diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..69fb1a9 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,9 +49,202 @@ "cell_type": "code", "execution_count": 3, "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
\n", + "
" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71.0 21.200991 US \n", + "1 Alana Beard LA G/F 185 73.0 21.329438 US \n", + "2 Alex Bentley CON G 170 69.0 23.875433 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba=pd.read_csv(\"wnba.csv\")\n", + "wnba.head(3)" ] }, { @@ -64,11 +257,54 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name False\n", + "Team False\n", + "Pos False\n", + "Height False\n", + "Weight True\n", + "BMI True\n", + "Birth_Place False\n", + "Birthdate False\n", + "Age False\n", + "College False\n", + "Experience False\n", + "Games Played False\n", + "MIN False\n", + "FGM False\n", + "FGA False\n", + "FG% False\n", + "3PM False\n", + "3PA False\n", + "3P% False\n", + "FTM False\n", + "FTA False\n", + "FT% False\n", + "OREB False\n", + "DREB False\n", + "REB False\n", + "AST False\n", + "STL False\n", + "BLK False\n", + "TO False\n", + "PTS False\n", + "DD2 False\n", + "TD3 False\n", + "dtype: bool" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.isnull().any()" ] }, { @@ -80,11 +316,124 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "\n", + "df_with_nan = wnba[wnba.isnull().any(axis=1)]\n", + "\n", + "display(df_with_nan)" ] }, { @@ -96,11 +445,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of values removed: 0.70%\n" + ] + } + ], "source": [ - "#your code here" + "total_values_before = wnba.size\n", + "total_values_after = wnba.dropna().size\n", + "\n", + "percentage_removed = ((total_values_before - total_values_after) / total_values_before) * 100\n", + "\n", + "print(f\"Percentage of values removed: {percentage_removed:.2f}%\")" ] }, { @@ -114,11 +476,165 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "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
\n", + "
" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71.0 21.200991 US \n", + "1 Alana Beard LA G/F 185 73.0 21.329438 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba_cleaned = wnba.dropna(subset=['Weight', 'BMI'], axis=0)\n", + "wnba_cleaned.head(2)" ] }, { @@ -134,7 +650,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "yes" ] }, { @@ -147,11 +663,54 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "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": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.dtypes" ] }, { @@ -170,11 +729,55 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight Int64\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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "wnba['Weight'] = pd.to_numeric(wnba['Weight'], errors='coerce').astype('Int64')\n", + "\n", + "display(wnba.dtypes)" ] }, { @@ -186,11 +789,345 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count143.000000142.0142.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000143.000000
mean184.56643478.97887323.09121427.07692324.356643496.97202873.895105167.62237842.90139914.72727343.42657324.80349739.27272749.11188875.57832221.92307761.16083983.08391644.23076917.6083929.71328732.090909201.7902101.1328670.006993
std8.68506810.996112.0736913.6791707.104259290.77732056.110895117.46709510.11149817.35591946.10619918.51218336.74774744.24485418.71219421.50927649.76191968.30219741.48301713.43897812.52019321.502017153.3815482.9003100.083624
min165.00000055.018.39067521.0000002.00000012.0000001.0000003.00000014.3000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%176.50000071.521.78587624.00000022.000000240.00000026.00000066.00000036.9500000.0000003.0000000.00000012.00000016.50000071.1500007.00000025.50000034.00000011.0000007.0000002.00000013.50000075.0000000.0000000.000000
50%185.00000079.022.87331427.00000027.000000504.00000069.000000152.00000042.00000010.00000032.00000030.30000029.00000035.00000080.00000013.00000050.00000062.00000033.00000015.0000005.00000028.000000177.0000000.0000000.000000
75%191.00000086.024.18071530.00000029.000000750.000000105.000000244.50000048.55000022.00000065.00000036.15000052.50000066.00000085.85000031.00000084.000000116.00000066.50000027.00000012.00000048.000000277.5000001.0000000.000000
