diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..3189a77 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": 23, "metadata": {}, "outputs": [], "source": [ @@ -47,11 +47,284 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "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": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba = pd.read_csv(\"/Users/ricardomendes/Desktop/LABS/Week5/M2-mini-project2/data/wnba.csv\")\n", + "wnba.head()" ] }, { @@ -64,11 +337,56 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name 0\n", + "Team 0\n", + "Pos 0\n", + "Height 0\n", + "Weight 1\n", + "BMI 1\n", + "Birth_Place 0\n", + "Birthdate 0\n", + "Age 0\n", + "College 0\n", + "Experience 0\n", + "Games Played 0\n", + "MIN 0\n", + "FGM 0\n", + "FGA 0\n", + "FG% 0\n", + "3PM 0\n", + "3PA 0\n", + "3P% 0\n", + "FTM 0\n", + "FTA 0\n", + "FT% 0\n", + "OREB 0\n", + "DREB 0\n", + "REB 0\n", + "AST 0\n", + "STL 0\n", + "BLK 0\n", + "TO 0\n", + "PTS 0\n", + "DD2 0\n", + "TD3 0\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba.isna().sum()" ] }, { @@ -80,11 +398,123 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba[wnba.isna().any(axis=1)]" ] }, { @@ -96,11 +526,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.043706293706293704\n" + ] + } + ], "source": [ - "#your code here" + "#your code here\n", + "total_values = wnba.size\n", + "nan_values = wnba.isna().sum().sum()\n", + "\n", + "nan_percentage = (nan_values / total_values) * 100\n", + "\n", + "print(nan_percentage)" ] }, { @@ -114,11 +558,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba = wnba.dropna()" ] }, { @@ -147,11 +593,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "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": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba.dtypes" ] }, { @@ -170,11 +660,58 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight int64\n", + "BMI float64\n", + "Birth_Place object\n", + "Birthdate object\n", + "Age int64\n", + "College object\n", + "Experience object\n", + "Games Played int64\n", + "MIN int64\n", + "FGM int64\n", + "FGA int64\n", + "FG% float64\n", + "3PM int64\n", + "3PA int64\n", + "3P% float64\n", + "FTM int64\n", + "FTA int64\n", + "FT% float64\n", + "OREB int64\n", + "DREB int64\n", + "REB int64\n", + "AST int64\n", + "STL int64\n", + "BLK int64\n", + "TO int64\n", + "PTS int64\n", + "DD2 int64\n", + "TD3 int64\n", + "dtype: object" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba[\"Weight\"] = wnba[\"Weight\"].astype(\"int64\")\n", + "wnba.dtypes" ] }, { @@ -186,13 +723,77 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Height Weight BMI Age Games Played \\\n", + "count 143.000000 142.000000 142.000000 143.000000 143.000000 \n", + "mean 184.566434 78.978873 23.091214 27.076923 24.356643 \n", + "std 8.685068 10.996110 2.073691 3.679170 7.104259 \n", + "min 165.000000 55.000000 18.390675 21.000000 2.000000 \n", + "25% 176.500000 71.500000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.000000 \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 143.000000 143.000000 143.000000 143.000000 143.000000 \n", + "mean 496.972028 73.895105 167.622378 42.901399 14.727273 \n", + "std 290.777320 56.110895 117.467095 10.111498 17.355919 \n", + "min 12.000000 1.000000 3.000000 14.300000 0.000000 \n", + "25% 240.000000 26.000000 66.000000 36.950000 0.000000 \n", + "50% 504.000000 69.000000 152.000000 42.000000 10.000000 \n", + "75% 750.000000 105.000000 244.500000 48.550000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 143.000000 143.000000 143.000000 143.000000 143.000000 143.000000 \n", + "mean 43.426573 24.803497 39.272727 49.111888 75.578322 21.923077 \n", + "std 46.106199 18.512183 36.747747 44.244854 18.712194 21.509276 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 12.000000 16.500000 71.150000 7.000000 \n", + "50% 32.000000 30.300000 29.000000 35.000000 80.000000 13.000000 \n", + "75% 65.000000 36.150000 52.500000 66.000000 85.850000 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 143.000000 143.000000 143.000000 143.000000 143.000000 143.000000 \n", + "mean 61.160839 83.083916 44.230769 17.608392 9.713287 32.090909 \n", + "std 49.761919 68.302197 41.483017 13.438978 12.520193 21.502017 \n", + "min 0.