~G0@}(3ag@R5=-)tWHbTh(FX=JVxO(RfSc{1g zGCI%Ui&Bz*C)0rTA|1;k5>+ug)fRt4e_e3P^Nc;jlzXmwp-~!5OFa5=w0%_RZqV0d zM3izWX=N2re=w6OVfq%gWF3XRJD#@hp@PXw=U8mhB6O#rfk?r5cn zqO#Vtmvk-V_UD(@23(Sc9rC^O$OhX>-*zOZy59OM$7oi*{!{IkYQZG*pP)F|g>P}L z2p^3S9+-tkFO0Vzk2B^(An&WRE348R1e_LhAk4!x2>ozx;wc=J0ZbGWuqIBoDQBJr zKcda0JlngfMwC)7u8cwq)C;8 zoY3N9v1#xmg(OScJ*6C{7T}9kF{pKYnkzh%cChc3$#rlZ<%^;gI(#Ox`S!5n#YdsU z-)lroLq|IGD^3&-mxjBlNZyVVS5?zD#v<4R1nm#{r4OT2Y5g7+_xI6LB^W9A;VXl3 z1WT#>TbUlA!V*s^)fm36UR%DUnB#EU9zCRWHkxJX%`)W4!{`WWRu7Olzq-j%$3X8J zv3gUuKQjdE`~;Ax;X};K>HL?w7fcm^{e0v4exYTc6W~uavxTzp_c9Tri2U+0yIm(s zaWeTNM7f~%HM3KaqDNI>%E!)1jmejR?K9_5ZGZVh=X5!LrsivQM?#`daw`>)-m&3B zShCcBK0YaXKiHK`B&!A_w^%&3Mbs&zPy_|4l0R(p2p$=q;mHV`D=PzX6EN-uTK?Wh z6=nrn^n|L&Uc_H$X<**G7-e_oVYB;cPr>=X#GE%bQ8NwglCMPeGkDw$ifXs?9^cvD zIz1>l2HVK$`}xZFc)DXb@_7lLOYu*s5&Li1mMU1*0Aqs^r!QOf1j2$T?d&Uca8+BU zSe_W{I~``%;O>FMM8B@X<%Oco{t=^j_1G>s=K-|(_&idM-}795UCQ7R4iuW36vU6cSi zNtRk$P~Jc8RY~NR z1F!l)`M-bGXJgNf^M{ksyVFIkMebqgP8jd0Th?PZyVJeKSp&CZGy0zWa^?E>Wv~9v zj~ zmd7GE)T(u#Z+aZrJ95?W4<%n|33u@C%^Cq#{3k`3Qxl1PKRehG==W$rq%59}Tj5uo zvmrsUiRP4|XVw>zVdnh7?uHDoCgtlrbf;&U&5|#C_+@49$GiSZMaSP23&%)1ueKYdx0~dz$O>gT5*UXPZcR2l<@FdUqLW zQVgT4>yx?Q(jFe2nd4GMK$u+|C;;B6JvAVLyC8nDr~)Na4;0B{2^Vz|cg(R)HY|^6 z>~=Na7L@k%0_znyrK+cg*_?h11k#n!8ctcaC)Uq;0pr`ytJ>Wi2#nU2^#ViQz{7J| z)(tPf1T}g;L(#Bi^k%8&1B35hn(sz6fEBOE(k~Hh$Pp5kKL96OI98XZy$)))`vA4= zSj@tI`m$*dStSL!vW3^%KsPLKNezI&0*^4)BT=N(%0NMKemU@`$==MScS`ST@}cJd za{b6R-fmc+dEZANpdyof99}YE=9n6fi?_jC30HRK&c(9(vU=$~FPG;=Eez`sb3f^a z3a`P8fq-rZaiOu9HyJtPMvfKZXoAn?`sf?yvAMZXGs-OMQR!D(7~YoV{XQ4t)}N*z`PLz#Fv&~`(m zsyVk()nwU?UMhpC_*<2`19@-+PyopTQ2Y+J^vZ01+#%yGi-F860OYNk;Afn=-3196 z96o<`=N^w%mLZlM5ze_glD6L`GK6AyoGaay-vc@P`uh5M*Vz=duVY~L!Z(0^NZr~6 z60`NVoI!IGx89Q{rTOI`-e1*9IaF%i8j~dLl5MjAklj_?{k%?!)Z1@cH_`vm&~Bec zh_X8tq+NS={K|58qExEVi-yg2#!yoCbV{Fmt(p+KUKN z_ag3VR7Z~aDTgMsrnmz z@r>B`bXrHizGz|2*tghfN6wY%+QGAbNDjz9K2b};PK=Q@CDFIJ>R$3r@zQusiVeox z;)5{&;}D&4@h6xF#u~ilCg{|@2Ntp<3A?0lcE0HY5S#vmiE)Wjlsd|6Z9r_@NGtC0 z4CnN0y4Y3%RLOma&5Flvf@6s2yP8CP1V=ut@z+4DV`P=iD=xGvBuB z9;@-nM*Vi_g*m?RB1GLwWyigS>3z9Jo7+5tOdG*cyQZmVOX z!EX-ky}#?^4{V{1+-L+GICWi^AJ?Fs^RbvXZZw7csztGw309JgX#@U$Kw!K16{8iD zVI{^R2b{|Bq}vV+D#?r?2a`>jhO`_~J+r%@TzA>(*n-$Zrl(UBd@I|@0qjx`zxKu% zRC_p~x^WV-yqDTEz0I=?k0I_(-nwP9n-@n~1 z8ZDNAz6;%6o^0XKdNa2ncUA|HbsOPX%bB#7sZq-7I^RwzE)~5af)<03iVsA7V3NOV z4d{CvZVhhzuuQMmH{G27S}39r`$LbU+_W!uGT+m&?V%K=bW4X~sZ zd2&?Qy-3GzF~B=y(`RpleukwdOKaU{V(d*6aO@2?-UCC2+*dsk9IpF}JxcS_@a_HS z;R3ZLsIY(;=^*;wlS#K! z=HfbE`>8eVzR4xziy}(&yls(@3=?Zm+H&tb7DV7g^8U7f_~px&%my`OhKH$jsBrkfVuaTl+W=A6P3N zuQ!1>#;b{|y@tYtR7r}23J3?9rk%qMLzj_Xv3(#uJ(DJIkZeg4=mEugKvEDx%XuL* zOS{NAplOH{%xqI@L(gGn(Qw=u+QR0~uasmx-`y%cFV=!mz%k%kXnwY`6yF-#T{4WX zK&oH%yzmFK3uM0|De~y)TN-K~I4-i~o-rIk;V9(t^oei}_|*Un5=OpH0YkF@@9$^H zK*YhO`zbxojFVkC3$QzoV!Vlj4H{0%i(GtJc!Or8@pGJYOp@cbEjPQ# zp0TxetI=AEzsSv+f%`<({2Ac8H}I9T?rkW$FbP@UPPYH7?|qAZ0~VsvrFs$|!!U14 z`u{ngd(le2rNsSa{H4(L>A{!uq-(6C?lQoFF$U`pzbl57M3kQ5p&eq5wuIAIU7(Lc zm3!AYPPl1#JIkBktoQi0Wp>Yg*;H)1c^IQ$*L$?L+E(3~hDTT1?XH9KOrIGal!y&unx1+h zDuNq{<&ARt{3pb1t|Ke3F>GN9%tws**gHB@f2%%(mUA8+2A#syW4L;z6fBtX0c%Bu zTCC^AJ(K_w^BGL|Rt3Z%a*Y*Djpj)MEqz%C!3EI@n&O0kVJx5oKH1df%7LTrSqCm+ z0_@88rTZTmDr6B@nv@FUbBcL+G!q^51!FpjXi34E8E#2KTyvEhGJFU0iWZ=d9j8eO z2qv9z(s)8zxn?H~ra+|8hIL@C!cHPLFuXW;3M7F!=800v6D~Zrs&U1b5$V_}HcLqX zG2&m2(Vo&iM%ltagd1P;%X7!CVrb@|6N_v&vkC(khsct+kUY5W^s$_A;ZzJm%VZ9L zbqor31?xjUVK#d~lm0$d5$4x>hQ>+s4CZ@BDfIxRCTGk_kNE5l_>z*dK6_Stn4eJG ze7Q#Dj>2_Ac)G1z2<3@m-JmJ<(c;cSoMNo#U-JW2sOnv35=*VrS9LY9tE2<4@|?Cg z!NVHO9nZ~^>Z7k?G;XxXIWksai%1K4M{A|OqPjvk1sq~LJ_Zx_8yb!oA+g1(-8sST zU`+X@$=;_TeNxCaJQjoXq<+>Cb+7MZPCpSR%P)eFq>VWt2Z7NJ)X-ivUWFKTSZGl9QPOOBfiDeDu~n@zp541KK9-8)zmn_ z2Hs2L*X0m2#|Y?h7&ry6B4?mFax_QKh$gqB5~Qb(RJeU-h*Uj@++t$En?XD3u*acAY~ky+ z)J{h&1%vA~scM$u=UxmOuHoV_sx%p?66e5xLOYBUodVNO6YI;;C)5^TyC%CPAI_dc zi#cJEOWts8AKPk?udj5Jh@4iKHS)TP`MI6s{h~W=eEZSf3GMb77T;9=K4t&IrH6)I znOfexg(Isz+1#*3qQY|b4D0MW`Gr_8Dkg17rJr|Oc)@e^u$CfDV?9|Z5T6OxMb{`O0_;sOMxn)awzp_2`r5V!i-!g8AG z&o~hXVFlAJ@$v=DLprQ#vPTOlUUz~rg9f=_yj~6O!5zoI)?mk=6M{(%*B;RH2e4+) z7f;IB#09uQUDP6w7r!JX(3{^8!&RNjLZU2%%V}^yF170(R%&1g5EDUOhg|w89h=W1 zne`XLes88h$6gb!s_A=An3ATzgf*wZX*P^L0v+_`7N1#h^&l+v1TOxWVBNm>8;oGQ z3MR(KGTRX1!9>aJdqkt$rsyeKHfc#$Qzx?3t2s1I8vB|%2RoM1N8j2r!m#7=|DKF5HTC;$glpS*x_B(+2sk6%ny+dc>TvZcX$0uAz?x*yfLXA9)fZ zs_JN4h$x1qr$|`|M$YjMC16v~hvE@Y4;1lOdj>bbY^?*A7T_u3W8zB@39}f{mdC-x zxL!L3H<(7`^JxEU^u(R?nDQdZv&xm+F)er`^6OFyO`0m=H4PZwJ&{90^FGj!K&767 z%@RFkGGhDOds$Fm63{qt2 zQp*hLI|WXxPiPY%@$hW70@or$O#+>s*f*4H0_WGR`%{#*3QP&FJ8L8Nj_c|Y+rsxU zjapgkW`G@eV_BU`dM72Z^q^uBe1$;qv3yIvZ;nrR`;=X05UpL?dFH`8TD8F3+R{9Y zjjm|k(clvOfS%^j9M)mZknn&9KiQSsqD1#a&3A9g7H!&Xqp3v?`TpPg^(Vq|BtJ!2 z!f1NntT$%t%F%O+-y?V>S3N$H0i}H>3HeXoaZ6stJ?@0N)|Qv(kmeJw#anGh8hMOB z>yLOW!)&4*%?z0r|Dp$0tqs%U_TuI{r8} zsgoI*^ole$teQyORpHK^)g33}Uj*o0;6K zYBBes7^Q;7mZ4*5^fc(GySa`mF%Nw^EA;<*^a?4FU#sC4MfNnSF3ORZO>h)A6Ju^c zWDkbjF<89-#a7q~226dFGSf8G?gIO;ge(IBV_OqNfX^0MHi9MB#W* z2|@UGDaVE1O9>V6fjdr(JP(P2x|fV8UsJ+yvVtiXWbzKhs*n24rhQ5+9Lm z$)J(O{O2IG6mo{dr3BgWnRQ<08Fax>DX~OUbQdEHg>6PJ-8cxEd@#}-a6+)KlzJVq zwY|BSi=(;esb_^w*n?lOQbcn@nL@Act}V|PKDM^$g|cxEcy2s@RypIfm{J;W%DD3y zGt;SlHfHO>+BS{K83D27sU`04Gs~*dJ86~1sm{vG=Ee-Kvot&)ZDs?v#0JPB@7&%) zaQ>d3Iz7}~4x3CQyt(w6&lCF~?pz!fD%zT9ibsAibrXjrP3}{<)*dP=8~~HwUM@~U zakheZxL7bS6Cq)~EL=1~Vq%$zb(=EE>0GGK>@NFN-Pv&80!kzt2o1zV`6(G>28K&V zgXjIf#Q?B(db*GBYc79KGShFc`PM)*K1Ul}E+qZapa}J+#=r(t|IbxhD`OuA*?P%= z8{AZI<=@G+KM3KY1tnaTjWjQ~<-{hCw;;Unb6C9vF|H2ArG6*hoEm=Ag0|lu&5F+n zwe>^6BGBzM@8}ZSzE43})eTBg3LBrf2R)%22Zs2->Rzpq>fXqMtOIPtXCN88+PhHX zaRG|d@aT!h-m=!hL@lWvnOHxfuq~yYeVYo6cg&!)sGyjdU>mtu2I9G`>pv>EE9hHz zcZsC%8_ehnw7A$xxM(P~)uX4Odq2I$-D+Lc)9cxTBxa1Y;EShi#AmY)f*KJ4E+Zd3 z)QbJmR2lcgCc?KVnJ)TmNP^|LITN*gI`(>l5?jS;o-ogf=)VtoW2Y6_ujT2~ed8i} z)xh^^>`+nPj_&%(Xfs!Ixv=bt#Omq0mN%f3?Zu{V`f8e&F%t6GaAPV!$7~u+`fI6P z8L;186sgknhQV>nf#wse8ONE|5NjWl=mVW*&amb)F$?;SQM=znmijc~SUtc=LvKaiQJ``sz@U|-FT_cUHg`< zS6Fs=|ZJwB8EyG5gFN2K4|k)sr~%L}jwZH?P;+`EL(@ZuU;AFh^jt||(F z0bwo;H%5Za*)Jx@oTpb1BK;zZG&>4$J}LdF4T{wJx zavX*^_vjuZOFa+w+5e-?;LH|5Ny24~0f}}E% zsZ@{zDY~oqB z%wDcPBH|?7c;i(-b?y2giTM!^lLvB|MN% zcai%YH1@c3C!F=Q>MYRzMu%cLT*OgZYKeG{}x*_%o{ z%5x`=y0jm?S>sJH(YP#X>oDax6?Pp%S6njuL;#33+lAB8ho2sAOugHNc8S>^yJo$> zK-8pR|9m9V*>gZ-PV`v?95i!G}lEQKfKk zCoR0I(%p3Yf}w)w$Wa8_O)$Z7^v!ZG}JDrbF};hDSg0}Zsmq5K%U}pf9}hVbo+*f;&h+jtytN@!R%m_ zC$l49D2+NPR_8q2`EeVk;TLChGQciFMqotQU^s#M3yM6G!sXB!+Yf-Jr=FwG5a0D`H{+<1r2uShH(z|bqk zpEM!CSZ}vga7+lX0+>?!v#F}?q=4RM{v(53+B{PRC8ohmk(^6L!Er_&A5*1WnqGxF zW~QEcTl0HmbZJb!ikvu)BP(RGb_bg0r zD>E!om?@vHeh@Av49E!IIFa$-#j|}kjK8ApGq?2CsBpTNZr%s{40crCrx9*J$@BF6 z7Y=`e^uY?(yH4(Av0ihp+o!%JKUp`fJr1Zh4}(k5Dk>^f+zQC<=15K#dTu{+M#TXz zG5P>;edB|-K_R)BF(6=h-fw58c2Xs1M;h=9rVT1QrIOOLOt$L1I-1n}8~2ZjesO`# z-DX&uf2jdVaF210a$a>(^aovobH8O-_0v&hN(~r@= z+%oqbJC~#F*SEpciuNOfW+XMwSm;L(ix8sa z+3KT?o|2nA-lb1yBm_BW+S=j)tz>%f1YbmG=vT>cpLalG%{Q)nYQJzg?FsJVHlbd> z2+X)9r#nD&R5q910m#OJ*Adbm3J9h4Yv;OwaZFe6 zy<21?&w|=&!oHT%PMGUX*8}mWs^-^@#vI_G70sDrj=ry+BI#f>=NG{)PS4ucbar|rWUof~VD=0NI+fJ$4~J&8;4*v)nc zcXc$_8h7jn05@UDtV(DoMD0Ew@-`TKdxzZ6{*2=I@_Wd}o`Hj3t>)ER^0@i+=cy}( z&Ezd6ji9p)CAu!l>ZbOr&3{Yk^$gTc@0fYfdNQ=mE9H{D1d5)RqzGM|*fvp5+DUUH zUIe_LE1!?m&y=Mf+cw3E*bQCT_NHxs#DXpyf(7Uz=$YG_au_RfrBKlu8z*|-WmH_- z7s33VsM85~J7tpIeJ~nsCO^F7zUr{_R zhXE7i=g}afOV8AG*{prftO&59^FgPp>u^s=kpf%1lg7<75-SCo`4h;+H+~eF-OpAE z*+1u%>1@l|Tqcj#`RA}f6aDd)hy8>^({<);t}%8EZKrOPFvo9-zH1-!y=l-zi668; zBYGTR9+{f2_1OH^Y0ZVw+KQEr7C+4Q#Olh@Yv$a?v}#^0O1x-)eA&t$K-Wh}*_))o z7SLaG5~>;L`z0(2_~|TZ*5cPx+o5@IbFN0^iG_zzlx`4p=bfLOe9Ca>b2DypcHfh)ppofm%Y++hpOxrWI z4cht^-g*QcIU4}l=$f|JS(_w}X3n=6NeZ~rrep+t0+xC590M}R9nuGgZ$0jrz=JjX zBaRzJ@(sNT#BDM)qS|9qBpY7in?`kQY;6EUZK(4lmNC^Y@>z#p^0;0-jFB8l{-ERV zV%Q4R)+SBT+yl$2tCJ~YvcTbc43}9=l<<8XNXQ+33Bd8I(yZ*gO&Yr9JO!J_DO>2< z=-1TJ=2x++_lNYp`1vOAE{03IS;;dS3fr3@e$Yxk&kZ_3n=4DDr|dM+j;a5!65cyK zb^=JWDhH*MzFp(7rKh%Oh^cXLGgH}$yWUZiZ(lUrF!Z{eE_g4)wjX6OpP$mM@c<-v zdRzdh*lLYal2fcz?xY8{_@g-ei4hG^Go_!+q}vUeb~e%J4p~TK3?9*#BzXp^Lp&D# zLfv|gMHYZZK~x@8qMnp-3$38J-8+CDzoqsy6tt0yd)&YQgs^}rbvR4}{r`leubl%j z<%hs{#Ewqp(toiK>yWe9R&hRc#Q%P?+rysF&n&LL=kHxG)N+gUzn{0sY`${;%Sz1n zW8(xRu>tj!Spad7vU)_MInTuNSv-E+o~Jgzhz?EHE{tRSKU}?eJk;O+{y$@wA~@XP?oXpzvK0Kf8L+Z_jkMX zM}H(U=XuU~p7VHI*Ztx{V);I_e28ckx1J+|lPB;S=$>_$OS|3tnlRO~J=B6w=Ll-@ z07_xHS*%evr3R7;+FQ-L*qb=G0NX2i^e*Nw!@gPwc-v^m*QFunI3WIk@3cRr^fF}i zbLE~jqEmx4x!np(7JfO$;pn!Zzt>%82IyqBgF2a={Zu4N&*30Jc2T2jdi2PU;BA5hpbltsoajEMY!8lPLJvtR?OsOJ*7XM#M%x7&g>~XYS-Fqws@!7M)4c9G|ahwlY zW{G#+&m+fRFJq5lQxHw_d0=>_^3rjXl#ok4OHx`^(R@@GWsibHGRioU^?>a-qY?P- zcM%)3V%AQwUkvBp3~te_$`NL#=Kcz~xjqcZo1G&ZfL}Vr-e&G`!Gj-~oEpYPD%;!H z$0>`EigWwEfj_MPN!H!q=&F&Wvhk{blAG?6ce?`unCSh?MmZ`R7>r@}WGOyGEEQin zA6+(8HsA)tW>B=@+dv(Jv+>+9uHN&n(rX(B*O0BiWJJ1WQ}>oQ(LRE^Wj^xCgeGmh zLDHAplCnnWzuY1!ae#TSPDX25GbeWCi3SSRMRKi-6>4m$OlG7VlxR{j8on?HL=ZwMv^3xx z$E^^_JaA2cvB&RIij;_&$prKZL<&2MlS8xf^A7}3fWm2jX!CsX24ysKgdT9TbXJoz z@S-U~)F`Cl1-9)Y;PR_EKut#k;cnofYV&*wpP(a%4hE%#+(j-U2>b(2pdzKT4_HXr z0zSW1r$k>Z!=4e~xn#&FcbaNarbFd5lxmU?aWbHGp>si@!GRz2K?UeYuz#S}i&p2u zRti1G*{X9`_r*$1wpNNL0_~uY|t1&vdlLcO2y)yh~c5)c(8e7*Rhw_KtTs6-Y;R&n=kc_*;a|xBH%!R+egC z;YiJ>%uqph1O+DF^_Aa%b@rieh?bJ z33DS-hniAwvH)rhqXWb@;y#X_uK`CJ;E`!%Os8^M4GK0oj38+yR8rC$@8c;dGZOQhq*KQKZKjzF6zB9`pm=JU-{Cps;^D*uA;yCu-mmK5dE zjnQ2Kz+hQE*-FtFReWS*fsZbRNW9GL26Adf6O4N)^Y{W@gaV67lH*?OxRB9#Twk zr&LJ_ba`nZkfq7@@?qPhXXLj4YUAJ)91It`=5|OnM~WwZ+Dzzm^AjS`R)7rZ6?9Xs zmPFhr-6^{rw^Hwn+?RsspC0<2+AS(mh6>)88Jh>BpEfctL>)TTPCu3D1H=Tvv(Hh_ z0HMUi2;jnJf>IhmhNNR$BN|GT!h0R1tug?+(+)Jr)?JwBabFoFC$^rj1n? zkA^XtVeT_yq%^GNImKCVEKh8j;1Z0{es>7m$4?C1+nMf#0_F)FdtAT<3oV(xI}$oU zcxQ%s?dDY{NYUbq4H?$+uL-_A_}@7KRtY_pE3R}Prm;kwcyH34?W-LtS@crw2$? z=w(Uzrp_Z*)uaNenUbmJ6r$PU72=^pN-mUyV3PUY4A~P%r=L&4g}rPH&5;IYru2t6 zbdxgmk)maM{IC0u^GHC%fYmk)#BqejYErU(I|>)!DGG`sF(n3y%5~%BJ@xC_#{ELJ z-k5JEAVYYnBA=iSHXK#M;_cj;1dwVnc+JAVYTXH zEj!HJX#N*F*KrYbH)$UnYOpmsXpK#^kVljJ-eOz6gt8F!cMFvj!C?$6!D$ot{@4;S zBOuAXPEB7*px8|0L~Ko@1E2FA=yVuZ(nnkK9$emiZhw&1>K88Ro#As&R({LqK*${> zP0VBhP%d43+3RGCx-KY!NV#D>Takafm_ z1yxrQ_v%gKMY%!h6Y+}2@UDE%b5ea;B(kAmFywx?A6uG;LG5qM3}Ouk)M^1nEk;1U z-bVFvh!6H+7%pPB8EEBGzM45fqMz9G#^l~9|9ftKpJ&-)&06JmH%VW@9L+@ncdhY)rc`YjlU%Yp?Uc z^+5mv2$RI0AJrVKFlFK5&*B>4G5`i$zCVoTN{JUDtNkR4+MgHSpFAJw1Q9{~`Di~S zj(0bXul?mwl!~F!K(@iEwS3T_-H`f?Lc38;vj{v(`eN~P9wnuqpqd`ZY~}eEO8bSL zEf0O{tba{cUj5tYp~W|{1Wk7Hd)b&fSpzIn{1+0WHj_X1P(^EIGCnGupZkPnEz>5Z zPv`N1)7jXW=^SnFg{CM72?7H=W;5`bDy-eSvtdy#K-`tj|ITTA(vI*zmLNIK9Hl~> z;SR!SOHn+P9m=OEt>2Uo!9{FmZpIC^%4pjyN7v>Vfc7%*F94|TnSX)yOZXY+9MMU2 z?ku|*C^Z7+fnaNR1CP|zoI#*O+mIiZzFrE$^?3~b-$hX0qD?;YOgx-qg{H#*1X_{`*=-I0cBs;szN=-~#T zeR9Q5{A8u+q?a1s3J9Ui5Efpc84~OPbz-Ucd8B>cWnDCh3IjsB=*)HJ3;eM7TgGS) z+|5xv)J1YgXh6vsaz71!F-ui{KCB75jP2~d2V1RdBSV#~eh(Lha^VFv6sEAemlbhk*=RLCAB~aCo{kaVqE(El zf9P`ncW&`G1$Cc=d{&$X5i`Bz-O?EgI>q?~SO4uY1eUT^{9>$n#@9&``9BfcG-)Lu zVEfxdNJ~yA>HLSPv~RY+Ycue^%IPsA+V%KKqy$J#{d;>O<;xv8rvuw>3l(6K?W9}p z&7=CSe=8qJ0+RO1M^njcZtQX!CmL3}lW^fOr)N|A?$|gxkS*x8%v|VM1TdvqKbg|7 zw+tw#3>z0;A)0i<>=V&O{=5S$D6O;q*73n3J~!;Py*Jri`-gBy=wN`LJIv+ifS}>t z;T-2>rpi7IVIN(Yv4L^}!k5sF@ij@R+xhD%)~m*?DQF-Q){W| z-8)QsVs0fAI7laG72yN7Q~9sX&}%@et$~@3^*Vcr&+F^^&Ce8n6tdGSr*xAohm~uw zwW+fVkH&@6LO+*|5>#hq!|!a2&E~&Wxi$922py*bFlVwtdVb=6<&zPA(g**llze~m_se(2%;AX)mf!OCEW@O*m~#GSYQ`z zcyOX_lpjSayN;QtWcl0H^iGd>AA2BdQ7{Op_><6o5ANWe=*U$8+^5l{03rTh^2=#x zYUNXPS!?v^TQ;Yu#8e;kgZXsf3rLHWg*%{Yi1{aUH4a)%A%UhjqAmNy7%r4BXN-m? zAusY2wc(=*ppI_fz1J74n99KNEl%yqEn%0}E+1vw*_fxv=0!k9C)M-YaGznS^L_-l z_iO}(+$?bLK&ht0W z2$bdUjfub?UKCHB-X3iEcFBe{L6I*$yC@@{L4E4ZVDPh(?Z-zhrqUh$Q7V8+2IQw7 zK=E9yNUzQT&RV2%Bdgu3>lUoY+uc5dN8{axtAZy0NUt6;l zOkRb!`$D1_fOFm9BO(3m{*d(<#q8x1Jk?6FL+%cLbu}7xr+O>}v($d)seY=T`2p2x zA2xbPq5AHeKd{XMw;>PsiA238PO~{b|4&2&vh!Wv@43yV2g9#0=*D-|ObBKa3|}`E z44J34ssw$_*EURRhTAVU;@p}4lWuJRy> ztDneh@NnOrb5t-T7*sv0U=DfwhP-Qxe`;h|=@9rIJ*-%!SQySl4?f%fE=?2N`8jlW ze{L+z)&GCmfHwbmF`OCOD7ut@$59;;Jto0R%+SEqFBNI4egEL_@xrZIowNbghUMGX ze_M0^HDsRITdrS#?k@*%EXuszc2fq{Ke|%|vxm=!Zym&Z*HMLS~HVWtUv{!<&PwMyR zji_h!+6u0QU;0Z`h|gd%Z6!t<709oC22gInNjL24{mZ)uKObvaDRURL3*uZ!J9iuu?hqV2j~)cP~k9(5hI#dzyH z`GzNZy3U-f(Pd(8;+M7JA@qj-t#1 zY-g>XpI-o2TbWzQb8xeNRBdl^>4xULX|K)KXECUtnPd&RX||>PYdzr{Qzd!;8aK8x z(h6W$EYa6c(7m@)%8C5mC3X;+I@dLAV9R@4sByKuy?yC&sy@60#v3RA=;HyMvRawI zXQm07qi0ldh9}p7^8a4Cr0XcKdKes91pPJxey8E@Wo9h*9-e>#E${8y+GpX!XNWLw z(2IDlUsKM7Ny<6{$^VAsjD6HA034-So7Rt88&0y2<+J?fGtQY|rt?E}!1r<(%mhF{ zam9h6r*>1kYCmP)w32M&Y(Uyn^#Z@jW{D9 zw=(p3UUTq#IX80U&ikLiwwEhxB53r_Nln;tU*?dNKdU%+?F9eY`nhblajR~S{;wVb zNi+;y4}y0=Q$-GlBdS&FijBIDsH(fSiV8t5?