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\n", + "138 False False False True True False False False False False \n", + "141 False False False False False False False True True False \n", + "\n", + " STL BLK TO PTS DD2 TD3 \n", + "0 False False False False False False \n", + "1 True False False False False False \n", + "7 False False False False False False \n", + "9 False False False False False False \n", + "11 False False False False True False \n", + "12 False False False False False False \n", + "14 False False False False False False \n", + "15 False False False False False False \n", + "17 False False False False False False \n", + "18 False False False False False False \n", + "19 False True False True True False \n", + "23 False False False False False False \n", + "24 False False False False False False \n", + "25 False True False False True False \n", + "28 False True False False True True \n", + "35 False False False False False False \n", + "36 False False False False False False \n", + "37 False False False False True False \n", + "39 False False False False False False \n", + "41 False False False False False False \n", + "44 False False False False False False \n", + "45 False True False False True False \n", + "46 False True False False True False \n", + "55 False False False False True False \n", + "56 False False False False False False \n", + "58 False False False False True False \n", + "64 False False False False False False \n", + "65 False False False False False False \n", + "66 False True False False True False \n", + "67 False False False False False False \n", + "69 False True False False True False \n", + "81 False True False False True False \n", + "82 False False False False False False \n", + "83 False True False False False False \n", + "84 False False False False False False \n", + "85 False False False False True False \n", + "89 False False False False False False \n", + "90 False False False False False False \n", + "91 False False False False False False \n", + "94 False False False False True False \n", + "103 False False False False True False \n", + "109 False False False False False False \n", + "115 False False False False False False \n", + "120 False False False False False False \n", + "122 False False False False False False \n", + "124 False False False False False False \n", + "125 False True False False True False \n", + "127 False False False False False False \n", + "128 False False False False False False \n", + "131 False True False False True False \n", + "138 False False False False False False \n", + "141 False False False True True False " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "numeric_columns = wnba.select_dtypes(include='number')\n", + "q1 = numeric_columns.quantile(0.25)\n", + "q3 = numeric_columns.quantile(0.75)\n", + "\n", + "iqr = q3 - q1\n", + "\n", + "threshold = 1.5\n", + "\n", + "lower_bound = q1 - (threshold * iqr)\n", + "upper_bound = q3 + (threshold * iqr)\n", + "\n", + "outliers = (numeric_columns < (q1 - threshold * iqr)) | (numeric_columns > (q3 + threshold * iqr))\n", + "\n", + "outliers[outliers.any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "\"\"\"we'll remove outliers\"\"\"\n", + "wnba_clean = wnba[~outliers.any(axis=1)]" ] }, { @@ -218,17 +2858,28 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "import os\n", + "\n", + "if not os.path.exists('data'):\n", + " os.makedirs('data')\n", + "wnba_clean.to_csv('data/wnba_clean.csv', index=False)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +2893,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 48b485c..7c7a10d 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -38,9 +38,202 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71 21.200991 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba=pd.read_csv(\"wnba_clean.csv\")\n", + "wnba.head(3)" ] }, { @@ -52,11 +245,400 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std8.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
max206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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" + ], + "text/plain": [ + " Height Weight BMI Age Games Played \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 184.612676 78.978873 23.091214 27.112676 24.429577 \n", + "std 8.698128 10.996110 2.073691 3.667180 7.075477 \n", + "min 165.000000 55.000000 18.390675 21.000000 2.000000 \n", + "25% 175.750000 71.500000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.500000 \n", + "75% 191.000000 86.000000 24.180715 30.000000 29.000000 \n", + "max 206.000000 113.000000 31.555880 36.000000 32.000000 \n", + "\n", + " MIN FGM FGA FG% 3PM \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 500.105634 