From ae7cc1d321272a351beb6656bd7329a290e7d998 Mon Sep 17 00:00:00 2001 From: Mariana Sousa Date: Mon, 14 Aug 2023 09:13:32 +0100 Subject: [PATCH] marianaslab --- data/codebook.md => codebook-Copy1.md | 0 codebook.md | 55 + data/wnba.csv => wnba-Copy1.csv | 0 data/wnba_clean.csv => wnba_clean-Copy1.csv | 0 your-code/1.-Data-Cleaning.ipynb | 929 +++- your-code/2.-Exploratory-Data-Analysis.ipynb | 5014 +++++++++++++++++- your-code/3.-Inferential-Analysis.ipynb | 1061 +++- your-code/wnba.csv | 144 + your-code/wnba_clean.csv | 143 + 9 files changed, 7267 insertions(+), 79 deletions(-) rename data/codebook.md => codebook-Copy1.md (100%) create mode 100644 codebook.md rename data/wnba.csv => wnba-Copy1.csv (100%) rename data/wnba_clean.csv => wnba_clean-Copy1.csv (100%) create mode 100644 your-code/wnba.csv create mode 100644 your-code/wnba_clean.csv diff --git a/data/codebook.md b/codebook-Copy1.md similarity index 100% rename from data/codebook.md rename to codebook-Copy1.md diff --git a/codebook.md b/codebook.md new file mode 100644 index 0000000..171a596 --- /dev/null +++ b/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/data/wnba.csv b/wnba-Copy1.csv similarity index 100% rename from data/wnba.csv rename to wnba-Copy1.csv diff --git a/data/wnba_clean.csv b/wnba_clean-Copy1.csv similarity index 100% rename from data/wnba_clean.csv rename to wnba_clean-Copy1.csv diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..aac6eab 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": [ @@ -47,11 +47,283 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF18371.021.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F18573.021.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG17069.023.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F18584.024.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING17578.025.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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" + ], + "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", + "3 Alex Montgomery SAN G/F 185 84.0 24.543462 US \n", + "4 Alexis Jones MIN G 175 78.0 25.469388 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", + "3 December 11, 1988 28 Georgia Tech 6 31 721 75 \n", + "4 August 5, 1994 23 Baylor R 24 137 16 \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", + "3 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 20 10 \n", + "4 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 7 0 \n", + "\n", + " TO PTS DD2 TD3 \n", + "0 12 93 0 0 \n", + "1 40 217 0 0 \n", + "2 24 218 0 0 \n", + "3 38 188 2 0 \n", + "4 14 50 0 0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "nba = pd.read_csv(\"wnba.csv\")\n", + "\n", + "nba.head()" ] }, { @@ -64,11 +336,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Weight 1\n", + "BMI 1\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "null_cols = nba.isnull().sum()\n", + "\n", + "null_cols[null_cols > 0]\n", + "\n" ] }, { @@ -80,11 +368,128 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "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 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "null_displ = nba[(nba[\"Weight\"].isnull()==True)]\n", + "\n", + "null_displ\n", + "\n", + "\n", + "\n", + "#null_displ = data[(data['displ'].isnull()==True)]" ] }, { @@ -96,11 +501,45 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(143, 32)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nba.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.006993006993006993" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "percent_null = 1/143\n", + "percent_null\n", + "\n", + "# we'll removing only 0.06% of our data" ] }, { @@ -114,11 +553,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "nba.drop(91, inplace = True)\n" ] }, { @@ -134,7 +573,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "# if waht i was going to look into had nothing to do with \"wheight\" and \"ibm\"" ] }, { @@ -147,11 +586,54 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "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": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "nba.dtypes" ] }, { @@ -170,11 +652,23 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "nba[\"Weight\"] = nba[\"Weight\"].astype(\"int64\")\n", + "nba[\"Weight\"].dtype\n" ] }, { @@ -186,11 +680,345 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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
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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": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "nba.describe()" ] }, { @@ -206,7 +1034,31 @@ "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "# 2 games played is an outlier\n", + "# 12 MIN could be an outlier\n", + "\n", + "## actually the minimum values are usually outliers, as they ar very far form the mean\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'describe'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[48], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnba\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdescribe\u001b[49m()\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'describe'" + ] + } + ], + "source": [ + "nba.describe()" ] }, { @@ -218,17 +1070,32 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'to_csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nba \u001b[38;5;241m=\u001b[39m \u001b[43mnba\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwnba_clean_checking.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'to_csv'" + ] + } + ], "source": [ - "#your code here" + "nba = nba.to_csv(\"wnba_clean.csv\")\n", + "\n", + "\n", + "## i think i saved it. tried to save it again an got this error, but i think i didi it right =)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1109,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 48b485c..56df89d 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -36,11 +36,289 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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Unnamed: 0HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
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25%35.250000175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%70.500000185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%106.750000191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
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6767Jia PerkinsMING1737525.059307USFebruary 23, 198235Texas Tech143093217842042.44712338.211413485.124729610341118351700
105106Plenette PiersonMINF/C1888824.898144USAugust 31, 198135Texas Tech15294025414238.0175133.3152075.0134962481243314000
109110Rebekkah BrunsonMINF1888423.766410USNovember 12, 198135Georgetown14267199721844.5226036.7628374.746135181403194227820
126127Sue BirdSEAG1756822.204082USOctober 16, 198036Connecticut152780610324442.25013437.3172470.8746531773135727310
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
55Alexis PetersonSEAG1706321.799308USJune 20, 199522SyracuseR149093426.52922.266100.0313161150112600
66Alexis PrincePHOG1888122.917610USFebruary 5, 199423BaylorR1611293426.541526.722100.01141554332400
88Allisha GrayDALG1857622.205990USOctober 20, 199224South Carolina23083413134637.92910328.210412980.652751274047193739500
1111Alyssa ThomasCONF1888423.766410USDecember 4, 199224Maryland32883315430350.8030.09115857.63415819213648118739940
1212Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
1515Angel RobinsonPHOF/C1988822.446689USAugust 30, 199521Arizona State115237254456.811100.077100.01642588111165800
1717Bashaara GravesCHIF1889125.746944USMarch 17, 199423Tennessee155981457.1000.03475.04131730131900
1818Breanna LewisDALC1969324.208663USJune 22, 199423Kansas StateR125021216.7000.03475.02792007700
1919Breanna StewartSEAF/C1937720.671696USAugust 27, 199422Connecticut22995220141748.24612337.413617179.5432062497829476858480
2020Bria HartleyNYG1736622.052190USSeptember 30, 199224Connecticut4295988019241.7329334.4253375.875057581554421700
2121Bria HolmesATLG1857722.498174USApril 19, 199423West VirginiaR286558523136.895018.0568466.7295685522373123500
2323Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
2424Brittany BoydNYG1757123.183673USNovember 6, 199323UC Berkeley323291560.0010.081172.735853022600
2626Brittney SykesATLG1756621.551020USJuly 2, 199423Rutgers103073414636240.3298733.37610274.525941195918174939710
3838Courtney WilliamsCONG1736220.715694USNovember 5, 199422South Florida12975516833849.783026.7313686.13884122601563937510
4040Damiris DantasATLC1918924.396261BRNovember 17, 199224Brazil4305699824340.3259127.5334376.729841131917182625400
4343Dearica HambySANF1918623.573915USJune 11, 199324Wake Forest2316509620746.43837.5589561.14891139322984325310
4747Elizabeth WilliamsATLF/C1918723.848030USJune 23, 199324Duke330377489650.0010.0325558.23561965542112800
4949Emma MeessemanWASC1938322.282477BEMay 13, 199324Belgium5236178923338.2257931.6566586.2235881703453025900
5555Evelyn AkhatorDALF1918222.477454NGMarch 2, 199522KentuckyR3092616536545.2206033.39211778.67319927250371367442130
5757Imani BoyetteATLC2018821.781639USNovember 10, 199224Texas1294105611947.11333.3142070.04375118149232212710
5858Isabelle HarrisonSANC1918322.751569USSeptember 27, 199323Kentucky33183215430051.31250.0558564.7661342004626246336450
6262Jazmon GwathmeyINDG1886518.390675PRJanuary 24, 199324James Madison2243715014035.7124924.5303976.91534491713193214200
6666Jewell LoydSEAG1786721.146320USMay 10, 199324Notre Dame32971511624547.382138.1283775.7501391894618505726840
6868Jonquel JonesCONF/C1988621.936537BSMay 1, 199423George Washington1294634712437.9113234.4111573.3114657393012411600
7070Kaela DavisDALG1887721.785876USMarch 15, 199522South CarolinaR23208277536.0205536.43475.02202257167700
7171Kahleah CopperCHIG/F1857020.452885USAugust 28, 199422Rutgers1294756216338.0123237.5496575.4103343321334818500
7272Kaleena Mosqueda-LewisSEAF1808225.308642USMarch 11, 199324Connecticut3293696014042.952321.7364580.011435411922216100
7777Kayla ThorntonDALF1858625.127831USOctober 20, 199224Texas–El Paso221224113036.7010.0101471.4192645136293200
7979Kelsey PlumSANG1736622.052190USAugust 24, 199423WashingtonR286107321034.8297837.2505886.2114253911347222500
8181Kiah StokesNYC1918723.848030USMarch 30, 199324Connecticut329576509851.0010.0415278.863122185218323314130
8484Lanay MontgomerySEAC1969624.989588USSeptember 17, 199323West VirginiaR7283742.9000.0000.00550142600
8787Lindsay AllenNYG1736521.718066USMarch 20, 199522Notre DameR23314215042.00110.06966.78283647131184800
9596Morgan TuckCONF1889125.746944USApril 30, 199423Connecticut1172943510134.782828.6131681.3934431970159110
9697Moriah JeffersonSANG1685519.486961USAugust 3, 199423Connecticut1215148115552.392045.0202774.163137923324319100