<~VMJxElOH15Szy$*0zjR3&;@PK6z z;M6Sx39aUEzA8)G_Maca&5wcC@C;=~z!Ud5NX2DT8F$anZ$_8+uo}QO=ZsSJ_OOxK zht-14Q6X^0S%VfV4Kw6iR`%VXa~{B)CF*nJW)-x9gJXoD&5NBk+4tFH(Zj$y##|J5 zA*<1T_AWwd{^rCrwzyot5?mfD$*j;8(EE%rsB?MzQTNjbFfg4#*|u|&k{Ctprt|>_ z-r2tA9t?F1da>_Z*QTt6g4IEzx3tEj6%O(aS|-^BYfA^_YVeyOO7P`76*>JM9=*!} zJ&^qL?s^dFnG2GpC*D#%g>^L#q7Q^e=YCXJ*??te{UkYq^IyNpV2FKgqt|9RLA~R{ ze#);NN&b?l%Nq%fQ3D?VeC%1vDg{kUf6URJ>tH+WqdwDR%;|AIwS~mVLy5Bxl%1~I zJ*~RuBcVy6<5edSp1!~`U0v(M{rH_WzLB6Y5M|BD5VJ-`J41Hr3F42jiRs(;{%tz>C@xr1S%N z`GJ84q0MC+T8izJ*`KFFaJ4mO-DVe5GW|EBi)6rRl`iwX?d*KNiEp3(fFhn}(HxApG_*$W}l!2DUmzeoJ?Z4KA_iF)UDH zu?lL-^^@|zS&+0;1r4U!nb8ncYnzX8wd4aLCZVyPIjEfPrg{Ox4^1?Mf*T>5FYe8X zV#rFNYw@P{8_!Ui-wGoi)>7w@8VeOw!R#%XY+H1M8C2$0SYLj;p}spe(A;J-PZgYTgY`f70M)g|Zoq&V#jmkJ+<&^i&~bb;TnFv? zF!>>?7!;Sno6S9L3889P7|ntFL??_v;P=6Tin1~lRUk>gr=rmSNHUOiit&l!I5f5Z zU6O}~r$>jGDNb1Ntb6q!f^im}NM&4Be(AafzUHrRs3pEv6bC=Gq?PnN@%mO`WMt73 zcXszOV6IlZz!o42_mmRD(G0b7UQX1U@NnR5FW>b&89e=}XV>?Z_ow@1%c_^@|N=2mIHxkY=k)ZI`F-?0;HprSz_cQIPLJZ?N%q*JVZL0~_}Xt;A-@#DpzUk0l<46@f9tWV3FXKI_6Wsaf)tGZaxL(MB$a=KFNa z<7S^^h@*f|parj6K>DmEVxY?K7^*3zTf#|ZrBq+my(5_=z#Ed=%EVmbT;XhFPZpya zq>zzA7;g><@8uDM#X%PEv26EZ_)UV5Y^!|3*u^d}Z4P)a1PQ|uFDNs`pDzuKigI0T z!I8o2i#}k;jg#S{U2MkdKz38(oIg)(znw5b!*Zuu5Qog_M_t-Oq!iLYGK4jZ@D6wH z`lpJ|NE-3e^e8j4k|yCGv~@3+2w6#6g|eu)CaOK4%7Hd2Q3|5cnu2TJMndixXF|oc z6>9wSk&l~m(hCu|OY0CSKNwNMZb~W<4)#A1l{^^Abdpm_u-kn+!4Yz4gmdYkY+QWU zMFKRx^tThs!ZxlC$Hv&B)K^C6IZhrY$oluj{yKLy-318))`oK|Vl#D-kB|Dc)*6B4}3b`iQwZ;2E64O9m){GBC+(S%SFJfR6#J=pJn~! zbJ{6;GFyB_+&-%pF?bDJ@p+J9&_(- zuoH2=r$0fLR`4gU-{^pTd!aeOtvd2V;m4ks1!pEk@r{gk{35@@p~Y2ZDY3$UzWjgZ zF5ocAy!<;V`cb+@ds^*q6bd+gKuMj`cb4|xfkGAJFRM8cn~dq8)-# zU#)J25_3!>D}Ck3(NjK#lG@B;eXOaX^It@Tbj+z9K`Euf+3 z#+O8O(#?R52kI1M{mvLNx-0hZf`D3B#&bvVtk#cKk5>69YOqAEwNxL?ZOCbyz z1G>iW{t?c7Wu!G;GQeV-GBl@{2?KM(vpDxQS&Q^6HgmAQ5g6)x)s9QQW$ijNBLa$! z;XBPK@OuwssRsO1(4@w2W4{xV*5I0Yq2G(lFI27!`C`@okfBL%TQ`=|{A5eXqz;8a z)s6V-F*EPYzYkJ1Y!8?anbI?6*>6w3WR}$nzk%65d=BMCgOwr^)5?qX3pXf87UW9|9g20qZQPEpZySnLJ2E)E2%SGOPA-#9!P zt}*Z)`a8S`R0p-jCJ$dj&8I>@@!U)_8qJ;TmP_xi4ZeZJhOYT#O8%}le2 zhfos0OE(2vluc?=rl1lIwLtZtsZ_`XVVLu50b(bwuiS#&g@3`_{?duNl4w!%5q%}V z%=oejfw!cd01v{dQ-pt`7e~p!JE^FSQ9^0B2u_+A3k_r*NfsWlV%L3TX=Who5!{?)94!R6l@hSFHdr(XLd*1s@zYlKhQ`u`4j38Euyk!b0 z;bUpJkwbc34qrJ==c#8nkd;jK)EHCXOl9iR3I@B2yJhLXJOt)}iV!BiRotQGxHt8J z5;)P6ZptBxQnl%9wQg?X&U@tUu>5XVG$qRP23M3JL^!LJ|Lc*@o(%MhhXu$yF^4=@ zz6^odCKWpbLkHHN=?j zEl{`hH-B=o6jZ`3FjL;#6-aO!A9|fjGFvDsW5so|DP!#4Xe@|KiCPkqznqGb$&SOffx)H6Ne9YWuL!(1Kiil zbM$_%)^zVCq&Qte#5ONsQ`9}QJcfdaeAWGsk|X)Vv=c*psLc!k_wy73Rc zw{mzy>LLQ>>BiK4yS#5;UB3VA8y%aJ1f|;VvlVQYBxK+9kHi*LFq+ zmuVwEr8kCcryL+7JJt0e3R|y%dtD~<3a|K=)wx9<82+j2XiscL=H7-WNnrO)eV?UX zc7@278^kq@9*i!gi0t2~3Q-)tBaPB}vgMg^b4w)?u^hrlhIt?;v3u5mCJPlXa-tK)`rS;<3)WV=4|65dtMfU{yXUVSmjqW;(7pVt-3O>die`fki| zy_{k`$zSIC2>yn>W9%X1yy=k>`Fyen@CGGNz;Dafjg3ou&79Y!!a#yc6FuZ1MbZq1 zH|JHMMC{E4nV!p8mUcprze$)&Uy_`!o^-rfO2q+tOf`ekgX5@NpqlVDpP4@B|sJru?2;|=DhZ7=?u zGl@8-6T3O#7`TMUf*n_7SF#3pjGc&A$(r@qmC9tl|^PPr%s8PvzZ?eI zJr)8RR#`fMTO^%fVs+H%5cG7MD`=Mfyos=S9;GjllcMtMeOKi*9;vsbGK`0Q71wqr zN5(!Ti!~&f!iwkWT%SIVJs9}kZoX{ujEU6a18IO4nq0hDw|OQ)?n*)~ zz!6EPTw8C!$E-lOY2Z~<8Gg(~LO5o6t|skBzgvTp6EsXS&>V;`Oc7oV*vhmd1x3AP z8t48PFL);nCBBh@DPnEP#!HoGV2({{^nxTu5Ni1Mc?3B-s8ShsUQ=VbP6so9*W+A( z$GbVeKA-7PVmR%;O}N~EucOR%H!RYPGb zZ1#$hP~|NI!Iv4GFMBB3g()>|-i(z5LeOopd6H-PD&E7lx2%d=RdC%GmlHN8=q9e< zn3|m16f$>gqHiGRF5+(<`^(7h@3U4ppd;M88G9ZuEqRVGp+iL9oIG$Rmj`@a1RlJP zi;l3GVW~&%#yL_GR$G~UkQ?r#jMgdyE7QP3zm( zS6ejiZbT;J>Wc%OzxVZ|dB6#ABeDFe`gPf{uL+p<>zHfPade^i8Bf`UxQ+7ErN1cL za02e(^3B@Hh*8uqgGlV?CU8~(#GZuzCAWbor3OH0)_l40pxkzfTq-HncBP}AgL6vx zWh(1GSyY5?YD^8M;-c%QCe}SDG|<&zusFTsXk{7^ohFj#1Ol1LE3T!sTwASCB$*$H zdz3EWbw4+?T|#W-8<4#jlcQyT=O6F>AKW7$pY2Ps{OM;RX?SDJ{vXTT%jE=-S1V)p zo0bomra$a$5#HAo6-OFMr3|ONZIgbO1uSF(5I%ojAs(x|INv=iFa3b)Fw|H zIXx&jb%fc%aVPj^ZN?0W#)2Ikdid+JP(^)jhf{Oo4pgihhc?TqDax-t$O#40d$5gvpFC9E;tGD{|Vi4(pJH5H_cGkML@+7Nn!s#nwq*9yIerE9v5x_KEK7>gc*EL6TP7_1S$&lonUZ!(a_ug#C;5 zS5#ZZCLH`jS;Y?D%<+^-kdP6{{aq79N)6+zG{d~}&lqgZ!luCf#;{P3JJ3UwY25m-H@c`dOKwg8{yi(($ji*f+#qQwRw@eZkN@mFmk)ji@k z(@*Yq7$hEKO)eprM>fEb*wIeY?{oI_SB3_aoBL#GUJ~T;Z23NZ+=MMOwYkX)QiA=} zg1_QkY&*xVYu`p`D9oqUf9b$v36`OMLVixzl~$Dgy>0I%w>RuivSv_ zR668>`3*{wZdc1855s@0nF_^_guJ$&19x1#)FBubgD+v;i7)s6P;*WqKU?q2HNH4 zJbHteJWLZ)#xeJA&ZDWTjUhDt$Ku2sNNG!;9aRyw-$Vom!1JiCLm$HntE1sf2&V7b zB(KVDk$4q#Z=##Cl5KELdYb(XuSegLdfwOyto}6_{c(&C;V))5a^ngLYTFtd>3*}O zKPTdYXmMs6GRqv~u)!1maJ_b>NYrNI2KEPh_n44x10_4XKC#16wtAn$iYkfek%&9` zhg&0bnd-a4KAeDi1TVzum0<}Plt~^Y1|2*gLxVq)8Whv zEe*KfLosGN|6g(xv>tNK;%#JDqT{vYgcYbS5h#|XDk*HDti^gTZU-{>2T~&qYl}tJ z9iAi~q-KA2g5xgj0qrj2xPQ%%W`-`<5#r&cE`??iR6lL2An9sxRQ<8!ixb;0vnIXp z&Xur`)wC~&HSHF|_ctuJOOu-mGu`$6nz%F^IA@skCy%;bNa9U$*YnVen5YCB&$NO&uZ_JLE~!r3$4qA<_dn+{kK8v^vV}0upbVkjUph z|J+^$le@~(*%*=-Pg2PkYDNZ?Irh84pVfnkgx;6|-UrWdWD1kM2UL@4xfJgnYF^rn zPqtws_jqUtFKBCZrJ@Cu=>{7PYp0K=G||Q=Wgo^$;ZSqDN@Hk%oSR@NB3zinMTY-0 zajRP_VB7eOFM+*(W(&aZV|`HxYvQ4)%{&P=;EO)^KP%L@**gL~3de6`HV&j7F1D%o z9O2Urww%=&mqUqpgB6J-dPZydi!1R`H7E4d^hq#f$TA|V{@^nwKk**33@cH=_iE$J z?O&4P0m5&PG~kgKF?exhZpF?J*X@=w*TQ0@G`NOx2E-Q+Lz)tt^5kUd#TA&_?u!p` zsvrGaq?lR!7@|DuN^(3F$IuxF_q=x325WOO8rs$Ee?b)O~qw8iz~G?zT8k(v^D zqGE=(?97bFqL%?3JZWB%a8-tImH9Zx@oQ&waQVIq7fbQx3kAcPj*zQ9#fviM8eM<$ z=E+TkK^UxpM3$xRgoJwsr`ypej~J zZplo}xC#I7xq#RPBf&N+SG^qJ2i&cXjiL$fT|aRy8`&i1J5Qcn$Q_l5t!$*OB*<7j zKepjb`QYD*`{!J*?tdFgs}udNjqA}&cZ^H?(%>sk{us#quh-^Pjg07w5ub(&maHS$ zw75RG>L7WSr}ho;x-u?RoA%_KqdjJ#C+0`o@0*Gme=(=cp=yTwl@CmZ7*gt8%k{ME zJj#Op`~RoweUc8(&@p*9R}=i<1}^9WyEdo3DwDD7AZ$eOk_J@6*rfArUvnUw-f-fP zLo3;%r}E=t=0r&N!f4%{Im_3*5(zf?N7BDjG9oe;x%$Y zYfwC*LKCYk)2EF3vC9YR?ztC+q)Yf@_U!^DSf`tZSINR+v?wk!{#$tjvm9TDJFTQ0 zmhG0OK*gileqEmL(~uoM$_*cPJO4iA^H_IUy4fctiDfI_g>tAU%6~VLx<7wzJA!{;=)16+M~U-}`<+bD{Ky zuSjLZr#C<2>)dY1(dIySCoMSh{@5||zA&GzAa=)3shnqntCY7YKS@;9{L#e5`_0ng zT~U1k$}lKhtZ2ex>C=FJyi7$Cw1~>*Ks2&N6lfe5Vx_w@WTeb&)LWJ<+IeR!nqf zNI~=^&B2OxOlTq)^(ss0?xLdYs&m4N`%Z?o{T?wtX82iKu3T;$4=eAqV|hqtzLSmd zmP1OB%Fzh%qaQw}Oi`KU>r%>iY#*zlP^2O;C{=O~?F`A(?a#-iG9)=MS;iD)yo*ft zlibG_=Z)sI=Ti8i$o)~F@ooiRp!0teSp1(ydps>ZtJH+- zpOF`mJxxojOW^u+6-{e`2#X_AYD~wA^qdX0;y;63m`E#?IGLfeqp7j9WfV|ILHv!s z_{^~`Xp=W~w}zp!>YV>XRSt87;!bgJ-CcOborDd{=7-XTNq!4{{lF5LhI1ho5d2+y zSR-O~wEPiqJ$SuckCs&K><{mT8dAO1FN-9{IQbEbG;Qr&y5c{NTH? zcR%Lnr&>u$J*Rdq)XHy0tB`%rG4ukOzST`7iZw@jQq^I;Q1!8b>7X?g@itc z9=le@ioC#z&uy6tLg~@#5}vo|FjGj9O!G{)>z)7K&+2aditXoqhj1InKO|$*#P3+$ zB>P#_?ytJ1*BFJlcvX)6A!()stU8H9AaI2o7+mJbHjkb zPt;F<^hepqc`;L4W6v3vljDh_H2Y^m-AezAWZ8waN6|l4g+JrdZ@O5i^zHun(Q1+O zTF~g+=e)GP`=)fK_q@y5D9Xv%_gV&-W|zdQ0vVzXCz-4W>DQk5OMRD)ET4C&?zKWq zlrvshV)2^ZI2$VKlTYi~{dcSH3oR3C8P+VI3DW#)yo;vw-F9>W?;@Xux?Sxuj5l;k z+M-?Y8fz}XQ<;gFNv&Z1eYo`ll5297y_Gtd4({#}(`)VYFDe!`Rho;-lNfvtXXvdoN~Ui+=FWFG-|?{-0qvRQAbK;L{MyOo${xUJ9Ga1y2{s<$NOc`}gtCle{F-W~GJ@D7eJ?ensx@_T+3Er-Me=T-f#c-e5VysS69bkvFq6;VPRqQVjeC#yS>!lWrJ7qOa9@G!SoxO znv&3&nI&B`)!p+^6Zict(PHLtd9!6lD3*JGlV$|%Cl~De2Yqw>M;0}Ue<*`4i!Yp8mI8zWGP#(8)HXK&< zh+kyC#M#_gxiix6u?O4&^GRgAT!=>qW7lYEE4h+s797FORgZDcYSMf6pWMeKJUA_9%(YwDu$Q92ps7Z%>PxEk748 zkD#P@=!Ty|<>TGW|1(5$vsQFTmsT@xFV)L}6l0Gvx5&8{1eL=mgd@x&E0W zDEhOv8b{nmThnIY&Z5(#k6u09-Lu0V5M*DmwHaiRCsp|Qcnc35^@{X#me0?B5MxjU z1qGWdy#RM|?nZ)vWwNFARKJhR;SWq4o2*X+h+QU9IayoZ5ZJV=s_{LQnVvoJ1j}*V zB-xCsb25PLsqLDayAQObZ)^4{H_el7$@Rx?g>?s#3`DUkhoNV)cH=cU894h|F_9mjz{BpgU{J71N(lwKh_kuIfrsjPesC_6V} zP^qb@W92S{y9m~s3_V=HQtqw zlIpvk-XLMsyn6ckYi?mliILQ+y)TTZDfc~3ufMYSN&AaHl6u9;#PJs@LT%OaJSN7Q zH7r+%hxulCdEwEatl_6JaXS{qCVYc7PrR@ z-k~MKN;v0OT}rdiJin&B5x($^?|a*dM#Z&8`u8)|n}mkaLDP&}lS02;Ha4Ur&BDut zL>H6*S*&Fk2Spl{Y}g^i?$#RMsy%zYQMQos``>O%z<6>X|Y@DJho+ z^~;QR8#rd#Lb|)=sm3#2XMKLhG8^dv&g{=lRs8#eyZLuZa%FB`zIpDP0EOd%>`6Cg zje>ZmrKhJ-^y#(lA-Za6YF-x1o_~cTPX1I38e0O_eOrjC%6(bFB=79Ob?PwIcwXfV zqOm5krD2Z}x!jivCNABOEKK5e! zcQLdLg4F;P5S4+BPK~LciG|KsMh0sJ)4|8q+9{4MOrY=HEW)R$*Cg?DZ!YS{QQN1# zr&Nb+ljvgxZc&Et0d0-4KkKQ+QLzn5Rx9tZI^5wPk$YWixu__0Pmn1+?fxSE`uKPq z?c!?Rf6w$g(e(kr1#h0diS^WpKHX;$s96=d-GOlx`xP8a&!w-fM%ZV*(szIR+Zt?V z-25?Xk#bPO4t5)UhFa3@p^l2uHa{;*@XM{G0F?aT`HJ9 zwXCc<2;wfyI;(kwmk7sau12mdOvk3Cl3{~~sIOCRuv=NYN|8WyZLP;f zg59r@KXQf?7v_47l9?+q=COKamGgPV)#X%^!NK*5 zNlL=K%pwx3>l<>J6(Zvn=3m|Rx>SZgS{)*u`fnTI3jaT{-omTOC)yuY=`QIG>F&lu zBP}7_-6b7z=stq9G)jYXcXyX`N_Xe`px=A%Z>{$qV4Y`}*|Ybj2Ej>8nI-#B_GR#V zo1u@p85^h$bZQ;S=$i+>V1<#R)68^@b-ha_OEY!rNSulFu=S*|yXx}EVYhxP=MPkh z$(XW!OxZBhH|l-odA!m5kd#%ha(x~3-?yzcBd`ymbAS?X#5*P?FrS{D1m~{V+1Ve4 znNnSb)!E8rCW~EeXMxlFJn`20iX-8tMkO$DsT5ycTv&u(N+HLE|8eF>BJ>C@D0IK* zPRi*Ugtm+hmagFu|5=GaAUedFzU#j`^C6P@-O2ym+lj_&f~Dr9H8#uO?TExNW0T17 z3zs$Sk|aXH9H;&3NZ_l1!IZ-G!|IRkL8>b$*gxDozERh#7l`5GZVo{_hvb-wF^!wM zgw=%SFzTS(HjXHACvag*mn}XhwR2>|?VR(5Mnh6*WyisaSzM(;&B05c##>cutxNI0 z^TH|ru{>lx(2DzQQC(e<`R7D~6%PDnk8|qpFsBCZW{P9n>Q7n*Vuvd3&(6*|0Bv_x zVd21i(!7Z_brYj@lQ&vLRu(y~$;)!AR1|X$8ga;&dqFf;*cyi?=<`>Q5Gq1hS^B!W z$kG#GjfKwuQ~v>L`bJc_j$fp?hXJ)7?&wDKNmDKCIVF_078hY(w`a7bCfCbGD8030 zN@3BJtSu#heRyDfiwXYPB`yd5XW}5!;aS!0aT%B$N(YZ6>rC02)G8s{`i4e0sRbstE?M#_)vZG$QItl2qxk&RycnSz)yQ7rH*>` zXHGl}9UALg%KKC=cmL`B{8H(^(l+?g9yj+e2I#0zO7~Wf>l}E!Kn<4^m1pKP^t*{O z)vSDG9fJeqt@lR)4>8ZDA}1@)k1G$t8X3-3R`jEcD|L=gz_r&2*nH|;I55l}dl)$c zxwyLh5@KU9kNM1g%obmyKKgz9h$$&4iR1U%J_K4*+#q?W9zU~;gchN#>DqEg`!Ud? z(w@z}7JqbO%0Gd~z(|_cE+K*$X~uZy?|O|AAvrA`uZ7v!+Ul9KIS%2`E2Z$e_w^jAN~K7(RJkUvGDUbLUazDHZ4(K}wsm73kZf1=<1j zZpd&99?vbZ*o^hqRtssTdJ(J9!%81YE6RLt?+ad~GoYg+@P6{@ALyaC)tsOKJnf9b z&CMMe6}6RYgu}Qn$ZQ;!GcT@1J$T?n-Cz$c(J;Dhk7i?KT{WGzd4(J2p#EXFqSZzM zD^+QDnYFUrz~)9>Pmct5;~mHxb4WbJv{rM4eNc;rit2>iu+oq09++B1Ssl{|U_6Wb ziQvfeAnSM%WHO(ACE^(r+k>(b5tviehhd%A@Xbu88J&kV>uw)BmNSNe-PL?9vG;0h zrSifNCFA1QMA+JeVYHgy)^*9Pz&dG64?gU4U&_q5*}=ZL5Qd50Ussj;^EC4ArVU&S zVz0@~KbwKL#IS+U?mf>A{BLlX(Zq?!xP2|jrJ@{Z_fXA5GHWIl0z#F7#S+nm6TT52f;pmhrhXeb-ld>{c;#sSVluE!U(AP zD0)6pdl;eP9ln-qfMN56m5V8x6B$zHE4Q2mTJB4t$_gv$L7nKt60i|e^PB<8Oykf% zWx>?l5+(2*@#A3zz_V7?TVxz{b`o@YB=Nh%(r1O>O0qti{8^=AXxz=*87k>_vWDiX zsB?7>fyzb4Y=#$ZRj}X1d`q#N&WXp+U6I5RRt)<#Ig)<+GQ*!yx17G|HBo~ zWnWIhyjB9S*?#<-10x?FpP=VOsNXS7C~RZ2X|QH+?aSb}ipljCH|nqm~pu|u$4<;$j+n{F{crIXNtdvgiQvvCo{ zkh_;kr)ug-(OpY0GEpFXR@(D^Hp!u3gtCE+pw`%a&0x&Vea<`D^3hMMY!FpdBpzhv z59NfErUX1#tuPvpH1YgtPWu66;i2|Dx!P;XL(7Pm;QyvP;Id{9=w~6!?WUFn0wd22 zRw@VQK4+sMoJmEKP0G1y+Oz%LOn-mtV=P&vPyb4ZXiOM_+5Md~WCaR1RHk%?C$l3G znRX-M1Uz&cv9^1wsP_uz5uB`D3pWuuomTYf?scLyI&^TChUia zi+Z9;+7VXK$2fr`_8Ygl!6>(+&bYmnnH4x(W4-;KU)h3i3LVO7tZe554h3yHeJJcpD^a}>TfLmbwHu- zmJ7%#xjq0by-Yj9; z`n+{|VfaR>-{_)u_S-&J)x~2(grd?a6u$`M+PE08F?#8XA2s&B^@UyPDLSMDSlY*^dfdbdbtj6n!JO zL&pTxlXAWj!BY5$){`K*rAA#&G1A!AEI3iS(sJ5{40oUJzvAO=XA`-3BlJ>X-mbh+ z+>#5#!v%nxS(xNQ`4Lp>FA+8bM&Ad<^*FwSh0)F7EEuIO@FPp*4GsLnt?Xb-B2tWw&a7^NE=NuHRqu#HZI0TpKH=y-5SBy5oFdCU zN>zz@5QgwS`>3f{t1f!;=JDkN@D|><1tABf;82JZTD~m&C5({ZVB(L}ZWK z2oFc3qNYBJSCEUHH+_73+}YV#eP($<^6})NVZH*xQda^3GuBc;JIIA+ioB!aRrGbK z6CXE!HEB;CTHDzf0&_oO+|Xdjd zpRJU4U&_wm%o{wRU@iQ+@r2a?c#(&Ti=3VwaeI3k^7ZSQS4{HTnCR#;Y+#+E`}+Dc z{^EK#Nsyneap@V@H-mD;^Mp()NQ=@@)874SW8NB7;=2Mun>tiqn31 zP_TZYF^Pl?u9@W@PqU!b|n$OcWMV)}?L!cuEc;~&@DF6k25UO_~nTa*2{%*tQv&phd$XGW)`j@?VkXo#Jw5&KY-?+a8-pVc+&NI{Z-xg7HMf%nPxq$1 zg|M_IyQu2blwU`&jh}qQ`ZK@21s>W=x}-9Pb~pC62xadhh0mty8~u|(`9}tdi1g=) zjf3Mu8IqH%2k8e)?Rqkia#~5>k9c0U;MlD?IQrt_aJKjbfdTaEeD1$btuApI?8roVPQv>ScrkK zaix2eg(#Sj;MW1$-=e`~?fd>PILdITxe*>r68-XP+2$@KXx3pHP5Xz<2;qe_6@OXYDOkXXx-Sl^A~rx&X#3Detwx){s;`djKci? zmL9k!D;gUGfMDCe#YG)F0HPu{v(D7g(n80@4FMp{Q^rbKG2kN{Ku3oXF<;rNW=o{b zw+44+raQlsk;2YvRgAGB_jF~JRlKPue>zc42#)Jw;+0(qiD^N_#N<$#o(gL(rvpJO z@n%E5^ubp+w8$xrP@JCvKyKL7k1*R8Ov0k*KnftAa5t6WM0119+vcAUOWjMm?N*}T z6(&VpTOMDIHG77z_?LT}1BZLqk6~zE=M#d*cgMS>r2b<>UUuC^)ckkol@TUlx|CrPfi|lE&(4kNVG#BWV`A4(PV) zXsq9p{mrMbEg0#<9bHi;CMz(AQc>{B7;j9tvfWHDE1kcAp3u>;LFu^A9!@r}z=H@5 zv2qf{&U~ZDWiS7cl0x7~X<~1#DdU_sRM0e?LziUCe11_mEve`U1vKy{15gcm;qEYj zcf{??F=}!sVaH#kQ6*s(lRAv0rT zWi>WFzNWc(b#>LP9Zn7OlpX6w>!;v17xnd7y=||C|BRl4bc=E8|AK=)0{qjeYQ&Sz3FxXKMytJ6q5#~wzg*K;WU-MBB^g1?SUiHd;%*~G*|9U~kZT<6>zUPVQP4rB=$M29YJ>h0})s-6wZ zkUJ+jkj?paP^6hz_U=$J4Bry~?sZLw@9X)fMJAYM7FG};`fA>YUecjz{A?GhL=eN` zP&In=Cu0W-3dD^2%jDiox5?0BBap=BOYdtahw4Wa4k@@7nUZS=z^p0wLdq9Hok#T7 zBv>b<6cyo zxnKU*Br8oGapuvQ`cCTVIG&!KtO5*oMUp^af~&dz>}i26wriQc(r9lOe#s8fOR-q` zQQ}|z)5@i4s*_eQW3XIN0cp=u(!=A@`D_F2jWWh29|#iSMFUyahB601;+hMUlAji! z>-meYUfMWyzwln1t2)!#*Lh2`tDqf6ar5W9FQwn#tXW5+bi}DTEIJc2-`WJ*Hc@$o zEZ~q5|LYn4^1cPpp`oi30Bi`HJs+SaHLvBaH}gSIKE@G%sh|-OqOx7{6z`_7Xu)6D&8VtByS`3 z(xT@(zuVu7aH5;NDia#mx7I(t3?KpXx0=XusF_X8uPnok@-bQi8f+Kbdy-6}>xs&=qIxK$ z#*7X9{bNNr-Bh!Ue<1~6AqCV+M~g6uOF+zFKu6Z35&#~IyUF>i#oWSxa9X6CwiNV! zNe6lmrcG9!7!DdFeAEhMrD#vdJo~`KH!|cVDi@42B--pS&-`k?O6(;518s|nTbo`{ z3Ge;|ld9-Qrcb8-$Uk~AR03!p6y@c!8XHruDFpcVK$4Pwd&n8UuOkG!S3cwwFM*F~ zGX4d@3AhX26h35PGlZnnk-5;)ou(U8_y3)5N9XaIYicaG*xsA^GNyQ1vGVd!E&AG> zriY@ze8m9h*b7cH%H?KEW_HUy_+l~n5qqpn0uUtN@*JIZ9|;!