74.401408 168.704225 43.102817 14.830986 \n", + "std 289.373393 55.980754 117.165809 9.855199 17.372829 \n", + "min 12.000000 1.000000 3.000000 16.700000 0.000000 \n", + "25% 242.250000 27.000000 69.000000 37.125000 0.000000 \n", + "50% 506.000000 69.000000 152.500000 42.050000 10.500000 \n", + "75% 752.500000 105.000000 244.750000 48.625000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 43.697183 24.978169 39.535211 49.422535 75.828873 22.063380 \n", + "std 46.155302 18.459075 36.743053 44.244697 18.536151 21.519648 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 13.000000 17.250000 71.575000 7.000000 \n", + "50% 32.000000 30.550000 29.000000 35.500000 80.000000 13.000000 \n", + "75% 65.500000 36.175000 53.250000 66.500000 85.925000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 61.591549 83.654930 44.514085 17.725352 9.781690 32.288732 \n", + "std 49.669854 68.200585 41.490790 13.413312 12.537669 21.447141 \n", + "min 2.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 26.000000 34.250000 11.250000 7.000000 2.000000 14.000000 \n", + "50% 50.000000 62.500000 34.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.500000 66.750000 27.500000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 \n", + "mean 203.169014 1.140845 0.007042 \n", + "std 153.032559 2.909002 0.083918 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 77.250000 0.000000 0.000000 \n", + "50% 181.000000 0.000000 0.000000 \n", + "75% 277.750000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 142 entries, 0 to 141\n", + "Data columns (total 32 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 142 non-null object \n", + " 1 Team 142 non-null object \n", + " 2 Pos 142 non-null object \n", + " 3 Height 142 non-null int64 \n", + " 4 Weight 142 non-null int64 \n", + " 5 BMI 142 non-null float64\n", + " 6 Birth_Place 142 non-null object \n", + " 7 Birthdate 142 non-null object \n", + " 8 Age 142 non-null int64 \n", + " 9 College 142 non-null object \n", + " 10 Experience 142 non-null object \n", + " 11 Games Played 142 non-null int64 \n", + " 12 MIN 142 non-null int64 \n", + " 13 FGM 142 non-null int64 \n", + " 14 FGA 142 non-null int64 \n", + " 15 FG% 142 non-null float64\n", + " 16 3PM 142 non-null int64 \n", + " 17 3PA 142 non-null int64 \n", + " 18 3P% 142 non-null float64\n", + " 19 FTM 142 non-null int64 \n", + " 20 FTA 142 non-null int64 \n", + " 21 FT% 142 non-null float64\n", + " 22 OREB 142 non-null int64 \n", + " 23 DREB 142 non-null int64 \n", + " 24 REB 142 non-null int64 \n", + " 25 AST 142 non-null int64 \n", + " 26 STL 142 non-null int64 \n", + " 27 BLK 142 non-null int64 \n", + " 28 TO 142 non-null int64 \n", + " 29 PTS 142 non-null int64 \n", + " 30 DD2 142 non-null int64 \n", + " 31 TD3 142 non-null int64 \n", + "dtypes: float64(4), int64(21), object(7)\n", + "memory usage: 35.6+ KB\n" + ] + } + ], + "source": [ + "wnba.info()" ] }, { @@ -70,11 +652,39 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Players with highest ages:\n", + " Name Age\n", + "126 Sue Bird 36\n", + "1 Alana Beard 35\n", + "67 Jia Perkins 35\n", + "105 Plenette Pierson 35\n", + "109 Rebekkah Brunson 35\n", + "Players with lowest weights:\n", + " Name Weight\n", + "96 Moriah Jefferson 55\n", + "42 Danielle Robinson 57\n", + "86 Leilani Mitchell 58\n", + "82 Kristi Toliver 59\n", + "141 Yvonne Turner 59\n" + ] + } + ], "source": [ - "#your code here" + "highest_ages = wnba.nlargest(5, 'Age')\n", + "print(\"Players with highest ages:\")\n", + "print(highest_ages[['Name', 'Age']])\n", + "\n", + "# Find players with the lowest weights\n", + "lowest_weights = wnba.nsmallest(5, 'Weight')\n", + "print(\"Players with lowest weights:\")\n", + "print(lowest_weights[['Name', 'Weight']])" ] }, { @@ -91,9 +701,36 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n", + "\n", + "axes[0, 0].hist(wnba['Height'], bins=20, color='skyblue')\n", + "axes[0, 0].set_title('Height Distribution')\n", + "\n", + "axes[0, 1].hist(wnba['Weight'], bins=20, color='lightgreen')\n", + "axes[0, 1].set_title('Weight Distribution')\n", + "\n", + "axes[1, 0].hist(wnba['Age'], bins=20, color='lightcoral')\n", + "axes[1, 0].set_title('Age Distribution')\n", + "\n", + "\n", + "axes[1, 1].hist(wnba['BMI'], bins=20, color='gold')\n", + "axes[1, 1].set_title('BMI Distribution')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -108,9 +745,7 @@ "execution_count": 6, "metadata": {}, "outputs": [], - "source": [ - "#your conclusions here" - ] + "source": [] }, { "cell_type": "markdown", @@ -134,11 +769,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "\n", + "\n", + "axes[0, 0].hist(wnba['REB'], bins=20, color='skyblue')\n", + "axes[0, 0].set_title('REB Distribution')\n", + "\n", + "axes[0, 1].hist(wnba['AST'], bins=20, color='lightgreen')\n", + "axes[0, 1].set_title('AST Distribution')\n", + "\n", + "axes[0, 2].hist(wnba['STL'], bins=20, color='lightcoral')\n", + "axes[0, 2].set_title('STL Distribution')\n", + "\n", + "axes[1, 0].hist(wnba['PTS'], bins=20, color='gold')\n", + "axes[1, 0].set_title('PTS Distribution')\n", + "\n", + "axes[1, 1].hist(wnba['BLK'], bins=20, color='purple')\n", + "axes[1, 1].set_title('BLK Distribution')\n", + "\n", + "if wnba.shape[1] % 2 != 0:\n", + " axes[1, 2].remove()\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" ] }, { @@ -173,11 +845,55 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "import matplotlib.pyplot as plt\n", + "\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", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "\n", + "axes[0, 0].hist(wnba['REB_per_min'], bins=20, color='skyblue')\n", + "axes[0, 0].set_title('REB per Minute Distribution')\n", + "\n", + "axes[0, 1].hist(wnba['AST_per_min'], bins=20, color='lightgreen')\n", + "axes[0, 1].set_title('AST per Minute Distribution')\n", + "\n", + "axes[0, 2].hist(wnba['STL_per_min'], bins=20, color='lightcoral')\n", + "axes[0, 2].set_title('STL per Minute Distribution')\n", + "\n", + "axes[1, 0].hist(wnba['PTS_per_min'], bins=20, color='gold')\n", + "axes[1, 0].set_title('PTS per Minute Distribution')\n", + "\n", + "axes[1, 1].hist(wnba['BLK_per_min'], bins=20, color='purple')\n", + "axes[1, 1].set_title('BLK per Minute Distribution')\n", + "\n", + "# Remove empty subplot(s) if necessary\n", + "if wnba.shape[1] % 2 != 0:\n", + " axes[1, 2].remove()\n", + "\n", + "# Adjust layout and spacing\n", + "plt.tight_layout()\n", + "\n", + "# Display the plot\n", + "plt.show()" ] }, { @@ -222,13 +938,16 @@ "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "\"\"\"Based on the data provided in the dataset, we have information about physical characteristics such as height, weight, and BMI, as well as game-related statistics like assists, free throws, and other performance metrics. However, the dataset does not provide specific data on muscle mass or free throw percentages for individual players. Therefore, we may not have all the necessary data to definitively answer each question.\n", + "While the dataset provides some insights, answering these questions definitively may require additional specific data or research. It's important to approach discussions and debates with an open mind and consider multiple perspectives.\n", + "\n", + "\"\"\"" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +961,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..b3766f6 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": 4, "metadata": {}, "outputs": [], "source": [ @@ -46,11 +46,204 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71 21.200991 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "2 October 27, 1990 26 Penn State 4 26 617 82 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "2 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 22 3 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba=pd.read_csv(\"wnba_clean.csv\")\n", + "wnba.head(3)" ] }, { @@ -74,7 +267,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "\"\"\"Beyond confidence intervals, other methods for inference include hypothesis testing and bootstrapping. Hypothesis testing involves formulating a null hypothesis (e.g., the average weight is equal to a specific value) and testing it against an alternative hypothesis. Bootstrapping is a resampling technique that helps estimate the sampling distribution of a statistic by generating multiple resamples from the original sample.\"\"\"" ] }, { @@ -86,11 +279,35 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 95% confidence interval for the average weight of professional female basketball players is: (77.17665079176093, 80.78109568711231)\n" + ] + } + ], "source": [ - "# your code here" + "import numpy as np\n", + "import scipy.stats as stats\n", + "\n", + "sample_mean = np.mean(wnba['Weight'])\n", + "sample_std = np.std(wnba['Weight'])\n", + "\n", + "sample_size = wnba.shape[0]\n", + "\n", + "confidence_level = 0.95\n", + "alpha = 1 - confidence_level\n", + "z_critical = stats.norm.ppf(1 - alpha / 2)\n", + "\n", + "margin_of_error = z_critical * (sample_std / np.sqrt(sample_size))\n", + "\n", + "confidence_interval = (sample_mean - margin_of_error, sample_mean + margin_of_error)\n", + "\n", + "print(\"The 95% confidence interval for the average weight of professional female basketball players is:\", confidence_interval)" ] }, { @@ -106,7 +323,13 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"\n", + "Based on the computed confidence interval, we can make the following statement at a 95% confidence level:\n", + "\n", + "\"The 95% confidence interval for the average weight of professional female basketball players is (lower bound, upper bound).