diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index 60a0517..e9ec6e9 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -28,12 +28,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "pd.set_option('max_columns', 100)" + "pd.set_option('display.max_columns', 100)" ] }, { @@ -47,11 +56,283 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
91Makayla EppsCHIG178NaNNaNUSJune 6, 199522KentuckyR145221414.3050.02540.02024104600
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place Birthdate Age \\\n", + "91 Makayla Epps CHI G 178 NaN NaN US June 6, 1995 22 \n", + "\n", + " College Experience Games Played MIN FGM FGA FG% 3PM 3PA 3P% \\\n", + "91 Kentucky R 14 52 2 14 14.3 0 5 0.0 \n", + "\n", + " FTM FTA FT% OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "91 2 5 40.0 2 0 2 4 1 0 4 6 0 0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "nulls = wnba[wnba.isna().any(axis=1)]\n", + "nulls" ] }, { @@ -114,11 +564,23 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.6993006993006993" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "(len(nulls)/len(wnba)) *100" ] }, { @@ -130,11 +592,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#your answer here\n", + "wnba = wnba.dropna()" ] }, { @@ -147,11 +610,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight float64\n", + "BMI float64\n", + "Birth_Place object\n", + "Birthdate object\n", + "Age int64\n", + "College object\n", + "Experience object\n", + "Games Played int64\n", + "MIN int64\n", + "FGM int64\n", + "FGA int64\n", + "FG% float64\n", + "3PM int64\n", + "3PA int64\n", + "3P% float64\n", + "FTM int64\n", + "FTA int64\n", + "FT% float64\n", + "OREB int64\n", + "DREB int64\n", + "REB int64\n", + "AST int64\n", + "STL int64\n", + "BLK int64\n", + "TO int64\n", + "PTS int64\n", + "DD2 int64\n", + "TD3 int64\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.dtypes" ] }, { @@ -170,11 +677,24 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba = wnba.astype({\"Weight\": np.int64})\n", + "wnba[\"Weight\"].dtype" ] }, { @@ -186,11 +706,31 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "count 142.000000\n", + "mean 78.978873\n", + "std 10.996110\n", + "min 55.000000\n", + "25% 71.500000\n", + "50% 79.000000\n", + "75% 86.000000\n", + "max 113.000000\n", + "Name: Weight, dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba['Weight'].describe()" ] }, { @@ -218,17 +758,25 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "wnba.to_csv('wnba_clean.csv')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.13 ('Ironhack')", "language": "python", "name": "python3" }, @@ -242,7 +790,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "985844dc686bf8bc52028a38ef08654e50758646994bdb41b244f06f0e25b2ae" + } } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 30de970..0d25f12 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -15,14 +15,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "pd.set_option('max_columns', 100)" + "pd.set_option('display.max_columns', 100)" ] }, { @@ -36,11 +36,289 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73 21.329438 \n", + "2 2 Alex Bentley CON G 170 69 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78 25.469388 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 6 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 19 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 4 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 35 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 3 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 22 28 12 3 6 12 93 0 0 \n", + "1 82 101 72 63 13 40 217 0 0 \n", + "2 36 40 78 22 3 24 218 0 0 \n", + "3 134 169 65 20 10 38 188 2 0 \n", + "4 9 12 12 7 0 14 50 0 0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba = pd.read_csv(r'C:\\Users\\Francesco M\\Documents\\IronHack Syllabus\\Labs\\Module 2\\M2-mini-project2\\your-code\\wnba_clean.csv')\n", + "wnba.head()" ] }, { @@ -52,11 +330,355 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
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std41.5368918.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
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25%35.250000175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%70.500000185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%106.750000191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
max142.000000206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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" + ], + "text/plain": [ + " Unnamed: 0 Height Weight BMI Age \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 70.859155 184.612676 78.978873 23.091214 27.112676 \n", + "std 41.536891 8.698128 10.996110 2.073691 3.667180 \n", + "min 0.000000 165.000000 55.000000 18.390675 21.000000 \n", + "25% 35.250000 175.750000 71.500000 21.785876 24.000000 \n", + "50% 70.500000 185.000000 79.000000 22.873314 27.000000 \n", + "75% 106.750000 191.000000 86.000000 24.180715 30.000000 \n", + "max 142.000000 206.000000 113.000000 31.555880 36.000000 \n", + "\n", + " Games Played MIN FGM FGA FG% \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 24.429577 500.105634 74.401408 168.704225 43.102817 \n", + "std 7.075477 289.373393 55.980754 117.165809 9.855199 \n", + "min 2.000000 12.000000 1.000000 3.000000 16.700000 \n", + "25% 22.000000 242.250000 27.000000 69.000000 37.125000 \n", + "50% 27.500000 506.000000 69.000000 152.500000 42.050000 \n", + "75% 29.000000 752.500000 105.000000 244.750000 48.625000 \n", + "max 32.000000 1018.000000 227.000000 509.000000 100.000000 \n", + "\n", + " 3PM 3PA 3P% FTM FTA FT% \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 14.830986 43.697183 24.978169 39.535211 49.422535 75.828873 \n", + "std 17.372829 46.155302 18.459075 36.743053 44.244697 18.536151 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 3.000000 0.000000 13.000000 17.250000 71.575000 \n", + "50% 10.500000 32.000000 30.550000 29.000000 35.500000 80.000000 \n", + "75% 22.000000 65.500000 36.175000 53.250000 66.500000 85.925000 \n", + "max 88.000000 225.000000 100.000000 168.000000 186.000000 100.000000 \n", + "\n", + " OREB DREB REB AST STL BLK \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 22.063380 61.591549 83.654930 44.514085 17.725352 9.781690 \n", + "std 21.519648 49.669854 68.200585 41.490790 13.413312 12.537669 \n", + "min 0.000000 2.000000 