*=HQohOq_qCAt1>^ zs83+&XJg3oO*wi4V^4a(gV0lJWjL3Q71%84%WGv8W<0atxOik83P&WVYaivHFv;oq z?~dpJO48n*m7woK656`a$Ag`nzRvb`|JJX%h~^-1Fc_?)rdI7PJ01wF$!P#y#-h?-W&q z_As+T1)BU&=Ym!6&=vXYwrknTA(eNf5xuBfW!XjXqJuf$(hre+x;Koc4agNNN`&SU zoW>50X|5x(pF!z)Z7dSnSSLC^i10xVwr=|ulGI^qJ71a5NxzJg&-KYVCDq@ubW`P# z2PVSA>=?}FvHcwg7|qv#xNk^eVn|UD!^GsIhh(y8f(Q;UgfVK&)26ZuF?2013T~VK zxk25W-<3g*y6kLSggZlEr!ABLDQdle^?qcK=fT#7`g%iZJDiEBU58StTg+}oc6a^i7=+RdSg4Q#`NWV3wt>Ty>JZm`DiCc*d z*s?_Pm2@!wu|;>j7E@Km`dRS#%RkU_3E(NX0Az~C_u*9P=@NkVSUEYxH8k*mlmS+5 z?%>;){Hhq}+fM)Har5R58*G*5owu+dy=>zmUv|^=dIE(~HYm^J#sz z83PEpJ+~wArGyeBqlYE>sp8w$TzSzsf^6{ey75hJ$-rL|1OA_$PBfg1%W6!j^d-oDA{~=!W(?Mb*oF%thL6dIuLH9_A?Tg_Q_bjC(p{W-g znkOxS=36#Je}(Uc%iP<-whi1G*;Sc4K5wO#e7n%D2p!%_p}8t+S`gy>iTy`Yozk5v zz|`mF1}gsYt*r1$YJ92(Bt%mW;7Z_ptgQHGv(?!T@fgZkK1de*sJURjpIGKseJM{kWY}VuZ+K6Ci1UAlf-GwzU-yoYPx( zQ32AiA^X%z$%icB6MnjGBsVi_%qc>8I5E-TD0OSM`Loh|{Kz8U=#Jgxg2&k1D)f_} zV1_pBv>xJGM|Xz8gBsAQDn^ZFm)GGcbHCGpAf3!-P#Na(|5HB|ENR+Z%R2m zOp_Q3Aq$w5F2`fyoBmU{&|TebVih=PxVJe4F>04eY5&w18O+BL5PX~ANkGi75=DfC z1%-r!wAkuL3cUQ@wetMr_HbzL7m%DhzdrtVDbW5l=SRu%b-O80t$|LP$-i0pP79|6 zIRIdeO{R3A0o&Hd2+#8j8f(im$;PV9!jU|++50&JqZE*EU&9#=*^I+L@N0(QjeFg0ksi?WaP0x4SeZrFXB(t!v3H`ji8$ zmwJuuhZA9L3&>qMN!D|Zg>77(Nca=-9Mopv?}t>rPLG9*(^--Ks{xZ+X^2kR+JyT1 zZwUwn@-ztX@jw)T=iI*j` zeI@ns7H46S!~Qv7vSZNgErWDZ5&SXS%owD)hq*CL$xS3k@Dt3YrgVq)9eI_)Qpm>9 zWa#fID38<`iv2k|ssSCzx@N&Qol$jUUOjm2ygDT24`{{_gKobn79t262ySF}NE8a9 z@()Qgy{rB^JtTm}i-Vm#v!o=->-uO7AfmlfxEP9BTy`-5wahK&er_%mCpWi|lhen| zN(ZVwKOqi4Gl!oJkW(-V=^b zp60-DMU5In1XfSLapHGo6%_u`-@q@tmV$DwrB}FAkPR+BAR0nJIX$ypf20I@TK)i_ zeGN>;Zr2rt`XOU$L8q??(N5pa0`+35?ek(~*s zKD|)dRi&5GSi9J7Gbq)l!GX^i|5kuI(`CB0Z>1Wc|9XUMN*>!EL-Hh=E!Z@do*9;? zSADi@&7hU!8s+V2fGg3F2QEcRh56|?=HKP<_5`Tk=!l37d?P|a!gPs|kr7E#(|4pj z)8pgq_pzGuT-uixY;u*`AD;4ZI=^sGRLe)OiJ`fkDkFjyuAWMhvnpXdIjEnnCuA8pg;aPZDDvYVK$F~C>U zR3VMb69*OaO61lTb#~9d|M|RF{(&5QBBjS+JXY*`2bW3OVnBzy$f@+VhCo(x)0?iO z4)PC#pWY}MMhxQqs(C_0#BrLlO$fK`>30~m=nzyB{l}(-odGvYX(=iK0s<{Fv!s`o zKu>S42qK0r7O*kcI=t@Y=RGda6e-G!krWWBrCF>%OL2HEg+)1b_g9J3syzC2ZF!n8 zKXVOug-M#%vhrDEK+@%CF3v875bG~Rie>9^J2I+>>x-r~7d;Av?%T$sai4GyL)kq--EWKf_^WXx2z|wmT1rAy zTKwA#i0h>G)+EO?ZCsy_dlH^ue55oN9fmsyN_Z`#)U%x}Vm}%Go4viQnrg{)Gu{AS z&vf2F=&9cEd!94!=Ya6kw~K7DRxEjJTfC8evD7L~f*2ABs8l7NZDh}q61Mb=nDQn; zL1#x$L@)tCg^@u@c9hCm9=a0o7pa062hJNZdMoy^g1qaVkB5K^h%F&r0{nnWP7f9o z7K~cI!4c%9|C0BSx{#e;KfxFWOjNtYm7}K&7jHe2efNOkGLX^9@d(xT6$=-79tSpd z8B)~g0O$i!l!dfF+2VqTYAK-Qtap6e7v$CW(veYOe7?SB&lk{Q zbt($r>J@69Ob&|asY2ZmHnOSeM1C!ni~RZ1F*%KvsoPx{!}IW9oSr)Pw{@aYEZ(c> z8>u7DMN*W0~4IHVY-!Wh6!Qj`XU0h#@w z*_ZNFgDiv#j1i@P8y3)%Z#(%_Weako_637ETcv86pdY%@49~_$2Wn;ww;x{NsGDgN zVTKA>ktmJv_-6#IkAfM+zkTJ!G-1wi*ifXMt3b7!jb1A+qwWe|HcTDFRr+=3<_;6u z$wQBk>XPj^A&jM9iQ+x?Z_w<@gGD&g4urlf^Y1?$nj+FqjN4R$LF-1ITb2c1risr* z(46~@g7czwY_J7r`Z+-IDdnp7LN&af{Ku6dSbg8xiTM#O}>X8=7$42nECO9euE@a>86 zDCqMHBKZ44s3a24)c2Oa!YFN}JX^4E&N-g?JsZ{Z6Rb zhj!kw-B(^Jp93}eo2mLR64>1fozd|Bpk^BE^9xr+#OsSV2?Xetu7EnWMb7AwV!_w_$e*DIRHafV@?(}Kjap|c5dl%I-;Y$;N`&{XQ4SPQFaCIg+68 znpEO4{n3J*ryFuh7n%Qrf>2m-0yzW6cNuGF$%=Kz5qeGgoaB6bMY5MdIkkt;kvMp2 zB%Ulop|?iwgla3*A5-+z`>gQfd2UWS1jn;u2`E53{76U)G&Ei8F&UesabS+Q<|3YexNa&pPjcf@O+y@ z1p8C+OR{4Eg|D%htEU}-7*?mnk)4>%d$v&=4st!_zT(B-`T}sa@y&P)F})^D)MgZK zS<@`Q<6yEXPE*QqW&*n(%nppduv%ior1X(sgEI`B!sC;f!^=+a=F*J;@(g8TosGLj zNnJj9qxCJ&JdOL)<-*NSNQ=Zs4Ksij)qh7N(Cp^V`$4PSM`s>A`CKF|w%4smUKBj5 z@Wx;pQ0_y?o;oBE;IDtHfof5P?-3%Fol7Y!W9%KIP~M|(LbDM&Rb1&jz2ny@pN}-Y z(r@oKg(G+*S(C96E^XXegFAFj>YC21vUNE9`cEP0O#mTll>o*mXgN`HYJY#~%y+V^ z=_Z+UC_9X>J#b&@le?kx&1}q&OTjf^d7DN~q&~(!h4$h?HS1gxNo$XOOh+$pdx8R_LC-q1^p5wwYPFTB zjg%e0q8{Unv9XcyNO}X8uXiRMP?;N?SiLq8 zChwJ%W}MnUpA{c6-@ekN6uN0)|8D&wbi5&ADwRg*?$Hfi_)chZnrHpX63;I;jR@wL z{XTkHu$%!Kt_j=mo32l;EW%EIQISwZsDG88G_ap0al?$P6VqWCMxz8HeN6uS*yl_( zHP*Ej?8>T~$sF24rXbH?YMn`MNSadT|7xSe(LkP))-bP6IvqlJeqs^fQq6BV52gA9 zCI5oMV|I`1iG358y5A|LKvWv*wpv6H&~(y^x3{|;E2p`Rh6`)}3=m|Vte&h{um!1} z`j08dE{y4YKnqvljP2%rT8f-JYhf)u7XM0?gqr~43U}Z+Vpffv zLQf!!ip>9La+5&WUH=MyWIo>|0+i@RcIW*#etZ-T;z1e;EHqp`q}{gVG2rhfx*R77 zt*-sD(OkqT))vZtxc=8@prj2VZBA+rycJ?55U=Aiv*HFtODPXnWM(fVAWO;(k=T(6kjd)PuFbv~pOcBNW-~>0~U$$%(5> zO3gzH`G+rY-t55CmzAMaS62hXo0yM}5ZaZQr-8wj#Nm#pPI$;k z?agiYOeoWMw56i6yAJaJJXSIV+t|&FbnqGvi$*Ry2e9q{SG}q1W0LB~+@a&Z2!Fb-&q$c{0fZ%Z4XbcR&X@Ai&1^h2ec>ADJ6wly~| zrk}s;U5>66owSY{+;M$~Yr;rWL7be^DjtvSMY$FfWZLgIfmT!eH$() z=8qX1i}4B3C4Z=Vy=sE^=Lh6zKunW&axCyYEv$6!4f0f?L3YhssO}(X9Oz`zh~yV~ zS>5iCjwX60{3D>SU=cQ07kj}B^WEMk*ZhDV>)+DfKf{5kFC;z09v#cH^)E!xR{DHK zq4oC2U~G@Vtm+ABNiqc8KF@y;=g;YT8C*{{BgYTy$Y)mDCmi2IAz9F(k?+vWsKXp& zld-#RB!0y!QlowK+(l>ieZVY*6A`<4e2)4!$)b#58-1`y1mLFm9`Enj2?Rj)D1Hi!p!TM{??*47^c7f76&Z2^ymI3QEob3J%Jxy4}dtKz+~ z2CJayv2(krrhz*B<5UNHHF>*5Y`nx$=~DhfuZZ&HE$%~&Pj`JEeU!MGEp;$~HJgUU z7eU3@?t9*Hvi`iQ^LrzqeheJg9n0e0q2pp&vSm`B6bfO%*U|^Kx-w@U4nl*DG|@K) zgsZS?vh+ZN@kM^`FQ@N!l$!mOH*}L`0Bu`Ry7-qIY6a~ya8fJ*vVDlC+#WY(3BApQ@z|BG1}M(w^mtVfuPbCnM@L?lov~xz zN_p@DoZFm+tVF|zegZAMXK=ao#DdNR}q#Vbm!wHN^PZNC^2P4+6@mTP2Hnn0XK$$Bdof@ z1h0_~bS*YX+hzrM70YsPd-o)3t2^E{ZXkM#mX0vuJ|WF2#b<;kEjJ7^=4!y@7Mjo+ z7>Kn1X}y!pC4EMt+V_t3EEC9=Mw2qJq#?P(HB7WbMAiP0lWmMw>GJfqvpPjMk%2#Z zF(2KBGchr(9UVniRdKqwxSR+ZQ;#+@H0Y%N3F7%OIXOwo$k+wEwrpTy)8x)l#=*ws z4@hkSLqg7Zwitl`_YOeC`-^2Ad+vQzGQRLm_~luJlWD@&t%?4RDmJ5R+FM;e)c}T; zE!>@?%)g!pPRlQZ{JRY&iBNE1S&Qi5jO`98qIm2N1l-^8A994Kjx)*GiAn|r zbP5H6%HGAKt;X$#su+Ty^L8^!Suu!RXLis>uo9xoI);xp+9&V|YPR#`BRe(hM?0Xc zKV^ZV0~swb-k^)PdRJ|co!&#)Zdj` zj@hhY@9&zL4l;5GhzK;2WiZaY3_6~gSosU}SW&pl znUg)i5pHf8cnwfctltwO0D4_8BUSssHWcmk)!MhXI4r<7Iil}%5Ku5Ge2;8qW)>mk zorLTXh18LPCNfOaa#YY_0D@?r6DV6BMh{HlLK^M3nyT!{y4{f)s-Ll!sA({49AAHw zq#^V26X9eg#u>St{8i@!gNjz#uKPqU7;o1mwnm_n%hG-o?T|*+Z}tc6)TScD;nA4V zQLr51D+B2qCZYInidZpQCV7|OM{zhXyTkcAyaCUPO6|Hspb;if$LSTQQBln?nHv0^Qv0mpwzsDiP*@@x!uCzJJ#1x^efe+LOoLPZl6IgYE2$ZzWj z+XeI6SQ2{h3;lPw_36Ran%f2-4YkyJnm;q>BXKP(49qxX6Bk=c4wV+@Lklj$RXideNimT)Io7GzkN}0D_N9mJm6Y> zCz3xp@KiA$1&!CIf{BVcL84G3;T0#-O_Yl(y5xZyQ}&(Omp{n{5hD+rE*um*+>6AuQdtba8_#QAvjMT9QY!?xeI zbP)#}J4~!0>6G8X;=s}vQ1~E!_;d~08_>+xR|}bz=i!x2ZI_k6c=~Nr7F4^(R43H% zrm->4&|`}h?Njm~`1^{NOe`tka?s;t271kgvuf$HKycSHw)|^?zT!hsPg@pM(ZTY0 zG6ZwtiknsD|7uBEj20yMgc&Bllx}|0s%At+MFk6BVdkhqaZq+W-G2QioR-r`H1|k^ zH8h3wH1>9fKaVgSsywg6OCtIr{}qM%&bEt(WW}Izg?bTVlbfvG7?zt!b6labNR zg@creIzNTn=e66N*E*!j!DSW5P7lrFXfdq&0lob=b~GA);t&fXfRy z!QC*w3f?v%pmmOn1$ZU%MO1Vi*W_7*gQHWn zq)fLF77%{9dwPm~faLI$co=QXwZdU;&Kmyh%NUA$KSO3Fx^_*_Xd+QAkibolAEw|% zfn}slK{4iD{RW@@R=cyMws-R^<3z)|<#>I=9TxIj!3)~s{a&dNV>&utYATE_re}?c zf8b-OsU_|0wB5{+?ZNFS&8dVIk1#$}8`q&s20Y>oj%@E|%zd-|E9uyp8zoh_yhNHe zR)3QB{uK%)8yyvrQXGVDf&pF44uCujm~zv`<-=d~a8Emyu!?x{3fqr8LU(sf|6IY93von#H!mwZGL%zf2)|sY*@Om| z{fP-8yuN-3(&D&@6% zfeE=13);LQ0W`=rcqh;Q*Lcg9@6|<0-WH_cq`IPI4}KK#2ZS1WystK7^BlC~kn{&( zSHFE1di(AH8WbHE_YF%hep>z8`lrrM8>owk{`z#u$MQ|SCao{Kiwv7uUwtyHExUN5|-N7BX3?R>)86*qiL-B7*kiT9xupj8r-bYr@#8#7jf}mUO#0G9}Wr>O8)MygtsZr zTd=HS_dDG6mY}}kW3lHs&gbjLVfdIhgwqIvd7D_{ub{Vo#iC4GKHw=c@;NfndJK6B zdXL}}GN~jP8?mbt7G+m&v~V?M3VkDNa9L^4TD`FnmRD%NNA8;|Wik?ayv~y>LZE`S z$xLY0v0P#Y-Bgb?Fi4--)=BFIPFHW}#HPJtZj*SXrm{g36Ezk>X7Tkun0|i$_(LfF zT1m>z@!f<~MmtL$OrxBN!yG;F-MHEgL5j{p)L+1gOjuZWk6ibd5OWh7ohq)M<$vaa z|IKFD*bNKAWgpmdcW^@Ig5m=5CAI?^fS8}G*sM44F4#}a`thWQ4M=00bAc5ju3f4% zZS>`^f6^+-^+YhvVr0K+On1tH{>V6qAc%t?G?G6(M#}-_j)V}K?Aa2_Dnj72YZy8q zQJviR@(-;@HFgj^4Z5c@$9I-P@Y8fe9dkj9ZNgrUm7PeC~rgjvY%G@Id>L8dU^=Yzvznd z)68S8lYXq%m7xgLstF#0p~y3$=fO>JGaiLqg2Q3uh6T8le^h&zh8R$VqZOp0$YyYpr!RqR-Zn2-FY zx3I<<-AEfvbbslM;7V&*60Z$g)jG>TxNMQ&+ArzC1ye3^(;)6MlkTP(; zB>Tn{v-+)Dl4$0Z$T9UPtYKI`N8te4i372YOe)?L#GD_9bmkku`K=1Y$n)x2ZT26B z8;VHPXODrosUHkjbN~sE7T5E@hq=Ulwz!?$at!3pwb>;FZ!b}9`wC>f82%*5jmypY zUK%;~Q$qtt^pWerCxYh-&B>x6BVmc^+LpuX#Wg5Wrbsj?3H@+^toT%_XCmr?0R!X) zrHZSW(Fnd}M>kUsT)g4$GW|g*u8#;35Rv-5O$u=`g6god54x;h`c?NCCM9O-w3AZA zChok;$Ae>zjvfJQgR;<=T>kTr3-aEas>8(>-G&> zh_KWpEV;sggq;%!?q*%MT~hDtdYvsSH$D}4MO9pD&42qIknXNfydXB9NX-?NRBEB` z-vSkoI5;f}vbx{HT`DAm$~Xn<`!jt1KxFvMI&g42QD3h1Y2;ks0P#1>G%^?7kKp&@ zRHTF#8-^!Uh@*2KQuqeQ{1Aal2klZJQ?MH9=nUW8NICL*m(|UWz}50Id8*=POggVx z@`FuHvE>@Yl-ZGs;>7YmV@qs>putq*0I>c50c1n1nE+H7ujGi*skIh}= zjF?weFL0~cn6<$horlD>!!k`Rw5bX4hjy!Ycj;jtQn05^OYS^+VQ%+ZEjh1SJw;#U+L)hd7|ul&zx2( z%9e9B$qIBLf8DCCJB&xV9->B;A2NvkQ2h5819?Kqv2(C5j+NpMkTjjwjd)Vfvgr~> zdu3l8Oh^S4Ndvd$n@?JuU!_d2^XBgKz}$v6aS>r-ua;85nbXMvGF3yCBNR87rTBL@ zJka}6uHSgkCXqtOoXrJD1hrz&!=m~YY7w{gd9ufb5c&E&q|1O}eE04#;+LcV>c`g- z(ipkP|Kv@^01N%X#`Pv;eD5-mfC17%l!07~#Ek*J2duwnWRbeF%#ov;gsrhK)dq<{ z(Q`r~w1?$moI(N#eWUi*j|)K!>B@R~&BfreC~s2wdDIkOd<933mzm7Jk?z*knrA z=4zqqM-l=%L$%qv*`FTNJ48m0UyXr4jdy%cf#|@e*%E{7Ot5rFe^CA|eOuoz!CK+> zYUMl?^0>rvB3OZB_s6YmzlWK3P^8Ti;F^Vm8SK%RKncl2&Y*-EbZ9;$bQ2K}a`Q?Z$ADK8^H=jBydpCp)J!xH{qB=aI$e@0sgg}*xlFHfn`_3`31z?T=P+5 z4OMWj^LQw(PiASKubkh?43G;Hyh@HwgJ-e{aTR~3Zr!uyZ%D>O_mAc0`G@blzg5V$ zAL@}-#M+jGy%9fHq1f)~4WNa0k~$>&_+Yh4krwQb=WWvcuH$foWRCRd^$1*T$K!#S z_{vJS(vP?~y5mTBr)6*6#OmxbBtBpnR6vkNARZ8$`h7fn;@(SaWEDPouQX`AME93a zD{+?8EctOGi?aJJ|Jh;yL~SwfX26d3C#OMxFU5#}X{S&%Mm+YYQtVVucPoD*5|plS zL@U!@Pevf%ws^p~-Gok#SB^n@6;;rN<=D1r|K;xamCIA!iHXgdGaX#2+fVHMSR5NW zBsO9anwW*f7vt`CoO^DElAZog7R?@5k9vALs-L7DkA(7{t^{0mEsV3PQ6CSt@bZVc zrPb*rCqJ?#q;-e*l!wjf#D*!t0(+vS@HLf(+9|SArZK^K4oT~6ls7$u>M4TePZ}Fe~*G84-xjnuX|_=D?cmSVLQrLwNKsd|S6+EpRlg>#b#GNi)Og}eJ*#3CpvEGbI$UKSQyu0;d z{-zCtseHNz!+7`zi^Saa(?v!h=hWvRWSM437b%FEtDxL5$es+10{q8gdb9VVO3ulm ztF^Tu)fL^(R6Kv8ltwutJtrE!uvZXye9jQh$>Y$R8GOyGfi^?^mG16by1QGtOS&6G8l+2>?v!p&M7kTKq*GFI z>5}fe2S4BYn>+XZb$6J7ncZQ}dDnU6<0%Fc-n+E!YlfX^^-Su4gkP;Sjw-aZO!3u} zdhl$*{IL#1-18hnXMZe;X}M%C4SDJ)_~;5H>%eCg|5snG?CA61Y;Uz#3o?ZeALYfM zx}`{*xVAi3;Ye#f$@{RP9oq#4a%hn=DbY`(k{ps4*VNQL+Gpk6z^lLMG*VN5SZ287fe5L?;u-a8dTLE)}$=5VlGyafQ zZ{dQS=#S?Hq$DF404ef|DnU$$k$gFLRH}sj+gz{3h=T+4gXKyYR^}P@5+ziI?J%9ZQ*BF{@?w*iiDP_0(e_qKA&)kIywlB4K-Qj`IxTfKHZwHmIJfM53s$@loHy4oA98aO);T3$Si2fYp3@av-)|;W zx*R%jwc;$=qJ?qWE0>fcscp*r-p=Qa+fF(SDjP2lN#sAv6nhoH^A0710Jo686BoPp zcXZc(eF7mL#9t5P?=TsW<)F0kw6rfwH!Z%V{7rAJ7#ifx%k}9?+td1d(f{@*W7>X+ zA5Motq4PFn)QA6VcAg&hd%(~Ei=BsddF@eRN4I(6tXq6$*NSRo9$LN4EosQL%?+SILC-|y7To7F zzTTa!0AbNUJjA$MxvFCsb1gol4h3gfO0;{=&9j*nlI>Ue#jtd~F&QWLfL)Ni;nv{4 zvabj{z*kI+mru}3O;ZlGhOW|8b?QCC2GWGGVQzRpQ=q9EAEtH_mZ`r`P_*l3N7iYm zA5-aTQH_N%_1p7>MYW`oM!o4d`UHQt#Khu<5;5yjaH-~2p?LYmd$vZ!bqxj4;$=4m zk1Ef5A zUVMEm9KF(C$odB20ZRa&)RGZzBZ9M0mfAAeg=&z;9~$%b4%VhBPs!C%yW$nkWtwT6 z-Y)T3#_Kf?+4i0l1cL_Ed+esrI*F3N+0O#vjWb;+h!?@ShwVt7_UM!|r?kUbd{oEB za0_`0XuFa^p(8PgsXDj;Nt;3D39iPkM~MG?!=eXRH| zs49j0Z;w(BUL+^`Sz%=%jny_bv|Z;538OKn0LpdD5gY)5ZJ^we8-Sv0GdM)lvf!9A);->2N-6KZw7pIT1bd<{-AIEhKG?C?WPeJ;(DXC zs7$X|Dyitr6z1c@@#l9i89x5Z$x=L!)CEjxy*gxhOg~tYTS%MzWlJZ; z0?*Mk(>bMnTV!y28tqg)Ei7;DX}%aRBXd@@1~bU|1$Sqg9oz6lf6c2E`oDq2I>hvT z_Fc$bMpR`$1Z0^gD!#u3d?hN!5;l{z*Ktu1?rh1#Cwr*Q%=om*Gy`1m*cK6g(3{W-aq#~w!9 zBgr+S=?|57f?mee@cDy5kKPU3OsuMT@b8;u(lH?2SY5u}>n0O}MLoiV5vj{`XUcwE(TRwBa+$uz%t=5y9zrE>R0xn%`6=HF?hgPD&l8T_BG@@q=#xJ#CaCcUz2Ah8}|l zkit9Yk(j*P!#waf@#ct$wrX1XTFh?zzQ;GtTaU*oPD}t;VTxFp>x{%uBV{+HZjT`0 z8>e^E-4ls!7fezNfBSH~Erj2*8>&a8g}1f0f_|lfL9z&iN6d0f8+mw^syR!3YV))u zJp2w_q~CV%6HP&3ykp7)2~U9-E(dh{BrAwnS!oz}7;xkxngpxLKP%vC)ar1G`D^ha zyBm5AMaYeZ_55}(W^QYTuKw!)qJ#n}n74|swrV(H@Nx3*js#YBM|}O+3?+(U=@g27 zv{2V|Zc0rkaHok#0t^hOPp**CY((-a`w#=XA^^L?5Wo{r&m!(S_9o01eoEUI$;66~ zwQB@+@%UQWQtVn1T($xxF%hi+O_f}`$$Q|mf$9D*ez?4mzUc5}n-i#c{xC-;0x(~9 zxww!;2B-(}NPtHstm=qq-~`&YUACY8C%k?3dZCZlfs!%lpu8yj{O|dQlG3rS2#)5F zvL3xZD7>Cj>-76bs<>9-3H>@afOp>c$J5Nw8PR|=@TYn>%Ve!^IK8AT_s758)yppu zLw8yTf}OB zkU|4Y3;?PDo#(GH8nEx-&8k<{+f*e6yqoN5OI>5F&qIs95f&N|@5BPa!Y*FG zYG)3UYF-FbuvQC9jFmufYp)Uixb!bwj4fw=(NEzewSte_FWI06M6-%O*tWm(8wtI% zg9bT$*i?F&cI2{M=Qevd4?@!_72u_3(&`p3BE0#T0?!fhG^@OgdKJ#T9M{Cmh0z;O zo2Y_pfF@UNLoDm_&VQ?@5c+i$SzuYxW?g7#uu%h`A-xv&k*mYKn(7`oWv(z^nW^_v z;h9dWIMRmT52Z)=h3FNyD<4=(Wm~Jr{Zfh{lmXaeNuC&v`H^!Q zP}G7;odl}@o&l)Ln?Bk}l2E$*bU`{`&&RbiHR{2GoySXF3#z+tlksBt`T&dQ>L(w; zg|JjhI9v9NJW=^1hLE(1&fx97z%blv0ZS3UBaD**BhI@HJ_11)mSq$%EF=H9{uR$! zB#lcE!Dz%3j&3joGMw~N9WiLJ4Fo#3_%pc=LOo5D;a7>|FzK72~fvNTZe^;ph5 z%&0@Q6~bSK|T1tL%a6p#oZl#5>Rfcr^@*tsBREmW7A8pMc0QJ1VFvi4tFwgeyIBK^KyY;g5aXO=9B<&LlO@txUjwC<)iquiu9`_zWeb}E5vCj z^nm9jRJ7)WO=25;GqD|w1P50e2gpE4@{eC7=APdBP$JYefks=wATd8>uRy1TL!(~8 ztP1}1>bHFMH;|ed?(@sz0+ok1v|2B)r1LL9m;OuZK3f>0s@WmWxEzDFu;yJm8unAl zQeN+|DUF69w0YyZ=+$$`|IdM_LHs}R1iLbT0;2Hese%9$^q7A{;)V`i;q z)%aY>T9s85Xg07AMbmA>0j=H`N+0w6osBi7pwaRqBU?PcCI+$;f8_vPi|&6oMKpjC z{7O)E2ljnf*b4^R1qJf??I}i7u1t@rGUm6ExZdMYRN=)IV(+ZyAkH&3jB+oeFr<&p zccHT?BfXO9Dj1dytb^AZqCoHq!`LLyIDz!4ax-dH<*3Odp6gl&!;(1?