\"\n", + "\n", + "The lower bound and upper bound values represent the range within which we can be confident (at a 95% confidence level) that the true average weight of professional female basketball players lies based on the sample data.\n", + "\"\"\"" ] }, { @@ -122,7 +345,13 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"If your sister weighs 67kg and we don't have the precise confidence interval for the average weight of professional female basketball players, we cannot definitively confirm or refute your grandmother's assumption based solely on her weight.\n", + "\n", + "Weight alone is not the sole determining factor for a player's ability to play in a professional basketball league. In addition, the dataset we have is specific to the WNBA and may not represent the entire population of professional female basketball players worldwide.\n", + "\n", + "Instead, we should focus on the skills, dedication, and other factors that contribute to success in basketball. Many players with varying body types have excelled in professional basketball leagues, and success is determined by a combination of factors such as skill, agility, athleticism, and basketball IQ.\n", + "\n", + "It's important to remember that each player is unique, and their abilities should be evaluated holistically rather than solely based on their weight or physique.\"\"\"" ] }, { @@ -158,7 +387,16 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "# To estimate the percentage of players that fail more than 40% of their free throws using the WNBA sample, we need to ensure that our sample fulfills certain requirements:\n", + "\n", + "# 1. Random Sampling: The sample of players from the WNBA dataset should be representative and randomly selected from the population of professional female basketball players. This means that the dataset should include players from various teams and positions and should not be biased towards a specific subset.\n", + "\n", + "# 2. Large Sample Size: A larger sample size generally provides more accurate estimates. If the dataset has a small number of players, the estimate of the proportion may be less reliable. It is ideal to have a sufficiently large sample size that provides a representative distribution of players.\n", + "\n", + "# 3. Independence: Each player's performance regarding free throws should be independent of other players. In other words, the success or failure of one player's free throws should not affect the success or failure of another player's free throws. This assumption ensures that the observations within the sample are not influenced by each other.\n", + "\n", + "# 4. Assumption of Randomness: It assumes that the success or failure of free throws is a random occurrence, and there are no systematic biases or factors affecting the performance of players.\n", + "\n" ] }, { @@ -170,11 +408,197 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71 21.200991 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 30 \n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK \\\n", + "0 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 3 6 \n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 13 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 95% confidence interval for the proportion of players that fail more than 40% of their free throws is: (1.0, 1.0)\n" + ] + } + ], "source": [ - "# your code here" + "import numpy as np\n", + "import scipy.stats as stats\n", + "\n", + "num_fail = len(wnba[wnba['REB'] > (0.4 * wnba['FTA'])])\n", + "total_sample_size = wnba.shape[0]\n", + "confidence_level = 0.95\n", + "alpha = 1 - confidence_level\n", + "z_critical = stats.norm.ppf(1 - alpha / 2)\n", + "\n", + "sample_proportion = num_fail / total_sample_size\n", + "standard_error = np.sqrt((sample_proportion * (1 - sample_proportion)) / total_sample_size)\n", + "\n", + "margin_of_error = z_critical * standard_error\n", + "\n", + "confidence_interval = (sample_proportion - margin_of_error, sample_proportion + margin_of_error)\n", + "\n", + "print(\"The 95% confidence interval for the proportion of players that fail more than 40% of their free throws is:\", confidence_interval)" ] }, { @@ -190,7 +614,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"looking at the values of the lower bound and upper bound, we can interpret the interval by considering the following:\n", + "\n", + "The estimate of the proportion is based on the sample data and assumes that the sample is representative of the population of professional female basketball players.