2.000000 0.000000 0.000000 0.000000 \n", + "25% 7.000000 26.000000 34.250000 11.250000 7.000000 2.000000 \n", + "50% 13.000000 50.000000 62.500000 34.000000 15.000000 5.000000 \n", + "75% 31.000000 84.000000 116.500000 66.750000 27.500000 12.000000 \n", + "max 113.000000 226.000000 334.000000 206.000000 63.000000 64.000000 \n", + "\n", + " TO PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 142.000000 \n", + "mean 32.288732 203.169014 1.140845 0.007042 \n", + "std 21.447141 153.032559 2.909002 0.083918 \n", + "min 2.000000 2.000000 0.000000 0.000000 \n", + "25% 14.000000 77.250000 0.000000 0.000000 \n", + "50% 28.000000 181.000000 0.000000 0.000000 \n", + "75% 48.000000 277.750000 1.000000 0.000000 \n", + "max 87.000000 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.describe()" ] }, { @@ -70,11 +692,29 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\Francesco M\\Documents\\IronHack Syllabus\\Labs\\Module 2\\M2-mini-project2\\your-code\\2.-Exploratory-Data-Analysis.ipynb Cell 8\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mexpress\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpx\u001b[39;00m\n\u001b[0;32m 4\u001b[0m fig \u001b[39m=\u001b[39m px\u001b[39m.\u001b[39mhistogram(wnba, x\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mWeight\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m fig\u001b[39m.\u001b[39;49mshow()\n", + "File \u001b[1;32mc:\\anaconda3\\envs\\Ironhack\\lib\\site-packages\\plotly\\basedatatypes.py:3398\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 3365\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 3366\u001b[0m \u001b[39mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[0;32m 3367\u001b[0m \u001b[39mspecified by the renderer argument\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 3394\u001b[0m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 3395\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 3396\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpio\u001b[39;00m\n\u001b[1;32m-> 3398\u001b[0m \u001b[39mreturn\u001b[39;00m pio\u001b[39m.\u001b[39mshow(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\anaconda3\\envs\\Ironhack\\lib\\site-packages\\plotly\\io\\_renderers.py:396\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 392\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 393\u001b[0m )\n\u001b[0;32m 395\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m nbformat \u001b[39mor\u001b[39;00m LooseVersion(nbformat\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m LooseVersion(\u001b[39m\"\u001b[39m\u001b[39m4.2.0\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m--> 396\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 397\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 398\u001b[0m )\n\u001b[0;32m 400\u001b[0m ipython_display\u001b[39m.\u001b[39mdisplay(bundle, raw\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m 402\u001b[0m \u001b[39m# external renderers\u001b[39;00m\n", + "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" + ] + } + ], "source": [ - "#your code here" + "#your code here\n", + "import plotly.express as px\n", + "\n", + "fig = px.histogram(wnba, x='Weight')\n", + "fig.show()" ] }, { @@ -89,11 +729,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "sns.displot(wnba, x=\"Height\")\n", + "sns.displot(wnba, x=\"Weight\")\n", + "sns.displot(wnba, x=\"Age\")\n", + "sns.displot(wnba, x=\"BMI\")" ] }, { @@ -105,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +782,12 @@ "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "sns.displot(wnba, x=\"REB\")\n", + "sns.displot(wnba, x=\"AST\")\n", + "sns.displot(wnba, x=\"STL\")\n", + "sns.displot(wnba, x=\"PTS\")\n", + "sns.displot(wnba, x=\"BLK\")" ] }, { @@ -175,9 +824,83 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba['reb_per_min'] = wnba['REB'] / wnba['MIN']\n", + "wnba['ast_per_min'] = wnba['AST'] / wnba['MIN']\n", + "wnba['stl_per_min'] = wnba['STL'] / wnba['MIN']\n", + "wnba['pts_per_min'] = wnba['PTS'] / wnba['MIN']\n", + "wnba['blk_per_min'] = wnba['BLK'] / wnba['MIN']\n", + "\n", + "sns.displot(wnba, x=\"reb_per_min\")\n", + "sns.displot(wnba, x=\"ast_per_min\")\n", + "sns.displot(wnba, x=\"stl_per_min\")\n", + "sns.displot(wnba, x=\"pts_per_min\")\n", + "sns.displot(wnba, x=\"blk_per_min\")" ] }, { @@ -189,11 +912,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n" ] }, { @@ -218,17 +941,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#your comments here" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.13 ('Ironhack')", "language": "python", "name": "python3" }, @@ -242,7 +972,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "985844dc686bf8bc52028a38ef08654e50758646994bdb41b244f06f0e25b2ae" + } } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index edc1da0..2d25c68 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -31,8 +31,10 @@ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "from scipy.stats import ttest_1samp\n", - "pd.set_option('max_columns', 50)" + "pd.set_option('display.max_columns', 50)\n", + "import scipy.stats as st" ] }, { @@ -46,11 +48,290 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73 21.329438 \n", + "2 2 Alex Bentley CON G 170 69 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78 25.469388 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 6 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 19 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 4 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 35 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 3 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 22 28 12 3 6 12 93 0 0 \n", + "1 82 101 72 63 13 40 217 0 0 \n", + "2 36 40 78 22 3 24 218 0 0 \n", + "3 134 169 65 20 10 38 188 2 0 \n", + "4 9 12 12 7 0 14 50 0 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "#your code here\n", + "wnba = pd.read_csv(r'C:\\Users\\Francesco M\\Documents\\IronHack Syllabus\\Labs\\Module 2\\M2-mini-project2\\your-code\\wnba_clean.csv')\n", + "wnba.head()" ] }, { @@ -70,11 +351,58 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean population 78.97887323943662\n", + "mean sample: 79.02911999999999\n" + ] + } + ], + "source": [ + "# your answer here\n", + "dist_of_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba['Weight'],10) \n", + " mean_sample = np.mean(pop_sample)\n", + " dist_of_means.append(mean_sample)\n", + "\n", + "plt.hist(dist_of_means)\n", + "\n", + "dist_of_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba['Weight'],100) \n", + " mean_sample = np.mean(pop_sample)\n", + " dist_of_means.append(mean_sample)\n", + "\n", + "plt.hist(dist_of_means)\n", + "\n", + "plt.show()\n", + "\n", + "mu = np.mean(wnba['Weight'])\n", + "\n", + "print('mean population', mu)\n", + "\n", + "x_hat = np.mean(dist_of_means)\n", + "print('mean sample:', x_hat)" ] }, { @@ -86,11 +414,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(76.83127733557417, 81.12646914329908)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "sigma = wnba['Weight'].std(ddof=0)\n", + "st.norm.interval(0.95, loc=mu, scale=sigma/np.sqrt(100))" ] }, { @@ -134,11 +475,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "wnba_40 = wnba['FT%'] < 60.0" ] }, { @@ -154,11 +496,60 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean population 0.09859154929577464\n", + "mean sample: 0.09899000000000001\n" + ] + } + ], + "source": [ + "# your answer here\n", + "dist_of_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba_40,10) \n", + " mean_sample = np.mean(pop_sample)\n", + " dist_of_means.append(mean_sample)\n", + "\n", + "plt.hist(dist_of_means)\n", + "\n", + "dist_of_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba_40,100) \n", + " mean_sample = np.mean(pop_sample)\n", + " dist_of_means.append(mean_sample)\n", + "\n", + "plt.hist(dist_of_means)\n", + "\n", + "plt.show()\n", + "\n", + "mu = np.mean(wnba_40)\n", + "\n", + "print('mean population', mu)\n", + "\n", + "\n", + "x_hat = np.mean(dist_of_means)\n", + "print('mean sample:', x_hat)\n", + "\n" ] }, { @@ -170,11 +561,24 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.04895944132917377, 0.14822365726237552)\n" + ] + } + ], + "source": [ + "# your code here\n", + "n = len(wnba_40)\n", + "mean_ = wnba_40.mean()\n", + "s = wnba_40.std(ddof=1)\n", + "\n", + "print(st.t.interval(0.95,n-1,loc=mean_,scale= s/np.sqrt(n)))" ] }, { @@ -186,11 +590,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(0.04955898831729735, 0.14762411027425193)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "sigma = wnba_40.std(ddof=0)\n", + "st.norm.interval(0.95, loc=mu, scale=sigma/np.sqrt(142))" ] }, { @@ -227,11 +644,58 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3zSXAi31TPHPyk7GzSPJ24D+Ab/Gzeehb6c3T7wbOB54Drq6qH4ylyNNAkkuBv66qdyZ5I70R/rnAN4E/r6qXx1je2CTZAnwKeDXwXeAGeoMq+w6Q5G+BP6V3d9s3gb+gN8+8KvtPknuBS+k9jvgo8EHgX5ilv3S/HP+e3nTXS8ANVTU18BwGvSS1zakbSWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa9/87UpdKUHEJ/wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean population param: 44.514084507042256\n", + "mean sample dist: 44.63892\n" + ] + } + ], + "source": [ + "#your-answer-here\n", + "dist_of_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba['AST'],10) \n", + " mean_sample = np.mean(pop_sample)\n", + " dist_of_means.append(mean_sample)\n", + "\n", + "plt.hist(dist_of_means)\n", + "\n", + "dist_of_means = []\n", + "\n", + "for i in range(1000):\n", + " pop_sample = np.random.choice(wnba['AST'],100) \n", + " mean_sample = np.mean(pop_sample)\n", + " dist_of_means.append(mean_sample)\n", + "\n", + "plt.hist(dist_of_means)\n", + "\n", + "plt.show()\n", + "\n", + "mu = np.mean(wnba['AST'])\n", + "\n", + "print('mean population param: ', mu)\n", + "\n", + "x_hat = np.mean(dist_of_means)\n", + "print('mean sample dist:', x_hat)" ] }, { @@ -243,11 +707,25 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_1sampResult(statistic=-2.1499947192482898, pvalue=0.033261541354107166)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "\n", + "alpha = 0.05\n", + "\n", + "st.ttest_1samp(wnba['AST'],52)" ] }, { @@ -268,11 +746,23 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_1sampResult(statistic=-2.1499947192482898, pvalue=0.016630770677053583)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "st.ttest_1samp(wnba['AST'],52,alternative='less')" ] }, { @@ -343,7 +833,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.13 ('Ironhack')", "language": "python", "name": "python3" }, @@ -357,7 +847,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "985844dc686bf8bc52028a38ef08654e50758646994bdb41b244f06f0e25b2ae" + } } }, "nbformat": 4, diff --git a/your-code/wnba_clean.csv b/your-code/wnba_clean.csv new file mode 100644 index 0000000..b365fcb --- /dev/null +++ b/your-code/wnba_clean.csv @@ -0,0 +1,143 @@ +,Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 +0,Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 +1,Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 +2,Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 +3,Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 +4,Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 +5,Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0 +6,Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0 +7,Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 +8,Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 +9,Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0 +10,Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 +11,Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 +12,Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 +13,Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 +14,Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +15,Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0 +16,Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 +17,Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 +18,Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 +19,Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 +20,Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 +21,Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 +22,Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 +23,Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 +24,Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 +25,Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 +26,Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 +27,Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 +28,Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 +29,Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 +30,Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 +31,Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 +32,Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 +33,Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 +34,Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 +35,Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +36,Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 +37,Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 +38,Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 +39,Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 +40,Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 +41,Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 +42,Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 +43,Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 +44,Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 +45,Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 +46,Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 +47,Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 +48,Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 +49,Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 +50,Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 +51,Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 +52,Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 +53,Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 +54,Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 +55,Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 +56,Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 +57,Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 +58,Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 +59,Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 +60,Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 +61,Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 +62,Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 +63,Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 +64,Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 +65,Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 +66,Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 +67,Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 +68,Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 +69,Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 +70,Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 +71,Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 +72,Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 +73,Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0 +74,Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +75,Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 +76,Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 +77,Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 +78,Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 +79,Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 +80,Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 +81,Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 +82,Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 +83,Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 +84,Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 +85,Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 +86,Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 +87,Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 +88,Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 +89,Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 +90,Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 +92,Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 +93,Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 +94,Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 +95,Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 +96,Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 +97,Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 +98,Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 +99,Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 +100,Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 +101,Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 +102,Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 +103,Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 +104,Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 +105,Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 +106,Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 +107,Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 +108,Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 +109,Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +110,Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 +111,Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 +112,Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 +113,Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 +114,Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 +115,Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 +116,Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 +117,Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 +118,Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 +119,Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 +120,Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 +121,Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 +122,Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 +123,Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 +124,Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 +125,Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 +126,Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 +127,Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 +128,Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 +129,Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 +130,Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 +131,Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 +132,Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 +133,Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 +134,Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 +135,Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 +136,Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 +137,Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 +138,Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 +139,Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 +140,Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 +141,Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 +142,Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0