diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..18954b0 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 +47,294 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(143, 32)\n" + ] + }, + { + "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": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba = pd.read_csv(\"../data/wnba.csv\")\n", + "\n", + "print(wnba.shape)\n", + "\n", + "wnba.head()" ] }, { @@ -64,11 +347,67 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 143 entries, 0 to 142\n", + "Data columns (total 32 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 143 non-null object \n", + " 1 Team 143 non-null object \n", + " 2 Pos 143 non-null object \n", + " 3 Height 143 non-null int64 \n", + " 4 Weight 142 non-null float64\n", + " 5 BMI 142 non-null float64\n", + " 6 Birth_Place 143 non-null object \n", + " 7 Birthdate 143 non-null object \n", + " 8 Age 143 non-null int64 \n", + " 9 College 143 non-null object \n", + " 10 Experience 143 non-null object \n", + " 11 Games Played 143 non-null int64 \n", + " 12 MIN 143 non-null int64 \n", + " 13 FGM 143 non-null int64 \n", + " 14 FGA 143 non-null int64 \n", + " 15 FG% 143 non-null float64\n", + " 16 3PM 143 non-null int64 \n", + " 17 3PA 143 non-null int64 \n", + " 18 3P% 143 non-null float64\n", + " 19 FTM 143 non-null int64 \n", + " 20 FTA 143 non-null int64 \n", + " 21 FT% 143 non-null float64\n", + " 22 OREB 143 non-null int64 \n", + " 23 DREB 143 non-null int64 \n", + " 24 REB 143 non-null int64 \n", + " 25 AST 143 non-null int64 \n", + " 26 STL 143 non-null int64 \n", + " 27 BLK 143 non-null int64 \n", + " 28 TO 143 non-null int64 \n", + " 29 PTS 143 non-null int64 \n", + " 30 DD2 143 non-null int64 \n", + " 31 TD3 143 non-null int64 \n", + "dtypes: float64(5), int64(20), object(7)\n", + "memory usage: 35.9+ KB\n", + "2\n" + ] + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba.info()\n", + "\n", + "wnba.isnull().sum()\n", + "\n", + "NaN_rows_count = wnba.isnull().sum(axis=1).sum()\n", + "NaN_rows_count" ] }, { @@ -80,11 +419,124 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
91Makayla EppsCHIG178NaNNaNUSJune 6, 199522KentuckyR145221414.3050.02540.02024104600
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place Birthdate Age \\\n", + "91 Makayla Epps CHI G 178 NaN NaN US June 6, 1995 22 \n", + "\n", + " College Experience Games Played MIN FGM FGA FG% 3PM 3PA 3P% \\\n", + "91 Kentucky R 14 52 2 14 14.3 0 5 0.0 \n", + "\n", + " FTM FTA FT% OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "91 2 5 40.0 2 0 2 4 1 0 4 6 0 0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba[wnba[\"Weight\"].isnull()]" ] }, { @@ -96,11 +548,28 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.7" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba_entries = len(wnba)\n", + "wnba_to_drop = 1\n", + "\n", + "wnba_removal = round(wnba_to_drop / wnba_entries * 100, 2)\n", + "wnba_removal" ] }, { @@ -114,11 +583,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba.drop(wnba[wnba[\"Weight\"].isnull()].index, inplace = True)" ] }, { @@ -130,11 +601,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#your answer here\n", + "\n", + "# if our interest is onto studying body characteristics of players, the decision is good (we miss a small part of our dataset)\n", + "# an exception would be if the player has outlier characteristics (e.g. the tallest, heavier, oldest player in the dataset)\n", + "\n", + "# if we want to study game characteristics, the decision might have a bigger effect as there are more categories to influence\n", + "# here outliers could appear even easier (think e.g. double-double or