\n", + "\n", + "The width of the interval reflects the uncertainty in the estimation. A wider interval indicates higher uncertainty, while a narrower interval indicates lower uncertainty. The margin of error we calculated represents half of the width.\"\"\"" ] }, { @@ -231,7 +659,19 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "\"\"\"\n", + "Here are the assumptions and requirements the sample should satisfy: \n", + "\n", + "1. Random sampling, The sample of WNBA player assists should be randomly selected from the population of interest. \n", + "\n", + "2. Independence: The assists of individual players should be independent of each other. \n", + ". \n", + "3. Normally distributed data (optional assumption): The distribution of assists within the sample should be normal\n", + "\n", + "4. Sufficient sample size: The sample size should be large enough to approximate a normal distribution of the sample mean. \n", + "\n", + "5. Reasonable variability: There should be sufficient variability in the sample data. \n", + "\"\"\"" ] }, { @@ -243,20 +683,35 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fail to reject the null hypothesis.\n", + "There is not enough evidence to conclude that the average assists for WNBA players is significantly different from the average of both WNBA and NBA players combined.\n" + ] + } + ], "source": [ - "#your-answer-here" + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "null_hypothesis_mean = 52 \n", + "alternative_hypothesis_mean = np.mean(wnba[\"AST\"])\n", + "\n", + "alpha = 0.05\n", + "\n", + "t_statistic, p_value = stats.ttest_1samp(wnba[\"AST\"], null_hypothesis_mean)\n", + "\n", + "if p_value < alpha/2: \n", + " print(\"Reject the null hypothesis.\")\n", + " print(\"There is evidence to support that the average assists for WNBA players is significantly different from the average of both WNBA and NBA players combined.\")\n", + "else:\n", + " print(\"Fail to reject the null hypothesis.\")\n", + " print(\"There is not enough evidence to conclude that the average assists for WNBA players is significantly different from the average of both WNBA and NBA players combined.\")" ] }, { @@ -268,11 +723,35 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fail to reject the null hypothesis.\n", + "There is not enough evidence to conclude that the average assists for WNBA players is significantly higher than the average of both WNBA and NBA players combined.\n" + ] + } + ], "source": [ - "#your-answer-here" + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "null_hypothesis_mean = 52 \n", + "alternative_hypothesis_mean = np.mean(wnba[\"AST\"])\n", + "\n", + "alpha = 0.05\n", + "\n", + "t_statistic, p_value = stats.ttest_1samp(wnba[\"AST\"], null_hypothesis_mean, alternative='greater')\n", + "\n", + "if p_value < alpha and np.mean(wnba[\"AST\"]) > null_hypothesis_mean:\n", + " print(\"Reject the null hypothesis.\")\n", + " print(\"There is evidence to support that the average assists for WNBA players is significantly higher than the average of both WNBA and NBA players combined.\")\n", + "else:\n", + " print(\"Fail to reject the null hypothesis.\")\n", + " print(\"There is not enough evidence to conclude that the average assists for WNBA players is significantly higher than the average of both WNBA and NBA players combined.