#a}Pwz_=&< zK5#vwPfXp_1s*ES7(6yr$KLLYCK!L|qS=is_VHJ@Xgkrf%GXEeI=UU{$6f#kfdKsc zZ#^KLQmI+(~2@>C{XH1Phpo!pc{)0GVkO;{Y|1_aXOVizzWj|b;>nY8H!d{Q8rkd>HSv&i9z}r5F;-GiQzw6 zxGsy##s0E!|L#;J<)kCjnk6{(el;kpkhSvx-~n>UTc3|?rK6q>dryDH*aXk}q-03> zGJPxHC?IhV4Eq51T*2Ml1XY#4yj0O!4?a%nt4F0yo7d~;0OOEsall8by&;La1r&GB z?Ar7yE1+28AOE~Bhax*^iTf^MfZ@HN=p|1Y+P1N>Ml-;?z-mccGhmotM4r)sEABDd zfk;?K#ScO_S1D;YWp9axfu^xM6qCxDvqmX>A{B96TOYzj<S&&ucam=D5NwbJcbq*yE2iI@a2 z-;#(@N_Z*ytB4}WwB#y^7L^j^|+jB=)HG zOPu?B2Q1HKGXKXmM1!2bkcveXDdOQX&O?Qq*^vgY~zaNj3(s~i~Jl-5q(N}Q3!dSB6mM}!o0K~{<@5h0-lOFQD;ztf${pg+q z&Rk_6=I@sj4yCS*BuVvs|+p4 z4^n7;_2}qnV{9>1w*t;Yv}nZIC&HodfOnTGRqHgK(8mg#ObVxLp|6(M;B$4`3Azm- zkc9kdf^AIOBMRnk8`S@)%`Ce?8NJD%pGSYNPG)uva&SU{Ki>6ViiJ zRZ&PYi6={LX`qf5yEogZpyKI!b{F&(a`;gi+-#iBD|u(GazUe&1Ze0Q31qsj;pAt1 zKkOr(;cFV3P+kxaQWX8DM<3%LLtYU<`Ffsgu)Q{7olha1_YvqHGM@G+p`+bYw(Iux z!GAD%^@R8?Iqr#h*U5=qLl+izg~#{g;KJU{gAr?2{PTc?}N-^BXa6q@mu2V&Z^O) ztt#7pt199K^`DIk90%&$UvLqf&dE=Qrbp)4Sd=qVJQ^iU#NP`3&7O4p5QS#p_DT0o z5gi3|4yylPyq%Q3GwS=tvP8W1v9~Gq)S7ffR@JzG9!a#=K02s9+!|}NuetP+knPYi zqpUo^Cxy15pey;yhq}?rjk7-4Az;|Bse0*^W26(}kn9}|r6Ph@+EaShKMkR~5UYaD zE(N8S>_e-jJ6^{P*oev)3T)yD(CgIuDCF)L^C4;Sb#?WmV!w7&_|e;ZgPfCG*Y&pM zA20r zyO&E2f(ZFu!ztKQ^F+S}1B2P0J%=Z#m+moz?2WLFuKUhkl!L2$c-M-uN#{<>7XQcc z@qGKwuEX6L0=_~K8N%qDg?#4v+WwKMu2Rh_?erc?|BL3N<<4M<1|O|fVM{)&gL}u` zF~K`o4Ke*6KI$fmk+(@WFe&hb-rota>#%{5ySeat7MQ?k(m@9el^uK)H?vP-J+!q< zQp(th&q9|^*tM|%%22=v$db+SYq%0n9B5|X5ZZ1?En80$xzthUScJAHV6xmsK|R_5 zDWql6#v3T0EI=8%%yxak=`nnM;JpG=ae?V;k24*mHMB(!lXEYW?>GPqigniwA6eKg~;XULDfQijFe zb{s^9C*U`W?d#tenR+hT*P`t#fW|v2o2?iMdMu6bEPJQ=o89``E?)e9tQ{}0e=%-t z@!VVjSpzKrlcv7fegKg$#BnIE;#MPR;)2+!ah`CPJEp^5!!T=)p;Ir(aDd$A?L=XK zc(qXc#x@c6gS}mEy7QXbMc|_{yD#+05+_)9*VT)@v-4vV01~CXaJ8FDR`N^nlLHI? zSqncH)ohku3Eib~kLu;wQ4M(`FJUaU6>Em#Q8bIJ&3Jx` zE>i2Ad1D0E_HS@KTVv}WydVsDw=D$|>AA??gKN#G`(ow6`x$m%kO>%)@teH^6#i~y zfL`UpcE&oNdd_U*fXv;i0ub1j=DaBDPNA6&umAh_@x64*e|j8V`2Plu_O;o2CwIck z)dQ=@qG-?7R$AK`^fQDN|5>cB{tax6GQkn)z|kl<{?>k^Uj01EDVfc6U8?G^wOF1Anzo~esM2;O zITtxV8|+*J9E`pNgP+giRv~+K2)icy5)~T|T5|X$J4ffvYS`7AYf80!(e{_0pIPya zkr=_3f(W~QpghaJ71w|pg7wi8R%9VcBA2jrNw7Z@?|Il;?! zWs_pQgq71i;7eN5W+$(+MZK`o_m)+%+QxqYU_3xawhW&uqO5>^#iyCSycEB+2-b+^ z`_Jt9-%_L%&&|pk*c!#T|8+klCLHmcOTqnx7qmEnpL_n^*~b~#ar)*=R~5eBC1bDr zP)b&roGsbUefMnIv{I^yF?Y;8d18H_+Cu06F0e-SY#!iUY0t8?FHRVzCv_a{vMP_N zpqFn3w*mAhV^J9K+`PQrtvv)+31|qVOSEiK8}#t|I!FaE>ca!L+!(+3`j{4SY&fgO*m&jhQo^^;|o&a3Pd-G}$kh&KRD!A-YBgJh|@HX#$E%J&MwI zG#tiAsGA!6W^J%pIRT$*X9=kZ>49*b8dqpjeG_O9b1=0v^zCdQrI8|BDc>fDBT_5N}-0xk9Vas+*7a=k0KKu&bD2E$i`p+u95A90; zz>UWSS1+wW=M!?LuK$Mfmg?UE#REu)fYg^c))Tbkhr@jdRaG$`{jdaLz3Amr$`tCY zd|?`5D|vyz_~Cwy0lyS#o}$ur{MCcH5_%b{-CTo51hfr)5%HjwKpfh6rP@IG zQQ&#W+ZPRUx4A8eVcA&MCMpK6s`Sas_X3KNC06a08_H^E(K!(v{8x*d-?$;<>k%41 zJ*mlSvfhiHzFTh?oTgfrw6AkR?8|8T^Q?d=%?wx|1)zyu^=Xynqe$lrA>ovIW30`u z?CiqfOxkUy$rp@rdgQ$tlHROCs+!D@=Sgyvo{5pPmKel_mso`{4Y{e+bXDNe|g(X}YB zLKD(5!I$KjEPLz&B1_UBgyNFtSuK^;|=5G&eid)DEutc8fgZ zo;DvYa>$40i$ces2x$N<%OKb=xIr4a!#rSnlvhvt&qjJBUO4;4&|Sql9em%s{e_O{ z^gRNDhL~T>3uX7AdoZyh2(zK9K=BEFt8`6bhRUdzTqr&URS4su*JFY7OV~c4e7cGG z7utvwzW zp(Mec zgnkEl02!^i&G8M>cA2wgTSwvT^W3SLCCv<6dsq4up)dW%UL8qSao2|9?yJO%QzDej z-(^OYF(%8e;sjS}yq>+GO9t&J?#4*69fxYX0zp?QWiCUGd`HSoRKL{Tb|YOoV*~&# zL+^@3v1FSLng}!=bTmKaNTsAQJ)P=V0-BFqB1zRkUlHpRKUOz?QpY&V60RHP zJPKYhl0ZRxzdUGCWz4Ae7|hj(-qKQzP7_%HQdX$OFM z`^nkaegHOC<9p*Mr=UPdPamOp?j$|~n-Tdj9#qc3q#RP zHi~wta4bEZXSb~>MsyKis@j zMh19^e|92L)cSuXH(@kv!nMZ{A`LwBny8yJ#}e2(<_4!dN*3QY7f|xxPkGz{r%k^_ zFVu{sXMvH|N~895QD;8<)b&^JXbA;X^D7rIdIHeXd8 zV1OP$0)XhHuTS*;{d;;AmVQ9!abwC!W(M}INh!n{=qs%qrF~Baq}Ju@R$?UyND2D- z>SSWJL-QFpNd!a*?-4pKWjeMT9u@C-{ZEtC{)BT4P0n0px8xl%OzpkU}kR?`T^GN(_mZ@tKn<>H>>?Vda34V zsNB=LfNxQYUmG@HM4?Wv+GsHKKCNaxfCWv^KBRsY5oVvETW8J)WA%fVd@GfqIJ&?O zzT5mt(W@%;3U?gUs~F55)@nU8ox~75pm`8rA%|yqwz7>z7;}GJ7y3~Ytx8qr3 z#jq!0N7Otb#NbPr7Tz+@9wKwB&IZ+VBeMRQ!B%QlentXJjGTUFC-ieFiS;U0^ar6d zjNK2hwSBrN_&D7>l-RT%=h%fAP^0a-Cj(8O4>?lWVXyeILGvRbTd(!guws(|{n#9SB{Fv08Kh(Qh!-&2LpxS^vTGN?9cYIx zL)K!#4YLrMzo>0(>Ze~^B)%7NU}=#TNV{W)X}-kS9IS!#bO_^7%h${8-r5pMe_yT( zewsB6IZl%vzH1xA>55)&n_vI*mxOsd4?L7{QQdN=Q3`K_ZRZ_J3Y zN-}xVBkxgdsJPMdqpnAm4(y`*30xoZwOQ4Z;8D#Ik^w49v%)ipP=nXw#0Z2b$nhB)s>t9Ec zkiW{}l}_2gV^?*^6n;*=hfrO)q=$^&@Bts=Z$%G!N>nG+HHP3rrq)RRg(Ct`u`Gk` z?|p9X?jQh@6afK2w;jhYDuaZHiHS;7G~jHSkLm#YXon57X|gs(eXVFEmp-egN|8r) z65h4`TqBxK3nw=XYmO7-^091eGuPA5i?@W2K7 z5(iPv#r=(WUF(q7@iN1H9|TMavP3sKq^*Avcys^$dQv+v^n!EfsSZMIi%MLh25C5e z^@Jr|4L<^WYOgW4*CHpj(ts=8BTf%ep)k@B-!v0UOTHBYH6DSWqQxEjn{u~V z$nJz%^EUz6jSx|jcZB2<3s!fSL+!!~o@!}=)7np)EvX@t-sIz0Hz9dcv|3X}dGmbW z`k4Mn3_XSR*Xi_8;zCh~ zV0tlVnbx2wGE;MB@!b6}`m6^+jR}*E^GH7{wEsU8+WzQF>(VSMA>qLs@a=E zkmrP|ryRMsabnG=$lj#Eiz8@piWmPFH~&Coas~Bqwo5X5bq!>7<@vbRjs)-Ev02%Q zG(#35aj}XmCqFDIPiP${SL-TK-atiJaRj^AARbY` z<2P$>=08HV%6KgI2~{fSl&@KsAudg=uHZwg^xX$mmW^(>!ddH*B4!S>7BlhoEx~0a za#4l%!WW~pqt~8ny^0Wuq-9dIoWx

H)ZW4#}tCf2l_1V88}Itc{KyA%@kHk=fx9 zw|0H}KV9e1KwG`pYumYW%J-g-n>1R9iLp`Sq|@vH;t||DWGCKT8~oyQ|C~H30oql@ zBc8*ptq_0@dofRzy!n6nz}ty+#h9>b)ujb{H!_N3jJT+HyiF0-sJ=<0 zTaL?>*{b}Fut*30z(i5)yN6&S?d{c0SsqAhXo-6FhxSki;+$kIQQmt)xwLWsRcBfm7qH_vDgr6^RA7&_Lh4{qdOMN>rj8x@>XMREo>H`_oT$6 zBG#gr&yZ2dJrFn=_y_x=JeVx zSgiU#u&_b8JDpfra4IKcCB-X2lNsNKl%)0EGoSYadpmi34ui@*HVI$(tJn@Bq^pA} zP!RMl0olhd&-u?hBLR*zKy~2u{=T!OPhVPky8+8CwcFfjy0^EtMXIjZbsH(LX#7~U z7}GG7Rsx%xv)q-6g$0$2jO?*t;0NCVyr#CccC>7}(T=svEzb6N3XGIqhO>Y*c9{if zxmreM#C?z7)-GNTH9aXG3fOu38*nN+ieHXvI184*j~E11r`3CxlHWRnFc~aSM>JjK z$StfOgM&lDPyKO7`d08B; z3r5e76T_D$rh4EArSHqEsqY1cp`$*<&M9gN;)KRxfdzdFOPslb298e%NR0Yz>9g&r zrV=;jl#0D0S;LHol z{RU?&Ix$l9uN>i)8ZfnP8z_uuX=(HN5?H|)TC4Kt=Wf~z9O0!k9G#-ynFY`aWb~^$ z1Mn)*oi56$nQk1I0rhudipM-_Nb{M7VIq6*-5Jy1qa%-mr%A{ZsSv^1MNm%6qFwF1 zO>mV%67O!Ioja3Cgs$#d95}i7Z2N#y%c?YTef=znuIq}2}{955c%^Ti#);=GFZQq$Y|!zvZ^qwgw9K5lSBBy)uNEdIQR-$_+tijV8 zDJy--w9U*yO8#)04X`&#Bg3u4=9Y-%OEXbRnV9UPI7fN}Y|l{;Jc%W$3FfRAxf0lj z+_v}FI3M(jLqa(8zX)K3*W(1mYugVHitfEOW?$ZlSueS21quBa>b^`wWQI-DPUh;R z>R39}(CNq4VqqP?0+XCHEX7>KtpQ!b;vpYiLTrA#L;=w(*`PmfZ;>_}0>uaf&8HGt z$U`5{g9}_=5bOK>otJM(Qk0xP3L3BiO(#W`Yi22dFI2wMkp}~dgG=tsPTwni zZu8@G?Y&S-)2V-TxlO+=C`;e9!?aP-khPcvMKp5Y2821(#q;i2LLO{$;(3^z2Sbc=l}U$VK5n>XQI_85ON^A zE(4Fj+KisgSY0pSTVn*S>7D65%D#6N1-t2gx5tJgB|efdgSnjNDWOWNNb)_d-%9YE zyn8-iTle@R!dyk)y>X1aQ2T5leyg22D~5lJG;HWWN^R(6+YlWy6!p8z1XV{58sg=d z>+g};7#yEj4jQ@%#IvmMb=H~;{nAXQbOH~lY$b`)JqgP`qoF4>>h`bGSg>e9Y;)jS z&%Ptu`)DK?abTOma!1-0X2lu&yBDT!p#;#Ja*{rKqUyd+KfI_7 zd5Do{&7;HLe#b)NHMkm{Re$~RH z>3?r6(9?#6bMW$iPV6*B@o>*?D~PLdGJlU#UIf)9lxFJkL}K6XL^U}|FjPQ6KRzts z;v=|Ggl&3)8Kiiy%I`UWa&FU?*!c{PQ+|g6S(;lYrOenK2v5jhdL5wJX zXxOP%>JD_rZKV~3FnJ_Zk6~V*>HEaY&y7DgMPYrb+mNZi>~(GtsLoj=q!60B-`LoO zg^PE*LM+*yJuQ5EymT)wM{;?D3M{;4Ppn>+l& zIk6^e<>{R+GL!?G10szwM|RCZEa~-O%~${_nx0d?NYUoxY-{)Sf}}j2vBWKd$kuDa zN-*O~nAw*=*@{OMYWZj?DSc?#+ScZZ{MeiW1zigtyfwj!ewj2>{)3;iW^2Jw1yI=` z58gfxyskH-Qq#v}2!e(1B?gdFd~}KS7-(1Szv{M|VA?8_-}{DmsV(#Sy?ah z!e|(LAqdI2q`FcFXd<$my6bB_K~BT$o~`50x;g9BUeU=#Rs?stweHc74C;TSwy$&d z+7=6QHh&5vpa_L%Y3u_Xvx{(Z|74!kE;?(77_*Y*mm-i`5qdExxC&T9=pt0pYtS~b z?Gwqi;F)gN4I3hUNtb!i+Y0n-cjEZP$0}Yxi|g5}@4$zHcOi%P>QF4h2%zB=-*IFY z`>&Up!x)eX>=CR5W#3r|V@b?j!ZBZx_h=bBe|@<8ttpf6O&E*2sz}pKOrK{iU)dd% z2$!!=#zn{#$CFaQI}NnqOHttlwKO)qNqqRE>(GUqMS8vQxfc=AB8o`Zi(Q>47O55q zFNeVuHo-#PY;qGm!J{hNVijFZ9QC~;0JXvMIA7)Q?Ljd$zJxx~)xpOPn0+lQVre&+ zFRLG_D%X8e%U~eZ9fP)=WD#!F8@s4Xz1HN}rx()368xUU>rB~_<Ly8_ow=rgKhpI5(dRVOq<IB{0bkzb0D_pehuNCtLUG2v#S%tl;ayTfyHM z%e@{S=;vMLTP4;%;=AoaF{FhOwmiMMWoY)^7zEo(>D%DX07xW-1=1oReX}NhqV_%K&_!Zj#?SAy1Jh z$D%51YAkwCDMC`&eO7Vz;5{G*aL!|Gp9S^L>OUa|%3>oD6EUB!o4Q=&tryEZ3GLU; zD~;NmFZZVYKXYU=f!cU{^rykj>wIfyBjX98VPq6Lmc_RjP9EU3?`fv77f&J>A+pqH zeyA!|POvoh9P#&X-fZ_$gUwbQ%`$#N5ZI67G$L^8bEdp^Lg&ILXG>X6rLBJ;#$ zz3s&Lbjb;Sqs^(tcdTil{n+5KdRwS`r4}!X&!M*wwXMctQ{`y69k;E~zPqx5Z2%3j z`&93P%lmY>X*b`|WEyWbq7A7z`gx7&TkY_)nr8kGX4JE}*156ya95#&8|hj{#H^6E*~)msLXAJywbM$_3bBXj zz`fBzrz5&Y`ATHRTGhkd+WHs%%cmRuc7LI`qqTUdaP+eUs&?8&aHemxXxxUJiTGjQ5D zJ{~V=ibev$6q`b$V`F1Fk2#`!`4`tuVc#FeKDoF+$WN_(4iQ}p7jjc{3d}v1j-hBK2?@eK0VdtlN zcTtC1jqv}rIimP(*!Oq4e;-1P@&5$_9s#W)=@ftv$S)vp29e4WJQ6USfb~^D5ag4B z6?9r-yKMcJv)yQV2WBp;#svYPp7iu|-S(~Dp|AfkzkwckvW0?Z()%HxU>^40g6@LF z`cF~vwaN;N0TTbOEaBh(dHer*3yDgKMB>ir9HJ_ zc8xDRXR!+k4zroGlJjo+02rMYl>h7z5nzlEXaxWhvKdhIxIS78PNq}s0mSHlq!j?Y z^Q-17wLd5?DWPLEZ2l}6js^juQ~ZI?!sX@V04=p=4WW*&1wha~wwTS=?aHXDWAgCu z05N0z_NI8`Yudby419e2f&OUxf`Wo>8gDNz0zho@@f;wfU0`5iOKNK31d7Thy$Axr zL-S<-?&tb=B^nUtd_B$qVx8emgvqYMv$APb=*)~}z}-}lY)#-xz;dfQvb?9Y;^Efs&=1wNz>|;zh>y|8l7@xiuHIl zp~3M>oZND|w_7Hsl>!mJvuUQVw=0o|&nJX<8pYIND%rT1?26|6^TE&ZuR*{MSAUoI zfEMKeD=RD8g=#a~xj_Eq=wwfBpKEB8e-g-Ro@Q`!{v7O|tGV zF^50(a`#5?jc4x?FiSM!FwGarBNu@!P~y1K`gOKUrvzXb&ylcPC03ol=%YDnVc?^@ zXJ7BL=@A|0vKh~APK`sx#7z7a3f1x^-=^}%55^zA7GN<&s2!=@5Xh4peo$U+;Sy7q zJXS>@W0^f$7p+yR=84)q-k#Ym)>@Um>UD}v+6=lxyknGGU;_}!25yZTZ(RI<4`68P zmSJ{i4qyyMz#T_6IxKw?@x2at)B7jz0nG=18i=62cWldF`!&4fZBAmMigxu2|xBpEP@R_HfG-GnO$6$Wp41NyY3 z%2~YX-fXmTfA;nIwFQCEtpMml>EXmyVK>QW+lB z_3h7M#VlTjD5*H=Oi>wAnZpLV`CkF|gcEwVUjB9hdnFVo2}zu$b1Qtz-7o8{`OeS82Aq5NL} zCh(T6-CTL;V%7#%QDLO61pr)#dASh^@_;eKe;hwUuw6F<+7+Xo!qG@;ZA_%3iX#i& zvA)3=B<6FB38@ebih0NA5u`+^^ff}>OF==Q2TJ}56*5(8HR46?${-V@bRO4CB>3rr ze#1wtW&}XrON%Mt=46fh<)jpb^NZ5m4Mfza+1acEDq})3$j|yQ=L80}79bSQysoT4 z<~iNxgIe9>L9+%H>u4TrvwKIKN<+frQ}#nP{|4s%o>~^&C~xwp6?0`1ju$#bzQkoJ zgl4+E4EvQ<_e=8MCB|v}nY*?%j>5Mnd$AQ0=41X9{jHG4m6MQB^Rb(cMtF9)(YkZy zER>Yf^=cseH;dFp)b`2_NtGx+wgDNg7)A!gE%r^ehtDCrha$|^@~vLz=?&R>9!pJ5 zd4N@v#@h9G{^|K{cOdN6Jk^qJU+{r{h1#gEHu)1zcGpAB&s_+$5ip^yl)(1>Or3e9 zUT2m2oi-VPG6ndf+7_=2H$ee2k_B((`RPISD)vQ8(Xb}FPE*lev9;Wwi8YQ1t{J_0 zS|het;n(hXh>>PuO}cs0H@=KDRl}Xgn*pm_B6P+?KVwoM4;1yVcU0C!JNu(U1D+C% zqu2x4&7;^IqpTOyz|C>1gYXd|Ea#4k12KgtW-XKKeYWvf^l@g-CPBy^Kez5s*qPB$E?Z693?Tv*O87uv%@_yFrJmbZm31MQgZuk)n|d93^nf)oL2b z<34ebRB@8rb*ZmY%`Jj62n%DkJnb~_r%9)Ve#g8cA zmxStd3nBwsP`n?Sm?D|kEaz(z+Jims!$%H@?d%YO5o&pk7rr`n8)A8OExr(PsNfs8 z2M*wh1an6OqHv%2q&ORG`#4UZ8`X{xnTy(G+sQRkYf>2Fi19_)M2SV2j;(k_>3ofd zRXV${)3c+W^vc;WhC0nZZ2_Y(xUUY127UuC&7yaBz?lWSN@aqWfr|oO?_;6#faeA9 zDkl23QGn0M@gQ0L_qoK6ID`Lvj&hbhf##%|md!2>Se|)2zR$>q@z0MJ6Ez+ePF+vh zbvHBB#te3uwYmEl<+vk6-+_g!Snf1@-*we}r)!YeU~5FBi-b9MNV5}gzhBavR!2Yq zr^}S?o*GXoq}^8O;={dhqQoEY=yeakY%Ow*m)*nNAUs%WedJxQx53Se zWH}_{wmYuev=-nSv1JIzPEVh2kNi$y(3rmdyJUBrOO&(=7#JmAm~_7Jt$G!8%M140 z=6J5kQ^~~00m+`KMPE*Y5CC6X45+_hTt-yuLP z`uCytx0~@6MF8}}3k`gq>w7G(n95uVTrBlVFg&_SRpveCDVmZTIPqp{n2*PDP-ii;$9l~^1Q#~)D^o~N-uW3VBzuzaI$-BT^I>cBuKCW zZF(;Ee`_o?I#_%z>ulZ@GMg%tw%wopS_*79>#En;-z<2S+WuQDNC_)XFI#@K>;9lj z?%*z`m~@0D{sownhE%nqf}^5ZrNW7`2s5c}zBNA$yT8_JhgDr|CdZZOWL41&zyu0QWi#sJMd&M)$!w75ccimAN9m zVLWXnT>)nC&ya9(O3bxdE5(JzMY&3Ew8l|ca)0g>mjTlK&WB^gWLjkfbm7x?6%E7` zNSKZEvnvYg@w1U~JlX}Xrq82ew%zFIOAVJ`YHBm$hTrh<9M z0Vj|~=fqI$!#smWu?Gx0mOuCrU2Vs$Od)r@=wjK{qv?sFkEgvDLkL@nY#rROgj}@a z-b)TmUk#i(c*Zig=r)q=RI~XxjJzPB`@pcO04AC|VAILJK4};5femhpf-{)pDc26% z#qwStHQ{)pdXbEDp$cd0yYc%#OrX#{j)93uaYFb)iFyq<&EqUq{Vv8ZRfV zjkW4QQd^CdFm7v^i#yygxy3M7b)Izof%)M+zn}9u=lh)V`Mkg9bzbL72iRP*VTH4o zEO#Wf2fw^+zVYOOp3Ja+%9zzmmOfID28zTDP&I!B(Qi%=M~3Fi5UD2MrZWc2kK6TB zLn$RrT2;cL7mKOGa_%3@PRT9NsG7L|b+e}&tbCz}=HJq3%4;CkrJy=g1Cq^y;Eq@K z&7scw@aurGxtkUH&IR+~^sgW|+uU%S=wtF~9^pSCYw0H30Vw$(^dI?~vcxl-TH;z& zRHs3fjRe;MiJ@@wwxv3EfR)ag7gZZKZc~z#0M<*t2j&pByjx*SP?93aa2O=jYwOcb z7{D%=32FIbw?IBU?ekbk*5VV(yKC)My}yga*dDKJMUGVZpApk8W5w8w2V&I7$jGvQ z0100M2d(n7>|>qCcvhQyM*h&Pv`^cMy@*=+xg+{Yccnakv_RY!G20d$?qP-0)J$4? zVEOI)Zdh*w6i-mqaNV}{MUZL!fH9{^&82#C?zz#%TlxwiW1i#8fKAF@k`U&#uiHv6 z&}id1dVH^0bh-ycs_S79(uu*^z=y-Jy07scw9bfZifw>?UoGj0=z`d zJYsaetmiu?*hNQ=R)i=@7*vAi@?&rGySSlu7ie0^ak3Vxi&hsLvYE(GiYcc$HTaA5R7XlZhUrFj zvMN`iGWk?u{5zk?D!2QLVO~_*zPxA;l-qO1ZG0Yq{aUzt6~8=hLw>kg^4h!AVQ2`O zrhES=L8sW`3?aj$$Cypo!V{y#11|1JUE%EL4-O6GL|1Na{5E37*HP0xdG|B)r%o1SsgqDQ)4wj_ zimTBHE5s5f8eRwFfhmjh(Y4m>8&aM18*}m&J}#Bv?l?kG*5X;8FU^5l{%p; z1Por4`;it5zR#TRO4uwj6beRcC70CRF4@v!gT52>)+YVj9L?Ae^L1LyK)e2-LK;U) zZv@#+h#RLBR?3+oivF!I#KM<5G#+Dz+~FmL+6h6|-oe@lPr`*z`gqOd;k~}k2h{TN zlFV#F)8CRoJog%5nG8g^YiI=uCg7u^(?#Ee;PsK*hwsu;m$Of(Hlh!9b&3Y%3gaLW zM$;HLN}cxZDH?7*)*Ud@u5fGtf8ljKCVY?