9798Natalie AchonwaINDC1938322.282477CANovember 22, 199224Notre Dame3305298215154.3000.0435578.231701012111162520700
101102Nia CoffeySANF1857722.498174USMay 21, 199522NorthwesternR25203165927.1040.0162272.7163046656144800
106107Rachel BanhamCONG1757624.816327USJuly 15, 199324Minnesota226238328736.8164833.3162080.0227292040129600
115116Saniya ChongDALG1736421.383942USJune 27, 199423ConnecticutR29348277436.583522.9252986.29192833213238700
118119Shatori Walker-KimbroughWASG1806419.753086USMay 18, 199522MarylandR22260297837.292634.6293290.64131710111129600
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight \\\n", + "0 0 Aerial Powers DAL F 183 71 \n", + "4 4 Alexis Jones MIN G 175 78 \n", + "5 5 Alexis Peterson SEA G 170 63 \n", + "6 6 Alexis Prince PHO G 188 81 \n", + "8 8 Allisha Gray DAL G 185 76 \n", + "11 11 Alyssa Thomas CON F 188 84 \n", + "12 12 Amanda Zahui B. NY C 196 113 \n", + "15 15 Angel Robinson PHO F/C 198 88 \n", + "17 17 Bashaara Graves CHI F 188 91 \n", + "18 18 Breanna Lewis DAL C 196 93 \n", + "19 19 Breanna Stewart SEA F/C 193 77 \n", + "20 20 Bria Hartley NY G 173 66 \n", + "21 21 Bria Holmes ATL G 185 77 \n", + "23 23 Brionna Jones CON F 191 104 \n", + "24 24 Brittany Boyd NY G 175 71 \n", + "26 26 Brittney Sykes ATL G 175 66 \n", + "38 38 Courtney Williams CON G 173 62 \n", + "40 40 Damiris Dantas ATL C 191 89 \n", + "43 43 Dearica Hamby SAN F 191 86 \n", + "47 47 Elizabeth Williams ATL F/C 191 87 \n", + "49 49 Emma Meesseman WAS C 193 83 \n", + "55 55 Evelyn Akhator DAL F 191 82 \n", + "57 57 Imani Boyette ATL C 201 88 \n", + "58 58 Isabelle Harrison SAN C 191 83 \n", + "62 62 Jazmon Gwathmey IND G 188 65 \n", + "66 66 Jewell Loyd SEA G 178 67 \n", + "68 68 Jonquel Jones CON F/C 198 86 \n", + "70 70 Kaela Davis DAL G 188 77 \n", + "71 71 Kahleah Copper CHI G/F 185 70 \n", + "72 72 Kaleena Mosqueda-Lewis SEA F 180 82 \n", + "77 77 Kayla Thornton DAL F 185 86 \n", + "79 79 Kelsey Plum SAN G 173 66 \n", + "81 81 Kiah Stokes NY C 191 87 \n", + "84 84 Lanay Montgomery SEA C 196 96 \n", + "87 87 Lindsay Allen NY G 173 65 \n", + "95 96 Morgan Tuck CON F 188 91 \n", + "96 97 Moriah Jefferson SAN G 168 55 \n", + "97 98 Natalie Achonwa IND C 193 83 \n", + "101 102 Nia Coffey SAN F 185 77 \n", + "106 107 Rachel Banham CON G 175 76 \n", + "115 116 Saniya Chong DAL G 173 64 \n", + "118 119 Shatori Walker-Kimbrough WAS G 180 64 \n", + "\n", + " BMI Birth_Place Birthdate Age College \\\n", + "0 21.200991 US January 17, 1994 23 Michigan State \n", + "4 25.469388 US August 5, 1994 23 Baylor \n", + "5 21.799308 US June 20, 1995 22 Syracuse \n", + "6 22.917610 US February 5, 1994 23 Baylor \n", + "8 22.205990 US October 20, 1992 24 South Carolina \n", + "11 23.766410 US December 4, 1992 24 Maryland \n", + "12 29.414827 SE August 9, 1993 24 Minnesota \n", + "15 22.446689 US August 30, 1995 21 Arizona State \n", + "17 25.746944 US March 17, 1994 23 Tennessee \n", + "18 24.208663 US June 22, 1994 23 Kansas State \n", + "19 20.671696 US August 27, 1994 22 Connecticut \n", + "20 22.052190 US September 30, 1992 24 Connecticut \n", + "21 22.498174 US April 19, 1994 23 West Virginia \n", + "23 28.507990 US December 18, 1995 21 Maryland \n", + "24 23.183673 US November 6, 1993 23 UC Berkeley \n", + "26 21.551020 US July 2, 1994 23 Rutgers \n", + "38 20.715694 US November 5, 1994 22 South Florida \n", + "40 24.396261 BR November 17, 1992 24 Brazil \n", + "43 23.573915 US June 11, 1993 24 Wake Forest \n", + "47 23.848030 US June 23, 1993 24 Duke \n", + "49 22.282477 BE May 13, 1993 24 Belgium \n", + "55 22.477454 NG March 2, 1995 22 Kentucky \n", + "57 21.781639 US November 10, 1992 24 Texas \n", + "58 22.751569 US September 27, 1993 23 Kentucky \n", + "62 18.390675 PR January 24, 1993 24 James Madison \n", + "66 21.146320 US May 10, 1993 24 Notre Dame \n", + "68 21.936537 BS May 1, 1994 23 George Washington \n", + "70 21.785876 US March 15, 1995 22 South Carolina \n", + "71 20.452885 US August 28, 1994 22 Rutgers \n", + "72 25.308642 US March 11, 1993 24 Connecticut \n", + "77 25.127831 US October 20, 1992 24 Texas–El Paso \n", + "79 22.052190 US August 24, 1994 23 Washington \n", + "81 23.848030 US March 30, 1993 24 Connecticut \n", + "84 24.989588 US September 17, 1993 23 West Virginia \n", + "87 21.718066 US March 20, 1995 22 Notre Dame \n", + "95 25.746944 US April 30, 1994 23 Connecticut \n", + "96 19.486961 US August 3, 1994 23 Connecticut \n", + "97 22.282477 CA November 22, 1992 24 Notre Dame \n", + "101 22.498174 US May 21, 1995 22 Northwestern \n", + "106 24.816327 US July 15, 1993 24 Minnesota \n", + "115 21.383942 US June 27, 1994 23 Connecticut \n", + "118 19.753086 US May 18, 1995 22 Maryland \n", + "\n", + " Experience Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA \\\n", + "0 2 8 173 30 85 35.3 12 32 37.5 21 26 \n", + "4 R 24 137 16 50 32.0 7 20 35.0 11 12 \n", + "5 R 14 90 9 34 26.5 2 9 22.2 6 6 \n", + "6 R 16 112 9 34 26.5 4 15 26.7 2 2 \n", + "8 2 30 834 131 346 37.9 29 103 28.2 104 129 \n", + "11 3 28 833 154 303 50.8 0 3 0.0 91 158 \n", + "12 3 25 133 20 53 37.7 2 8 25.0 9 12 \n", + "15 1 15 237 25 44 56.8 1 1 100.0 7 7 \n", + "17 1 5 59 8 14 57.1 0 0 0.0 3 4 \n", + "18 R 12 50 2 12 16.7 0 0 0.0 3 4 \n", + "19 2 29 952 201 417 48.2 46 123 37.4 136 171 \n", + "20 4 29 598 80 192 41.7 32 93 34.4 25 33 \n", + "21 R 28 655 85 231 36.8 9 50 18.0 56 84 \n", + "23 R 19 112 14 26 53.8 0 0 0.0 16 19 \n", + "24 3 2 32 9 15 60.0 0 1 0.0 8 11 \n", + "26 10 30 734 146 362 40.3 29 87 33.3 76 102 \n", + "38 1 29 755 168 338 49.7 8 30 26.7 31 36 \n", + "40 4 30 569 98 243 40.3 25 91 27.5 33 43 \n", + "43 2 31 650 96 207 46.4 3 8 37.5 58 95 \n", + "47 3 30 377 48 96 50.0 0 1 0.0 32 55 \n", + "49 5 23 617 89 233 38.2 25 79 31.6 56 65 \n", + "55 R 30 926 165 365 45.2 20 60 33.3 92 117 \n", + "57 1 29 410 56 119 47.1 1 3 33.3 14 20 \n", + "58 3 31 832 154 300 51.3 1 2 50.0 55 85 \n", + "62 2 24 371 50 140 35.7 12 49 24.5 30 39 \n", + "66 3 29 715 116 245 47.3 8 21 38.1 28 37 \n", + "68 1 29 463 47 124 37.9 11 32 34.4 11 15 \n", + "70 R 23 208 27 75 36.0 20 55 36.4 3 4 \n", + "71 1 29 475 62 163 38.0 12 32 37.5 49 65 \n", + "72 3 29 369 60 140 42.9 5 23 21.7 36 45 \n", + "77 2 21 224 11 30 36.7 0 1 0.0 10 14 \n", + "79 R 28 610 73 210 34.8 29 78 37.2 50 58 \n", + "81 3 29 576 50 98 51.0 0 1 0.0 41 52 \n", + "84 R 7 28 3 7 42.9 0 0 0.0 0 0 \n", + "87 R 23 314 21 50 42.0 0 11 0.0 6 9 \n", + "95 1 17 294 35 101 34.7 8 28 28.6 13 16 \n", + "96 1 21 514 81 155 52.3 9 20 45.0 20 27 \n", + "97 3 30 529 82 151 54.3 0 0 0.0 43 55 \n", + "101 R 25 203 16 59 27.1 0 4 0.0 16 22 \n", + "106 2 26 238 32 87 36.8 16 48 33.3 16 20 \n", + "115 R 29 348 27 74 36.5 8 35 22.9 25 29 \n", + "118 R 22 260 29 78 37.2 9 26 34.6 29 32 \n", + "\n", + " FT% OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 80.8 6 22 28 12 3 6 12 93 0 0 \n", + "4 91.7 3 9 12 12 7 0 14 50 0 0 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+ "66 75.7 50 139 189 46 18 50 57 268 4 0 \n", + "68 73.3 11 46 57 39 30 1 24 116 0 0 \n", + "70 75.0 2 20 22 5 7 1 6 77 0 0 \n", + "71 75.4 10 33 43 32 13 3 48 185 0 0 \n", + "72 80.0 11 43 54 11 9 2 22 161 0 0 \n", + "77 71.4 19 26 45 13 6 2 9 32 0 0 \n", + "79 86.2 11 42 53 91 13 4 72 225 0 0 \n", + "81 78.8 63 122 185 21 8 32 33 141 3 0 \n", + "84 0.0 0 5 5 0 1 4 2 6 0 0 \n", + "87 66.7 8 28 36 47 13 1 18 48 0 0 \n", + "95 81.3 9 34 43 19 7 0 15 91 1 0 \n", + "96 74.1 6 31 37 92 33 2 43 191 0 0 \n", + "97 78.2 31 70 101 21 11 16 25 207 0 0 \n", + "101 72.7 16 30 46 6 5 6 14 48 0 0 \n", + "106 80.0 2 27 29 20 4 0 12 96 0 0 \n", + "115 86.2 9 19 28 33 21 3 23 87 0 0 \n", + "118 90.6 4 13 17 10 11 1 12 96 0 0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nba.loc[nba[\"Age\"] < 25]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + 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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
1212Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
2323Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
3636Courtney ParisDALC19311330.336385USSeptember 21, 198729Oklahoma716217325756.1000.061250.0283462568187000
4141Danielle AdamsCONF/C18510831.555880USFebruary 19, 198928Texas A&M51881164337.2123040.055100.0641044474900
8989Lynetta KizerCONC19310427.920213USApril 4, 199027Maryland5202384810048.0010.0233076.722355761171011900
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
4242Danielle RobinsonPHOG1755718.612245USOctober 5, 198927Oklahoma7286807917844.4050.0516183.61373861063345820900
8282Kristi ToliverWASG1705920.415225USJanuary 27, 198730Maryland92984511928441.96719434.5444989.895059912084834900
8686Leilani MitchellPHOG1655821.303949USJune 15, 198532Utah9306237018238.5319233.7627582.71257691082695023300
9697Moriah JeffersonSANG1685519.486961USAugust 3, 199423Connecticut1215148115552.392045.0202774.163137923324319100
141142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "42 42 Danielle Robinson PHO G 175 57 18.612245 \n", + "82 82 Kristi Toliver WAS G 170 59 20.415225 \n", + "86 86 Leilani Mitchell PHO G 165 58 21.303949 \n", + "96 97 Moriah Jefferson SAN G 168 55 19.486961 \n", + "141 142 Yvonne Turner PHO G 175 59 19.265306 \n", + "\n", + " Birth_Place Birthdate Age College Experience Games Played \\\n", + "42 US October 5, 1989 27 Oklahoma 7 28 \n", + "82 US January 27, 1987 30 Maryland 9 29 \n", + "86 US June 15, 1985 32 Utah 9 30 \n", + "96 US August 3, 1994 23 Connecticut 1 21 \n", + "141 US October 13, 1987 29 Nebraska 2 30 \n", + "\n", + " MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB \\\n", + "42 680 79 178 44.4 0 5 0.0 51 61 83.6 13 73 86 \n", + "82 845 119 284 41.9 67 194 34.5 44 49 89.8 9 50 59 \n", + "86 623 70 182 38.5 31 92 33.7 62 75 82.7 12 57 69 \n", + "96 514 81 155 52.3 9 20 45.0 20 27 74.1 6 31 37 \n", + "141 356 59 140 42.1 11 47 23.4 22 28 78.6 11 13 24 \n", + "\n", + " AST STL BLK TO PTS DD2 TD3 \n", + "42 106 33 4 58 209 0 0 \n", + "82 91 20 8 48 349 0 0 \n", + "86 108 26 9 50 233 0 0 \n", + "96 92 33 2 43 191 0 0 \n", + "141 30 18 1 32 151 0 0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "nba.loc[nba[\"Weight\"] < 60]" ] }, { @@ -89,11 +3325,74 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "plt.hist(nba[\"Height\"], 10)\n", + "plt.title(\"Player's Height\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Heitght (cm)\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"Weight\"], 10)\n", + "plt.title(\"Player's Weight\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Weight (Kg)\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"Age\"], 10)\n", + "plt.title(\"Player's Age\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Age (years)\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"BMI\"], 10)\n", + "plt.title(\"Player's BMI\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"BMI\")\n", + "plt.show()" ] }, { @@ -109,7 +3408,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# most players have more than 180cm\n", + "# players weight is faily distributed\n", + "# most players have less than 30years old\n", + "# BMI fairly disctributed around the mean" ] }, { @@ -134,11 +3436,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "plt.hist(nba[\"REB\"])\n", + "plt.title(\"Rebounds\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Amount of rebounds per player\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"AST\"])\n", + "plt.title(\"Assists\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Amount of assists per player\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"STL\"])\n", + "plt.title(\"Steals\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Amount of steals per player\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"PTS\"])\n", + "plt.title(\"Points\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Amount of points per player\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"BLK\"])\n", + "plt.title(\"Blocks\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Amount of blocks per player\")\n", + "plt.show()" ] }, { @@ -154,7 +3535,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# all are skewed to the right. most players have a lot of those things" ] }, { @@ -173,11 +3554,701 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3REB_per_minAST_per_minSTL_per_minPTS_per_minBLK_per_min
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.86222812361293000.1618500.0693640.0173410.5375720.034682
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.0198210172631340217000.1066530.0760300.0665260.2291450.013728
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.3436407822324218000.0648300.1264180.0356560.3533230.004862
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.03513416965201038188200.2343970.0901530.0277390.2607490.013870
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.7391212701450000.0875910.0875910.0510950.3649640.000000
.....................................................................................................................
137138Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.528891176937850467000.1358890.0801390.0429730.5423930.009292
138139Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.751823313828000.1811020.0236220.0078740.2204720.023622
139140Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.21670863931540277000.1281670.0581220.0462000.4128170.007452
140141Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.556212268752122715821100.2815130.0787820.0220590.6113450.023109
141142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.61113243018132151000.0674160.0842700.0505620.4241570.002809
\n", + "

142 rows × 38 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73 21.329438 \n", + "2 2 Alex Bentley CON G 170 69 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78 25.469388 \n", + ".. ... ... ... ... ... ... ... \n", + "137 138 Tiffany Hayes ATL G 178 70 22.093170 \n", + "138 139 Tiffany Jackson LA F 191 84 23.025685 \n", + "139 140 Tiffany Mitchell IND G 175 69 22.530612 \n", + "140 141 Tina Charles NY F/C 193 84 22.550941 \n", + "141 142 Yvonne Turner PHO G 175 59 19.265306 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + ".. ... ... ... ... ... \n", + "137 US September 20, 1989 27 Connecticut 6 \n", + "138 US April 26, 1985 32 Texas 9 \n", + "139 US September 23, 1984 32 South Carolina 2 \n", + "140 US May 12, 1988 29 Connecticut 8 \n", + "141 US October 13, 1987 29 Nebraska 2 \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 6 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 19 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 4 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 35 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 3 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... ... \n", + "137 29 861 144 331 43.5 43 112 38.4 136 161 84.5 28 \n", + "138 22 127 12 25 48.0 0 1 0.0 4 6 66.7 5 \n", + "139 27 671 83 238 34.9 17 69 24.6 94 102 92.2 16 \n", + "140 29 952 227 509 44.6 18 56 32.1 110 135 81.5 56 \n", + "141 30 356 59 140 42.1 11 47 23.4 22 28 78.6 11 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 REB_per_min AST_per_min \\\n", + "0 22 28 12 3 6 12 93 0 0 0.161850 0.069364 \n", + "1 82 101 72 63 13 40 217 0 0 0.106653 0.076030 \n", + "2 36 40 78 22 3 24 218 0 0 0.064830 0.126418 \n", + "3 134 169 65 20 10 38 188 2 0 0.234397 0.090153 \n", + "4 9 12 12 7 0 14 50 0 0 0.087591 0.087591 \n", + ".. ... ... ... ... ... .. ... ... ... ... ... \n", + "137 89 117 69 37 8 50 467 0 0 0.135889 0.080139 \n", + "138 18 23 3 1 3 8 28 0 0 0.181102 0.023622 \n", + "139 70 86 39 31 5 40 277 0 0 0.128167 0.058122 \n", + "140 212 268 75 21 22 71 582 11 0 0.281513 0.078782 \n", + "141 13 24 30 18 1 32 151 0 0 0.067416 0.084270 \n", + "\n", + " STL_per_min PTS_per_min BLK_per_min \n", + "0 0.017341 0.537572 0.034682 \n", + "1 0.066526 0.229145 0.013728 \n", + "2 0.035656 0.353323 0.004862 \n", + "3 0.027739 0.260749 0.013870 \n", + "4 0.051095 0.364964 0.000000 \n", + ".. ... ... ... \n", + "137 0.042973 0.542393 0.009292 \n", + "138 0.007874 0.220472 0.023622 \n", + "139 0.046200 0.412817 0.007452 \n", + "140 0.022059 0.611345 0.023109 \n", + "141 0.050562 0.424157 0.002809 \n", + "\n", + "[142 rows x 38 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nba[\"REB_per_min\"] = nba[\"REB\"]/nba[\"MIN\"]\n", + "\n", + "nba[\"AST_per_min\"] = nba[\"AST\"]/nba[\"MIN\"]\n", + "\n", + "nba[\"STL_per_min\"] = nba[\"STL\"]/nba[\"MIN\"]\n", + "\n", + "nba[\"PTS_per_min\"] = nba[\"PTS\"]/nba[\"MIN\"]\n", + "\n", + "nba[\"BLK_per_min\"] = nba[\"BLK\"]/nba[\"MIN\"]\n", + "\n", + "nba" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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UunRplS5dWtHR0frqq68c+40xGjNmjCpUqCB/f3+1atVKO3fu9GDFAADA23g0zFSsWFETJkzQ1q1btXXrVrVp00ZdunRxBJZJkyZp8uTJevfdd7VlyxaFhYWpXbt2ysjI8GTZAADAi3g0zHTu3Fl33XWXatSooRo1amjcuHEKCAjQpk2bZIzRlClTNGLECHXr1k316tXTnDlzdPr0ac2bN8+TZQMAAC/iNWtmcnJyFB8fr8zMTEVHR+vAgQNKTU1V+/btHX3sdrtatmypDRs2FHierKwspaenO70AAMD1y+PfALxjxw5FR0fr7NmzCggI0KJFi1SnTh1HYAkNDXXqHxoaqoMHDxZ4vtjYWI0dO9atNQOFYcVv0wUAK/L4zEzNmjWVmJioTZs26ZlnnlGfPn20a9cux36bzebU3xiTp+1Sw4cPV1pamuN1+PBht9UOAAA8z+MzM35+fqpWrZokqUmTJtqyZYumTp2ql156SZKUmpqq8PBwR/9jx47lma25lN1ul91ud2/RAADAa3h8ZubPjDHKyspSVFSUwsLCtHLlSse+c+fOafXq1WrWrJkHKwQAAN7EozMzL7/8sjp16qSIiAhlZGQoPj5eCQkJWrZsmWw2mwYNGqTx48erevXqql69usaPH6+SJUuqV69eniwbAAB4EY+GmV9//VUPP/ywUlJSFBQUpAYNGmjZsmVq166dJGno0KE6c+aM+vfvr5MnT6pp06ZasWKFAgMDPVk2AADwIjZjjPF0Ee6Unp6uoKAgpaWlqXTp0p4uB1eJTwahIMkT7vZ0CQDcwJX3b69bMwMAAOAKwgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0j4aZ2NhY3XLLLQoMDFRISIi6du2q3bt3O/Xp27evbDab0+u2227zUMUAAMDbeDTMrF69WjExMdq0aZNWrlyp7OxstW/fXpmZmU79OnbsqJSUFMdr6dKlHqoYAAB4m+KevPiyZcuctuPi4hQSEqJt27bpjjvucLTb7XaFhYVd6/IAAIAFeNWambS0NElS2bJlndoTEhIUEhKiGjVq6IknntCxY8cKPEdWVpbS09OdXgAA4PrlNWHGGKMhQ4aoefPmqlevnqO9U6dO+uijj/TNN9/orbfe0pYtW9SmTRtlZWXle57Y2FgFBQU5XhEREdfqFgAAgAfYjDHG00VIUkxMjL788kutW7dOFStWLLBfSkqKIiMjFR8fr27duuXZn5WV5RR00tPTFRERobS0NJUuXdottcP9Kg/70tMlwEslT7jb0yUAcIP09HQFBQUV6v3bo2tmLnr22Wf1+eefa82aNZcNMpIUHh6uyMhI7dmzJ9/9drtddrvdHWUCAAAv5NEwY4zRs88+q0WLFikhIUFRUVFXPObEiRM6fPiwwsPDr0GFAADA2/3lNTPp6elavHixkpKSXD42JiZG//nPfzRv3jwFBgYqNTVVqampOnPmjCTp1KlTeuGFF7Rx40YlJycrISFBnTt3VnBwsO67776/WjoAALgOuBxmHnjgAb377ruSpDNnzqhJkyZ64IEH1KBBAy1cuNClc02fPl1paWlq1aqVwsPDHa8FCxZIknx8fLRjxw516dJFNWrUUJ8+fVSjRg1t3LhRgYGBrpYOAACuQy4/ZlqzZo1GjBghSVq0aJGMMfrjjz80Z84cvf766+revXuhz3Wltcf+/v5avny5qyUCAIC/EZdnZtLS0hzfA7Ns2TJ1795dJUuW1N13313golwAAAB3cTnMREREaOPGjcrMzNSyZcvUvn17SdLJkydVokSJIi8QAADgclx+zDRo0CD17t1bAQEBioyMVKtWrSRdePxUv379oq4PAADgslwOM/3791fTpk116NAhtWvXTsWKXZjcqVKlil5//fUiLxAAAOByXHrMdP78eVWpUkX+/v667777FBAQ4Nh399136/bbby/yAgEAAC7HpTDj6+urrKws2Ww2d9UDAADgEpcXAD/77LOaOHGisrOz3VEPAACAS1xeM7N582Z9/fXXWrFiherXr69SpUo57f/000+LrDgAAIArcTnMlClTxqUvxgMAAHAnl8NMXFycO+oAAAC4Klf1Q5PZ2dlatWqV3n//fWVkZEiSjh49qlOnThVpcQAAAFfi8szMwYMH1bFjRx06dEhZWVlq166dAgMDNWnSJJ09e1b/+te/3FEnAABAvlyemRk4cKCaNGmikydPyt/f39F+33336euvvy7S4gAAAK7E5ZmZdevWaf369fLz83Nqj4yM1C+//FJkhQEAABSGyzMzubm5ysnJydN+5MgRBQYGFklRAAAAheVymGnXrp2mTJni2LbZbDp16pRGjx6tu+66qyhrAwAAuCKXHzO9/fbbat26terUqaOzZ8+qV69e2rNnj4KDgzV//nx31AgAAFAgl8NMhQoVlJiYqPnz5+v7779Xbm6uHnvsMfXu3dtpQTAAAMC14HKYyczMVKlSpdSvXz/169fPHTUBAAAUmstrZkJDQ9WvXz+tW7fOHfUAAAC4xOUwM3/+fKWlpenOO+9UjRo1NGHCBB09etQdtQEAAFyRy2Gmc+fOWrhwoY4ePapnnnlG8+fPV2RkpO655x59+umnys7OdkedAAAA+bqq32aSpHLlymnw4MH63//+p8mTJ2vVqlW6//77VaFCBY0aNUqnT58uyjoBAADy5fIC4ItSU1M1d+5cxcXF6dChQ7r//vv12GOP6ejRo5owYYI2bdqkFStWFGWtAAAAebgcZj799FPFxcVp+fLlqlOnjmJiYvTQQw+pTJkyjj4NGzZUo0aNirJOAACAfLkcZh599FH17NlT69ev1y233JJvnypVqmjEiBF/uTgAAIArcTnMpKSkqGTJkpft4+/vr9GjR191UQAAAIXlcpi5NMicOXNG58+fd9pfunTpv14VAABAIbn8aabMzEwNGDBAISEhCggI0A033OD0AgAAuJZcDjNDhw7VN998o2nTpslut2vmzJkaO3asKlSooLlz57qjRgAAgAK5/Jjpiy++0Ny5c9WqVSv169dPLVq0ULVq1RQZGamPPvpIvXv3dkedAAAA+XJ5Zub3339XVFSUpAvrY37//XdJUvPmzbVmzZqirQ4AAOAKXA4zVapUUXJysiSpTp06+u9//yvpwozNpd81AwAAcC24HGYeffRR/e9//5MkDR8+3LF2ZvDgwXrxxReLvEAAAIDLcXnNzODBgx3/u3Xr1vrpp5+0detWVa1aVTfddFORFgcAAHAlV/3bTBdVqlRJlSpVKopaAAAAXFaoMPPOO+8U+oTPPffcVRcDAADgqkKFmbfffrtQJ7PZbIQZAABwTRUqzBw4cMDddQAAAFwVlz/NdCljjIwxV318bGysbrnlFgUGBiokJERdu3bV7t2781xjzJgxqlChgvz9/dWqVSvt3Lnzr5QNAACuI1cVZmbNmqV69eqpRIkSKlGihOrVq6eZM2e6fJ7Vq1crJiZGmzZt0sqVK5Wdna327dsrMzPT0WfSpEmaPHmy3n33XW3ZskVhYWFq166dMjIyrqZ0AABwnXH500wjR47U22+/rWeffVbR0dGSpI0bN2rw4MFKTk7W66+/XuhzLVu2zGk7Li5OISEh2rZtm+644w4ZYzRlyhSNGDFC3bp1kyTNmTNHoaGhmjdvnp566ilXywcAANcZl8PM9OnTNWPGDD344IOOtnvvvVcNGjTQs88+61KY+bO0tDRJUtmyZSVdWKuTmpqq9u3bO/rY7Xa1bNlSGzZsIMwAAADXw0xOTo6aNGmSp71x48bKzs6+6kKMMRoyZIiaN2+uevXqSZJSU1MlSaGhoU59Q0NDdfDgwXzPk5WVpaysLMd2enr6VdcEAAC8n8trZh566CFNnz49T/sHH3zwl34xe8CAAfrhhx80f/78PPtsNpvTtjEmT9tFsbGxCgoKcrwiIiKuuiYAAOD9ruobgGfNmqUVK1botttukyRt2rRJhw8f1iOPPKIhQ4Y4+k2ePLlQ53v22Wf1+eefa82aNapYsaKjPSwsTNKFGZrw8HBH+7Fjx/LM1lw0fPhwpxrS09MJNAAAXMdcDjM//vijbr75ZknSvn37JEnly5dX+fLl9eOPPzr6FTRzciljjJ599lktWrRICQkJioqKctofFRWlsLAwrVy5Uo0aNZIknTt3TqtXr9bEiRPzPafdbpfdbnf1tgAAgEW5HGa+/fbbIrt4TEyM5s2bp88++0yBgYGONTJBQUHy9/eXzWbToEGDNH78eFWvXl3Vq1fX+PHjVbJkSfXq1avI6gAAANb1l39o8q+4uPamVatWTu1xcXHq27evJGno0KE6c+aM+vfvr5MnT6pp06ZasWKFAgMDr3G1AADAG3k0zBTm24NtNpvGjBmjMWPGuL8gAABgOX/p5wwAAAA8jTADAAAsrVBh5uabb9bJkyclSa+++qpOnz7t1qIAAAAKq1BhJikpyfHjj2PHjtWpU6fcWhQAAEBhFWoBcMOGDfXoo4+qefPmMsbozTffVEBAQL59R40aVaQFAgAAXE6hwszs2bM1evRoLVmyRDabTV999ZWKF897qM1mI8wAAIBrqlBhpmbNmoqPj5ckFStWTF9//bVCQkLcWhgAAEBhuPw9M7m5ue6oAwAA4Kpc1Zfm7du3T1OmTFFSUpJsNptq166tgQMHqmrVqkVdHwAAwGW5/D0zy5cvV506dfTdd9+pQYMGqlevnjZv3qy6detq5cqV7qgRAACgQC7PzAwbNkyDBw/WhAkT8rS/9NJLateuXZEVBwAAcCUuz8wkJSXpsccey9Per18/7dq1q0iKAgAAKCyXw0z58uWVmJiYpz0xMZFPOAEAgGvO5cdMTzzxhJ588knt379fzZo1k81m07p16zRx4kQ9//zz7qgRAACgQC6HmZEjRyowMFBvvfWWhg8fLkmqUKGCxowZo+eee67ICwQAALgcl8OMzWbT4MGDNXjwYGVkZEiSAgMDi7wwAACAwriq75m5iBADAAA8zeUFwAAAAN6EMAMAACyNMAMAACzNpTBz/vx5tW7dWj///LO76gEAAHCJS2HG19dXP/74o2w2m7vqAQAAcInLj5keeeQRzZo1yx21AAAAuMzlj2afO3dOM2fO1MqVK9WkSROVKlXKaf/kyZOLrDgAAIArcTnM/Pjjj7r55pslKc/aGR4/AQCAa83lMPPtt9+6ow4AAICrctUfzd67d6+WL1+uM2fOSJKMMUVWFAAAQGG5HGZOnDihO++8UzVq1NBdd92llJQUSdLjjz/Or2YDAIBrzuUwM3jwYPn6+urQoUMqWbKko71Hjx5atmxZkRYHAABwJS6vmVmxYoWWL1+uihUrOrVXr15dBw8eLLLCAAAACsPlmZnMzEynGZmLjh8/LrvdXiRFAQAAFJbLYeaOO+7Q3LlzHds2m025ubl644031Lp16yItDgAA4Epcfsz0xhtvqFWrVtq6davOnTunoUOHaufOnfr999+1fv16d9QIAABQIJdnZurUqaMffvhBt956q9q1a6fMzEx169ZN27dvV9WqVd1RIwAAQIFcnpmRpLCwMI0dO7aoawEAAHDZVYWZkydPatasWUpKSpLNZlPt2rX16KOPqmzZskVdHwAAwGW5/Jhp9erVioqK0jvvvKOTJ0/q999/1zvvvKOoqCitXr3aHTUCAAAUyOWZmZiYGD3wwAOaPn26fHx8JEk5OTnq37+/YmJi9OOPPxZ5kQAAAAVxeWZm3759ev755x1BRpJ8fHw0ZMgQ7du3r0iLAwAAuBKXw8zNN9+spKSkPO1JSUlq2LBhUdQEAABQaIUKMz/88IPj9dxzz2ngwIF68803tW7dOq1bt05vvvmmBg8erEGDBrl08TVr1qhz586qUKGCbDabFi9e7LS/b9++stlsTq/bbrvNpWsAAIDrW6HWzDRs2FA2m03GGEfb0KFD8/Tr1auXevToUeiLZ2Zm6qabbtKjjz6q7t2759unY8eOiouLc2z7+fkV+vwAAOD6V6gwc+DAAbdcvFOnTurUqdNl+9jtdoWFhbnl+gAAwPoKFWYiIyPdXUeBEhISFBISojJlyqhly5YaN26cQkJCCuyflZWlrKwsx3Z6evq1KBMAAHjIVX1p3i+//KL169fr2LFjys3Nddr33HPPFUlh0oWZm3/84x+KjIzUgQMHNHLkSLVp00bbtm0r8Be6Y2Nj+XZiAAD+Rmzm0oUwhRAXF6enn35afn5+KleunGw22/+dzGbT/v37r64Qm02LFi1S165dC+yTkpKiyMhIxcfHq1u3bvn2yW9mJiIiQmlpaSpduvRV1QbPqzzsS0+XAC+VPOFuT5cAwA3S09MVFBRUqPdvl2dmRo0apVGjRmn48OEqVszlT3b/JeHh4YqMjNSePXsK7GO32wuctQEAANcfl9PI6dOn1bNnz2seZCTpxIkTOnz4sMLDw6/5tQEAgHdyOZE89thj+vjjj4vk4qdOnVJiYqISExMlXfjUVGJiog4dOqRTp07phRde0MaNG5WcnKyEhAR17txZwcHBuu+++4rk+gAAwPpcfswUGxure+65R8uWLVP9+vXl6+vrtH/y5MmFPtfWrVvVunVrx/aQIUMkSX369NH06dO1Y8cOzZ07V3/88YfCw8PVunVrLViwQIGBga6WDQAArlMuh5nx48dr+fLlqlmzpiTlWQDsilatWuly64+XL1/uankAAOBvxuUwM3nyZH344Yfq27evG8oBAABwjctrZux2u26//XZ31AIAAOAyl8PMwIED9c9//tMdtQAAALjM5cdM3333nb755hstWbJEdevWzbMA+NNPPy2y4gAAAK7E5TBTpkyZAr99FwAA4FpzOczExcW5ow4AAICrcu2/xhcAAKAIuTwzExUVddnvk7naH5oEAAC4Gi6HmUGDBjltnz9/Xtu3b9eyZcv04osvFlVdAAAAheJymBk4cGC+7e+99562bt36lwsCAABwRZGtmenUqZMWLlxYVKcDAAAolCILM5988onKli1bVKcDAAAoFJcfMzVq1MhpAbAxRqmpqfrtt980bdq0Ii0OAADgSlwOM127dnXaLlasmMqXL69WrVqpVq1aRVUXAFy3Kg/70tMluCx5wt2eLgEokMthZvTo0e6oAwAA4KrwpXkAAMDSCj0zU6xYsct+WZ4k2Ww2ZWdn/+WiAAAACqvQYWbRokUF7tuwYYP++c9/yhhTJEUBAAAUVqHDTJcuXfK0/fTTTxo+fLi++OIL9e7dW6+99lqRFgcAAHAlV7Vm5ujRo3riiSfUoEEDZWdnKzExUXPmzFGlSpWKuj4AAIDLcinMpKWl6aWXXlK1atW0c+dOff311/riiy9Ur149d9UHAABwWYV+zDRp0iRNnDhRYWFhmj9/fr6PnQAAAK61QoeZYcOGyd/fX9WqVdOcOXM0Z86cfPt9+umnRVYcAADAlRQ6zDzyyCNX/Gg2rMGK3z4KAEBBCh1mZs+e7cYyAAAArg7fAAwAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACzNo2FmzZo16ty5sypUqCCbzabFixc77TfGaMyYMapQoYL8/f3VqlUr7dy50zPFAgAAr+TRMJOZmambbrpJ7777br77J02apMmTJ+vdd9/Vli1bFBYWpnbt2ikjI+MaVwoAALxVcU9evFOnTurUqVO++4wxmjJlikaMGKFu3bpJkubMmaPQ0FDNmzdPTz311LUsFQAAeCmvXTNz4MABpaamqn379o42u92uli1basOGDQUel5WVpfT0dKcXAAC4fnl0ZuZyUlNTJUmhoaFO7aGhoTp48GCBx8XGxmrs2LFurQ2A96g87EtPlwDAw7x2ZuYim83mtG2MydN2qeHDhystLc3xOnz4sLtLBAAAHuS1MzNhYWGSLszQhIeHO9qPHTuWZ7bmUna7XXa73e31AQAA7+C1MzNRUVEKCwvTypUrHW3nzp3T6tWr1axZMw9WBgAAvIlHZ2ZOnTqlvXv3OrYPHDigxMRElS1bVpUqVdKgQYM0fvx4Va9eXdWrV9f48eNVsmRJ9erVy4NVAwAAb+LRMLN161a1bt3asT1kyBBJUp8+fTR79mwNHTpUZ86cUf/+/XXy5Ek1bdpUK1asUGBgoKdKBgAAXsZmjDGeLsKd0tPTFRQUpLS0NJUuXdrT5XgFPv0BwFXJE+72dAn4m3Hl/dtr18wAAAAUBmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYWnFPF2B1/AI1AACexcwMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNK8OM2PGjJHNZnN6hYWFebosAADgRYp7uoArqVu3rlatWuXY9vHx8WA1AADA23h9mClevDizMQAAoEBe/ZhJkvbs2aMKFSooKipKPXv21P79+y/bPysrS+np6U4vAABw/fLqMNO0aVPNnTtXy5cv14wZM5SamqpmzZrpxIkTBR4TGxuroKAgxysiIuIaVgwAAK41mzHGeLqIwsrMzFTVqlU1dOhQDRkyJN8+WVlZysrKcmynp6crIiJCaWlpKl26dJHXVHnYl0V+TgDwNskT7vZ0CfibSU9PV1BQUKHev71+zcylSpUqpfr162vPnj0F9rHb7bLb7dewKgAA4Ele/Zjpz7KyspSUlKTw8HBPlwIAALyEV4eZF154QatXr9aBAwe0efNm3X///UpPT1efPn08XRoAAPASXv2Y6ciRI3rwwQd1/PhxlS9fXrfddps2bdqkyMhIT5cGAAC8hFeHmfj4eE+XAAAAvJxXP2YCAAC4EsIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwtOKeLgAAAFxQediXni7BZckT7vZ0CczMAAAAayPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAAS+MbgAEAV8Q308KbMTMDAAAsjTADAAAsjTADAAAsjTADAAAszRJhZtq0aYqKilKJEiXUuHFjrV271tMlAQAAL+H1YWbBggUaNGiQRowYoe3bt6tFixbq1KmTDh065OnSAACAF/D6MDN58mQ99thjevzxx1W7dm1NmTJFERERmj59uqdLAwAAXsCrw8y5c+e0bds2tW/f3qm9ffv22rBhg4eqAgAA3sSrvzTv+PHjysnJUWhoqFN7aGioUlNT8z0mKytLWVlZju20tDRJUnp6ultqzM067ZbzAgD+Gnf9/747WfE9xV3jfPG8xpgr9vXqMHORzWZz2jbG5Gm7KDY2VmPHjs3THhER4ZbaAADeKWiKpyv4e3D3OGdkZCgoKOiyfbw6zAQHB8vHxyfPLMyxY8fyzNZcNHz4cA0ZMsSxnZubq99//13lypUrMABdrfT0dEVEROjw4cMqXbp0kZ4bV8b4exbj71mMv2cx/u5njFFGRoYqVKhwxb5eHWb8/PzUuHFjrVy5Uvfdd5+jfeXKlerSpUu+x9jtdtntdqe2MmXKuLNMlS5dmn+YPYjx9yzG37MYf89i/N3rSjMyF3l1mJGkIUOG6OGHH1aTJk0UHR2tDz74QIcOHdLTTz/t6dIAAIAX8Pow06NHD504cUKvvvqqUlJSVK9ePS1dulSRkZGeLg0AAHgBrw8zktS/f3/179/f02XkYbfbNXr06DyPtXBtMP6exfh7FuPvWYy/d7GZwnzmCQAAwEt59ZfmAQAAXAlhBgAAWBphBgAAWBphBgAAWBph5hLTpk1TVFSUSpQoocaNG2vt2rWX7b969Wo1btxYJUqUUJUqVfSvf/0rT5+FCxeqTp06stvtqlOnjhYtWuSu8i2vqMd/xowZatGihW644QbdcMMNatu2rb777jt33oKlueOf/4vi4+Nls9nUtWvXIq76+uKOv8Eff/yhmJgYhYeHq0SJEqpdu7aWLl3qrluwNHeM/5QpU1SzZk35+/srIiJCgwcP1tmzZ911C39fBsYYY+Lj442vr6+ZMWOG2bVrlxk4cKApVaqUOXjwYL799+/fb0qWLGkGDhxodu3aZWbMmGF8fX3NJ5984uizYcMG4+PjY8aPH2+SkpLM+PHjTfHixc2mTZuu1W1ZhjvGv1evXua9994z27dvN0lJSebRRx81QUFB5siRI9fqtizDHeN/UXJysrnxxhtNixYtTJcuXdx8J9bljr9BVlaWadKkibnrrrvMunXrTHJyslm7dq1JTEy8VrdlGe4Y///85z/Gbrebjz76yBw4cMAsX77chIeHm0GDBl2r2/rbIMz8f7feeqt5+umnndpq1aplhg0blm//oUOHmlq1ajm1PfXUU+a2225zbD/wwAOmY8eOTn06dOhgevbsWURVXz/cMf5/lp2dbQIDA82cOXP+esHXGXeNf3Z2trn99tvNzJkzTZ8+fQgzl+GOv8H06dNNlSpVzLlz54q+4OuMO8Y/JibGtGnTxqnPkCFDTPPmzYuoalzEYyZJ586d07Zt29S+fXun9vbt22vDhg35HrNx48Y8/Tt06KCtW7fq/Pnzl+1T0Dn/rtw1/n92+vRpnT9/XmXLli2awq8T7hz/V199VeXLl9djjz1W9IVfR9z1N/j8888VHR2tmJgYhYaGql69eho/frxycnLccyMW5a7xb968ubZt2+Z4vL1//34tXbpUd999txvu4u/NEt8A7G7Hjx9XTk5Onl/iDg0NzfOL3Relpqbm2z87O1vHjx9XeHh4gX0KOufflbvG/8+GDRumG2+8UW3bti264q8D7hr/9evXa9asWUpMTHRX6dcNd/0N9u/fr2+++Ua9e/fW0qVLtWfPHsXExCg7O1ujRo1y2/1YjbvGv2fPnvrtt9/UvHlzGWOUnZ2tZ555RsOGDXPbvfxdEWYuYbPZnLaNMXnartT/z+2unvPvzB3jf9GkSZM0f/58JSQkqESJEkVQ7fWnKMc/IyNDDz30kGbMmKHg4OCiL/Y6VdT/DuTm5iokJEQffPCBfHx81LhxYx09elRvvPEGYSYfRT3+CQkJGjdunKZNm6amTZtq7969GjhwoMLDwzVy5Mgirv7vjTAjKTg4WD4+PnkS+LFjx/Ik74vCwsLy7V+8eHGVK1fusn0KOufflbvG/6I333xT48eP16pVq9SgQYOiLf464I7x37lzp5KTk9W5c2fH/tzcXElS8eLFtXv3blWtWrWI78S63PXvQHh4uHx9feXj4+PoU7t2baWmpurcuXPy8/Mr4juxJneN/8iRI/Xwww/r8ccflyTVr19fmZmZevLJJzVixAgVK8ZKj6LCSEry8/NT48aNtXLlSqf2lStXqlmzZvkeEx0dnaf/ihUr1KRJE/n6+l62T0Hn/Lty1/hL0htvvKHXXntNy5YtU5MmTYq++OuAO8a/Vq1a2rFjhxITEx2ve++9V61bt1ZiYqIiIiLcdj9W5K5/B26//Xbt3bvXESQl6eeff1Z4eDhB5hLuGv/Tp0/nCSw+Pj4yFz58U4R3AD7N9P9d/FjerFmzzK5du8ygQYNMqVKlTHJysjHGmGHDhpmHH37Y0f/ix/IGDx5sdu3aZWbNmpXnY3nr1683Pj4+ZsKECSYpKclMmDCBj2YXwB3jP3HiROPn52c++eQTk5KS4nhlZGRc8/vzdu4Y/z/j00yX546/waFDh0xAQIAZMGCA2b17t1myZIkJCQkxr7/++jW/P2/njvEfPXq0CQwMNPPnzzf79+83K1asMFWrVjUPPPDANb+/6x1h5hLvvfeeiYyMNH5+fubmm282q1evduzr06ePadmypVP/hIQE06hRI+Pn52cqV65spk+fnuecH3/8salZs6bx9fU1tWrVMgsXLnT3bVhWUY9/ZGSkkZTnNXr06GtwN9bjjn/+L0WYuTJ3/A02bNhgmjZtaux2u6lSpYoZN26cyc7OdvetWFJRj//58+fNmDFjTNWqVU2JEiVMRESE6d+/vzl58uQ1uJu/F5sxzHUBAADrYs0MAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAMsbM2aMGjZs6Oky3CY5OVk2m41fIAcKQJgB3GzDhg3y8fFRx44dPV2KR/GGfPUiIiKUkpKievXqFel5W7VqpUGDBhXpOQFPIMwAbvbhhx/q2Wef1bp163To0CG3XisnJ8fpRwXhOefOnSuyc/n4+CgsLEzFixcvsnMC1xPCDOBGmZmZ+u9//6tnnnlG99xzj2bPnu3YFx0drWHDhjn1/+233+Tr66tvv/1W0oU3xKFDh+rGG29UqVKl1LRpUyUkJDj6z549W2XKlNGSJUtUp04d2e12HTx4UFu2bFG7du0UHBysoKAgtWzZUt9//73TtX766Sc1b95cJUqUUJ06dbRq1SrZbDYtXrzY0eeXX35Rjx49dMMNN6hcuXLq0qWLkpOTC7zfkydPqnfv3ipfvrz8/f1VvXp1xcXFSZKioqIkSY0aNZLNZlOrVq0cx8XFxal27doqUaKEatWqpWnTpjmd96WXXlKNGjVUsmRJValSRSNHjtT58+cLrCMhIUG33nqrSpUqpTJlyuj222/XwYMH8+17ccYoPj5ezZo1U4kSJVS3bl2ncZakXbt26a677lJAQIBCQ0P18MMP6/jx4479rVq10oABAzRkyBAFBwerXbt2+V6vb9++6tq1q8aPH6/Q0FCVKVNGY8eOVXZ2tl588UWVLVtWFStW1IcffpinxouzWgkJCbLZbPr666/VpEkTlSxZUs2aNdPu3bvzXOdSgwYNcox73759tXr1ak2dOlU2m002m83xt73SvQLehjADuNGCBQtUs2ZN1axZUw899JDi4uJ08efQevfurfnz5+vSn0dbsGCBQkND1bJlS0nSo48+qvXr1ys+Pl4//PCD/vGPf6hjx47as2eP45jTp08rNjZWM2fO1M6dOxUSEqKMjAz16dNHa9eu1aZNm1S9enXdddddysjIkCTl5uaqa9euKlmypDZv3qwPPvhAI0aMcKr99OnTat26tQICArRmzRqtW7dOAQEB6tixY4GzDiNHjtSuXbv01VdfKSkpSdOnT1dwcLAk6bvvvpMkrVq1SikpKfr0008lSTNmzNCIESM0btw4JSUlafz48Ro5cqTmzJnjOG9gYKBmz56tXbt2aerUqZoxY4befvvtfGvIzs5W165d1bJlS/3www/auHGjnnzySdlstsv+rV588UU9//zz2r59u5o1a6Z7771XJ06ckCSlpKSoZcuWatiwobZu3aply5bp119/1QMPPOB0jjlz5qh48eJav3693n///QKv9c033+jo0aNas2aNJk+erDFjxuiee+7RDTfcoM2bN+vpp5/W008/rcOHD1+25hEjRuitt97S1q1bVbx4cfXr1++y/S81depURUdH64knnlBKSopSUlIcj7MKc6+AV/Hs71wC17dmzZqZKVOmGGMu/IJucHCwWblypTHGmGPHjpnixYubNWvWOPpHR0ebF1980RhjzN69e43NZjO//PKL0znvvPNOM3z4cGOMMXFxcUaSSUxMvGwd2dnZJjAw0HzxxRfGGGO++uorU7x4cZOSkuLos3LlSiPJLFq0yBhjzKxZs0zNmjVNbm6uo09WVpbx9/c3y5cvz/c6nTt3No8++mi++w4cOGAkme3btzu1R0REmHnz5jm1vfbaayY6OrrA+5k0aZJp3LixY3v06NHmpptuMsYYc+LECSPJJCQkFHh8fnVNmDDB0Xb+/HlTsWJFM3HiRGOMMSNHjjTt27d3Ou7w4cNGktm9e7cxxpiWLVuahg0bXvF6ffr0MZGRkSYnJ8fRVrNmTdOiRQvHdnZ2tilVqpSZP3++U40Xx+7bb781ksyqVascx3z55ZdGkjlz5ozjOn/+lfKBAwc6/fJzy5YtzcCBA536FOZeAW/DA1jATXbv3q3vvvvOMQNRvHhx9ejRQx9++KHatm2r8uXLq127dvroo4/UokULHThwQBs3btT06dMlSd9//72MMapRo4bTebOyslSuXDnHtp+fnxo0aODU59ixYxo1apS++eYb/frrr8rJydHp06cda3Z2796tiIgIhYWFOY659dZbnc6xbds27d27V4GBgU7tZ8+e1b59+/K952eeeUbdu3fX999/r/bt26tr165q1qxZgWP022+/6fDhw3rsscf0xBNPONqzs7MVFBTk2P7kk080ZcoU7d27V6dOnVJ2drZKly6d7znLli2rvn37qkOHDmrXrp3atm2rBx54QOHh4QXWIV147HdR8eLF1aRJEyUlJTnG4ttvv1VAQECe4/bt2+f4GzVp0uSy17iobt26Klbs/ybGQ0NDnRb3+vj4qFy5cjp27Nhlz3Pp3/3i/R07dkyVKlUqVB35Key9At6EMAO4yaxZs5Sdna0bb7zR0WaMka+vr06ePKkbbrhBvXv31sCBA/XPf/5T8+bNU926dXXTTTdJuvAoyMfHR9u2bZOPj4/TuS99o/H398/zCKVv37767bffNGXKFEVGRsputys6OtrxeMgYc8XHLrm5uWrcuLE++uijPPvKly+f7zGdOnXSwYMH9eWXX2rVqlW68847FRMTozfffLPAa0gXHjU1bdrUad/Fe960aZN69uypsWPHqkOHDgoKClJ8fLzeeuutAmuPi4vTc889p2XLlmnBggV65ZVXtHLlSt12222Xvec/uzhGubm56ty5syZOnJinz6UhqVSpUoU6r6+vb57r5Nd2pcXclx5zaa2SVKxYMadHmJIuu87oosLeK+BNCDOAG2RnZ2vu3Ll666231L59e6d93bt310cffaQBAwaoa9eueuqpp7Rs2TLNmzdPDz/8sKNfo0aNlJOTo2PHjqlFixYuXX/t2rWaNm2a7rrrLknS4cOHnRZw1qpVS4cOHdKvv/6q0NBQSdKWLVucznHzzTdrwYIFCgkJKXAWJD/ly5dX37591bdvX7Vo0UIvvvii3nzzTfn5+Um68Imri0JDQ3XjjTdq//796t27d77nW79+vSIjI53W9BS0mPdSjRo1UqNGjTR8+HBFR0dr3rx5lw0zmzZt0h133CHpwt9v27ZtGjBggKQLY7Fw4UJVrlzZMp8oKl++vH788UentsTERKcA5Ofn5/T3kKx5rwALgAE3WLJkiU6ePKnHHntM9erVc3rdf//9mjVrlqQL/yXfpUsXjRw5UklJSerVq5fjHDVq1FDv3r31yCOP6NNPP9WBAwe0ZcsWTZw4UUuXLr3s9atVq6Z///vfSkpK0ubNm9W7d2/5+/s79rdr105Vq1ZVnz599MMPP2j9+vWOsHDxv/B79+6t4OBgdenSRWvXrtWBAwe0evVqDRw4UEeOHMn3uqNGjdJnn32mvXv3aufOnVqyZIlq164tSQoJCZG/v79jQWlaWpqkC194Fxsbq6lTp+rnn3/Wjh07FBcXp8mTJzvu5dChQ4qPj9e+ffv0zjvvaNGiRQXe+4EDBzR8+HBt3LhRBw8e1IoVK/Tzzz876ijIe++9p0WLFumnn35STEyMTp486VhQGxMTo99//10PPvigvvvuO+3fv18rVqxQv3798oQBb9GmTRtt3bpVc+fO1Z49ezR69Og84aZy5cravHmzkpOTdfz4ceXm5lryXgHCDOAGs2bNUtu2bZ3WfVzUvXt3JSYmOj4q3bt3b/3vf/9TixYt8qx1iIuL0yOPPKLnn39eNWvW1L333qvNmzcrIiListf/8MMPdfLkSTVq1EgPP/ywnnvuOYWEhDj2+/j4aPHixTp16pRuueUWPf7443rllVckSSVKlJAklSxZUmvWrFGlSpXUrVs31a5dW/369dOZM2cKnKnx8/PT8OHD1aBBA91xxx3y8fFRfHy8pAvrUN555x29//77qlChgrp06SJJevzxxzVz5kzNnj1b9evXV8uWLTV79mzHR7m7dOmiwYMHa8CAAWrYsKE2bNigkSNHFnjvJUuW1E8//aTu3burRo0aevLJJzVgwAA99dRTlx2zCRMmaOLEibrpppu0du1affbZZ45PYlWoUEHr169XTk6OOnTooHr16mngwIEKCgpyWvviTTp06KCRI0dq6NChuuWWW5SRkaFHHnnEqc8LL7wgHx8f1alTR+XLl9ehQ4csea+Azfz5oSqAv6X169erefPm2rt3r6pWrerpcq6Z5ORkRUVFafv27df1TyIA1zMeiAJ/U4sWLVJAQICqV6+uvXv3auDAgbr99tv/VkEGwPWBMAP8TWVkZGjo0KE6fPiwgoOD1bZt28t+QggAvBWPmQAAgKWxmgsAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFja/wMVgtDCOxfhAAAAAABJRU5ErkJggg==\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "plt.hist(nba[\"BLK_per_min\"])\n", + "plt.title(\"Blocks