\")" ] }, { @@ -288,7 +767,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n" ] }, { @@ -343,7 +822,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +836,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/your-code/codebook.md b/your-code/codebook.md new file mode 100644 index 0000000..171a596 --- /dev/null +++ b/your-code/codebook.md @@ -0,0 +1,55 @@ +# Codebook + +## Dataset + +The dataset we are working with contains personal data and game statistics for the 142 players of the WNBA. The data represents the performances of the players during all the games of the 2016/2017 season. + +For those of you that are less accustomed to basketball lingo here are some definitions: +- **Field Goal**: any shot made from inside the 3-point line. +- **Free Throws**: shots that are given to a player after they suffer a foul. The play stops and the player can freely shot from behind the free throw line. +- **Rebound**: a recovered basketball after a failed shot. If the shot was made by a teammate it's an Offensive Rebound, if instead the shot was made by an opponent is a Defensive Rebound. +- **Turnover**: losing a basketball before your team has had a chance of shooting the ball. +- **Blocks**: blocking an opponent's shot. +- **Double doubles**: a player is said to have performed a double-double when they accumulate at least a double digit number in two out of five of the main statistics: points, rebounds, blocks, steals and assists. +- **Triple doubles**: same as double-double but with three out of five statistics. +- **Positions**: here's the wikipedia page if you'd like to better understand the various positions in basketball: https://en.wikipedia.org/wiki/Basketball_positions + +## Features Description + +| Feature | Description | +|:---|:---| +| Name | Name | +| Team | Team | +| Pos | Position | +| Height | Height | +| Weight | Weight | +| BMI | Body Mass Index | +| Birth_Place | Birth place | +| Birthdate | Birthdate | +| Age | Age | +| College | College | +| Experience | Experience | +| G | Games Played | +| MIN | Minutes Played | +| FGM | Field Goals Made | +| FGA | Field Goals Attempts | +| FG% | Field Goals % | +| 3PM | 3Points Made | +| 3PA | 3Points Attempts | +| 3P% | 3Points % | +| FTM | Free Throws made | +| FTA | Free Throws Attempts | +| FT% | Free Throws % | +| OREB | Offensive Rebounds | +| DREB | Defensive Rebounds | +| REB | Total Rebounds | +| AST | Assists | +| STL | Steals | +| BLK | Blocks | +| TO | Turnovers | +| PTS | Total points | +| DD2 | Double doubles | +| TD3 | Triple doubles | + +## Source +[WNBA Player Stats 2017](https://www.kaggle.com/jinxbe/wnba-player-stats-2017) diff --git a/your-code/data/wnba_clean.csv b/your-code/data/wnba_clean.csv new file mode 100644 index 0000000..e5811cf --- /dev/null +++ b/your-code/data/wnba_clean.csv @@ -0,0 +1,92 @@ +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 diff --git a/your-code/wnba.csv b/your-code/wnba.csv new file mode 100644 index 0000000..bb13374 --- /dev/null +++ b/your-code/wnba.csv @@ -0,0 +1,144 @@ +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 +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 +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 +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 +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 +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 +Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 +Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 +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 +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 +Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 +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 +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 +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 +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 +Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +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 +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 +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 +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 +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 +Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 \ No newline at end of file diff --git a/your-code/wnba_clean.csv b/your-code/wnba_clean.csv new file mode 100644 index 0000000..3d702f3 --- /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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +Danielle Adams,CON,F/C,185,108,31.555880199999997,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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +Kayla Alexander,SAN,C,193,88,23.624795300000002,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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +Rebecca Allen,NY,G/F,188,74,20.937075600000004,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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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 +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