diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..d938583 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 +47,519 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "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
...................................................................................................
138Tiffany HayesATLG17870.022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
139Tiffany JacksonLAF19184.023.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
140Tiffany MitchellINDG17569.022.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
141Tina CharlesNYF/C19384.022.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
142Yvonne TurnerPHOG17559.019.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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143 rows × 32 columns

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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", + "138 Tiffany Hayes ATL G 178 70.0 22.093170 US \n", + "139 Tiffany Jackson LA F 191 84.0 23.025685 US \n", + "140 Tiffany Mitchell IND G 175 69.0 22.530612 US \n", + "141 Tina Charles NY F/C 193 84.0 22.550941 US \n", + "142 Yvonne Turner PHO G 175 59.0 19.265306 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 \n", + "1 May 14, 1982 35 Duke 12 30 947 \n", + "2 October 27, 1990 26 Penn State 4 26 617 \n", + "3 December 11, 1988 28 Georgia Tech 6 31 721 \n", + "4 August 5, 1994 23 Baylor R 24 137 \n", + ".. ... ... ... ... ... ... \n", + "138 September 20, 1989 27 Connecticut 6 29 861 \n", + "139 April 26, 1985 32 Texas 9 22 127 \n", + "140 September 23, 1984 32 South Carolina 2 27 671 \n", + "141 May 12, 1988 29 Connecticut 8 29 952 \n", + "142 October 13, 1987 29 Nebraska 2 30 356 \n", + "\n", + " FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST \\\n", + "0 30 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 \n", + "1 90 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 \n", + "2 82 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 \n", + "3 75 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 \n", + "4 16 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "138 144 331 43.5 43 112 38.4 136 161 84.5 28 89 117 69 \n", + "139 12 25 48.0 0 1 0.0 4 6 66.7 5 18 23 3 \n", + "140 83 238 34.9 17 69 24.6 94 102 92.2 16 70 86 39 \n", + "141 227 509 44.6 18 56 32.1 110 135 81.5 56 212 268 75 \n", + "142 59 140 42.1 11 47 23.4 22 28 78.6 11 13 24 30 \n", + "\n", + " STL BLK TO PTS DD2 TD3 \n", + "0 3 6 12 93 0 0 \n", + "1 63 13 40 217 0 0 \n", + "2 22 3 24 218 0 0 \n", + "3 20 10 38 188 2 0 \n", + "4 7 0 14 50 0 0 \n", + ".. ... ... .. ... ... ... \n", + "138 37 8 50 467 0 0 \n", + "139 1 3 8 28 0 0 \n", + "140 31 5 40 277 0 0 \n", + "141 21 22 71 582 11 0 \n", + "142 18 1 32 151 0 0 \n", + "\n", + "[143 rows x 32 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba = pd.read_csv(\"wnba.csv\")\n", + "wnba" ] }, { @@ -64,11 +572,73 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name False\n", + "Team False\n", + "Pos False\n", + "Height False\n", + "Weight True\n", + "BMI True\n", + "Birth_Place False\n", + "Birthdate False\n", + "Age False\n", + "College False\n", + "Experience False\n", + "Games Played False\n", + "MIN False\n", + "FGM False\n", + "FGA False\n", + "FG% False\n", + "3PM False\n", + "3PA False\n", + "3P% False\n", + "FTM False\n", + "FTA False\n", + "FT% False\n", + "OREB False\n", + "DREB False\n", + "REB False\n", + "AST False\n", + "STL False\n", + "BLK False\n", + "TO False\n", + "PTS False\n", + "DD2 False\n", + "TD3 False\n", + "dtype: bool" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.isnull().