per minute\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Average blocks per minute\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"REB_per_min\"])\n", + "plt.title(\"Rebounds per minute\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Average rebounds per minute\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"STL_per_min\"])\n", + "plt.title(\"Steals per minute\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Average steals per minute\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"PTS_per_min\"])\n", + "plt.title(\"Points per minute\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Average points per minute\")\n", + "plt.show()\n", + "\n", + "plt.hist(nba[\"AST_per_min\"])\n", + "plt.title(\"Assists per minute\")\n", + "plt.ylabel(\"Number of players\")\n", + "plt.xlabel(\"Average assists per minute\")\n", + "plt.show()" ] }, { @@ -193,7 +4264,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# most of the plots are more balanced, closer to normal distributions, \n", + "# execpt for the blocks plot" ] }, { @@ -216,19 +4288,913 @@ "**Do you think you have all the necessary data to answer these questions?**" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3REB_per_minAST_per_minSTL_per_minPTS_per_minBLK_per_min
4242Danielle RobinsonPHOG1755718.612245USOctober 5, 198927Oklahoma7286807917844.4050.0516183.613738610633458209000.1264710.1558820.0485290.3073530.005882
8282Kristi ToliverWASG1705920.415225USJanuary 27, 198730Maryland92984511928441.96719434.5444989.8950599120848349000.0698220.1076920.0236690.4130180.009467
8686Leilani MitchellPHOG1655821.303949USJune 15, 198532Utah9306237018238.5319233.7627582.712576910826950233000.1107540.1733550.0417340.3739970.014446
9697Moriah JeffersonSANG1685519.486961USAugust 3, 199423Connecticut1215148115552.392045.0202774.1631379233243191000.0719840.1789880.0642020.3715950.003891
141142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.61113243018132151000.0674160.0842700.0505620.4241570.002809
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "42 42 Danielle Robinson PHO G 175 57 18.612245 \n", + "82 82 Kristi Toliver WAS G 170 59 20.415225 \n", + "86 86 Leilani Mitchell PHO G 165 58 21.303949 \n", + "96 97 Moriah Jefferson SAN G 168 55 19.486961 \n", + "141 142 Yvonne Turner PHO G 175 59 19.265306 \n", + "\n", + " Birth_Place Birthdate Age College Experience Games Played \\\n", + "42 US October 5, 1989 27 Oklahoma 7 28 \n", + "82 US January 27, 1987 30 Maryland 9 29 \n", + "86 US June 15, 1985 32 Utah 9 30 \n", + "96 US August 3, 1994 23 Connecticut 1 21 \n", + "141 US October 13, 1987 29 Nebraska 2 30 \n", + "\n", + " MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB \\\n", + "42 680 79 178 44.4 0 5 0.0 51 61 83.6 13 73 86 \n", + "82 845 119 284 41.9 67 194 34.5 44 49 89.8 9 50 59 \n", + "86 623 70 182 38.5 31 92 33.7 62 75 82.7 12 57 69 \n", + "96 514 81 155 52.3 9 20 45.0 20 27 74.1 6 31 37 \n", + "141 356 59 140 42.1 11 47 23.4 22 28 78.6 11 13 24 \n", + "\n", + " AST STL BLK TO PTS DD2 TD3 REB_per_min AST_per_min STL_per_min \\\n", + "42 106 33 4 58 209 0 0 0.126471 0.155882 0.048529 \n", + "82 91 20 8 48 349 0 0 0.069822 0.107692 0.023669 \n", + "86 108 26 9 50 233 0 0 0.110754 0.173355 0.041734 \n", + "96 92 33 2 43 191 0 0 0.071984 0.178988 0.064202 \n", + "141 30 18 1 32 151 0 0 0.067416 0.084270 0.050562 \n", + "\n", + " PTS_per_min BLK_per_min \n", + "42 0.307353 0.005882 \n", + "82 0.413018 0.009467 \n", + "86 0.373997 0.014446 \n", + "96 0.371595 0.003891 \n", + "141 0.424157 0.002809 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1)\n", + "\n", + "nba.loc[nba[\"Weight\"] < 60]\n", + "\n", + "# there are players weighting less than 60kg, that played over 20 games, \n", + "# so i think my sister could do it." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3REB_per_minAST_per_minSTL_per_minPTS_per_minBLK_per_min
1414Aneika HenryATLF/C1938723.356332JMFebruary 13, 198631Florida642244100.0000.0000.004412038000.1818180.0454550.0909090.3636360.000000
3535Clarissa dos SantosSANC1858926.004383BROctober 3, 198828Brazil475281457.111100.0000.03710711517000.1923080.1346150.0192310.3269230.019231
5656Glory JohnsonDALF1917721.106878USJuly 27, 199027Tennessee44423933.33650.0000.003310049000.0714290.0238100.0000000.2142860.000000
8484Lanay MontgomerySEAC1969624.989588USSeptember 17, 199323West VirginiaR7283742.9000.0000.005501426000.1785710.0000000.0357140.2142860.142857
114115Sandrine GrudaLAF/C1938422.550941FRJune 25, 198730France54121333.3000.0000.002200022000.1666670.0000000.0000000.1666670.000000
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" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "14 14 Aneika Henry ATL F/C 193 87 23.356332 \n", + "35 35 Clarissa dos Santos SAN C 185 89 26.004383 \n", + "56 56 Glory Johnson DAL F 191 77 21.106878 \n", + "84 84 Lanay Montgomery SEA C 196 96 24.989588 \n", + "114 115 Sandrine Gruda LA F/C 193 84 22.550941 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "14 JM February 13, 1986 31 Florida 6 \n", + "35 BR October 3, 1988 28 Brazil 4 \n", + "56 US July 27, 1990 27 Tennessee 4 \n", + "84 US September 17, 1993 23 West Virginia R \n", + "114 FR June 25, 1987 30 France 5 \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "14 4 22 4 4 100.0 0 0 0.0 0 0 0.0 0 \n", + "35 7 52 8 14 57.1 1 1 100.0 0 0 0.0 3 \n", + "56 4 42 3 9 33.3 3 6 50.0 0 0 0.0 0 \n", + "84 7 28 3 7 42.9 0 0 0.0 0 0 0.0 0 \n", + "114 4 12 1 3 33.3 0 0 0.0 0 0 0.0 0 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 REB_per_min AST_per_min \\\n", + "14 4 4 1 2 0 3 8 0 0 0.181818 0.045455 \n", + "35 7 10 7 1 1 5 17 0 0 0.192308 0.134615 \n", + "56 3 3 1 0 0 4 9 0 0 0.071429 0.023810 \n", + "84 5 5 0 1 4 2 6 0 0 0.178571 0.000000 \n", + "114 2 2 0 0 0 2 2 0 0 0.166667 0.000000 \n", + "\n", + " STL_per_min PTS_per_min BLK_per_min \n", + "14 0.090909 0.363636 0.000000 \n", + "35 0.019231 0.326923 0.019231 \n", + "56 0.000000 0.214286 0.000000 \n", + "84 0.035714 0.214286 0.142857 \n", + "114 0.000000 0.166667 0.000000 " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2) \n", + "\n", + "nba.loc[nba[\"FTM\"] == 0]\n", + "\n", + "# all the girls that have 0 free throws is because they also have 0 attempts." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3REB_per_minAST_per_minSTL_per_minPTS_per_minBLK_per_min
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" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "90 90 Maimouna Diarra LA C 198 90 22.956841 \n", + "\n", + " Birth_Place Birthdate Age College Experience Games Played MIN \\\n", + "90 SN January 30, 1991 26 Sengal R 9 16 \n", + "\n", + " FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "90 1 3 33.3 0 0 0.0 1 2 50.0 3 4 7 1 1 \n", + "\n", + " BLK TO PTS DD2 TD3 REB_per_min AST_per_min STL_per_min \\\n", + "90 0 3 3 0 0 0.4375 0.0625 0.0625 \n", + "\n", + " PTS_per_min BLK_per_min \n", + "90 0.1875 0.0 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2)\n", + "\n", + "nba.loc[(nba[\"FTA\"] >0) & (nba[\"FTM\"] == 1)]\n", + "\n", + "# actually there in only one girl that attemped 2 and made 1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3REB_per_minAST_per_minSTL_per_minPTS_per_minBLK_per_min
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Unnamed: 0, 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, REB_per_min, AST_per_min, STL_per_min, PTS_per_min, BLK_per_min]\n", + "Index: []" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2)\n", + "\n", + "nba.loc[(nba[\"FTA\"] >0) & (nba[\"FTM\"] == 0)]\n", + "\n", + "# no one failed all their free throws" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "# 3)\n", + "\n", + "## how do i know if they are males or females?? i do not find that column." ] + }, + { + "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 +5208,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..d87d72f 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": 2, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,11 @@ "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import ttest_1samp\n", - "pd.set_option('display.max_columns', 50)" + "pd.set_option('display.max_columns', 50)\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import scipy.stats as st" ] }, { @@ -46,11 +50,909 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
55Alexis PetersonSEAG1706321.799308USJune 20, 199522SyracuseR149093426.52922.266100.0313161150112600
66Alexis PrincePHOG1888122.917610USFebruary 5, 199423BaylorR1611293426.541526.722100.01141554332400
77Allie QuigleyCHIG1786420.199470USJune 20, 198631DePaul82684716631952.07015046.7404687.0983929520135944200
88Allisha GrayDALG1857622.205990USOctober 20, 199224South Carolina23083413134637.92910328.210412980.652751274047193739500
99Allison HightowerWASG1787724.302487USJune 4, 198829LSU57103143836.821118.266100.03710105023600
1010Alysha ClarkSEAF1807623.456790USJuly 7, 198730Middle Tennessee6308439318350.8206232.3385174.52997126502243224400
1111Alyssa ThomasCONF1888423.766410USDecember 4, 199224Maryland32883315430350.8030.09115857.63415819213648118739940
1212Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
1313Amber HarrisCHIF1968822.907122USJanuary 16, 198829Xavier322146184440.90100.05862.512284053964100
1414Aneika HenryATLF/C1938723.356332JMFebruary 13, 198631Florida642244100.0000.0000.00441203800
1515Angel RobinsonPHOF/C1988822.446689USAugust 30, 199521Arizona State115237254456.811100.077100.01642588111165800
1616Asia TaylorWASF1857622.205990USAugust 22, 199126Louisville320128103132.3000.0111861.1162137952103100
1717Bashaara GravesCHIF1889125.746944USMarch 17, 199423Tennessee155981457.1000.03475.04131730131900