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 25.500000 34.000000 11.000000 7.000000 2.000000 13.500000 \n", + "50% 50.000000 62.000000 33.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.000000 66.500000 27.000000 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 143.000000 143.000000 143.000000 \n", + "mean 201.790210 1.132867 0.006993 \n", + "std 153.381548 2.900310 0.083624 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 75.000000 0.000000 0.000000 \n", + "50% 177.000000 0.000000 0.000000 \n", + "75% 277.500000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 \n" + ] + } + ], "source": [ - "#your code here" + "#your code here\n", + "print(wnba.describe())" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -222,13 +823,14 @@ "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "df.to_csv(\"data/wnba_clean.csv\", index=False)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +844,7 @@ "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..183c0d1 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -36,13 +36,292 @@ }, { "cell_type": "code", - 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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
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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", + " 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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba = pd.read_csv(\"/Users/ricardomendes/Desktop/LABS/Week5/M2-mini-project2/data/wnba_clean.csv\")\n", + "wnba.head()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -52,11 +331,346 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
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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
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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
23Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
15Angel RobinsonPHOF/C1988822.446689USAugust 30, 199521Arizona State115237254456.811100.077100.01642588111165800
70Kaela DavisDALG1887721.785876USMarch 15, 199522South CarolinaR23208277536.0205536.43475.02202257167700
38Courtney WilliamsCONG1736220.715694USNovember 5, 199422South Florida12975516833849.783026.7313686.13884122601563937510
55Evelyn AkhatorDALF1918222.477454NGMarch 2, 199522KentuckyR3092616536545.2206033.39211778.67319927250371367442130
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place \\\n", + "23 Brionna Jones CON F 191 104 28.507990 US \n", + "15 Angel Robinson PHO F/C 198 88 22.446689 US \n", + "70 Kaela Davis DAL G 188 77 21.785876 US \n", + "38 Courtney Williams CON G 173 62 20.715694 US \n", + "55 Evelyn Akhator DAL F 191 82 22.477454 NG \n", + "\n", + " Birthdate Age College Experience Games Played MIN FGM \\\n", + "23 December 18, 1995 21 Maryland R 19 112 14 \n", + "15 August 30, 1995 21 Arizona State 1 15 237 25 \n", + "70 March 15, 1995 22 South Carolina R 23 208 27 \n", + "38 November 5, 1994 22 South Florida 1 29 755 168 \n", + "55 March 2, 1995 22 Kentucky R 30 926 165 \n", + "\n", + " FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB AST STL \\\n", + "23 26 