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "1\n" + ] + } + ], + "source": [ + "print(wnba[\"BMI\"].isnull().sum())\n", + "print(wnba[\"Weight\"].isnull().sum())" ] }, { @@ -80,11 +650,123 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba[wnba[\"BMI\"].isnull()]\n", + "# wnba[wnba[\"Weight\"].isnull()]" ] }, { @@ -96,11 +778,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6993006993006993 % of the Data will be lost\n" + ] + } + ], "source": [ - "#your code here" + "percent = 1/len(wnba)\n", + "print(f\"{percent*100} % of the Data will be lost\")" ] }, { @@ -114,11 +805,55 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name False\n", + "Team False\n", + "Pos False\n", + "Height False\n", + "Weight False\n", + "BMI False\n", + "Birth_Place False\n", + "Birthdate False\n", + "Age False\n", + "College False\n", + "Experience False\n", + "Games Played False\n", + "MIN False\n", + "FGM False\n", + "FGA False\n", + "FG% False\n", + "3PM False\n", + "3PA False\n", + "3P% False\n", + "FTM False\n", + "FTA False\n", + "FT% False\n", + "OREB False\n", + "DREB False\n", + "REB False\n", + "AST False\n", + "STL False\n", + "BLK False\n", + "TO False\n", + "PTS False\n", + "DD2 False\n", + "TD3 False\n", + "dtype: bool" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.dropna(inplace=True)\n", + "wnba.isnull().any()" ] }, { @@ -130,11 +865,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "# as the NaN is only a small fraction of the code and BMI and Weight is important for us to compute the questioned analysis\n", + "# i think it a good decision.\n", + "# in a case where we wouldn't need information about Weight or BMI and some of her other values are especially interesting." ] }, { @@ -147,11 +884,54 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "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": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.dtypes" ] }, { @@ -170,11 +950,34 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 71\n", + "1 73\n", + "2 69\n", + "3 84\n", + "4 78\n", + " ..\n", + "138 70\n", + "139 84\n", + "140 69\n", + "141 84\n", + "142 59\n", + "Name: Weight, Length: 142, dtype: int32" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba[\"Weight\"] = wnba[\"Weight\"].astype(int)\n", + "wnba[\"Weight\"]" ] }, { @@ -186,11 +989,345 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std8.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
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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": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.describe()" ] }, { @@ -202,11 +1339,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#std for demographic stats and games played rather low, for in game stats varies quite a lot. Biggest STD for Rebounds and Field\n", + "# Golas attempts" ] }, { @@ -218,17 +1356,17 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba.to_csv(\"wnba_clean.csv\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1380,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 48b485c..6f62a7a 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -36,11 +36,520 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
...................................................................................................
137Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
138Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
139Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
140Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
141Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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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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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
12Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
23Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
36Courtney ParisDALC19311330.336385USSeptember 21, 198729Oklahoma716217325756.1000.061250.0283462568187000
41Danielle AdamsCONF/C18510831.555880USFebruary 19, 198928Texas A&M51881164337.2123040.055100.0641044474900