1818Breanna LewisDALC1969324.208663USJune 22, 199423Kansas StateR125021216.7000.03475.02792007700
1919Breanna StewartSEAF/C1937720.671696USAugust 27, 199422Connecticut22995220141748.24612337.413617179.5432062497829476858480
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73 21.329438 \n", + "2 2 Alex Bentley CON G 170 69 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78 25.469388 \n", + "5 5 Alexis Peterson SEA G 170 63 21.799308 \n", + "6 6 Alexis Prince PHO G 188 81 22.917610 \n", + "7 7 Allie Quigley CHI G 178 64 20.199470 \n", + "8 8 Allisha Gray DAL G 185 76 22.205990 \n", + "9 9 Allison Hightower WAS G 178 77 24.302487 \n", + "10 10 Alysha Clark SEA F 180 76 23.456790 \n", + "11 11 Alyssa Thomas CON F 188 84 23.766410 \n", + "12 12 Amanda Zahui B. NY C 196 113 29.414827 \n", + "13 13 Amber Harris CHI F 196 88 22.907122 \n", + "14 14 Aneika Henry ATL F/C 193 87 23.356332 \n", + "15 15 Angel Robinson PHO F/C 198 88 22.446689 \n", + "16 16 Asia Taylor WAS F 185 76 22.205990 \n", + "17 17 Bashaara Graves CHI F 188 91 25.746944 \n", + "18 18 Breanna Lewis DAL C 196 93 24.208663 \n", + "19 19 Breanna Stewart SEA F/C 193 77 20.671696 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + "5 US June 20, 1995 22 Syracuse R \n", + "6 US February 5, 1994 23 Baylor R \n", + "7 US June 20, 1986 31 DePaul 8 \n", + "8 US October 20, 1992 24 South Carolina 2 \n", + "9 US June 4, 1988 29 LSU 5 \n", + "10 US July 7, 1987 30 Middle Tennessee 6 \n", + "11 US December 4, 1992 24 Maryland 3 \n", + "12 SE August 9, 1993 24 Minnesota 3 \n", + "13 US January 16, 1988 29 Xavier 3 \n", + "14 JM February 13, 1986 31 Florida 6 \n", + "15 US August 30, 1995 21 Arizona State 1 \n", + "16 US August 22, 1991 26 Louisville 3 \n", + "17 US March 17, 1994 23 Tennessee 1 \n", + "18 US June 22, 1994 23 Kansas State R \n", + "19 US August 27, 1994 22 Connecticut 2 \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 \n", + "5 14 90 9 34 26.5 2 9 22.2 6 6 100.0 \n", + "6 16 112 9 34 26.5 4 15 26.7 2 2 100.0 \n", + "7 26 847 166 319 52.0 70 150 46.7 40 46 87.0 \n", + "8 30 834 131 346 37.9 29 103 28.2 104 129 80.6 \n", + "9 7 103 14 38 36.8 2 11 18.2 6 6 100.0 \n", + "10 30 843 93 183 50.8 20 62 32.3 38 51 74.5 \n", + "11 28 833 154 303 50.8 0 3 0.0 91 158 57.6 \n", + "12 25 133 20 53 37.7 2 8 25.0 9 12 75.0 \n", + "13 22 146 18 44 40.9 0 10 0.0 5 8 62.5 \n", + "14 4 22 4 4 100.0 0 0 0.0 0 0 0.0 \n", + "15 15 237 25 44 56.8 1 1 100.0 7 7 100.0 \n", + "16 20 128 10 31 32.3 0 0 0.0 11 18 61.1 \n", + "17 5 59 8 14 57.1 0 0 0.0 3 4 75.0 \n", + "18 12 50 2 12 16.7 0 0 0.0 3 4 75.0 \n", + "19 29 952 201 417 48.2 46 123 37.4 136 171 79.5 \n", + "\n", + " OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 6 22 28 12 3 6 12 93 0 0 \n", + "1 19 82 101 72 63 13 40 217 0 0 \n", + "2 4 36 40 78 22 3 24 218 0 0 \n", + "3 35 134 169 65 20 10 38 188 2 0 \n", + "4 3 9 12 12 7 0 14 50 0 0 \n", + "5 3 13 16 11 5 0 11 26 0 0 \n", + "6 1 14 15 5 4 3 3 24 0 0 \n", + "7 9 83 92 95 20 13 59 442 0 0 \n", + "8 52 75 127 40 47 19 37 395 0 0 \n", + "9 3 7 10 10 5 0 2 36 0 0 \n", + "10 29 97 126 50 22 4 32 244 0 0 \n", + "11 34 158 192 136 48 11 87 399 4 0 \n", + "12 5 18 23 7 4 5 12 51 0 0 \n", + "13 12 28 40 5 3 9 6 41 0 0 \n", + "14 0 4 4 1 2 0 3 8 0 0 \n", + "15 16 42 58 8 1 11 16 58 0 0 \n", + "16 16 21 37 9 5 2 10 31 0 0 \n", + "17 4 13 17 3 0 1 3 19 0 0 \n", + "18 2 7 9 2 0 0 7 7 0 0 \n", + "19 43 206 249 78 29 47 68 584 8 0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "nba = pd.read_csv(\"wnba_clean.csv\")\n", + "\n", + "nba.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(142, 33)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nba.shape" ] }, { @@ -74,7 +976,14 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "\"\"\"\n", + "I would calculate the mean of the sample and tried to find an accurate confidence \n", + "interval, 95,5 or 98%, to find the range where the population's mean sits in.\n", + "One of the sample's requirements is the size, and this one with 140 datapoints \n", + "seems satisfactory.\n", + "Because I have already plotted the Weights i know it looks like a normal distribution.\n", + "\n", + "\"\"\"" ] }, { @@ -86,11 +995,48 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "78.97887323943662" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "sample_mean = nba[\"Weight\"].mean()\n", + "\n", + "n = 142\n", + "\n", + "sample_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77.15461406720749, 80.80313241166576)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_std = nba[\"Weight\"].std(ddof=1)\n", + "\n", + "stats.t.interval(0.95, 142-1, loc = sample_mean, scale = sample_std/np.sqrt(n))" ] }, { @@ -106,7 +1052,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "## with 95% confidence, i can say that the mean weight of female professional players\n", + "# is between 77,15kg and 80,8kg." ] }, { @@ -122,7 +1069,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# indeed, my sisters is lighter than the vast majority of female professional players,\n", + "# but she also weights more the the lightest player, so she should persue that\n", + "# career if that is what she loves doing." ] }, { @@ -134,11 +1083,26 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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ojvdvIdIfj3Nv8cp8M0lWY7x/C5H+eJx7i5fmmwGKpnj/FiL98Tj3Fq/NNwMUTfH+LUT643HuLV6bbwYomuL9W4j0x+PcW7w230yS1RTv30JkjttVEZnsn8e5t3htvm0JUFpaWvDQQw9hzZo1aG5uRkVFBX71q1/hwgsvBAAIIVBTU4OnnnoKhw4dwkUXXYQnnngCw4cPt6M5ntR5/5ZwpDXp+UofOq5Cyvu3ELlfFZHp/nmce4vX5tuWUzw/+clPUFtbixdffBHbt29HZWUlLrvsMuzbtw8AsHjxYixZsgTLli3D1q1bEQwGMWHCBLS0tNjRHE/i/VuIjHG7KiKb/fM49xavzbfll7o/duwYCgoK8Morr2DSpEnxxy+44AJMnjwZjzzyCEKhEKqqqjB37lwAQFtbG0pKSrBo0SLceuutve6Dl7o3zu3/GRLJrD0mcMmiP6dMPOz8H+mmud+15U3fqv3zOPcWlefb1Uvdnzx5Eu3t7cjPTzwH1rdvX2zatAmNjY0Ih8OorKyMP+f3+zF+/Hhs3rw5aYDS1taGtra2+O/RaNTqZmtrYnkpJgwLan/FQaJMmKmKsOMWA1btn8e5t3hlvi0PUAoKCnDxxRfjkUcewXnnnYeSkhL87ne/w3vvvYehQ4ciHA4DAEpKShJeV1JSgt27dyf9mwsXLkRNTY3VTfUM3r+FKDm3qyKs3D+Pc2/xwnzbkoPy4osvQgiBM844A36/H7/+9a8xffp05Obmxrfx+RIjPSFEj8c6zZs3D5FIJP6zZ88eO5pNimuPCbyz6wu8Ur8P7+z6wvTFirJ9PanH7aoIt/dPavHae5QtVTznnHMONm7ciCNHjiAajaK0tBQ/+tGPUFZWhmAwCAAIh8MoLf36XFlzc3OPb1U6+f1++P1+O5pKmsj2nKzK53Qpc25XRbi9f1KHF9+jbL1QW//+/VFaWopDhw7htddew5VXXhkPUmpra+PbHT9+HBs3bsTYsWPtbA5pKtsqDLerOMg9bldFuL1/UoNX36NsCVBee+01bNiwAY2NjaitrcV3vvMdfPOb38SPf/xj+Hw+VFVVYcGCBVizZg0aGhpw0003oV+/fpg+fbodzSGNZXtvCq/d24J6mlheiuUzRiIYSDyNEgzkY/mMkbb/79Tt/ZPcvPweZcspnkgkgnnz5mHv3r0oKirCD37wAzz66KPIy8sDAMyZMwfHjh3DHXfcEb9Q2+uvv46CggI7mkMay7YKwu0qDpKD21URbu+f5OXl9yhbApRp06Zh2rRpKZ/3+Xyorq5GdXW1HbvXWrrLYbt9qW43ZFsF4XYVhxN0XhdW9s1IVYSdY6l6VYbO68xNXniPSoX34lFIuiQpAJ5LoAKyr4LQvYpC58Q6p/um81hmi2NjH93fo9Lh3YwVkS5J6raVdbjNgwlUwNdVEKn+n+ZDxxtlqiqIbF8vM50T65zum85jmS2Ojb10fo/qDQMUBRhJkkpG9wQqIPsqCF2rKHROrHO6bzqPZbY4NvbT9T3KCAYoCugtSSqdrglUusq2CkLHKgoziXWqcbpvOo9ltjg2ztDxPcoI5qAowIrkJx0TqLrKtgpCtyoKnRPrnO6bzmOZLY6Nc3R7jzKCAYoCrEh+UjmBymh1QLZVEHZVUbhR3aBzYp0Tfes6Zwda2np/QZb7U5XO68wsJ47zdO9ROlZRMUBRQG+Xw05H9Utlq14d4Fb7db6Eut19SzZnOT4gVRqFymOZLZ3XmRluv0+5vX+7MAdFAemSpNJRPYFK9eoAN9uvc2KdnX1LNWfpgpNs9qc6ndeZUW6/T7m9fzsxQFFEqiSpdFROoFK9OkCG9uucWGdH39LNWafun7M6jGW2dF5nvXH7OHd7/3bjKR6FdCZJPf+XRjzyh7/1uv3/+eEIjBta7EDLrKf65Z1lab/OiXVW981ItVxMAA9NOg/FBX6txjJbOq+zdNw+zt3ev90YoCgmN8eH4gK/oW0PHDGW3Ccj1asDZGq/6pdQT8fKvhmdi+ICP6684AxL9qkTnddZKm4f527v324MUBTkhcx5o20v7u/HO7u+cOx/bUYz5c2036p9qsCpvnTdT3F/P+ADDhxus2TOVD6uyFpurxm39283BigK8kLmvJE+Bvrl4b7ff4hwVL57sRitvLrv9x+iemrq9uqUne9UX5Ltp6tM50yH44qs5faacXv/dmOSrIK8kDnfWx8FgC+PnkgITgB57sVitPLq82jq9uqUne9UX1Ltx8g+vXBckbXcXjNu799uDFAU5YXM+VR9LCn049R+eUlfI9O9WDrbX1KY+jROqtfrlJ3vVF+MVOH0tk8vHFdkLbfXjNv7txNP8SjMC5nzyfoYEwLXP/NeytdYnbmeTab8xPJSFOTnmW6vTtn5TvXFzD2repsz3Y8rspbba8bt/duFAYrivJA5372Pr9TvM/Q6We7FcuCwsWqqrq/XKTvfqb5k8vpUr/HCcUXWcnvNuL1/OzBAIeU4nbmeyf6yvZeLTtn5TvUlk9erMH5kD52q43TFAIWU43Tmutn9WXEvF52y853qi5l7Vqk0fmQ9narjdMYkWVKO05nrZvZn1b1cdMrOd6ovRiunVBs/spZO1XG6Y4BCSnI6c93I/qy+l4tO2flO9cXIPatUHD+yhk7VcV7gE0IoNxPRaBSBQACRSASFhYVuN4dc5PR55HT7e2fXF7ju6Xd7/Rtm7+Wi07lyma8kS/ozeoz+7qff0i7hVBZmPr+Zg2KSG2+wfFNNLdvMdbPjnG5/dt3LxansfCfWXPe+tMeELbcq0LGiQTfZrrdMXq9TdVwm0o2ZjJ85DFBMcPNS3Uzgsp7V46xy5Y0ba47r3LuynftMX6/yMZqtdGMGQMpjkad4DOpMrOo+WJ3xpVXntJ3aj9fZMc7tMYFLFv2512qVTXO/6/r/TLpyY81xnXtXtnOfzetVPUazlW7MUgUAdh2LZj6/mSRrgAyX6mYCl3XsGmcVK2/cWHNc596V7dxn+3oVj9FsGRmzZGQ4FhmgGGDmUt0q7Mfr7Bxn1Spv3FhzXOfele3cW7F2VDtGs2XmFhDduX0sMgfFANku1a1rApdT7B5nle6L4caa4zr3rmzn3qq1o9Ixmi0rjiO3jkUGKAbIdqluHRO4nOTEOButInG7TLr4f6W+03JXVq45rnNnyVSdke3cW7l2dK70yuRWG+m4dSwyQDFAlkt18/Lc1pBlnJ2uYkm2v2ChH6f2y0Pk6AnHxkKW8fcC2Sqlsp17rp3emb3VRjpujydzUAyQ4VLduiZwuUGGcXb6ctup9vd5tA1ffhWcODUWMoy/F8h4Sfds555rJ71Mb7XR/d9df3dzPBmgGOT2pbp1TeByi5vj7HQVS2/78wE4tV8eSgqdGwuuc3vJXCmV7dxz7SSX6a02VswYiRWSjievg2ISrySrFzfG2enLbRvd329vuQg5OT5Hx4Lr3B4qXNLdjSvJ6izbW204NZ681L2NnEqs0jmBSyZujLPTVSxG/86BI22mLsFvBa5ze6hQKZXt3HPtJMr2VhsyjicDFLId/6eTyK4qllTjrHrVDG/8Z57qc07m6TjnDFDIVrJVEcjAjkqEdOM8YVhQ2cqHZP3qyutrKRVWu3iPjnPOJFmyjYxVBDKwuhKht3Gu3RlWsvIhVb+68vpaSoXVLt6j45wzQCFbyFxFIAOrKhGMjvOEYUGlKh+MVCQAXEvpsNrFe3Sbc57iIVuYuWeGbIlZTrHicttmxlmly3ubuX8I11JqKs05WUOnOWeAQrZQoYpABtlmzpsdZxkz9ZPJZF14fS2losqck3V0mXMGKDbxeuWK0Uzx4v5+vLPrC8+OU1eZrBkdM/eBzNqrWh9V5JX3Na/0U3YMUGzAyhVjGeWBfnm47/cfIhz17jh1ynTN6Ji5D/Ter65U7aNqvPK+5pV+qoBJshZj5UqH3jLKBYAvj55ICE4A740TkN2a0TFzH0jfr65U7qNKvPK+5pV+qoIBioVYuZIoVUZ5yVd30E3Ga+NkxZrRLXO/U6p+daV6H1Xglfc1r/RTJTzFYyFWrvSULKM8JgSuf+a9lK/x0jhZtWZ0ytzvqnu/eCVZ53nlfc0r/VQJAxQLsXIlue4Z5a/U7zP0Oi+Mk5VrRpfM/e507ZcqvPK+5pV+qsTyUzwnT57Ez3/+c5SVlaFv3744++yz8fDDDyMWi8W3EUKguroaoVAIffv2xaWXXoodO3ZY3RTH6VpRYTWO09c4FmRG+4mT2PHbV/D+wiew/Tersf03q/H+wiew47evoP3ESVv2me0a7dpmO9uZLR6L8rH8G5RFixZhxYoVeOGFFzB8+HC8//77+PGPf4xAIIBZs2YBABYvXowlS5bg+eefxze+8Q384he/wIQJE/DRRx+hoKDA6iY5RteKCqtxnL7GsSCjPlj6LELzH8Dw6IGkz39+RzH21zyGiqpbLN1vNms0WZvtame2eCzKx/JvUN555x1ceeWVmDRpEs466yz88Ic/RGVlJd5//30AHd+eLF26FA8++CCuvvpqlJeX44UXXsDRo0fx0ksvWd0cR+laUWE1jtPXOBZkxAdLn8WIe3+C01MEJwBwevQARtz7E3yw9FlL953pGk3VZrvamS0ei/KxPEC55JJL8Kc//Qkff/wxAODDDz/Epk2b8P3vfx8A0NjYiHA4jMrKyvhr/H4/xo8fj82bN1vdHMfpWlFhNY7T1zgWlE77iZMIzX8AQPo37M7nSqvnWX4axewaTddmO9uZLR6LcrH8FM/cuXMRiURw7rnnIjc3F+3t7Xj00Udx3XXXAQDC4TAAoKSkJOF1JSUl2L17d9K/2dbWhra2tvjv0WjU6mZbSteKCqtxnL7GsaBU/t9//CHlaZ3ucgAEI/+DHf/xBwy//kpL22FmjfbWZjvbmS0ei/KwPEB5+eWXsXLlSrz00ksYPnw46uvrUVVVhVAohJkzZ8a38/kSJ1sI0eOxTgsXLkRNTY3VTbUVKw+Saz9xEv/vP/6AY5/tRd8hg3DutElKj1Oy/uTmZXZYcc1kx8q5kMmxz/Y68ppU49f98THTJiE3L/06Nbp/s+10ao55LEpCWGzQoEFi2bJlCY898sgj4pvf/KYQQohdu3YJAKKuri5hm6lTp4obb7wx6d9sbW0VkUgk/rNnzx4BQEQiEaubTzaqe/wZES4sFgKI/4QLi0Xd48+43bSM6NYflek8Fw0r1yb0y8hPw8q1pvaRavw2X/3jjMbVaJvNtFPnOfaSSCRi+PPb8gClqKhIPPnkkwmPLViwQAwdOlQIIUQsFhPBYFAsWrQo/nxbW5sIBAJixYoVhvZhpoMkh7rHnxHtgGjv9gbV+ZhqbzK69Udlus/FyeMnRLiwuEf/kv