{jIjukLPkB`6LIx@Sd#;Cme_SKQf$=D zj^g{an}@;LCDZ4bY#5#Rwc_2K;Bw^YN4adPfEF%S-I&SNhyLBLr^3&b&uR;(e-ntc zjFkKBl{~W!rYjNQAoRbMf-_$-m9Rd_uBh+V@I#rxL3B z;|2ClX){-}$Cn8c@yrJ111lNH*Ej@G)r?5!V#S~YDk-8Zz2*1m5zKt+#RKv{O~#vs zH1=*&_*qg?CHxsc0M9-M#o~vG1n{^eQ!V#c&ycvQ`5Jkt@*`=fX8ndJ^kfL~ZQNNY zrTw|3FYe_lZgtet1(5(bADbFJMC;J-w_9c2<)~2h&G#R z!ruYw&<`FMAH4Y7KD$D(pALIGZChJYrZ&1T51cro99n%EcQ1EDC*0>HXKcGkBlw@= zD=G`NgyLXp^vt4F4EP{gOn3`TYRM{cBm--=adt+fobt`GzIo zi1FcvC9XeaN=ZiRp#v)uB2#cu>2hf3i9z+khO%K^Y`zeEtRo`m)Ovi>{$vM23c*5s ornA%=Fc|Ltjh6pXhoxS>D=b+;K1U1b)I;Ehb-spabqdV<1Ee~wGynhq literal 0 HcmV?d00001 diff --git a/your-project/README.md b/your-project/README.md index 7192196a..353cf505 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -1,9 +1,9 @@ Ironhack Logo # Size & Intelligence in Dogs -*[Santiago Mougán]* +*Santiago Mougán* -*[Your Cohort, Campus & Date]* +*Data Analytics (BCN - 01/20)* ## Content - [Project Description](#project-description) @@ -19,32 +19,34 @@ - [Links](#links) ## Project Description -Write a short description of your project: 3-5 sentences about what your project is about, why you chose this topic (if relevant), and what you are trying to show. +Analysis and observations on size, mass and intelligence relationships on dogs, and exploration of prediction possibilities between this parameters. ## Hypotheses / Questions -* What data/business/research/personal question you would like to answer? -* What is the context for the question and the possible scientific or business application? -* What are the hypotheses you would like to test in order to answer your question? -Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required. +* Is there a connection between physical features and intelligence? +* Can intelligence be predicted from physical features? ## Dataset -* Where did you get your data? If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset. -* Did you build your own datset? If so, did you use an API or a web scraper? PRovide the relevant scripts in your repo. -* For all types of datasets, provide a description of the size, complexity, and data types included in your dataset, as well as a schema of the tables if necessary. -* If the question cannot be answered with the available data, why not? What data would you need to answer it better? +* Dataset on dog breeds and intelligence measurements from task competences from data.world. +* Dataset on dog breeds related to height and weight from data.world. +* Dataset with the info from the two main tables combined and clean. ## Cleaning -Describe your full process of data wrangling and cleaning. Document why you chose to fill missing values, extract outliers, or create the variables you did as well as your reasoning behind the process. +* The numerical measurements were actually strings, that had to be converted to integers. Also some non numerical characters had to be fixed. +* Height and weight data were presented in pairs (minimun and maximum for each breed), so I converted them to unique (mean) values to ease the analysis. +* Since height and weight data corresponded to specific breeds, the NaNs could be easily investigated and fixed one by one. +* A few outlyiers were actually mistakes (e.g. weight value in the place of the height value), but again, easy to investigate and fix. +* There were NaNs on intelligence measurementes with complete and good data about the tasks response, so it could be assigned to their correct measurements instead of deleted. +* With the intelligence measurements fixed, there is no need for the details about tasks. +* Once height, weight and intelligence classification are related and fixed, there is no need for the breed label. ## Analysis -* Overview the general steps you went through to analyze your data in order to test your hypothesis. -* Document each step of your data exploration and analysis. -* Include charts to demonstrate the effect of your work. -* If you used Machine Learning in your final project, describe your feature selection process. +* First analysis with plotting of all parameters, getting an obvious relationship between height and weight. +* Further analysis through observation of both physical parameters versus intelligence measurements. +![Height&Weight vs Intelligence categories](data:image/png;base64,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%0A) +* The machine learning work was through clustering, since there are six defined intelligence groups. ## Model Training and Evaluation -*Include this section only if you chose to include ML in your project.* -* Describe how you trained your model, the results you obtained, and how you evaluated those results. +All the data was clustered in six main groups, corresponding to six intelligence categories. With this, the program can be feed new data on height and weight and relate it to its corresponding intelligence level. ## Conclusion * Summarize your results. What do they mean? From 27cb8205ac9fb85e9378b3db10052d743cc4ad94 Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 18:30:54 +0100 Subject: [PATCH 3/8] Add files via upload --- your-project/README.md | 39 +++++++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 16 deletions(-) diff --git a/your-project/README.md b/your-project/README.md index 353cf505..a50f0da2 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -42,33 +42,40 @@ Analysis and observations on size, mass and intelligence relationships on dogs, ## Analysis * First analysis with plotting of all parameters, getting an obvious relationship between height and weight. * Further analysis through observation of both physical parameters versus intelligence measurements. -![Height&Weight vs Intelligence categories](data:image/png;base64,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%0A) +![Height&Weight vs Intelligence categories](https://raw.githubusercontent.com/Yaguit/Project-Week-8-Final-Project/master/your-project/HW-I.PNG) * The machine learning work was through clustering, since there are six defined intelligence groups. ## Model Training and Evaluation -All the data was clustered in six main groups, corresponding to six intelligence categories. With this, the program can be feed new data on height and weight and relate it to its corresponding intelligence level. +All the data was clustered in six main groups, corresponding to the six intelligence categories. From this a function can be made to input new data on height and weight and get the corresponding intelligence level in return. ## Conclusion -* Summarize your results. What do they mean? -* What can you say about your hypotheses? -* Interpret your findings in terms of the questions you try to answer. +* Relationship between intelligence and height and weight is not obvious to the human eye. +* The hypothesis of their relation should be discarded. +* More information about other physical features may rescue a modified version of the hypothesis. ## Future Work -Address any questions you were unable to answer, or any next steps or future extensions to your project. +The relationship between height, weight and intelligence is not clear, so further investigation should be done to determine if phsysical differences and similarities between breeds are related to ther intelligence differences and similarities. ## Workflow -Outline the workflow you used in your project. What were the steps? -How did you test the accuracy of your analysis and/or machine learning algorithm? +* Looking for topics and general ideas +* I started growing interest in relationship between physical and mental features +* Finding the data on dog intelligence and physical measurements +* Once started the coding, ohter ideas were found and tested ## Organization -How did you organize your work? Did you use any tools like a trello or kanban board? - -What does your repository look like? Explain your folder and file structure. +Trello: + * List of general brainstorm ideas + * To do list + * Tasks in progress + * Tasks already done +Github repository: + * Original datasets on .csv format + * Jupyter notebook file with all the coding + * Graph example image + * ReadMe ## Links -Include links to your repository, slides and trello/kanban board. Feel free to include any other links associated with your project. - -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) +[Repository](https://github.com/Yaguit/Project-Week-8-Final-Project) +[Slides](https://slides.com/srmg/size-intelligence/#/) +[Trello](https://trello.com/b/PCUuBxi8/final-project) From e717b99eec954ecf038bc5181638598b641f8413 Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 18:32:59 +0100 Subject: [PATCH 4/8] Add files via upload --- your-project/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/your-project/README.md b/your-project/README.md index a50f0da2..0ca2594a 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -64,15 +64,15 @@ The relationship between height, weight and intelligence is not clear, so furthe ## Organization Trello: - * List of general brainstorm ideas - * To do list - * Tasks in progress - * Tasks already done +* List of general brainstorm ideas +* To do list +* Tasks in progress +* Tasks already done Github repository: - * Original datasets on .csv format - * Jupyter notebook file with all the coding - * Graph example image - * ReadMe +* Original datasets on .csv format +* Jupyter notebook file with all the coding +* Graph example image +* ReadMe ## Links From 9e9efccc045442366794293fed83a0f13fd748db Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 18:34:33 +0100 Subject: [PATCH 5/8] Add files via upload --- README.md | 82 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 00000000..aa24645f --- /dev/null +++ b/README.md @@ -0,0 +1,82 @@ +Ironhack Logo + +# Size & Intelligence in Dogs +*Santiago Mougán* + +*Data Analytics (BCN - 01/20)* + +## Content +- [Project Description](#project-description) +- [Hypotheses / Questions](#hypotheses-questions) +- [Dataset](#dataset) +- [Cleaning](#cleaning) +- [Analysis](#analysis) +- [Model Training and Evaluation](#model-training-and-evaluation) +- [Conclusion](#conclusion) +- [Future Work](#future-work) +- [Workflow](#workflow) +- [Organization](#organization) +- [Links](#links) + +## Project Description +Analysis and observations on size, mass and intelligence relationships on dogs, and exploration of prediction possibilities between this parameters. + +## Hypotheses / Questions +* Is there a connection between physical features and intelligence? +* Can intelligence be predicted from physical features? + +## Dataset +* Dataset on dog breeds and intelligence measurements from task competences from data.world. +* Dataset on dog breeds related to height and weight from data.world. +* Dataset with the info from the two main tables combined and clean. + +## Cleaning +* The numerical measurements were actually strings, that had to be converted to integers. Also some non numerical characters had to be fixed. +* Height and weight data were presented in pairs (minimun and maximum for each breed), so I converted them to unique (mean) values to ease the analysis. +* Since height and weight data corresponded to specific breeds, the NaNs could be easily investigated and fixed one by one. +* A few outlyiers were actually mistakes (e.g. weight value in the place of the height value), but again, easy to investigate and fix. +* There were NaNs on intelligence measurementes with complete and good data about the tasks response, so it could be assigned to their correct measurements instead of deleted. +* With the intelligence measurements fixed, there is no need for the details about tasks. +* Once height, weight and intelligence classification are related and fixed, there is no need for the breed label. + +## Analysis +* First analysis with plotting of all parameters, getting an obvious relationship between height and weight. +* Further analysis through observation of both physical parameters versus intelligence measurements. +![Height&Weight vs Intelligence categories](https://raw.githubusercontent.com/Yaguit/Project-Week-8-Final-Project/master/your-project/HW-I.PNG) +* The machine learning work was through clustering, since there are six defined intelligence groups. + +## Model Training and Evaluation +All the data was clustered in six main groups, corresponding to the six intelligence categories. From this a function can be made to input new data on height and weight and get the corresponding intelligence level in return. + +## Conclusion +* Relationship between intelligence and height and weight is not obvious to the human eye. +* The hypothesis of their relation should be discarded. +* More information about other physical features may rescue a modified version of the hypothesis. + +## Future Work +The relationship between height, weight and intelligence is not clear, so further investigation should be done to determine if phsysical differences and