84Lanay MontgomerySEAC1969624.989588USSeptember 17, 199323West VirginiaR7283742.9000.0000.00550142600
89Lynetta KizerCONC19310427.920213USApril 4, 199027Maryland5202384810048.0010.0233076.722355761171011900
124Stefanie DolsonCHIC1969725.249896USAugust 1, 199225Connecticut32882316229355.3246040.0505886.2351211566514376539830
130Sylvia FowlesMINC1989624.487297USJune 10, 198532LSU102989522233666.1000.012816279.011318429739396171572160
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
30Cappie PondexterCHIG1757323.836735USJuly 1, 198334Rutgers11246769425836.483225.0546780.61059691041755625020
45Diana TaurasiPHOG1837422.096808USNovember 6, 198234Connecticut132059112125547.5226633.311211894.931981293220312837630
52Érika de SouzaSANC1968622.386506BRSeptember 3, 198234Brazil13305796511258.0000.0293290.65874132351873715900
67Jia PerkinsMING1737525.059307USFebruary 23, 198235Texas Tech143093217842042.44712338.211413485.124729610341118351700
88Lindsay WhalenMING1757825.469388USSeptember 5, 198234Minnesota14225206915345.1123435.3273675.084654901124417700
94Monique CurriePHOG/F1838023.888441USFebruary 25, 198334Duke113271712128442.6379339.88510382.5191031226722114836400
105Plenette PiersonMINF/C1888824.898144USAugust 31, 198135Texas Tech15294025414238.0175133.3152075.0134962481243314000
109Rebekkah BrunsonMINF1888423.766410USNovember 12, 198135Georgetown14267199721844.5226036.7628374.746135181403194227820
126Sue BirdSEAG1756822.204082USOctober 16, 198036Connecticut152780610324442.25013437.3172470.8746531773135727310
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "30 Cappie Pondexter CHI G 175 73 23.836735 US \n", + "45 Diana Taurasi PHO G 183 74 22.096808 US \n", + "52 Érika de Souza SAN C 196 86 22.386506 BR \n", + "67 Jia Perkins MIN G 173 75 25.059307 US \n", + "88 Lindsay Whalen MIN G 175 78 25.469388 US \n", + "94 Monique Currie PHO G/F 183 80 23.888441 US \n", + "105 Plenette Pierson MIN F/C 188 88 24.898144 US \n", + "109 Rebekkah Brunson MIN F 188 84 23.766410 US \n", + "126 Sue Bird SEA G 175 68 22.204082 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "1 May 14, 1982 35 Duke 12 30 947 90 \n", + "30 July 1, 1983 34 Rutgers 11 24 676 94 \n", + "45 November 6, 1982 34 Connecticut 13 20 591 121 \n", + "52 September 3, 1982 34 Brazil 13 30 579 65 \n", + "67 February 23, 1982 35 Texas Tech 14 30 932 178 \n", + "88 September 5, 1982 34 Minnesota 14 22 520 69 \n", + "94 February 25, 1983 34 Duke 11 32 717 121 \n", + "105 August 31, 1981 35 Texas Tech 15 29 402 54 \n", + "109 November 12, 1981 35 Georgetown 14 26 719 97 \n", + "126 October 16, 1980 36 Connecticut 15 27 806 103 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "1 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 63 \n", + "30 258 36.4 8 32 25.0 54 67 80.6 10 59 69 104 17 \n", + "45 255 47.5 22 66 33.3 112 118 94.9 31 98 129 32 20 \n", + "52 112 58.0 0 0 0.0 29 32 90.6 58 74 132 35 18 \n", + "67 420 42.4 47 123 38.2 114 134 85.1 24 72 96 103 41 \n", + "88 153 45.1 12 34 35.3 27 36 75.0 8 46 54 90 11 \n", + "94 284 42.6 37 93 39.8 85 103 82.5 19 103 122 67 22 \n", + "105 142 38.0 17 51 33.3 15 20 75.0 13 49 62 48 12 \n", + "109 218 44.5 22 60 36.7 62 83 74.7 46 135 181 40 31 \n", + "126 244 42.2 50 134 37.3 17 24 70.8 7 46 53 177 31 \n", + "\n", + " BLK TO PTS DD2 TD3 \n", + "1 13 40 217 0 0 \n", + "30 5 56 250 2 0 \n", + "45 31 28 376 3 0 \n", + "52 7 37 159 0 0 \n", + "67 11 83 517 0 0 \n", + "88 2 44 177 0 0 \n", + "94 11 48 364 0 0 \n", + "105 4 33 140 0 0 \n", + "109 9 42 278 2 0 \n", + "126 3 57 273 1 0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba[wnba[\"Age\"]>33]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "# Aslo correct Data" ] }, { @@ -89,11 +1813,102 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = wnba[\"Height\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Height\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = wnba[\"Weight\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Weight\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "x = wnba[\"Age\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Age\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = wnba[\"BMI\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"BMI\")\n", + "plt.show()" ] }, { @@ -105,11 +1920,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "## Height --> a lot between 1.70 - 1.80, most 1.87-1.95., 1.70, betwenn 1.82 -1.87 and oer 2.00 are the lowest.