53.8 0 0 0.0 16 19 84.2 11 14 25 2 7 \n", + "15 44 56.8 1 1 100.0 7 7 100.0 16 42 58 8 1 \n", + "70 75 36.0 20 55 36.4 3 4 75.0 2 20 22 5 7 \n", + "38 338 49.7 8 30 26.7 31 36 86.1 38 84 122 60 15 \n", + "55 365 45.2 20 60 33.3 92 117 78.6 73 199 272 50 37 \n", + "\n", + " BLK TO PTS DD2 TD3 \n", + "23 1 7 44 0 0 \n", + "15 11 16 58 0 0 \n", + "70 1 6 77 0 0 \n", + "38 6 39 375 1 0 \n", + "55 13 67 442 13 0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba.sort_values(by = \"Weight\", ascending = False).head(5)\n", + "wnba.sort_values(by = \"Weight\").head(5)\n", + "\n", + "wnba.sort_values(by = \"Age\", ascending = False).head(5)\n", + "wnba.sort_values(by = \"Age\").head(5)" ] }, { @@ -89,11 +979,47 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "#your code here\n", + "plot_options, ((chart_1, chart_2), (chart_3, chart_4)) = plt.subplots(nrows = 2, ncols = 2)\n", + "\n", + "n_bin = 15\n", + "chart_1.hist(wnba[\"Height\"], bins = n_bin)\n", + "chart_2.hist(wnba[\"Weight\"], bins = n_bin)\n", + "chart_3.hist(wnba[\"Age\"], bins = n_bin)\n", + "chart_4.hist(wnba[\"BMI\"], bins = n_bin)\n", + "\n", + "ylim = 30\n", + "\n", + "chart_1.set_ylim(0, ylim)\n", + "chart_2.set_ylim(0, ylim)\n", + "chart_3.set_ylim(0, ylim)\n", + "chart_4.set_ylim(0, ylim)\n", + "\n", + "chart_1.set_xlabel(\"Height\")\n", + "chart_2.set_xlabel(\"Weight\")\n", + "chart_3.set_xlabel(\"Age\")\n", + "chart_4.set_xlabel(\"BMI\")\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" ] }, { @@ -109,7 +1035,13 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\n", + "\"\"\"Height and Weight appear to follow a standard distribution, possibly bi- or even trimodal\n", + "This can be related to the different body-types of players depending on their game duties (playmaker vs. guard vs. center)\n", + "BMI (BMI = [weight / (height ** 2)]) shows less variance and looks more unimodal, this can be due to the ^2 in the calculation\n", + "Age is somehow right skewed (younger ages) and shows greater variance\n", + "\"\"\"" ] }, { @@ -134,11 +1066,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "plot_options, (chart_1, chart_2, chart_3, chart_4, chart_5) = plt.subplots(nrows = 1, ncols = 5)\n", + "\n", + "n_bin = 20\n", + "chart_1.hist(wnba[\"REB\"], bins = n_bin)\n", + "chart_2.hist(wnba[\"AST\"], bins = n_bin)\n", + "chart_3.hist(wnba[\"STL\"], bins = n_bin)\n", + "chart_4.hist(wnba[\"PTS\"], bins = n_bin)\n", + "chart_5.hist(wnba[\"BLK\"], bins = n_bin)\n", + "\n", + "ylim = 51\n", + "\n", + "chart_1.set_ylim(0, ylim)\n", + "chart_2.set_ylim(0, ylim)\n", + "chart_3.set_ylim(0, ylim)\n", + "chart_4.set_ylim(0, ylim)\n", + "chart_5.set_ylim(0, ylim)\n", + "\n", + "chart_1.set_xlabel(\"REB\")\n", + "chart_2.set_xlabel(\"AST\")\n", + "chart_3.set_xlabel(\"STL\")\n", + "chart_4.set_xlabel(\"PTS\")\n", + "chart_5.set_xlabel(\"BLK\")\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" ] }, { @@ -154,7 +1124,15 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\n", + "\"\"\"\n", + "All the distributions are skewed to the right (right side = player with very high stats on the specific category)\n", + "Rebounds, Steals and Points are quite similar in frequency and behavior of the distribution\n", + "we could conclude that these are essential to the game\n", + "Assists -and to an even higher extent- Blocks, start with a high frequency (many players can give a few of them)\n", + "but as we go to the right, we see that very few players can perform\n", + "\"\"\"" ] }, { @@ -173,11 +1151,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "plot_options, (chart_1, chart_2, chart_3, chart_4, chart_5) = plt.subplots(nrows = 1, ncols = 5)\n", + "\n", + "n_bin = 