similarities between breeds are related to ther intelligence differences and similarities. + +## Workflow +* Looking for topics and general ideas +* I started growing interest in relationship between physical and mental features +* Finding the data on dog intelligence and physical measurements +* Once started the coding, ohter ideas were found and tested + +## Organization +Trello: +* List of general brainstorm ideas +* To do list +* Tasks in progress +* Tasks already done + +Github repository: +* Original datasets on .csv format +* Jupyter notebook file with all the coding +* Graph example image +* ReadMe + +## Links + +[Repository](https://github.com/Yaguit/Project-Week-8-Final-Project) +[Slides](https://slides.com/srmg/size-intelligence/#/) +[Trello](https://trello.com/b/PCUuBxi8/final-project) From f061589af054924d2495685b98d4f930c74970e5 Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 18:35:02 +0100 Subject: [PATCH 6/8] Add files via upload --- your-project/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/your-project/README.md b/your-project/README.md index 0ca2594a..aa24645f 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -68,6 +68,7 @@ Trello: * To do list * Tasks in progress * Tasks already done + Github repository: * Original datasets on .csv format * Jupyter notebook file with all the coding From fbf0f3c218118f6e8b7f2cb0ba0b441145f1b630 Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 18:36:05 +0100 Subject: [PATCH 7/8] Delete Untitled.ipynb --- your-project/Untitled.ipynb | 2672 ----------------------------------- 1 file changed, 2672 deletions(-) delete mode 100644 your-project/Untitled.ipynb diff --git a/your-project/Untitled.ipynb b/your-project/Untitled.ipynb deleted file mode 100644 index b23c7be2..00000000 --- a/your-project/Untitled.ipynb +++ /dev/null @@ -1,2672 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import re\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.cluster import KMeans\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import r2_score" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BreedClassificationobeyreps_lowerreps_upper
0Border CollieBrightest Dogs95%14
1PoodleBrightest Dogs95%14
2German ShepherdBrightest Dogs95%14
3Golden RetrieverBrightest Dogs95%14
4Doberman PinscherBrightest Dogs95%14
..................
131BorzoiLowest Degree of Working/Obedience IntelligenceNaN81100
132Chow ChowLowest Degree of Working/Obedience IntelligenceNaN81100
133BulldogLowest Degree of Working/Obedience IntelligenceNaN81100
134BasenjiLowest Degree of Working/Obedience IntelligenceNaN81100
135Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN81100
\n", - "

136 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Breed Classification obey \\\n", - "0 Border Collie Brightest Dogs 95% \n", - "1 Poodle Brightest Dogs 95% \n", - "2 German Shepherd Brightest Dogs 95% \n", - "3 Golden Retriever Brightest Dogs 95% \n", - "4 Doberman Pinscher Brightest Dogs 95% \n", - ".. ... ... ... \n", - "131 Borzoi Lowest Degree of Working/Obedience Intelligence NaN \n", - "132 Chow Chow Lowest Degree of Working/Obedience Intelligence NaN \n", - "133 Bulldog Lowest Degree of Working/Obedience Intelligence NaN \n", - "134 Basenji Lowest Degree of Working/Obedience Intelligence NaN \n", - "135 Afghan Hound Lowest Degree of Working/Obedience Intelligence NaN \n", - "\n", - " reps_lower reps_upper \n", - "0 1 4 \n", - "1 1 4 \n", - "2 1 4 \n", - "3 1 4 \n", - "4 1 4 \n", - ".. ... ... \n", - "131 81 100 \n", - "132 81 100 \n", - "133 81 100 \n", - "134 81 100 \n", - "135 81 100 \n", - "\n", - "[136 rows x 5 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dog_int = pd.read_csv('dog_intelligence.csv')\n", - "dog_int" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Breedheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Akita262880120
1Anatolian Sheepdog2729100150
2Bernese Mountain Dog232785110
3Bloodhound242680120
4Borzoi262870100
..................
145Papillon811510
146Pomeranian121237
147Poodle Toy10101010
148Toy Fox Terrier101047
149Yorkshire Terrier8837
\n", - "

150 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Breed height_low_inches height_high_inches weight_low_lbs \\\n", - "0 Akita 26 28 80 \n", - "1 Anatolian Sheepdog 27 29 100 \n", - "2 Bernese Mountain Dog 23 27 85 \n", - "3 Bloodhound 24 26 80 \n", - "4 Borzoi 26 28 70 \n", - ".. ... ... ... ... \n", - "145 Papillon 8 11 5 \n", - "146 Pomeranian 12 12 3 \n", - "147 Poodle Toy 10 10 10 \n", - "148 Toy Fox Terrier 10 10 4 \n", - "149 Yorkshire Terrier 8 8 3 \n", - "\n", - " weight_high_lbs \n", - "0 120 \n", - "1 150 \n", - "2 110 \n", - "3 120 \n", - "4 100 \n", - ".. ... \n", - "145 10 \n", - "146 7 \n", - "147 10 \n", - "148 7 \n", - "149 7 \n", - "\n", - "[150 rows x 5 columns]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "breed_info = pd.read_csv('breed_info.csv', encoding= 'unicode_escape')\n", - "breed_info" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "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", - " \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", - " \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", - " \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", - "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Border CollieBrightest Dogs95%1419214040
1Golden RetrieverBrightest Dogs95%1421245575
2Doberman PinscherBrightest Dogs95%14262860100
3Labrador RetrieverBrightest Dogs95%1421245580
4PapillonBrightest Dogs95%14811510
..............................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN81100242680120
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN81100262870100
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110019224555
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017172022
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110025275060
\n", - "

105 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " Breed Classification \\\n", - "0 Border Collie Brightest Dogs \n", - "1 Golden Retriever Brightest Dogs \n", - "2 Doberman Pinscher Brightest Dogs \n", - "3 Labrador Retriever Brightest Dogs \n", - "4 Papillon Brightest Dogs \n", - ".. ... ... \n", - "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", - "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", - "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", - "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", - "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", - "\n", - " obey reps_lower reps_upper height_low_inches height_high_inches \\\n", - "0 95% 1 4 19 21 \n", - "1 95% 1 4 21 24 \n", - "2 95% 1 4 26 28 \n", - "3 95% 1 4 21 24 \n", - "4 95% 1 4 8 11 \n", - ".. ... ... ... ... ... \n", - "100 NaN 81 100 24 26 \n", - "101 NaN 81 100 26 28 \n", - "102 NaN 81 100 19 22 \n", - "103 NaN 81 100 17 17 \n", - "104 NaN 81 100 25 27 \n", - "\n", - " weight_low_lbs weight_high_lbs \n", - "0 40 40 \n", - "1 55 75 \n", - "2 60 100 \n", - "3 55 80 \n", - "4 5 10 \n", - ".. ... ... \n", - "100 80 120 \n", - "101 70 100 \n", - "102 45 55 \n", - "103 20 22 \n", - "104 50 60 \n", - "\n", - "[105 rows x 9 columns]" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "breed_int = dog_int.merge(breed_info, left_on = 'Breed', right_on = 'Breed')\n", - "breed_int" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "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", - "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
70Alaskan MalamuteAverage Working/Obedience Intelligence50%2640nananana
\n", - "
" - ], - "text/plain": [ - " Breed Classification obey reps_lower \\\n", - "70 Alaskan Malamute Average Working/Obedience Intelligence 50% 26 \n", - "\n", - " reps_upper height_low_inches height_high_inches weight_low_lbs \\\n", - "70 40 na na na \n", - "\n", - " weight_high_lbs \n", - "70 na " - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "breed_int.loc[breed_int.weight_high_lbs == 'na']" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "breed_int.replace({'height_high_inches':{'na':'25'}, 'height_low_inches':{'na':'25'}, 'weight_high_lbs':{'na':'82'}, 'weight_low_lbs':{'na':'82'}}, inplace= True)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "breed_int['height_low_inches'] = pd.to_numeric(breed_int['height_low_inches'])\n", - "breed_int['height_high_inches'] = pd.to_numeric(breed_int['height_high_inches'])\n", - "breed_int['weight_low_lbs'] = pd.to_numeric(breed_int['weight_low_lbs'])\n", - "breed_int['weight_high_lbs'] = pd.to_numeric(breed_int['weight_high_lbs'])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 105 entries, 0 to 104\n", - "Data columns (total 9 columns):\n", - "Breed 105 non-null object\n", - "Classification 105 non-null object\n", - "obey 96 non-null object\n", - "reps_lower 105 non-null int64\n", - "reps_upper 105 non-null int64\n", - "height_low_inches 105 non-null float64\n", - "height_high_inches 105 non-null float64\n", - "weight_low_lbs 105 non-null int64\n", - "weight_high_lbs 105 non-null int64\n", - "dtypes: float64(2), int64(4), object(3)\n", - "memory usage: 8.2+ KB\n" - ] - } - ], - "source": [ - "breed_int.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "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", - " \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", - " \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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbsHeightWeight
0Border CollieBrightest Dogs95%1419.021.0404020.040.0
1Golden RetrieverBrightest Dogs95%1421.024.0557522.565.0
2Doberman PinscherBrightest Dogs95%1426.028.06010027.080.0
3Labrador RetrieverBrightest Dogs95%1421.024.0558022.567.5
4PapillonBrightest Dogs95%148.011.05109.57.5
....................................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN8110024.026.08012025.0100.0
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN8110026.028.07010027.085.0
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110019.022.0455520.550.0
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017.017.0202217.021.0
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110025.027.0506026.055.0
\n", - "

105 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " Breed Classification \\\n", - "0 Border Collie Brightest Dogs \n", - "1 Golden Retriever Brightest Dogs \n", - "2 Doberman Pinscher Brightest Dogs \n", - "3 Labrador Retriever Brightest Dogs \n", - "4 Papillon Brightest Dogs \n", - ".. ... ... \n", - "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", - "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", - "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", - "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", - "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", - "\n", - " obey reps_lower reps_upper height_low_inches height_high_inches \\\n", - "0 95% 1 4 19.0 21.0 \n", - "1 95% 1 4 21.0 24.0 \n", - "2 95% 1 4 26.0 28.0 \n", - "3 95% 1 4 21.0 24.0 \n", - "4 95% 1 4 8.0 11.0 \n", - ".. ... ... ... ... ... \n", - "100 NaN 81 100 24.0 26.0 \n", - "101 NaN 81 100 26.0 28.0 \n", - "102 NaN 81 100 19.0 22.0 \n", - "103 NaN 81 100 17.0 17.0 \n", - "104 NaN 81 100 25.0 27.0 \n", - "\n", - " weight_low_lbs weight_high_lbs Height Weight \n", - "0 40 40 20.0 40.0 \n", - "1 55 75 22.5 65.0 \n", - "2 60 100 27.0 80.0 \n", - "3 55 80 22.5 67.5 \n", - "4 5 10 9.5 7.5 \n", - ".. ... ... ... ... \n", - "100 80 120 25.0 100.0 \n", - "101 70 100 27.0 85.0 \n", - "102 45 55 20.5 50.0 \n", - "103 20 22 17.0 21.0 \n", - "104 50 60 26.0 55.0 \n", - "\n", - "[105 rows x 11 columns]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "breed_int['Height'] = (0.5 * breed_int['height_low_inches']) + (0.5 * breed_int['height_high_inches'])\n", - "breed_int['Weight'] = (0.5 * breed_int['weight_low_lbs']) + (0.5 * breed_int['weight_high_lbs'])\n", - " \n", - "breed_int" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "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", - " \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", - " \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", - " \n", - "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs95%1420.040.0
1Golden RetrieverBrightest Dogs95%1422.565.0
2Doberman PinscherBrightest Dogs95%1427.080.0
3Labrador RetrieverBrightest Dogs95%1422.567.5
4PapillonBrightest Dogs95%149.57.5
........................