\n", + "## Weight --> continuesly increasing from 55 to 80. Then decreasing till 100. 100 no values (suspicious). low values for over 100\n", + "## Age --> 23-24 followed by 26-30 most values. More 32 than 30 -> better year 6?, then decreasing\n", + "## BMI --> around 21 - 22.5 and 23.5 to 24 most fregmented. after 23 rapidly decreasing, apart from 20-24.5 rest outliers" ] }, { @@ -134,11 +1952,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = wnba[\"REB\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Rebounds\")\n", + "plt.show()\n", + "\n", + "x = wnba[\"AST\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Assists\")\n", + "plt.show()\n", + "\n", + "x = wnba[\"STL\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Steals\")\n", + "plt.show()\n", + "\n", + "plt.tight_layout" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "x = wnba[\"PTS\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Points\")\n", + "plt.show()\n", + "\n", + "x = wnba[\"BLK\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Blocks\")\n", + "plt.show()\n", + "\n", + "plt.tight_layout" ] }, { @@ -154,7 +2083,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# all of them skewed a lot to the left. After that quite constant decrease, though some exepctions (small spikes). \n", + "# More than where the majority of data lies -> extordinary achievements | The better the achievement the less the freq\n", + "# achievement dependent on minutes played\n", + "# Similiar Distribution" ] }, { @@ -173,11 +2105,136 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba_filtered = pd.DataFrame({\"REB\":[]})\n", + "wnba_filtered[\"REB\"]=wnba[\"REB\"]/wnba[\"MIN\"]\n", + "wnba_filtered[\"AST\"]=wnba[\"AST\"]/wnba[\"MIN\"]\n", + "wnba_filtered[\"STL\"]=wnba[\"STL\"]/wnba[\"MIN\"]\n", + "wnba_filtered[\"PTS\"]=wnba[\"PTS\"]/wnba[\"MIN\"]\n", + "wnba_filtered[\"BLK\"]=wnba[\"BLK\"]/wnba[\"MIN\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = wnba_filtered[\"REB\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Rebounds per min\")\n", + "plt.show()\n", + "\n", + "x = wnba_filtered[\"AST\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Assists per min\")\n", + "plt.show()\n", + "\n", + "x = wnba_filtered[\"STL\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Steals per min\")\n", + "plt.show()\n", + "\n", + "plt.tight_layout" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = wnba_filtered[\"PTS\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Points per min\")\n", + "plt.show()\n", + "\n", + "x = wnba_filtered[\"BLK\"]\n", + "plt.hist(x, bins=\"auto\", edgecolor=\"black\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.xlabel(\"Blocks per min\")\n", + "plt.show()\n", + "\n", + "plt.tight_layout" ] }, { @@ -193,7 +2250,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# except for block (no diff) allt graphs sifted to the middle, still a little bit skewed to the left, but lot less than before.\n", + "# wider and more equally distributed, also within a smaller range, tho still after a certrain point gradually declining.\n", + "# still some perfomance extraordinary and reason for outliers.