20\n", + "chart_1.hist(wnba[\"REB\"] / wnba[\"MIN\"], bins = n_bin)\n", + "chart_2.hist(wnba[\"AST\"] / wnba[\"MIN\"], bins = n_bin)\n", + "chart_3.hist(wnba[\"STL\"] / wnba[\"MIN\"], bins = n_bin)\n", + "chart_4.hist(wnba[\"PTS\"] / wnba[\"MIN\"], bins = n_bin)\n", + "chart_5.hist(wnba[\"BLK\"] / wnba[\"MIN\"], bins = n_bin)\n", + "\n", + "ylim = 51\n", + "\n", + "chart_1.set_ylim(0, ylim)\n", + "chart_2.set_ylim(0, ylim)\n", + "chart_3.set_ylim(0, ylim)\n", + "chart_4.set_ylim(0, ylim)\n", + "chart_5.set_ylim(0, ylim)\n", + "\n", + "chart_1.set_xlabel(\"REB/MIN\")\n", + "chart_2.set_xlabel(\"AST/MIN\")\n", + "chart_3.set_xlabel(\"STL/MIN\")\n", + "chart_4.set_xlabel(\"PTS/MIN\")\n", + "chart_5.set_xlabel(\"BLK/MIN\")\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" ] }, { @@ -193,7 +1209,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\n", + "\"\"\"\n", + "Every stat except blocks get less skewed when normalized on playing time\n", + "\"\"\"" ] }, { @@ -228,7 +1248,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1262,7 @@ "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..229f944 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": 16, "metadata": {}, "outputs": [], "source": [ @@ -46,11 +46,284 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 13, + "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
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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", + " 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": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "\n", + "wnba = pd.read_csv(\"/Users/ricardomendes/Desktop/LABS/Week5/M2-mini-project2/data/wnba_clean.csv\")\n", + "wnba.head()" ] }, { @@ -70,11 +343,33 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(136, 32)\n" + ] + } + ], + "source": [ + "# your answer here\n", + "\n", + "# from our plots in exploratory data analysis we saw that weight of wnba players follows a rather\n", + "# normal distribution. therefore it would be possible to use this sample and normal distribution in\n", + "# order to inger from the sample to the population (women basketball players)\n", + "\n", + "# for a more accurate outcome, we could consider removing weight outliers -> see previous step:\n", + "\n", + "# Checking top/bottom 5 comparing weight to height and age to birthdate:\n", + "# 104-113 kg for 185-196 cm, 55-59 kg for 165-175 cm \n", + "\n", + "# Drop outliers based on weight column\n", + "wnba = wnba[(wnba[\"Weight\"] >= 59) & (wnba[\"Weight\"] <= 104)]\n", + "\n", + "print(wnba.shape)" ] }, { @@ -86,11 +381,39 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78.97887323943662\n", + "10.996110408297898\n", + "142\n", + "0.95\n", + "(77.17027122332428, 80.78747525554897)\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "import scipy.stats as st\n", + "\n", + "sample_mean = wnba[\"Weight\"].mean()\n", + "sample_std = wnba[\"Weight\"].std(ddof = 1)\n", + "sample_n = len(wnba[\"Weight\"])\n", + "alpha = 0.95\n", + "\n", + "\n", + "ci = st.norm.interval(confidence = alpha, loc = sample_mean, scale = sample_std / np.sqrt(sample_n))\n", + "\n", + "print(sample_mean)\n", + "print(sample_std)\n", + "print(sample_n)\n", + "print(alpha)\n", + "print(ci)" ] }, { @@ -106,7 +429,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "\"\"\"\n", + "With 95% confidence, the mean weight of a female basketball player is between 77.1 and 80.4 kg\n", + "\"\"\"" ] }, { @@ -122,7 +449,14 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "\"\"\"\n", + "Based on the 95% confidence interval we calculated using the WNBA dataset, \n", + "it appears that the average weight of professional female basketball players is between 75.78 and 81.91 kg.