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": [ + "
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BreedClassificationobeyreps_lowerreps_upper
0Border CollieBrightest Dogs95%14
1PoodleBrightest Dogs95%14
2German ShepherdBrightest Dogs95%14
3Golden RetrieverBrightest Dogs95%14
4Doberman PinscherBrightest Dogs95%14
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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
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136 rows × 5 columns

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" + ], + "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": [ + "
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Breedheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
0Akita262880120
1Anatolian Sheepdog2729100150
2Bernese Mountain Dog232785110
3Bloodhound242680120
4Borzoi262870100
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145Papillon811510
146Pomeranian121237
147Poodle Toy10101010
148Toy Fox Terrier101047
149Yorkshire Terrier8837
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150 rows × 5 columns

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" + ], + "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": [ + "
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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
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105 rows × 9 columns

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" + ], + "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": [ + "
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BreedClassificationobeyreps_lowerreps_upperheight_low_inchesheight_high_inchesweight_low_lbsweight_high_lbs
70Alaskan MalamuteAverage Working/Obedience Intelligence50%2640nananana
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" + ], + "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": [ + "
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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
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105 rows × 11 columns

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" + ], + "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": [ + "
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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
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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": [ + "
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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": [ + "
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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
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" + ], + "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": [ + "
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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
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105 rows × 5 columns

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" + ], + "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", 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" + ] + }, + "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": 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\n", 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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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+ "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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" + ], + "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": [ + "
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" + ], + "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": [ + { + 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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 00000000..d1f10d0f Binary files /dev/null and b/your-project/HW-I.PNG differ diff --git a/your-project/README.md b/your-project/README.md index 9563cd70..aa24645f 100644 --- a/your-project/README.md +++ b/your-project/README.md @@ -1,9 +1,9 @@ Ironhack Logo -# Title of My Project -*[Your Name]* +# Size & Intelligence in Dogs +*Santiago Mougán* -*[Your Cohort, Campus & Date]* +*Data Analytics (BCN - 01/20)* ## Content - [Project Description](#project-description) @@ -19,54 +19,64 @@ - [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](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 -*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 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) 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