triple-double)" ] }, { @@ -147,11 +624,56 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight int64\n", + "BMI float64\n", + "Birth_Place object\n", + "Birthdate object\n", + "Age int64\n", + "College object\n", + "Experience object\n", + "Games Played int64\n", + "MIN int64\n", + "FGM int64\n", + "FGA int64\n", + "FG% float64\n", + "3PM int64\n", + "3PA int64\n", + "3P% float64\n", + "FTM int64\n", + "FTA int64\n", + "FT% float64\n", + "OREB int64\n", + "DREB int64\n", + "REB int64\n", + "AST int64\n", + "STL int64\n", + "BLK int64\n", + "TO int64\n", + "PTS int64\n", + "DD2 int64\n", + "TD3 int64\n", + "dtype: object" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba.dtypes" ] }, { @@ -170,11 +692,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba[\"Weight\"] = wnba[\"Weight\"].astype(\"int64\")" ] }, { @@ -186,11 +710,388 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Height142.0184.6126768.698128165.000000175.750000185.000000191.000000206.00000
Weight142.078.97887310.99611055.00000071.50000079.00000086.000000113.00000
BMI142.023.0912142.07369118.39067521.78587622.87331424.18071531.55588
Age142.027.1126763.66718021.00000024.00000027.00000030.00000036.00000
Games Played142.024.4295777.0754772.00000022.00000027.50000029.00000032.00000
MIN142.0500.105634289.37339312.000000242.250000506.000000752.5000001018.00000
FGM142.074.40140855.9807541.00000027.00000069.000000105.000000227.00000
FGA142.0168.704225117.1658093.00000069.000000152.500000244.750000509.00000
FG%142.043.1028179.85519916.70000037.12500042.05000048.625000100.00000
3PM142.014.83098617.3728290.0000000.00000010.50000022.00000088.00000
3PA142.043.69718346.1553020.0000003.00000032.00000065.500000225.00000
3P%142.024.97816918.4590750.0000000.00000030.55000036.175000100.00000
FTM142.039.53521136.7430530.00000013.00000029.00000053.250000168.00000
FTA142.049.42253544.2446970.00000017.25000035.50000066.500000186.00000
FT%142.075.82887318.5361510.00000071.57500080.00000085.925000100.00000
OREB142.022.06338021.5196480.0000007.00000013.00000031.000000113.00000
DREB142.061.59154949.6698542.00000026.00000050.00000084.000000226.00000
REB142.083.65493068.2005852.00000034.25000062.500000116.500000334.00000
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countmeanstdmin25%50%75%maxstd > mean50% =! mean50% vs. mean
Height142.0184.6126768.698128165.000000175.750000185.000000191.000000206.00000FalseFalseFalse
Weight142.078.97887310.99611055.00000071.50000079.00000086.000000113.00000FalseFalseFalse
BMI142.023.0912142.07369118.39067521.78587622.87331424.18071531.55588FalseFalseFalse
Age142.027.1126763.66718021.00000024.00000027.00000030.00000036.00000FalseFalseFalse
Games Played142.024.4295777.0754772.00000022.00000027.50000029.00000032.00000FalseFalseFalse
MIN142.0500.105634289.37339312.000000242.250000506.000000752.5000001018.00000FalseFalseFalse
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DREB142.061.59154949.6698542.00000026.00000050.00000084.000000226.00000FalseFalseFalse
REB142.083.65493068.2005852.00000034.25000062.500000116.500000334.00000FalseFalseFalse
AST142.044.51408541.4907900.00000011.25000034.00000066.750000206.00000FalseFalseFalse
STL142.017.72535213.4133120.0000007.00000015.00000027.50000063.00000FalseFalseFalse
BLK142.09.78169012.5376690.0000002.0000005.00000012.00000064.00000TrueTrueTrue
TO142.032.28873221.4471412.00000014.00000028.00000048.00000087.00000FalseFalseFalse
PTS142.0203.169014153.0325592.00000077.250000181.000000277.750000584.00000FalseFalseFalse
DD2142.01.1408452.9090020.0000000.0000000.0000001.00000017.00000TrueTrueTrue