20A6IpcLo4efyE4b+fbvxiX/2YHdfe2my2nbrPsZe4GqDMnDlTnHHGGeLVV18VjY2NYvXq1aK4uFjMmTMnvs1jjz0mAoGAWL16tdi+fbu47rrrRGlpqYhGo4b2wQBFLVa/WblNt/6ozCtzkeoDOtsP697Gr3twYmZcrQoqvDLHXuFqgBKNRsWsWbPEkCFDRH5+vjj77LPFgw8+KNra2uLbxGIxMX/+fBEMBoXf7xff/va3xfbt2w3vgwGKWuz4utdNuvVHZV6ai2SnOLr+NAVON/1NQianj8yMa7I2m22nl+bYC8x8flueXVRQUIClS5di6dKlKbfx+Xyorq5GdXW11bsnCdmVMOcW3fqjMi/NRUXVLWi/cyZ2fJUk6j+jo+S1bV9TPGE0aDJhNNtx6e313ducSTu9NMeUSP0Ud5fpWjlgpb5DBlm6ndt0648Zsq13s3Phdvuz3X9uXh9Ly3KzXaNGXp9tm90+3txeM57mwDc6lpPlFA+zyo3R7Ryybv0xSsb1bmYu3G6/2/tPxs4cFKfaaGdbZJwz1bmag+IEGQIUZpWbo9t46daf3sjcXyNtc7v9bu8/07ZlWsXjZBvtaovMc6YyBig28+r/oLNlRcKcTHTrTyoqrPd0c+F2+93evxGpxi/ZdVDcWuNOHm8qzJmqzHx++4QQws1TTJmIRqMIBAKIRCIoLCx0fP87fvsKhs+4qvftVq6V7jLObtPtfK5u/UlGlfWeai7cbr/b+zfK6JVk3VzjTrVFlTlTkZnPb73eSR3CrPLMWZ3k5zbd+pOMKus91Vy43X63929UqvGTaY071RZV5kx3DFAyYGVWeXtMuHpTqlT7d7tdZttLiawcJ6Pr3T94EN7Z9UXG+7Rrbt2uAnF7/5lQ/TjLtv1uz5nq428VnuLJQPuJkzhQXIrToweS3v48BqA5cDpO/5/9ab9+3NDQhJr1O9EUaY0/VhrIx/wpwxy5rXeq/U8dUYp1Hza51q5U3B4vVVg9TkbW++eFp+MH96/E/sMnMtqnnXNr1fGq6v7NUv04s6L9bs6Z6uPfGzOf38nGnnqRm9cH+2seA9CxULvq/L2pemGvwcntK+sSFiEAhCOtuH1lHTY0NFnYYuP7b4q04v++1ehau1Jxe7xUYcc4GVnv1d/9aUJwYmafds+tFceryvs3Q/XjzKr2uzVnqo+/1RigZKii6hZ8+Pgz+J/C4oTHmwOn48PHn0FF1S0pX9seE6hZvxPJvrrqfKxm/U60x+z5civd/lNxol2puD1eqrBznNKt95/96CG89s2xGe3TqbnN5ni1gtv7N0L148zq9js9Z6qPvx3cD9kVlullnLc0HuwRIXcl0PFNxpbGg7j4nAEWt7r3/bvVrlTcHi9V2D1Oydb7l6O/hdXPvZ/xPp2cWysuu67y/nuj+nFmR/udnDPVx98OchwZCsskq7y5xVhwYHQ7s7L9u3a1K9v9Od0u2TgxTt3X+yv1+7Lap9Nz63ZFitv7T0f148yu9js1Z6qPvx0YoDisPSZwoKXN0LbF/f1Z7SdVFvjAgvyM/64Vr7drf063SzZujFO2++TcysPKuXCjCkX1taR6++3AAMVBybKz07nv9x+ieqr5zO3essDHlBWhNJCPcKTVVB6KD0Aw0PFm46Te2utWu2Tjxjhlu0/OrTysmgu3qlBUX0uqt98OTJJ1SKrs7HQ+j5rP3DaSBZ6b48P8KcMAdCx6Izq3mz9lmOP1+Ona62a7ZOPGOGW7T86tPKyYCzerUFRfS6q33w4MUByQSdUMYD5z20wW+MTyUiyfMRLBQOLXhaWBfNz67TKUdns8GMjH8hkjXavDT9Vet9slGzfGKdt9cm7lkc1cyFCFovpaUr39VuOF2hzwzq4vcN3T72b1N37302/1mrltdD9d/xavJKsnN8Yp231ybuWRyVxk8v5jF9XXkurtT4f34pGMFVnXRv5GJlnguTm+pG8WqR53m6ztko0b45TtPjm38shkLmSqQlF9LanefqswQHGAFVnXRv4Gs8BJRjr/b1BlVs+L6lVAdtGpL05jgOKATKtmAHOZ28wCJ9nofl8RVdkxL6pXAdlBp764gUmyDjCSnZ3uOaOZ28wCJ5nwviJysmteVK8CsppOfXELAxSHpMvOXjFjJFZYlLnNLHCSgQwVHdST3fOiehWQVXTqi5t4isdBE8tLMWFYMOX5yHTPWbkfIrvxviJycmJeMn3/0WnN6NQXNzFAcVi67GwrM7eZBe48FUt77SJTRQd9zal5SfX+k269ur1mrDyW3O6LLhigEFnAjWQ4mRPwWFEmJzfnpbf1KnPbzOL6twZzUIiy5EYynOwJeJ0VHan+/+lDxwcAK8qc5da8GFmvMrfNLK5/azBAIcqCG8lwKiTgsaJMTm7Mi9H12rlvGdtm9lji+rcGAxSiLJhJhlN5n5lgRZmcnJ4XM+tV5raZxfWfPeagEGXBjWQ4lRLwWFEmJyfnxex6lbltZnH9Z4cBSgqyVkfoTMUxdyMZTrUEPFaUmdf1WCju7wd8wIHDbVkfF1YeY0b/Vibr1ak148SxxPWfOQYoSchcHaErVcfcjdsL8JYGekt2LHSV6XFh5TFm5m/JvF5lbhsxB6UH2asjdKTymLuRDMcEPH2lOha6yuS4sPIYM/u3ZF6vMreNGKAkUKE6Qjc6jLkbyXBMwNNPumOhK7PHhZXHWKZ/S+b1KnPbvI6neLrg5Ymdp8uYu5EMxwQ8vfR2LHRl5riw8hjL5m/JvF5lbpuXMUDpQqXqCF3oNOZuJMMxAU8fmaxxI6+x8hjL9m/JvF5lbptX8RRPF6pVR+iAY07UIZM1buQ1Vh5jPF7JSQxQuuDliZ3HMSfq0Nux0JWZ48LKY4zHKzmJAUoXzOh2HsecqEO6Y6Ers8eFlccYj1dyEgOUbpjR7TyOOVGHVMdCV5kcF1YeYzxeySk+IYS89ZspRKNRBAIBRCIRFBYW2rIPFa9qqjqOOVEHna4kS9SVmc9vBihERETkCDOf3ywz7oL/IzCPY0ZERHZggPIVVe8F4yaOGRER2YVJslD7XjBu4ZgREZGdPB+g6HAvGKdxzIiIyG6eD1DM3FuCOnDMiIjIbp4PUHS6F4xTOGZERGQ3ywOUs846Cz6fr8fPnXfeCQAQQqC6uhqhUAh9+/bFpZdeih07dljdDMN4bwnzOGZkt/aYwDu7vsAr9fvwzq4veLqQMub1taRy/y2v4tm6dSva29vjvzc0NGDChAm45pprAACLFy/GkiVL8Pzzz+Mb3/gGfvGLX2DChAn46KOPUFBQYHVzetV5b4lwpDVpToUPHVdI5L0lvsYxIzuxOoys4vW1pHr/Lf8G5fTTT0cwGIz/vPrqqzjnnHMwfvx4CCGwdOlSPPjgg7j66qtRXl6OF154AUePHsVLL71kdVMM4b0lzOOYkV1YHUZW8fpa0qH/tuagHD9+HCtXrsTNN98Mn8+HxsZGhMNhVFZWxrfx+/0YP348Nm/ebGdT0uK9JczjmJHVWB1GVvH6WtKl/7ZeqG3t2rX48ssvcdNNNwEAwuEwAKCkpCRhu5KSEuzevTvl32lra0NbW1v892g0anlbJ5aXYsKwIK+KagLHjKxkpjrs4nMGONcwUo7X15Iu/bc1QHn22WdxxRVXIBQKJTzu8yV+gAkhejzW1cKFC1FTU2NLG7vKzfElnSxezj21VGNGZBarw8gqXl9LRvv1l0//R+rPNdsClN27d+ONN97A6tWr448Fg0EAHd+klJZ+fQqgubm5x7cqXc2bNw+zZ8+O/x6NRjF48GAbWt2T6klGRKpgdRhZxetryWi/lr25K/5vGT/XbMtBee655zBw4EBMmjQp/lhZWRmCwSBqa2vjjx0/fhwbN27E2LFjU/4tv9+PwsLChB8n6JBkRKSKzuqwVP+H86HjTZTVYdQbr6+l3vqfjIyfa7YEKLFYDM899xxmzpyJPn2+/pLG5/OhqqoKCxYswJo1a9DQ0ICbbroJ/fr1w/Tp0+1oSsZ0STIiUgWrw8gqXl9L6fqfioyfa7YEKG+88QY+++wz3HzzzT2emzNnDqqqqnDHHXdg9OjR2LdvH15//XVXroGSDi/nTuQ8VoeRVby+llL1Px3ZPtd8Qgg5QiUTotEoAoEAIpGIbad7Xqnfh1mr6nvd7lfXXoArLzjDljYQeRUT08kqXl9LXfv/yeeHsezNT3t9jZ2fa2Y+v22t4lGZ15OsiNzE6jCyihtryUhQ5FTg1LX/7+z6wlCAIsvnGgOUFHg5dyIiMstI5adb1aGqfa55/m7GqXg9yYqIiMwxUvnpZnWoap9rDFDS8HqSFRERGWOk8rN63Q5Ur3O3OlSlzzWe4ukFL+dORES9MVL5GY62pXy+cxsnLkGvyucaAxQDmLBHRETpWHnZfCcuwa/C5xoDFCKynNdLO8l7rKx8kaWKxm0MUIjIUrx/FXmRkQqZkkI/AB8+j6pRReM2JskSkWV4/yryKiMVMtVTh6N6qjpVNG5jgEJEluD9q8jrjFTIqFRF4zae4iEiS5i5f5XsyXlEmTJSIaNKFY3bGKAQkSWMVh44UaFA5CYjFTIqVNG4jQEKxVlZeSFTFYdMbVFFJmOm4v2ruDbkJ9McudEWmfrvNAYoBMDayguZqjhkaosqMh0z1e7zwbUhP5nmyI22yNR/N/iEEMplrJm5XTP1rrPyovtC6IzRzSRuWfm3siVTW1SR7Zh1vh5Awt+Qbcy5NuQn0xy50RaZ+m8lM5/frOLxOCsrL2Sq4pCpLaqwYsxUqFDg2pCfTHPkRltk6r+beIrH46ysvJCpikOmtqjCqjGTvUKBa0N+Ms2RG22Rqf9uYoDicVZWXshUxSFTW1Rh5ZjJXKHAtSE/mebIjbbI1H83MUDxOKMVFQda2vBK/b60/xuWqYpDprZkwo3MfaNjUdzfb2s77Ob02vByFUamZDp+3WiLTP13EwMUj+ut8gIAcnzAI3/4W/z3VFnkMlVxyNQWs9zK3DeyFgDgvt9/iOqp6lYROLk2vF6FkSmZjl832iJT/93EJFmPS3f/iE7d87BS3VfFyL0onLrPhExtMcPNe9kYWQsA8HlU7fvqOLU2eF+izMl0/LrRFpn67yYGKJSy8iLV2k+XRS5TFYdMbTFChsz9zjHruOtqcjpUEdi9NmSYS9XJdPy60RaZ+u8WXgeF4rqeKz/Q0pZwWieV3/30W0mTIWU67y5TW9J5Z9cXuO7pd3vdLtWYW+kvnx7A9c+8J0Vb7GTX2pBpLlUn0/HLK8lmz8znN3NQHKDKAutaefFK/T5Dr0mVRS5TFYdMbUlHpsz9A4fbDG2nehWBlWuj63H+yeeHDb1G9fFzgtE5MvI+m+17cbbrJZP9q/L+ZQcGKDZTNUmOWeTOk2nMZWqLCpId50Zw/Kxh5H3W7fdit/evIuag2EjlJLnOLPJUsb0PHQeX7lnkTpJpzGVqi+xSHefpcPysY+R91u33Yrf3ryoGKDZRPUmOWeTOk2nMZWqLzNId56lw/Kxj5H22et0OVK9z771Y9c8CNzFAsYmZSxXLilnkzpNpzGVqi6x6O86T4fhZx8j7bDjahnDUvfdiHT4L3MIcFJvIlPCYDdnvq6IjmcZcprbIyOjxe9d3zsHQkgKOn8XcuLy8XX9X9s8CNzBAsVD3Ml0jVEiS83IWuVucGnMjVQWc/9SMHr/j/vfpHMNeZFLh4sbl5e36uyp8FjiNAYpFkmVo5/h6XoW1k1cuVUzyYlVB9nhJcmtkuhaNjH/HRQd9+DzqzhxxjWSOOSgWSJWhnS44AZgkR+5hVYE1mEycvWzWopHxr546HNVT3ZsjrpHMMUDJkpEs/u7rjkly5CZWFViLycSZs2ItGhl/t+fI7f2riqd4smQkiz8mgIcmnYfiAj+T5Mh1ZqoKmDdhDJOJM2PVWjQy/m7Pkdv7VxEDlCwZzbwuLvDjygvOsLk1RL1jVYE9mExsnpVr0cj4uz1Hbu9fNQxQssQMbbWocl8kO3HNkiy4FikdBihZYoa2Oli10oFrlmTBtUjpMEk2S8zQVgOrVr7GNUuy4FqkdBigWIAZ2nJj1UpPXLMkC65FSsUnhFDuXTkajSIQCCASiaCwsNDt5sQxv0FO7+z6Atc9/W6v2/3up9/yXAIb1yzJgmvRG8x8fjMHxULM0JYTq1ZS45olWXAtUnc8xUPaY6UAEZF6GKCQ9jorBVJ9WexDRzUPKwWIiOTBAIW0x0oBIiL1MEAhT2ClABGRWpgkS57Be2EQEamDAQp5CisFiIjUYMspnn379mHGjBkYMGAA+vXrhwsuuADbtm2LPy+EQHV1NUKhEPr27YtLL70UO3bssKMpREREpCDLA5RDhw5h3LhxyMvLwx//+Efs3LkTv/zlL3HqqafGt1m8eDGWLFmCZcuWYevWrQgGg5gwYQJaWlqsbg4REREpyPIryT7wwAP4y1/+grfffjvp80IIhEIhVFVVYe7cuQCAtrY2lJSUYNGiRbj11lt73YesV5IlIiKi1Mx8flv+Dcq6deswevRoXHPNNRg4cCAqKirw9NNPx59vbGxEOBxGZWVl/DG/34/x48dj8+bNVjeHiIiIFGR5gPL3v/8dy5cvx9ChQ/Haa6/htttuwz333IPf/OY3AIBwOAwAKCkpSXhdSUlJ/Lnu2traEI1GE36IiIhIX5ZX8cRiMYwePRoLFiwAAFRUVGDHjh1Yvnw5brzxxvh2Pl9iaacQosdjnRYuXIiamhqrm0pE1CvexI68RpY1b3mAUlpaimHDhiU8dt555+E///M/AQDBYBBAxzcppaVfXxyrubm5x7cqnebNm4fZs2fHf49Goxg8eLDVTSciSrChoQk163eiKfL1jSRLA/mYP2UYL+5HWpJpzVt+imfcuHH46KOPEh77+OOPceaZZwIAysrKEAwGUVtbG3/++PHj2LhxI8aOHZv0b/r9fhQWFib8EBHZaUNDE25fWZfwRg0A4Ugrbl9Zhw0NTS61jMgesq15ywOUe++9F++++y4WLFiATz/9FC+99BKeeuop3HnnnQA6Tu1UVVVhwYIFWLNmDRoaGnDTTTehX79+mD59utXNISIyrT0mULN+J5KVOHY+VrN+J9pjlhZBErlGxjVv+SmeCy+8EGvWrMG8efPw8MMPo6ysDEuXLsX1118f32bOnDk4duwY7rjjDhw6dAgXXXQRXn/9dRQUFFjdHCIi07Y0Huzxv8iuBICmSCu2NB7klYlJCzKueVsudT958mRMnjw55fM+nw/V1dWorq62Y/dERFlpbkn9Rp3JdkSyk3HN8148RETdDCzI732jNNvJUgVBZFS2a94ODFCIiLoZU1aE0kA+wpHWpOfkfQCCgY7AozuZqiCIjMpmzdvFlpsFEhGpLDfHh/lTOi6X0P17j87f508Z1uNbEdmqIIiMynTN24kBChFREhPLS7F8xkgEA4lfaQcD+Vg+Y2SPb0NkrIIgMsPsmrcbT/EQEaUwsbwUE4YFDeWTyFgFQWSWmTVvNwYoRERp5Ob4DAUUMlZBEGXC6Jq3GwMUol6wIoOMkLEKgkhlDFCI0mBFBhklYxUEkcqYJEuUAisyyAwZqyCIVMYAhSgJVmRQJmSrgiBSGU/xECXBigzKlExVEEQqY4BClAQrMigbslRBEKmMp3iIkmBFBhGRuxigECXRWZGR6kt5HzqqeViRQURkDwYoREmwIoOIyF0MUIhSYEUGEZF7mCRLlAYrMoiI3MEAhagXrMgg1fD2DKQDBihERBrh7RlIF8xBISLSBG/PQDphgEJEpAHenoF0wwCFiEgDZm7PQKQCBihERBrg7RlINwxQiIg0wNszkG4YoBARaYC3ZyDdMEAhItIAb89AumGAQkSkCd6egXTCC7UREWmEt2cgXTBAISLSDG/PQDrgKR4iIiKSDgMUIiIikg4DFCIiIpIOAxQiIiKSDgMUIiIikg4DFCIiIpIOAxQiIiKSDgMUIiIikg4DFCIiIpKOkleSFUIAAKLRqMstISIiIqM6P7c7P8fTUTJAaWlpAQAMHjzY5ZYQERGRWS0tLQgEAmm38QkjYYxkYrEY9u/fj4KCAvh81t4AKxqNYvDgwdizZw8KCwst/dsqYP/Zf/af/Wf/2X+7+i+EQEtLC0KhEHJy0meZKPkNSk5ODgYNGmTrPgoLCz25QDux/+w/+8/+exX7b2//e/vmpBOTZImIiEg6DFCIiIhIOgxQuvH7/Zg/fz78fr/bTXEF+8/+s//sP/vP/stAySRZIiIi0hu/QSEiIiLpMEAhIiIi6TBAISIiIukwQCEiIiLpMEDp4sknn0RZWRny8/MxatQovP322243yRYLFy7EhRdeiIKCAgwcOBBXXXUVPvroo4RthBCorq5GKBRC3759cemll2LHjh0utdheCxcuhM/nQ1VVVfwx3fu/b98+zJgxAwMGDEC/fv1wwQUXYNu2bfHnde7/yZMn8fOf/xxlZWXo27cvzj77bDz88MOIxWLxbXTq/1tvvYUpU6YgFArB5/Nh7dq1Cc8b6WtbWxvuvvtuFBcXo3///pg6dSr27t3rYC8yl67/J06cwNy5c3H++eejf//+CIVCuPHGG7F///6Ev6Fr/7u79dZb4fP5sHTp0oTH3eo/A5SvvPzyy6iqqsKDDz6IDz74AP/8z/+MK664Ap999pnbTbPcxo0bceedd+Ldd99FbW0tTp48icrKShw5ciS+zeLFi7FkyRIsW7YMW7duRTAYxIQJE+L3QdLF1q1b8dRTT+Gf/umfEh7Xuf+HDh3CuHHjkJeXhz/+8Y/YuXMnfvnLX+LUU0+Nb6Nz/xctWoQVK1Zg2bJl+Nvf/obFixfj3/7t3/Dv//7v8W106v+RI0cwYsQILFu2LOnzRvpaVVWFNWvWYNWqVdi0aRMOHz6MyZMno7293aluZCxd/48ePYq6ujo89NBDqKurw+rVq/Hxxx9j6tSpCdvp2v+u1q5di/feew+hUKjHc671X5AQQogxY8aI2267LeGxc889VzzwwAMutcg5zc3NAoDYuHGjEEKIWCwmgsGgeOyxx+LbtLa2ikAgIFasWOFWMy3X0tIihg4dKmpra8X48ePFrFmzhBD693/u3LnikksuSfm87v2fNGmSuPnmmxMeu/rqq8WMGTOEEHr3H4BYs2ZN/Hcjff3yyy9FXl6eWLVqVXybffv2iZycHLFhwwbH2m6F7v1PZsuWLQKA2L17txDCG/3fu3evOOOMM0RDQ4M488wzxeOPPx5/zs3+8xsUAMePH8e2bdtQWVmZ8HhlZSU2b97sUqucE4lEAABFRUUAgMbGRoTD4YTx8Pv9GD9+vFbjceedd2LSpEm47LLLEh7Xvf/r1q3D6NGjcc0112DgwIGoqKjA008/HX9e9/5fcskl+NOf/oSPP/4YAPDhhx9i06ZN+P73vw9A//53ZaSv27Ztw4kTJxK2CYVCKC8v1248gI73Q5/PF/9GUff+x2Ix3HDDDbj//vsxfPjwHs+72X8lbxZotQMHDqC9vR0lJSUJj5eUlCAcDrvUKmcIITB79mxccsklKC8vB4B4n5ONx+7dux1vox1WrVqFuro6bN26tcdzuvf/73//O5YvX47Zs2fjX/7lX7Blyxbcc8898Pv9uPHGG7Xv/9y5cxGJRHDuueciNzcX7e3tePTRR3HdddcB0H/+uzLS13A4jFNOOQWnnXZaj210e39sbW3FAw88gOnTp8dvlqd7/xctWoQ+ffrgnnvuSfq8m/1ngNKFz+dL+F0I0eMx3dx1113461//ik2bNvV4Ttfx2LNnD2bNmoXXX38d+fn5KbfTtf+xWAyjR4/GggULAAAVFRXYsWMHli9fjhtvvDG+na79f/nll7Fy5Uq89NJLGD58OOrr61FVVYVQKISZM2fGt9O1/8lk0lfdxuPEiRO49tprEYvF8OSTT/a6vQ7937ZtG371q1+hrq7OdF+c6D9P8QAoLi5Gbm5uj2iwubm5x/8sdHL33Xdj3bp1ePPNNzFo0KD448FgEAC0HY9t27ahubkZo0aNQp8+fdCnTx9s3LgRv/71r9GnT594H3Xtf2lpKYYNG5bw2HnnnRdPCNd9/u+//3488MADuPbaa3H++efjhhtuwL333ouFCxcC0L//XRnpazAYxPHjx3Ho0KGU26juxIkTmDZtGhobG1FbWxv/9gTQu/9vv/02mpubMWTIkPh74e7du3HffffhrLPOAuBu/xmgADjllFMwatQo1NbWJjxeW1uLsWPHutQq+wghcNddd2H16tX485//jLKysoTny8rKEAwGE8bj+PHj2Lhxoxbj8b3vfQ/bt29HfX19/Gf06NG4/vrrUV9fj7PPPlvr/o8bN65HWfnHH3+MM888E4D+83/06FHk5CS+9eXm5sbLjHXvf1dG+jpq1Cjk5eUlbNPU1ISGhgYtxqMzOPnkk0/wxhtvYMCAAQnP69z/G264AX/9618T3gtDoRDuv/9+vPbaawBc7r+tKbgKWbVqlcjLyxPPPvus2Llzp6iqqhL9+/cX//jHP9xumuVuv/12EQgExH//93+Lpqam+M/Ro0fj2zz22GMiEAiI1atXi+3bt4vrrrtOlJaWimg06mLL7dO1ikcIvfu/ZcsW0adPH/Hoo4+KTz75RPz2t78V/fr1EytXroxvo3P/Z86cKc444wzx6quvisbGRrF69WpRXFws5syZE99Gp/63tLSIDz74QHzwwQcCgFiyZIn44IMP4lUqRvp62223iUGDBok33nhD1NXVie9+97tixIgR4uTJk251y7B0/T9x4oSYOnWqGDRokKivr094P2xra4v/DV37n0z3Kh4h3Os/A5QunnjiCXHmmWeKU045RYwcOTJedqsbAEl/nnvuufg2sVhMzJ8/XwSDQeH3+8W3v/1tsX37dvcabbPuAYru/V+/fr0oLy8Xfr9fnHvuueKpp55KeF7n/kejUTFr1iwxZMgQkZ+fL84++2zx4IMPJnwg6dT/N998M+nxPnPmTCGEsb4eO3ZM3HXXXaKoqEj07dtXTJ48WXz22Wcu9Ma8dP1vbGxM+X745ptvxv+Grv1PJlmA4lb/fUIIYe93NERERETmMAeFiIiIpMMAhYiIiKTDAIWIiIikwwCFiIiIpMMAhYiIiKTDAIWIiIikwwCFiIiIpMMAhYiIiKTDAIWIiIikwwCFiIiIpMMAhYiIiKTDAIWIiIik8/8BIFQ489r2uXEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "critical = nba[(nba[\"Weight\"] > 77.15) & (nba[\"Weight\"] <80.8)]\n", + "\n", + "plt.plot(nba[\"Weight\"], \"o\")\n", + "plt.plot(critical[\"Weight\"], \"o\", color = \"red\")\n", + "plt.show()" ] }, { @@ -154,11 +1118,37 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here" + "# FT% percent of free thows made. to find the percent of missed: 100-FT%\n", + "\n", + "nba[\"FT_failed\"] = 100 - nba[\"FT%\"]\n", + "\n", + "# how many have more than 40% missed free thows\n", + "\n", + "missed_40 = nba[nba[\"FT_failed\"] > 40]\n", + "\n", + "missed_40[\"Name\"].count()\n", + "\n", + "## actually only 14 players failed more than 40 % of their three throws.