\n", + "# => better way to look at the distribution of stats" ] }, { @@ -218,17 +2278,200 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15% of Players have a lower Weight than 66.0. \n", + " In Specific those 26 players have following weights \n", + " [63, 64, 66, 65, 66, 66, 62, 57, 65, 63, 66, 65, 66, 59, 64, 58, 65, 55, 63, 66, 64, 64, 66, 64, 66, 59]\n", + "\n", + " 15% of Players have a higher BMI than 24.89. \n", + " In Specific those 22 players have following BMIs \n", + " [25.47, 29.41, 25.75, 28.51, 26.0, 30.34, 31.56, 26.45, 27.17, 25.06, 25.31, 25.13, 24.99, 25.47, 27.92, 25.75, 24.9, 25.95, 26.83, 25.25, 25.42, 26.2]\n" + ] + } + ], + "source": [ + "### Argument 1:\n", + "lb = wnba[\"Weight\"].quantile(0.15)\n", + "x = wnba[wnba[\"Weight\"]<=lb][\"Weight\"].tolist()\n", + "print(f\"15% of Players have a lower Weight than {lb}. \\n In Specific those {len(x)} players have following weights \\n {x}\")\n", + "\n", + "ub = wnba[\"BMI\"].quantile(0.85)\n", + "y = wnba[wnba[\"BMI\"]>=ub][\"BMI\"].tolist()\n", + "y = [round(i,2) for i in y]\n", + "print(f\"\\n 15% of Players have a higher BMI than {round(ub,2)}. \\n In Specific those {len(y)} players have following BMIs \\n {y}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# AS the Data shows there a lot of players with a lot below average (74 in us) Weight. Moreover a lot of players have aorund \n", + "# or above average BMIs. This suggest that having a lot of muscles and not being skinny is not a neccessity for getting to\n", + "# the WNBA. \n", + "\n", + "# Therefore, it can be said, that even if my sister wouldn't be able to reach the wnba, because of her WEight and lack of \n", + "# muscles, the mentioned precondition most definetively don't rule out her chance to reach any professional BB league." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexFT%
count142.000000142.000000
mean70.50000075.828873
std41.13595318.536151
min0.0000000.000000
25%35.25000071.575000
50%70.50000080.000000
75%105.75000085.925000
max141.000000100.000000
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" + ], + "text/plain": [ + " index FT%\n", + "count 142.000000 142.000000\n", + "mean 70.500000 75.828873\n", + "std 41.135953 18.536151\n", + "min 0.000000 0.000000\n", + "25% 35.250000 71.575000\n", + "50% 70.500000 80.000000\n", + "75% 105.750000 85.925000\n", + "max 141.000000 100.000000" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Argument 2:\n", + "x = wnba[\"FT%\"].reset_index()\n", + "x.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "# As the mean of percentage of FreeThrows made is 75.8 and 75% of players make over 71.5% of the Freethrows, this statement\n", + "# is proven wrong." ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "44.514084507042256" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Argument 3\n", + "\n", + "mean = wnba[\"AST\"].mean()\n", + "mean" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "# The last statement is also wrong, as the mean for WNBA players is 44.5" + ] + }, + { + "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 +2485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..343a86b 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -46,11 +46,520 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
...................................................................................................
137Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
138Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
139Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
140Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
141Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
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142 rows × 32 columns