\n", + "Since your sister's weight of 67 kg falls below this range,\n", + "it is possible that her weight could be a disadvantage in playing professionally.\n", + "\"\"\"" ] }, { @@ -154,11 +488,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of players who miss more than 40% of their free throws: 0.099\n" + ] + } + ], + "source": [ + "# your answer here\n", + "\n", + "\n", + "missed_ft = wnba[wnba['FT%'] < 60] # Select players who miss more than 40% of their free throws\n", + "prop_missed_ft = len(missed_ft) / len(wnba) # Calculate proportion of players who miss more than 40% of their free throws\n", + "print(f\"Proportion of players who miss more than 40% of their free throws: {prop_missed_ft:.3f}\")\n" ] }, { @@ -170,11 +517,32 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% confidence interval for proportion of players who miss more than 40% of their free throws: [0.050, 0.148]\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "# Calculate standard error and margin of error\n", + "n = len(wnba)\n", + "p = prop_missed_ft\n", + "se = math.sqrt(p*(1-p)/n)\n", + "z = 1.96 # z-value for 95% confidence interval\n", + "me = z * se\n", + "\n", + "# Calculate confidence interval\n", + "lower_bound = p - me\n", + "upper_bound = p + me\n", + "\n", + "print(f\"95% confidence interval for proportion of players who miss more than 40% of their free throws: [{lower_bound:.3f}, {upper_bound:.3f}]\")\n" ] }, { @@ -225,15 +593,6 @@ "**How would you do it? Try and think about the requirements that your sample must satisfy in order to do that. Do you feel it actually fulfills those requirements? Do you need to make any assumptions?**" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -243,11 +602,44 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can reject the null hypothesis\n" + ] + } + ], + "source": [ + "#your code here\n", + "\n", + "p = (wnba[\"AST\"]).count()\n", + "\n", + "# 1. Set the hypothesis\n", + "# H0: mu = 52\n", + "# H1: mu != 52\n", + "mu = 52\n", + "cit = \"two-sided\"\n", + "\n", + "# 2. Choose significance level\n", + "alpha = 0.05\n", + "\n", + "# 3. Sample\n", + "n = 142\n", + "sample = wnba[\"AST\"].sample(n)\n", + "\n", + "# 4. Compute the statistic & 5. Get p-value -> st.ttest_1samp\n", + "t_test_result = st.ttest_1samp(sample, mu, alternative = cit)\n", + "\n", + "# 6. Decide\n", + "if t_test_result.pvalue < alpha: \n", + " print(\"We can reject the null hypothesis\")\n", + "else:\n", + " print(\"We can not reject the null hypothesis\")\n", + " " ] }, { @@ -256,7 +648,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\"\"\"\n", + "The average number of assists for WNBA players is not 52\n", + "\"\"\"" ] }, { @@ -268,11 +663,43 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can not reject the null hypothesis\n" + ] + } + ], + "source": [ + "#your-answer-here\n", + "\n", + "p = (wnba[\"AST\"]).count()\n", + "\n", + "# 1. Set the hypothesis\n", + "# H0: mu <= 52\n", + "# H1: mu > 52\n", + "mu = 52\n", + "cit = \"greater\"\n", + "\n", + "# 2. Choose significance level\n", + "alpha = 0.05\n", + "\n", + "# 3. Sample\n", + "n = 142\n", + "sample = wnba[\"AST\"].sample(n)\n", + "\n", + "# 4. Compute the statistic & 5. Get p-value -> st.ttest_1samp\n", + "t_test_result = st.ttest_1samp(sample, mu, alternative = cit)\n", + "\n", + "# 6. Decide\n", + "if t_test_result.pvalue < alpha and t_test_result.statistic > 0: \n", + " print(\"We can reject the null hypothesis\")\n", + "else:\n", + " print(\"We can not reject the null hypothesis\")" ] }, { @@ -343,7 +770,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +784,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.9" } }, "nbformat": 4,