\n", + "\n", + "\n", + "\n", + "### or its easier to just say, that if they missed 40%, they scored mess than 60%, \n", + "# so FT% < 60%" ] }, { @@ -170,11 +1160,33 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(72.78011163304726, 78.87763484582598)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "alpha = 0.95\n", + "\n", + "# H0: FT% =< 60\n", + "# H1: FT% > 60\n", + "\n", + "mean = nba[\"FT%\"].mean()\n", + "\n", + "std = nba[\"FT%\"].std()\n", + "\n", + "n = 142\n", + "\n", + "stats.norm.interval(0.95, loc=mean, scale = std/np.sqrt(n))" ] }, { @@ -190,7 +1202,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "### with a 95% confidence level, the mean of sucess of female nba players three thorws\n", + "# is 72-78%." ] }, { @@ -231,7 +1244,7 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "### Wheres is the NBA data???" ] }, { @@ -343,7 +1356,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +1370,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/wnba.csv b/your-code/wnba.csv new file mode 100644 index 0000000..0e35127 --- /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..b365fcb --- /dev/null +++ b/your-code/wnba_clean.csv @@ -0,0 +1,143 @@ +,Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 +0,Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 +1,Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 +2,Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 +3,Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 +4,Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 +5,Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0 +6,Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0 +7,Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 +8,Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 +9,Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0 +10,Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 +11,Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 +12,Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 +13,Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 +14,Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +15,Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0 +16,Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 +17,Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 +18,Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 +19,Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 +20,Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 +21,Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 +22,Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 +23,Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 +24,Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 +25,Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 +26,Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 +27,Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 +28,Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 +29,Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 +30,Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 +31,Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 +32,Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 +33,Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 +34,Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 +35,Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +36,Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 +37,Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 +38,Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 +39,Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 +40,Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 +41,Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 +42,Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 +43,Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 +44,Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 +45,Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 +46,Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 +47,Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 +48,Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 +49,Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 +50,Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 +51,Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 +52,Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 +53,Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 +54,Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 +55,Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 +56,Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 +57,Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 +58,Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 +59,Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 +60,Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 +61,Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 +62,Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 +63,Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 +64,Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 +65,Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 +66,Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 +67,Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 +68,Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 +69,Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 +70,Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 +71,Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 +72,Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 +73,Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0 +74,Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +75,Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 +76,Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 +77,Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 +78,Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 +79,Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 +80,Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 +81,Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 +82,Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 +83,Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 +84,Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 +85,Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 +86,Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 +87,Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 +88,Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 +89,Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 +90,Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 +92,Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 +93,Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 +94,Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 +95,Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 +96,Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 +97,Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 +98,Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 +99,Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 +100,Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 +101,Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 +102,Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 +103,Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 +104,Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 +105,Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 +106,Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 +107,Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 +108,Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 +109,Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +110,Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 +111,Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 +112,Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 +113,Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 +114,Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 +115,Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 +116,Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 +117,Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 +118,Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 +119,Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 +120,Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 +121,Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 +122,Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 +123,Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 +124,Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 +125,Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 +126,Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 +127,Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 +128,Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 +129,Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 +130,Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 +131,Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 +132,Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 +133,Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 +134,Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 +135,Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 +136,Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 +137,Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 +138,Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 +139,Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 +140,Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 +141,Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 +142,Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0