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mean \\\n", + "Height 185.000000 191.000000 206.00000 False False \n", + "Weight 79.000000 86.000000 113.00000 False False \n", + "BMI 22.873314 24.180715 31.55588 False False \n", + "Age 27.000000 30.000000 36.00000 False False \n", + "Games Played 27.500000 29.000000 32.00000 False False \n", + "MIN 506.000000 752.500000 1018.00000 False False \n", + "FGM 69.000000 105.000000 227.00000 False False \n", + "FGA 152.500000 244.750000 509.00000 False False \n", + "FG% 42.050000 48.625000 100.00000 False False \n", + "3PM 10.500000 22.000000 88.00000 True False \n", + "3PA 32.000000 65.500000 225.00000 True False \n", + "3P% 30.550000 36.175000 100.00000 False False \n", + "FTM 29.000000 53.250000 168.00000 False False \n", + "FTA 35.500000 66.500000 186.00000 False False \n", + "FT% 80.000000 85.925000 100.00000 False False \n", + "OREB 13.000000 31.000000 113.00000 False True \n", + "DREB 50.000000 84.000000 226.00000 False False \n", + "REB 62.500000 116.500000 334.00000 False False \n", + "AST 34.000000 66.750000 206.00000 False False \n", + "STL 15.000000 27.500000 63.00000 False False \n", + "BLK 5.000000 12.000000 64.00000 True True \n", + "TO 28.000000 48.000000 87.00000 False False \n", + "PTS 181.000000 277.750000 584.00000 False False \n", + "DD2 0.000000 1.000000 17.00000 True True \n", + "TD3 0.000000 0.000000 1.00000 True True \n", + "\n", + " 50% vs. mean \n", + "Height False \n", + "Weight False \n", + "BMI False \n", + "Age 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 True \n", + "DREB False \n", + "REB False \n", + "AST False \n", + "STL False \n", + "BLK True \n", + "TO False \n", + "PTS False \n", + "DD2 True \n", + "TD3 True " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your answer here" + "#your answer here\n", + "\n", + "# two rough ways to look for outliers from the describe() function:\n", + "# standard deviation higher than the mean (std > mean): 3PA, 3PM, BLK\n", + "# large deviation (e.g. p = 0.4) of the mean and the 50% (median) (can also be because of skewed distribution): OREB, BLK, \n", + "\n", + "# i didn't comment on the DD2 and TD3 as they are by definition an \"outlier\" statistic of the game\n", + "\n", + "# implementation in Python\n", + "outliers[\"std > mean\"] = outliers.apply(lambda row: \n", + " True if row[\"std\"] > row['mean'] else False, axis=1)\n", + "\n", + "p = 0.4\n", + "outliers[\"50% vs. mean\"] = outliers.apply(lambda row: \n", + " True if abs((row[\"50%\"] - row[\"mean\"]) / row[\"mean\"]) > p else False, axis=1)\n", + "\n", + "outliers" ] }, { @@ -222,13 +1616,15 @@ "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba.to_csv('../data/wnba_clean.csv')" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1638,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 48b485c..60ea57a 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": 24, "metadata": {}, "outputs": [], "source": [ @@ -36,11 +36,294 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(142, 32)\n" + ] + }, + { + "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": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wnba = pd.read_csv(\"../data/wnba_clean.csv\")\n", + "\n", + "print(wnba.shape)\n", + "\n", + "wnba.head()" ] }, { @@ -52,11 +335,385 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Height142.0184.6126768.698128165.000000175.750000185.000000191.000000206.00000
Weight142.078.97887310.99611055.00000071.50000079.00000086.000000113.00000
BMI142.023.0912142.07369118.39067521.78587622.87331424.18071531.55588
Age142.027.1126763.66718021.00000024.00000027.00000030.00000036.00000
Games Played142.024.4295777.0754772.00000022.00000027.50000029.00000032.00000