100BloodhoundLowest Degree of Working/Obedience IntelligenceNaN8110025.0100.0
101BorzoiLowest Degree of Working/Obedience IntelligenceNaN8110027.085.0
102Chow ChowLowest Degree of Working/Obedience IntelligenceNaN8110020.550.0
103BasenjiLowest Degree of Working/Obedience IntelligenceNaN8110017.021.0
104Afghan HoundLowest Degree of Working/Obedience IntelligenceNaN8110026.055.0
\n", - "

105 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " Breed Classification \\\n", - "0 Border Collie Brightest Dogs \n", - "1 Golden Retriever Brightest Dogs \n", - "2 Doberman Pinscher Brightest Dogs \n", - "3 Labrador Retriever Brightest Dogs \n", - "4 Papillon Brightest Dogs \n", - ".. ... ... \n", - "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", - "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", - "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", - "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", - "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", - "\n", - " obey reps_lower reps_upper Height Weight \n", - "0 95% 1 4 20.0 40.0 \n", - "1 95% 1 4 22.5 65.0 \n", - "2 95% 1 4 27.0 80.0 \n", - "3 95% 1 4 22.5 67.5 \n", - "4 95% 1 4 9.5 7.5 \n", - ".. ... ... ... ... ... \n", - "100 NaN 81 100 25.0 100.0 \n", - "101 NaN 81 100 27.0 85.0 \n", - "102 NaN 81 100 20.5 50.0 \n", - "103 NaN 81 100 17.0 21.0 \n", - "104 NaN 81 100 26.0 55.0 \n", - "\n", - "[105 rows x 7 columns]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "del breed_int['height_low_inches']\n", - "del breed_int['height_high_inches']\n", - "del breed_int['weight_low_lbs']\n", - "del breed_int['weight_high_lbs']\n", - "breed_int" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "scrolled": true - }, - "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", - " \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", - " \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", - " \n", - "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs951420.040.0
1Golden RetrieverBrightest Dogs951422.565.0
2Doberman PinscherBrightest Dogs951427.080.0
3Labrador RetrieverBrightest Dogs951422.567.5
4PapillonBrightest Dogs95149.57.5
........................
80Sealyham TerrierFair Working/Obedience Intelligence30418012.019.0
81PugFair Working/Obedience Intelligence30418010.518.0
82French BulldogFair Working/Obedience Intelligence30418011.522.5
83MalteseFair Working/Obedience Intelligence3041809.05.0
84Italian GreyhoundFair Working/Obedience Intelligence30418013.58.0
\n", - "

85 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " Breed Classification obey reps_lower \\\n", - "0 Border Collie Brightest Dogs 95 1 \n", - "1 Golden Retriever Brightest Dogs 95 1 \n", - "2 Doberman Pinscher Brightest Dogs 95 1 \n", - "3 Labrador Retriever Brightest Dogs 95 1 \n", - "4 Papillon Brightest Dogs 95 1 \n", - ".. ... ... ... ... \n", - "80 Sealyham Terrier Fair Working/Obedience Intelligence 30 41 \n", - "81 Pug Fair Working/Obedience Intelligence 30 41 \n", - "82 French Bulldog Fair Working/Obedience Intelligence 30 41 \n", - "83 Maltese Fair Working/Obedience Intelligence 30 41 \n", - "84 Italian Greyhound Fair Working/Obedience Intelligence 30 41 \n", - "\n", - " reps_upper Height Weight \n", - "0 4 20.0 40.0 \n", - "1 4 22.5 65.0 \n", - "2 4 27.0 80.0 \n", - "3 4 22.5 67.5 \n", - "4 4 9.5 7.5 \n", - ".. ... ... ... \n", - "80 80 12.0 19.0 \n", - "81 80 10.5 18.0 \n", - "82 80 11.5 22.5 \n", - "83 80 9.0 5.0 \n", - "84 80 13.5 8.0 \n", - "\n", - "[85 rows x 7 columns]" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "breed_int.head(85)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "breed_int['obey'] = breed_int['obey'].fillna('15%')" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "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", - " \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", - "
BreedClassificationobeyreps_lowerreps_upperHeightWeight
0Border CollieBrightest Dogs951420.040.0
1Golden RetrieverBrightest Dogs951422.565.0
2Doberman PinscherBrightest Dogs951427.080.0
3Labrador RetrieverBrightest Dogs951422.567.5
4PapillonBrightest Dogs95149.57.5
\n", - "
" - ], - "text/plain": [ - " Breed Classification obey reps_lower reps_upper Height \\\n", - "0 Border Collie Brightest Dogs 95 1 4 20.0 \n", - "1 Golden Retriever Brightest Dogs 95 1 4 22.5 \n", - "2 Doberman Pinscher Brightest Dogs 95 1 4 27.0 \n", - "3 Labrador Retriever Brightest Dogs 95 1 4 22.5 \n", - "4 Papillon Brightest Dogs 95 1 4 9.5 \n", - "\n", - " Weight \n", - "0 40.0 \n", - "1 65.0 \n", - "2 80.0 \n", - "3 67.5 \n", - "4 7.5 " - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def percentage_sign_remover(string):\n", - " return int(re.sub('%', '', string))\n", - "\n", - "breed_int['obey'] = breed_int['obey'].apply(percentage_sign_remover)\n", - "breed_int.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 105 entries, 0 to 104\n", - "Data columns (total 7 columns):\n", - "Breed 105 non-null object\n", - "Classification 105 non-null object\n", - "obey 105 non-null int64\n", - "reps_lower 105 non-null int64\n", - "reps_upper 105 non-null int64\n", - "Height 105 non-null float64\n", - "Weight 105 non-null float64\n", - "dtypes: float64(2), int64(3), object(2)\n", - "memory usage: 6.6+ KB\n" - ] - } - ], - "source": [ - "breed_int.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BreedClassificationResponsivityHeightWeight
0Border CollieBrightest Dogs9520.040.0
1Golden RetrieverBrightest Dogs9522.565.0
2Doberman PinscherBrightest Dogs9527.080.0
3Labrador RetrieverBrightest Dogs9522.567.5
4PapillonBrightest Dogs959.57.5
..................
100BloodhoundLowest Degree of Working/Obedience Intelligence1525.0100.0
101BorzoiLowest Degree of Working/Obedience Intelligence1527.085.0
102Chow ChowLowest Degree of Working/Obedience Intelligence1520.550.0
103BasenjiLowest Degree of Working/Obedience Intelligence1517.021.0
104Afghan HoundLowest Degree of Working/Obedience Intelligence1526.055.0
\n", - "

105 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Breed Classification \\\n", - "0 Border Collie Brightest Dogs \n", - "1 Golden Retriever Brightest Dogs \n", - "2 Doberman Pinscher Brightest Dogs \n", - "3 Labrador Retriever Brightest Dogs \n", - "4 Papillon Brightest Dogs \n", - ".. ... ... \n", - "100 Bloodhound Lowest Degree of Working/Obedience Intelligence \n", - "101 Borzoi Lowest Degree of Working/Obedience Intelligence \n", - "102 Chow Chow Lowest Degree of Working/Obedience Intelligence \n", - "103 Basenji Lowest Degree of Working/Obedience Intelligence \n", - "104 Afghan Hound Lowest Degree of Working/Obedience Intelligence \n", - "\n", - " Responsivity Height Weight \n", - "0 95 20.0 40.0 \n", - "1 95 22.5 65.0 \n", - "2 95 27.0 80.0 \n", - "3 95 22.5 67.5 \n", - "4 95 9.5 7.5 \n", - ".. ... ... ... \n", - "100 15 25.0 100.0 \n", - "101 15 27.0 85.0 \n", - "102 15 20.5 50.0 \n", - "103 15 17.0 21.0 \n", - "104 15 26.0 55.0 \n", - "\n", - "[105 rows x 5 columns]" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint = breed_int.rename(columns={'obey': 'Responsivity'})\n", - "del brint['reps_lower']\n", - "del brint['reps_upper']\n", - "brint" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Breed Vizsla\n", - "Classification Excellent Working Dogs\n", - "Responsivity 85\n", - "Height 23.5\n", - "Weight 57\n", - "Name: 19, dtype: object" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint.Height.loc[19] = 23.5\n", - "brint.Weight.loc[19] = 57\n", - "brint.Height.loc[92] = 29.5\n", - "brint.loc[19]" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Breed Saint Bernard\n", - "Classification Fair Working/Obedience Intelligence\n", - "Responsivity 30\n", - "Height 29.5\n", - "Weight 150\n", - "Name: 92, dtype: object" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint.loc[92]" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(brint)" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEJCAYAAACE39xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAZMklEQVR4nO3df7RdZX3n8feXJJIA0SAJFIwQltIqWo02pHSAKv5AoA7qkIqIlrHOZNaMWUSqVmZNl9rOzBKnFRjGGV1YGNAqtBUFOloVGSgVU8cEQviRIj+MGBLhAiqBJOTXd/549oXD5d57ft9zsu/7tdZZ99k/nuc+Z99zPmfvZ++zb2QmkqS93z6D7oAkqTcMdEmqCQNdkmrCQJekmjDQJakmDHRJqomZU/nL5s+fn4sWLZrKXylJe701a9Y8mpkLmq03pYG+aNEiVq9ePZW/UpL2ehHx01bWc8hFkmrCQJekmjDQJakmpnQMXZIa7dy5k40bN7J9+/ZBd2UozJ49m4ULFzJr1qyO6hvokgZm48aNzJ07l0WLFhERg+7OQGUmjz32GBs3buTII4/sqA2HXCQNzPbt2znooIOmfZgDRAQHHXRQV0crBrqkgTLMn9XttjDQJU1rBxxwwHOmL7/8clasWDFpneuuu47zzz9/0nVuuukm3v72t4+77KKLLmLr1q3tdbQFjqFLe6FF530TgA3n/96Ae9Jbo8+rV/q1fU477TROO+20jutfdNFFvO9972O//fbrYa/cQ5ekCY2MjHD66adzzDHHcMwxx3DLLbcAz92Lv//++zn22GM55phj+MQnPvGcPf4nn3ySZcuW8YpXvIKzzjqLzOTiiy9m06ZNnHjiiZx44ok97a976JKmtW3btrF48eJnph9//PFn9r5XrlzJueeey/HHH8+DDz7I2972NtavX/+c+itXrmTlypWceeaZfOELX3jOsttuu4277rqLww47jOOOO45bbrmFc845hwsuuIAbb7yR+fPn9/S5GOiqhUXnfbN2ww+aGnPmzGHt2rXPTF9++eXP3HPqe9/7Hnffffczy5544gm2bNnynPqrVq3immuuAeC9730vH/3oR59ZtnTpUhYuXAjA4sWL2bBhA8cff3zfnouBLkkT2LNnD6tWrWLOnDkd1d93332fKc+YMYNdu3b1qmvjcgxdkiZw0kkn8bnPfe6Z6cY9+VHHHnssV199NQBXXXVVS+3OnTv3eXv6vWCgS9IELr74YlavXs1rXvMajj766OeNkUO5YuWCCy5g6dKlbN68mRe96EVN212+fDmnnHJKz0+KRmb2tMHJLFmyJL0fuvphuo2h1+WyxfXr1/PKV75y0N3oytatW5kzZw4RwVVXXcWVV17Jtdde23F7422TiFiTmUua1XUMXZK6sGbNGlasWEFmMm/ePC677LKB9aVpoEfEbOBmYN9q/a9l5icj4kjgKuDFwK3A+zNzRz87K0nD5oQTTuD2228fdDeA1sbQnwbelJmvBRYDJ0fEscBngAsz8yjgF8AH+9dNSVIzTQM9iyeryVnVI4E3AV+r5l8BvLMvPZRUa1N5Hm/YdbstWrrKJSJmRMRa4BHgeuB+4JeZOXpR5UbgJV31RNK0M3v2bB577DFDnWfvhz579uyO22jppGhm7gYWR8Q84BvAeKelx/2LRMRyYDnA4Ycf3mE3pemp7lfvLFy4kI0bNzIyMjLorgyF0f9Y1Km2rnLJzF9GxE3AscC8iJhZ7aUvBDZNUOcS4BIoly123FNJtTNr1qyO/zuPnq/pkEtELKj2zImIOcBbgPXAjcCyarWzgc4vvJQkda2VMfRDgRsjYh3wI+D6zPw/wMeBP4qI+4CDgEv7101J6s5E91pfdN43e34f9kFpOuSSmeuA140z/wFgaT86JUlqn/dykaSaMNBVO3U5fJbaZaBLUk0Y6JJUEwa6JNWEgS4NQJ0uldOzBv13NdAlqSYMdEmqCQNdkmrCQJekmjDQJakmDHRJqgkDXZJqwkCXpJoYmkAf9AX505HbW73m+7g1/dpGQxPokqTuGOiSVBMGuiTVhIE+5ByTlNQqA12SasJAl6SaMNCbcMijfVOxzfy7aKpN9eutk9e4gS5JNWGgS1JNNA30iHhpRNwYEesj4q6IWFnN/1REPBQRa6vHqf3vrtQ5h2la07iN3F57l5ktrLML+Ehm3hoRc4E1EXF9tezCzPyL/nVPktSqpoGemZuBzVV5S0SsB17S745JktrT1hh6RCwCXgf8sJq1IiLWRcRlEXHgBHWWR8TqiFg9MjLSVWeHzTAdjvarL/0apnD4Y+q5zQdrKrZ9y4EeEQcAVwMfzswngM8DLwMWU/bgPztevcy8JDOXZOaSBQsW9KDLkqTxtBToETGLEuZfycyvA2Tmw5m5OzP3AF8Elvavm5KkZlq5yiWAS4H1mXlBw/xDG1Z7F3Bn77snSWpVK3voxwHvB9405hLF/xYRd0TEOuBE4Nx+dnSUY4CSNL5WrnL5PhDjLPpW77sjSeqU3xSVpJqofaD3+lIth3ymFy/1096k9oEuSdOFgS5JNTGtA30qDqUnO2T3UL7+xt7oyr+5+mlaB7ok1YmBLkk1YaBrKDgU0ZxDNmrGQJekmjDQJakmBhroEx1CemipqebrTXXgHrok1YSBLkk1YaBLUk0Y6NKAec5IvWKgS1JNGOiSVBMG+l6kk0NzD+f7w+1aT3v739VAl6SaMNAlqSYMdLVsEIeje/shsPrD18X4DHRJqgkDXZJqommgR8RLI+LGiFgfEXdFxMpq/osj4vqIuLf6eWD/uyu1x0NzDbtevkZb2UPfBXwkM18JHAt8KCKOBs4DbsjMo4AbqmlJ0oA0DfTM3JyZt1blLcB64CXAO4ArqtWuAN7Zr05Kkppraww9IhYBrwN+CBySmZuhhD5wcK87J0lqXcuBHhEHAFcDH87MJ9qotzwiVkfE6pGRkb6NZzpWKrXO90s9tRToETGLEuZfycyvV7MfjohDq+WHAo+MVzczL8nMJZm5ZMGCBb3osyRpHK1c5RLApcD6zLygYdF1wNlV+Wzg2t53T5LUqlb20I8D3g+8KSLWVo9TgfOBt0bEvcBbq2npOTq9oZik9s1stkJmfh+ICRa/ubfdkSR1ym+KSlJNGOjj8AqA9rm99l6+3uvDQJekmjDQJakm9upA9zBRGgyHaYbTXh3okqRnGeiSVBMGuiTVxLQL9InG/abDmGCrz3E6bAupjqZdoEtSXRnoklQTBnoNOWTSnNtIdWSgS1JNGOiSVBMGuiQNiW6HAg10SaoJA12SasJA7wP/7Vo9eWWMhp2BLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNNA30iLgsIh6JiDsb5n0qIh6KiLXV49T+dlPyskGNz9fFs1rZQ78cOHmc+Rdm5uLq8a3edkuS1K6mgZ6ZNwOPT0FfJEld6GYMfUVErKuGZA6caKWIWB4RqyNi9cjISBe/TlK7ejkU4bDG8Os00D8PvAxYDGwGPjvRipl5SWYuycwlCxYs6PDXSZKa6SjQM/PhzNydmXuALwJLe9stSVK7Ogr0iDi0YfJdwJ0TrStpak310IhXmYxvENtlZrMVIuJK4I3A/IjYCHwSeGNELAYS2AD8uz72UZLUgqaBnplnjjP70j70RZLUBb8pKkldGKYhJwNdkmrCQJekmjDQJakmDPS92LCM20n90u1rfLq9Rwx0SaoJA12SasJAl6SaMNAlqSYMdEmqCQNdGmO6XRmh+jDQJakmDHRJqgkDvUeG6QY90jDyS0L9Z6BLUk0Y6JJUEwa6JNVELQPd8Wxp7+H7tXdqGeiSNB0Z6JJUEwb6FPPQUs34Gum/ug7zGOiSVBMGuiTVRNNAj4jLIuKRiLizYd6LI+L6iLi3+nlgf7upOqjrYW5dn1c/uc36o5U99MuBk8fMOw+4ITOPAm6opiVJA9Q00DPzZuDxMbPfAVxRla8A3tnjfkmS2tTpGPohmbkZoPp58EQrRsTyiFgdEatHRkY6/HXqNw9/e8dt+Sy3xdTq+0nRzLwkM5dk5pIFCxb0+9dJ0rTVaaA/HBGHAlQ/H+ldlyRJneg00K8Dzq7KZwPX9qY7kqROtXLZ4pXAKuA3ImJjRHwQOB94a0TcC7y1mu4bx+GGk38XDZupuhxyWF/7M5utkJlnTrDozT3uiySpC35TVJJqwkCXNDT8Bml3DHRJqgkDXZJqwkCX9nIOU2iUgS5JNWGgS1JNGOhDyENoSZ0w0CWpJgx0SaoJA12SasJAl6Rx7I3nsgx0SaoJA12SasJAl6SaMNAlqSYMdEmqCQNdkmrCQJekmjDQJakmDHRJqgkDXZJqwkCXpJqY2U3liNgAbAF2A7syc0kvOiVJal9XgV45MTMf7UE7kqQuOOQiSTXRbaAn8N2IWBMRy3vRIUlSZ7odcjkuMzdFxMHA9RHxz5l5c+MKVdAvBzj88MOJLn+hJGl8Xe2hZ+am6ucjwDeApeOsc0lmLsnMJQsWLOjm10mSJtFxoEfE/hExd7QMnATc2auOSZLa082QyyHANyJitJ2vZua3e9IrSVLbOg70zHwAeG0P+yJJ6oKXLUpSTRjoklQTBrok1YSBLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNGOiSVBMGuiTVhIEuSTVhoEtSTRjoklQTBrok1YSBLkk1YaBLUk0Y6JJUEwa6JNWEgS5JNWGgS1JNGOiSVBMGuiTVhIEuSTXRVaBHxMkRcU9E3BcR5/WqU5Kk9nUc6BExA/ifwCnA0cCZEXF0rzomSWpPN3voS4H7MvOBzNwBXAW8ozfdkiS1KzKzs4oRy4CTM/PfVNPvB347M1eMWW85sLya/A3gHmA+8Gg1r5vydK8/TH3xuQxnX3wu9dgWR2TmAprJzI4ewO8Df9kw/X7gf7RYd3UvytO9/jD1xecynH3xudRnW7Ty6GbIZSPw0obphcCmLtqTJHWhm0D/EXBURBwZES8A3gNc15tuSZLaNbPTipm5KyJWAN8BZgCXZeZdLVa/pEfl6V5/mPrSbf1h6ku39YepL93WH6a+DLr+oPvSVMcnRSVJw8VvikpSTRjoklQTBrok1YSBLkk10fFVLq2IiJMz89tV+V8BvwccQ/m26EGUb0HdCVwPHF8t+yXwM+A3Jyn/HBipyncBTwEHV239HfBmyq0JRijXy79ukvLdwIuAl/S5/v3V83st5aqg7cAeICgfrLOrn3uqzddOeTuwu4u2Bl1/mPric+murT1AAluBncALgKeBxyiv+6jaObBq4+lq/XbK3bbVSf3HKN+zuRa4NDN3Moza+RZSuw/g1obyo8B/AY4A/onyAjkCOJeywUaXPUUJ/FbLNwI7gDcAFwLbgMur6a3AfU3Ka6q+9Lv+r6pt8K5qvYeA/1w9HgU+TvmA+jFwbxvlj1f1O21r0PWHqS8+l+7aWkvZ6TqVsqO1Cfgg8CXKjs3V1WNjNe+DwE+ADW2Uu22rk/pfAlYBxwKfB24BLgXWATcDXxmn/LfA/63KXwU+0EKdsfW/0lD/kJYydwoDfWtDee2Y6T2N6wFr2yg/0xblE3cPMKthvXVNymuBbVNQf3tDOYDtDc95R0P5HuCeVsvj1G+rrUHXH6a++FwG1pcft1rutq1O6o++fxvKu2m+A9rtTus/NZTPBa4Z/f2TPfo65AIcHBF/RAmwFzSUXwjMbJiOxvWqeh+ZpDwLmBcRpwP7AjOqIZ2ofu+/jIigDCm9sFpvovK+wD5TUH8XMD8iDsnMhyNiZ0T8WVVnS0T8MfDNhu3xqhbLb6/qd9rWoOsPU198Lt21NQf4ZUT8WkN5BrAMeDoi3k0Zmnk6In4f+Ho1PXo77lbK3bbVSf1lwO6I2IdyDysy80+q5UHZofvpmPJsyo7qT4ELI+KzLdQZW39bQ/2zaUFfv1gUEZ9smHwD5XYBW4H9KYdlfwscALyPclizFXgrMK9aNlF5WdXGP1BeOL9NGXrZDzgNuIbyifiGat7fT1KeA7wR+Faf67+QMq7+FPBrVf9/RXlDzKR8YO1DGbejmt9qeTvlA6PTtgZdf5j64nPpvK3Rn/sAT1LGqfenvOZ/QTnPBfAIZdx6f8p7Hsr7pJVyt211Uv8pyvt8G+Vc2KuBP66e62eqNj47pvwhyv2tzqvW+wvgI03qjK3/ZGa+BiAi1o2WJ9PXPfTM/NOIeDll3PgOyknQeZROf4cyPPEE8DcNy+6gvFheM0n5B8DhlED8MOU+7H9AOUG5gLLxDgX+O+WfbxwySfn6qi9n9bn+zmr+UcB6ylHG1Zn5s4g4B/hGJ2WAvbn+ZG2NfT1FxAmUD8U7KG+uScuZ+d126/Sy/jD1ZQqfyyLKa/4fKRc+vIryfn0IeDfwAPBl4PSGZS9qs9xtW53UP4kS0CPA45T39Fbgh9V6B4wpX0355z9zq+mft1BnbP3DAaojnrW0opVxmU4fwDnAd4E/qTbez4Cbqie1rpr3COXkyeiyHdW8ycq7KCcz7qVc/fIIZczpZ8CWatmtVfmpJuX7KXsT/a6/u+r3PZQTLDuq5/2PlDfEzzss/3vKh2KnbQ26/mRtbQDmV6+lnwC3AZ+k7C0+NE75vmq7fpJy4urxFur0sv4w9WUQz+Wxar2HKDtbo8serOqMvj82VMsforx/t7dR7ratTuo/SHmff4Jy4vI/jcm5D7RT7qTO2PoTZm6fA/0OYEZVvhO4qSr/OiXgZlA+hbY1LLsH2NKk/M/VH2Mm5RNz9KTkTMoh4IyqvJPywTFZ+eEprn8S5Wx3At8G/rx6gY1QhqS2UK4iaLX8D5QPik7bGnT9Vtr6NmUIa1G1nbcCd49TXtPwt9ifcgS4oEmdXtYfpr4M4rlsoxyl7kd5f99VlZ+ghOMM4MWU1/68hvXubKPcbVud1H8CuLN6jnOoLm5oyLkH2yl3Umds/YkeU/HFotFhndGToVDepAHMzMwHxyzb1VBnovKeqjyXcp1oRLmF7/xq+fxq2T6Uk5aTlfejnNSckvqZ+V3K+Nh24H9RDuPmAodRLgnbSXmRtVreVJU7bWvQ9Sdr64WUw/cvU96AqyPioGob7xqnnAARMXpd8UTr9av+MPVlEM8lgaczcyvlA3hHVb6fcoJwd2Y+DmRm/rJhvV1tlLttq5P69wMvi4h1lGGRV0fEtuqxB3jpOOVtE5UnqTO2/sKIWBcRd1CGa5rr8x76Sspe6SWUPdmfVeV7q+l1lJOhOxqWjVDCrll5B/BT4D9S9hKeqObdTdlT2EI5lNrZpLyJcqjY7/o7KUNMX6QcuWxu2E5rG8q3AXNaLY9Tv622Bl2/SVs/rbbZT6rt92BDecc45Z9U5Qcoh9JZtTFZnV7WH6a+DOK5bAfuqv52PwJur8rzKEOP+1HGiJ8C9quWrQZua6PcbVud1J9XPc/FlBOiuyjj4/+CchT52Djl4yh7+aeMKU9WZ2z9hymXLS4CNrWUuf0M9GpjvIpyVcorGsuTLWuxfBhwWMMG/7fAqdX0K6rppS2Wp6L+6dV6y6r5v96wjTou7+31J2trktfUfsCRrZY7qdPL+sPUlz4/l3kTlOcDrx9brqYPA36zjXK3bXVSfz7lssbjG8vVskuB68eWq+n7G9a7v1mdcep/dbzyZA/vhy5JNeHNuSSpJgx0SaoJA11DJSJ2R8TaiLgzIv4uIuYNuk/jiYhvTda3iDgsIr5WlRdHxKlT1ztNV46ha6hExJOZeUBVvoJyg6T/OuBudSUi/jWwJDNXDLovqjf30DXMVlHuMw9ARHwsIn5UXZv7p9W8/SPimxFxe7VXf0Y1f0NEfCYi/l/1eHk1/4iIuKFq44aIGP169eURcXFE/CAiHoiIZdX8QyPi5oajhhMa2p9f/Y7/0NDHT0XERyJiUbX+C4A/A86o2jgjIu6NiAXV+vtExH0RMfodBqljBrqGUpS73L0ZuK6aPolyH5yllOuBfysifhc4mXKN7msz89WUb5WOeiIzlwKfAy6q5n0O+FKWGx19Bbi4Yf1DKZemvR04v5r3XuA7mbmY8s9Jxt5T4yrgjIbpd1NuIAdAZu6gfGX8rzNzcWb+NfBXlHv/ALyFcr32o61uG2kiBrqGzZyIWEv5gsWLKTc/g3LLhJMoX0S6lXJ9/1GUL2m9pdpTPiEzf9XQ1pUNP3+nKv8O5R8GQPkG6vEN61+TmXsy826e/Wbej4APRMSnKNclb2nsbGbeRrmt82ER8VrgF1m+/TyZyyg3kwP4Q+B/N1lfaomBrmGzrdobPoJyd80PVfMD+HS1l7s4M1+emZdm5o+B36IE+6cj4hMNbeUEZSaY/3RDOQAy82bgdyk3afpyRPwBz/c1yhfGzqDssU8qy50kH46IN1Fu/fz3zepIrTDQNZSqPe1zgI9GxCzKLYr/MCJGT5i+JCIOjojDKP+x6q8otyd+fUMzZzT8XFWVfwC8pyqfBXx/sn5ExBHAI5n5Rco3+V4/zmpXVW0uo4T7WFso97hp9JeUoZe/yczdk/VBalW//2OR1LHMvC0ibgfek5lfjohXAqsiAsoti98HvBz48+rGRjspt+AdtW9E/JCy43JmNe8c4LKI+BjlnkAfaNKNNwIfi4id1e983h56Zt4VEXOBhzJz8zht3AicVw0lfboaR7+OMtTicIt6xssWVUsRsYFyqeBQnmyMiCXAhZl5wqD7ovpwD12aYhFxHuVI4qxm60rtcA9dkmrCk6KSVBMGuiTVhIEuSTVhoEtSTRjoklQTBrok1cT/Bw9TyWt5vsonAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "brint.plot('Responsivity', y='Height')\n", - "brint.plot('Responsivity', y='Weight')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "grouped = brint.groupby('Responsivity', as_index=False).mean()\n", - "\n", - "plt.plot(grouped['Height'], grouped['Responsivity'], c='b')\n", - "plt.plot(grouped['Weight'], grouped['Responsivity'], c='r')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "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", - " \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", - "