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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "0 Aerial Powers DAL F 183 71 21.200991 US \n", + "1 Alana Beard LA G/F 185 73 21.329438 US \n", + "2 Alex Bentley CON G 170 69 23.875433 US \n", + "3 Alex Montgomery SAN G/F 185 84 24.543462 US \n", + "4 Alexis Jones MIN G 175 78 25.469388 US \n", + ".. ... ... ... ... ... ... ... \n", + "137 Tiffany Hayes ATL G 178 70 22.093170 US \n", + "138 Tiffany Jackson LA F 191 84 23.025685 US \n", + "139 Tiffany Mitchell IND G 175 69 22.530612 US \n", + "140 Tina Charles NY F/C 193 84 22.550941 US \n", + "141 Yvonne Turner PHO G 175 59 19.265306 US \n", + "\n", + " Birthdate Age College Experience Games Played MIN \\\n", + "0 January 17, 1994 23 Michigan State 2 8 173 \n", + "1 May 14, 1982 35 Duke 12 30 947 \n", + "2 October 27, 1990 26 Penn State 4 26 617 \n", + "3 December 11, 1988 28 Georgia Tech 6 31 721 \n", + "4 August 5, 1994 23 Baylor R 24 137 \n", + ".. ... ... ... ... ... ... \n", + "137 September 20, 1989 27 Connecticut 6 29 861 \n", + "138 April 26, 1985 32 Texas 9 22 127 \n", + "139 September 23, 1984 32 South Carolina 2 27 671 \n", + "140 May 12, 1988 29 Connecticut 8 29 952 \n", + "141 October 13, 1987 29 Nebraska 2 30 356 \n", + "\n", + " FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST \\\n", + "0 30 85 35.3 12 32 37.5 21 26 80.8 6 22 28 12 \n", + "1 90 177 50.8 5 18 27.8 32 41 78.0 19 82 101 72 \n", + "2 82 218 37.6 19 64 29.7 35 42 83.3 4 36 40 78 \n", + "3 75 195 38.5 21 68 30.9 17 21 81.0 35 134 169 65 \n", + "4 16 50 32.0 7 20 35.0 11 12 91.7 3 9 12 12 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "137 144 331 43.5 43 112 38.4 136 161 84.5 28 89 117 69 \n", + "138 12 25 48.0 0 1 0.0 4 6 66.7 5 18 23 3 \n", + "139 83 238 34.9 17 69 24.6 94 102 92.2 16 70 86 39 \n", + "140 227 509 44.6 18 56 32.1 110 135 81.5 56 212 268 75 \n", + "141 59 140 42.1 11 47 23.4 22 28 78.6 11 13 24 30 \n", + "\n", + " STL BLK TO PTS DD2 TD3 \n", + "0 3 6 12 93 0 0 \n", + "1 63 13 40 217 0 0 \n", + "2 22 3 24 218 0 0 \n", + "3 20 10 38 188 2 0 \n", + "4 7 0 14 50 0 0 \n", + ".. ... ... .. ... ... ... \n", + "137 37 8 50 467 0 0 \n", + "138 1 3 8 28 0 0 \n", + "139 31 5 40 277 0 0 \n", + "140 21 22 71 582 11 0 \n", + "141 18 1 32 151 0 0 \n", + "\n", + "[142 rows x 32 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba = pd.read_csv(\"wnba_clean.csv\")\n", + "wnba.drop(\"Unnamed: 0\", axis=1, inplace=True)\n", + "wnba" ] }, { @@ -70,11 +579,46 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + } + ], + "source": [ + "# H0: 67 kg is fine\n", + "# H1: 67 kg is too skinny\n", + "\n", + "s = wnba[\"Weight\"]\n", + "p_value = ttest_1samp(s,67,alternative=\"less\")[1]\n", + "print(p_value)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can not reject null hypothesis\n" + ] + } + ], + "source": [ + "alpha = 0.05\n", + "\n", + "if p_value" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "plt.hist(wnba[\"Weight\"])\n", + "plt.show()" ] }, { @@ -154,11 +753,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24.171126760563382\n", + "1.0\n" + ] + } + ], "source": [ - "# your answer here" + "s = 100-wnba[\"FT%\"]\n", + "print(s.mean())\n", + "p_value = ttest_1samp(s,60,alternative=\"greater\")[1]\n", + "print(p_value)" ] }, { @@ -170,11 +781,26 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21.122365154174023, 27.21988836695274)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_mean = s.mean()\n", + "s_std = s.std(ddof=1)\n", + "n = len(s)\n", + "\n", + "stats.norm.interval(0.95, loc=s_mean, scale=s_std/np.sqrt(n))" ] }, { @@ -186,11 +812,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# actually wnba players only miss between 21 - 27% of their freethrows" ] }, { @@ -227,11 +853,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "s = wnba[\"AST\"]" ] }, { @@ -243,20 +869,29 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "we can reject null hypothesis\n" + ] + } + ], + "source": [ + "#H0: Is not higher\n", + "#H1: is higer\n", + "\n", + "\n", + "p_value = ttest_1samp(s,52,alternative=\"two-sided\")[1]\n", + "alpha = 0.05\n", + "\n", + "if p_value