BreedClassificationResponsivityHeightWeight
0Border CollieBrightest Dogs9520.040.0
1Golden RetrieverBrightest Dogs9522.565.0
2Doberman PinscherBrightest Dogs9527.080.0
3Labrador RetrieverBrightest Dogs9522.567.5
4PapillonBrightest Dogs959.57.5
5RottweilerBrightest Dogs9524.5100.0
6Australian Cattle DogBrightest Dogs9518.540.0
\n", - "
" - ], - "text/plain": [ - " Breed Classification Responsivity Height Weight\n", - "0 Border Collie Brightest Dogs 95 20.0 40.0\n", - "1 Golden Retriever Brightest Dogs 95 22.5 65.0\n", - "2 Doberman Pinscher Brightest Dogs 95 27.0 80.0\n", - "3 Labrador Retriever Brightest Dogs 95 22.5 67.5\n", - "4 Papillon Brightest Dogs 95 9.5 7.5\n", - "5 Rottweiler Brightest Dogs 95 24.5 100.0\n", - "6 Australian Cattle Dog Brightest Dogs 95 18.5 40.0" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint.loc[brint.Responsivity == 95]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "#ml predictions on any given size" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([95, 85, 70, 50, 30, 15], dtype=int64)" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint.Responsivity.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Brightest Dogs', 'Excellent Working Dogs',\n", - " 'Above Average Working Dogs',\n", - " 'Average Working/Obedience Intelligence',\n", - " 'Fair Working/Obedience Intelligence',\n", - " 'Lowest Degree of Working/Obedience Intelligence '], dtype=object)" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint.Classification.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Border Collie', 'Golden Retriever', 'Doberman Pinscher',\n", - " 'Labrador Retriever', 'Papillon', 'Rottweiler',\n", - " 'Australian Cattle Dog', 'English Springer Spaniel', 'Schipperke',\n", - " 'Belgian Sheepdog', 'Keeshond', 'German Shorthaired Pointer',\n", - " 'Standard Schnauzer', 'Brittany', 'Weimaraner', 'Belgian Malinois',\n", - " 'Bernese Mountain Dog', 'Pomeranian', 'Irish Water Spaniel',\n", - " 'Vizsla', 'Cardigan Welsh Corgi', 'Chesapeake Bay Retriever',\n", - " 'Puli', 'Yorkshire Terrier', 'Giant Schnauzer',\n", - " 'Portuguese Water Dog', 'Border Terrier', 'Briard',\n", - " 'Welsh Springer Spaniel', 'Samoyed', 'Field Spaniel',\n", - " 'Newfoundland', 'Australian Terrier',\n", - " 'American Staffordshire Terrier', 'Gordon Setter',\n", - " 'Bearded Collie', 'Cairn Terrier', 'Kerry Blue Terrier',\n", - " 'Irish Setter', 'Norwegian Elkhound', 'Affenpinscher',\n", - " 'English Setter', 'Pharaoh Hound', 'Clumber Spaniel', 'Dalmatian',\n", - " 'Bedlington Terrier', 'Curly Coated Retriever', 'Irish Wolfhound',\n", - " 'Kuvasz', 'Australian Shepherd', 'Saluki', 'Finnish Spitz',\n", - " 'Pointer', 'Cavalier King Charles Spaniel',\n", - " 'German Wirehaired Pointer', 'American Water Spaniel',\n", - " 'Siberian Husky', 'Bichon Frise', 'Tibetan Spaniel',\n", - " 'English Foxhound', 'American Foxhound', 'Greyhound',\n", - " 'Wirehaired Pointing Griffon', 'West Highland White Terrier',\n", - " 'Scottish Deerhound', 'Boxer', 'Great Dane', 'Dachshund',\n", - " 'Shiba Inu', 'Staffordshire Bull Terrier', 'Alaskan Malamute',\n", - " 'Whippet', 'Chinese Shar Pei', 'Rhodesian Ridgeback',\n", - " 'Ibizan Hound', 'Welsh Terrier', 'Irish Terrier', 'Boston Terrier',\n", - " 'Akita', 'Skye Terrier', 'Sealyham Terrier', 'Pug',\n", - " 'French Bulldog', 'Maltese', 'Italian Greyhound',\n", - " 'Chinese Crested', 'Dandie Dinmont Terrier', 'Tibetan Terrier',\n", - " 'Japanese Chin', 'Lakeland Terrier', 'Great Pyrenees',\n", - " 'Scottish Terrier', 'Saint Bernard', 'Bull Terrier', 'Chihuahua',\n", - " 'Bullmastiff', 'Shih Tzu', 'Basset Hound', 'Mastiff', 'Beagle',\n", - " 'Bloodhound', 'Borzoi', 'Chow Chow', 'Basenji', 'Afghan Hound'],\n", - " dtype=object)" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brint.Breed.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pre_ml_data = brint[['Classification','Height','Weight']].copy()\n", - "\n", - "sns.pairplot(data= pre_ml_data, hue='Classification')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [], - "source": [ - "# pre_ml_data.replace({'Classification':{'Brightest Dogs': 'Brightest', 'Excellent Working Dogs': 'Excellent',\n", - "# 'Above Average Working Dogs': 'Above_Average',\n", - "# 'Average Working/Obedience Intelligence': 'Average',\n", - "# 'Fair Working/Obedience Intelligence': 'Below_Average',\n", - "# 'Lowest Degree of Working/Obedience Intelligence ': 'Lowest'}}, inplace= True)\n", - "# pre_ml_data = pd.get_dummies(ml_data)\n", - "# \n", - "# pre_ml_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "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", - " \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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ClassificationHeightWeight
0020.040.0
1022.565.0
2027.080.0
3022.567.5
409.57.5
5024.5100.0
6018.540.0
7120.050.0
8111.515.0
9124.067.5
10118.042.5
11123.565.0
12118.033.0
13119.035.0
14126.077.5
15124.062.5
16125.097.5
17112.05.0
18116.555.0
19123.557.0
\n", - "
" - ], - "text/plain": [ - " Classification Height Weight\n", - "0 0 20.0 40.0\n", - "1 0 22.5 65.0\n", - "2 0 27.0 80.0\n", - "3 0 22.5 67.5\n", - "4 0 9.5 7.5\n", - "5 0 24.5 100.0\n", - "6 0 18.5 40.0\n", - "7 1 20.0 50.0\n", - "8 1 11.5 15.0\n", - "9 1 24.0 67.5\n", - "10 1 18.0 42.5\n", - "11 1 23.5 65.0\n", - "12 1 18.0 33.0\n", - "13 1 19.0 35.0\n", - "14 1 26.0 77.5\n", - "15 1 24.0 62.5\n", - "16 1 25.0 97.5\n", - "17 1 12.0 5.0\n", - "18 1 16.5 55.0\n", - "19 1 23.5 57.0" - ] - }, - "execution_count": 193, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pre_ml_data.replace({'Classification':{'Brightest Dogs': 0, 'Excellent Working Dogs': 1,\n", - " 'Above Average Working Dogs': 2,\n", - " 'Average Working/Obedience Intelligence': 3,\n", - " 'Fair Working/Obedience Intelligence': 4,\n", - " 'Lowest Degree of Working/Obedience Intelligence ': 5}}, inplace= True)\n", - "\n", - "pre_ml_data.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [], - "source": [ - "ml_data = pre_ml_data[['Height','Weight']].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], - "source": [ - "model = KMeans(n_clusters=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": {}, - "outputs": [], - "source": [ - "clusters = model.fit(ml_data)\n", - "ml_data['Kmeans_labels'] = clusters.fit_predict(ml_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "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", - " \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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
HeightWeightKmeans_labels
020.040.04
122.565.00
227.080.00
322.567.50
49.57.55
524.5100.03
618.540.04
720.050.04
811.515.05
924.067.50
1018.042.54
1123.565.00
1218.033.01
1319.035.01
1426.077.50
1524.062.50
1625.097.53
1712.05.05
1816.555.04
1923.557.04
\n", - "
" - ], - "text/plain": [ - " Height Weight Kmeans_labels\n", - "0 20.0 40.0 4\n", - "1 22.5 65.0 0\n", - "2 27.0 80.0 0\n", - "3 22.5 67.5 0\n", - "4 9.5 7.5 5\n", - "5 24.5 100.0 3\n", - "6 18.5 40.0 4\n", - "7 20.0 50.0 4\n", - "8 11.5 15.0 5\n", - "9 24.0 67.5 0\n", - "10 18.0 42.5 4\n", - "11 23.5 65.0 0\n", - "12 18.0 33.0 1\n", - "13 19.0 35.0 1\n", - "14 26.0 77.5 0\n", - "15 24.0 62.5 0\n", - "16 25.0 97.5 3\n", - "17 12.0 5.0 5\n", - "18 16.5 55.0 4\n", - "19 23.5 57.0 4" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ml_data.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [], - "source": [ - "ml_data['True_labels'] = pre_ml_data['Classification']" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(18, 8))\n", - "ax1.scatter(ml_data['Height'], ml_data['Weight'], c=ml_data['Kmeans_labels'])\n", - "ax2.scatter(ml_data['Height'], ml_data['Weight'], c=ml_data['True_labels'])\n", - "ax1.set_title('K-means clusters')\n", - "ax2.set_title('True clusters')\n", - "ax1.set_xlabel('Height (ins)')\n", - "ax2.set_xlabel('Height (ins)')\n", - "ax1.set_ylabel('Weight (lbs)')\n", - "ax2.set_ylabel('Weight (lbs)')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4 27\n", - "0 23\n", - "5 21\n", - "1 21\n", - "3 10\n", - "2 3\n", - "Name: Kmeans_labels, dtype: int64" - ] - }, - "execution_count": 203, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ml_data.Kmeans_labels.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [], - "source": [ - "test_df = pd.DataFrame({'Height': [26, 30], 'Weight': [100, 120]})\n", - "test_df['Kmeans_labels'] = clusters.predict(test_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "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", - "
HeightWeightKmeans_labels
0261003
1301203
\n", - "
" - ], - "text/plain": [ - " Height Weight Kmeans_labels\n", - "0 26 100 3\n", - "1 30 120 3" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def imaginary_dog \n", - " jumps = int(input\n", - " if 0 >= jumps >= -95:\n", - " coded_index.append(i+jumps)\n", - " #print(f'Encrypting the message <{message}> with <{jumps}> jumps.')\n", - " test_df = pd.DataFrame({'Height': [26, 30], 'Weight': [100, 120]})\n", - " test_df['Kmeans_labels'] = clusters.predict(test_df)" - ] - } - ], - "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": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 579dcf952e00b9ebe2847bf195f1b032f725aaf3 Mon Sep 17 00:00:00 2001 From: Yaguit <59540825+Yaguit@users.noreply.github.com> Date: Fri, 13 Mar 2020 18:36:26 +0100 Subject: [PATCH 8/8] Delete README.md --- README.md | 82 ------------------------------------------------------- 1 file changed, 82 deletions(-) delete mode 100644 README.md diff --git a/README.md b/README.md deleted file mode 100644 index aa24645f..00000000 --- a/README.md +++ /dev/null @@ -1,82 +0,0 @@ -Ironhack Logo - -# Size & Intelligence in Dogs -*Santiago Mougán* - -*Data Analytics (BCN - 01/20)* - -## Content -- [Project Description](#project-description) -- [Hypotheses / Questions](#hypotheses-questions) -- [Dataset](#dataset) -- [Cleaning](#cleaning) -- [Analysis](#analysis) -- [Model Training and Evaluation](#model-training-and-evaluation) -- [Conclusion](#conclusion) -- [Future Work](#future-work) -- [Workflow](#workflow) -- [Organization](#organization) -- [Links](#links) - -## Project Description -Analysis and observations on size, mass and intelligence relationships on dogs, and exploration of prediction possibilities between this parameters. - -## Hypotheses / Questions -* Is there a connection between physical features and intelligence? -* Can intelligence be predicted from physical features? - -## Dataset -* Dataset on dog breeds and intelligence measurements from task competences from data.world. -* Dataset on dog breeds related to height and weight from data.world. -* Dataset with the info from the two main tables combined and clean. - -## Cleaning -* The numerical measurements were actually strings, that had to be converted to integers. Also some non numerical characters had to be fixed. -* Height and weight data were presented in pairs (minimun and maximum for each breed), so I converted them to unique (mean) values to ease the analysis. -* Since height and weight data corresponded to specific breeds, the NaNs could be easily investigated and fixed one by one. -* A few outlyiers were actually mistakes (e.g. weight value in the place of the height value), but again, easy to investigate and fix. -* There were NaNs on intelligence measurementes with complete and good data about the tasks response, so it could be assigned to their correct measurements instead of deleted. -* With the intelligence measurements fixed, there is no need for the details about tasks. -* Once height, weight and intelligence classification are related and fixed, there is no need for the breed label. - -## Analysis -* First analysis with plotting of all parameters, getting an obvious relationship between height and weight. -* Further analysis through observation of both physical parameters versus intelligence measurements. -![Height&Weight vs Intelligence categories](https://raw.githubusercontent.com/Yaguit/Project-Week-8-Final-Project/master/your-project/HW-I.PNG) -* The machine learning work was through clustering, since there are six defined intelligence groups. - -## Model Training and Evaluation -All the data was clustered in six main groups, corresponding to the six intelligence categories. From this a function can be made to input new data on height and weight and get the corresponding intelligence level in return. - -## Conclusion -* Relationship between intelligence and height and weight is not obvious to the human eye. -* The hypothesis of their relation should be discarded. -* More information about other physical features may rescue a modified version of the hypothesis. - -## Future Work -The relationship between height, weight and intelligence is not clear, so further investigation should be done to determine if phsysical differences and similarities between breeds are related to ther intelligence differences and similarities. - -## Workflow -* Looking for topics and general ideas -* I started growing interest in relationship between physical and mental features -* Finding the data on dog intelligence and physical measurements -* Once started the coding, ohter ideas were found and tested - -## Organization -Trello: -* List of general brainstorm ideas -* To do list -* Tasks in progress -* Tasks already done - -Github repository: -* Original datasets on .csv format -* Jupyter notebook file with all the coding -* Graph example image -* ReadMe - -## Links - -[Repository](https://github.com/Yaguit/Project-Week-8-Final-Project) -[Slides](https://slides.com/srmg/size-intelligence/#/) -[Trello](https://trello.com/b/PCUuBxi8/final-project)