MIN142.0500.105634289.37339312.000000242.250000506.000000752.5000001018.00000
FGM142.074.40140855.9807541.00000027.00000069.000000105.000000227.00000
FGA142.0168.704225117.1658093.00000069.000000152.500000244.750000509.00000
FG%142.043.1028179.85519916.70000037.12500042.05000048.625000100.00000
3PM142.014.83098617.3728290.0000000.00000010.50000022.00000088.00000
3PA142.043.69718346.1553020.0000003.00000032.00000065.500000225.00000
3P%142.024.97816918.4590750.0000000.00000030.55000036.175000100.00000
FTM142.039.53521136.7430530.00000013.00000029.00000053.250000168.00000
FTA142.049.42253544.2446970.00000017.25000035.50000066.500000186.00000
FT%142.075.82887318.5361510.00000071.57500080.00000085.925000100.00000
OREB142.022.06338021.5196480.0000007.00000013.00000031.000000113.00000
DREB142.061.59154949.6698542.00000026.00000050.00000084.000000226.00000
REB142.083.65493068.2005852.00000034.25000062.500000116.500000334.00000
AST142.044.51408541.4907900.00000011.25000034.00000066.750000206.00000
STL142.017.72535213.4133120.0000007.00000015.00000027.50000063.00000
BLK142.09.78169012.5376690.0000002.0000005.00000012.00000064.00000
TO142.032.28873221.4471412.00000014.00000028.00000048.00000087.00000
PTS142.0203.169014153.0325592.00000077.250000181.000000277.750000584.00000
DD2142.01.1408452.9090020.0000000.0000000.0000001.00000017.00000
TD3142.00.0070420.0839180.0000000.0000000.0000000.0000001.00000
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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": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#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)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "# 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", + "# 35-36 years born in '80-'82, 21-22 years born in '94-'95" ] }, { @@ -89,11 +1033,45 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 86, "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", + "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()\n" ] }, { @@ -109,7 +1087,12 @@ "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" ] }, { @@ -134,11 +1117,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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MoNgBAAAYQbEDAAAwgmIHAABgBMUOAADACIodAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATFDgAAwAiKHQAAgBEUOwAAACModgAAAEZQ7AAAAIyg2AEAABhBsQMAADCCYgcAAGAExQ4AAMAIih0AAIARFDsAAAAjKHYAAABGUOwAAACMoNgBAAAYQbEDAAAwgmIHAABgBMUOAADACIodAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATFDgAAwAiKHQAAgBEUOwAAACModgAAAEYkxXoAnTF08Zbgvw+smBLDkQAAgGiiA4SHM3YAAABGUOwAAACMoNgBAAAYEVaxKy0t1ZgxY5SWlqbLL79cd955pz788MOQdZxzKi4uVnZ2tlJSUlRYWKjq6uqIDhrRRe7eRfbeRO7eRfaJL6xiV1FRoUcffVTvvPOOysvLdfbsWRUVFenEiRPBdVauXKlVq1aprKxMVVVV8vv9mjhxopqbmyM+eEQHuXsX2XsTuXsX2RvguqG+vt5JchUVFc45586dO+f8fr9bsWJFcJ3Tp0+7jIwM9/zzz3fqNRsbG50k19jYGFyWu2hz8Ib2OtpnPakncneu/Xa0zZ3sO2Yh+2hvgwUWcneO7LvCQvaf933+YjevCif3bv2OXWNjoyRpwIABkqSamhrV1dWpqKgouE5ycrImTJigysrKDl8jEAioqakp5Ib4FoncJbJPRMx5b2LOexdzPvF0udg557RgwQLddNNNGjlypCSprq5OkpSZmRmybmZmZvCx85WWliojIyN4y8nJ6eqQEAWRyl0KP/uhi7cEb4g+5rw3xXLOI7aY84mpy8Vu7ty5ev/99/Wzn/2s3WM+ny/kvnOu3bJWS5YsUWNjY/BWW1vb1SEhCiKVu0T2iYY5703Mee9iziemLv3liXnz5mnTpk36zW9+o8GDBweX+/1+SZ81+qysrODy+vr6du2+VXJyspKTk7syDERZJHOXyD6RMOe9iTnvXcz5xBXWGTvnnObOnatXX31Vb775pvLy8kIez8vLk9/vV3l5eXBZS0uLKioqNG7cuMiMGFFH7t5F9t5E7t5F9okvrDN2jz76qNavX69f/OIXSktLC/48PSMjQykpKfL5fJo/f75KSkqUn5+v/Px8lZSUqF+/fpo1a1aPbAB6Hrl7F9l7E7l7F9knvrCK3XPPPSdJKiwsDFm+Zs0a3XfffZKkhQsX6tSpU5ozZ44aGhpUUFCgbdu2KS0tLSIDRvSRu3eRvTeRu3eRfeLzOedcrAfRVlNTkzIyMtTY2Kj09HRJCvkU5IEVU2I1tLjV0T5LROdvx8U+/cpx8BkL2VvYhmizss+sbEc0Wdhnn/d9/mK8+t4fTu78rVgAAAAjuvSp2HjCWR3AHs7SA0DXcMYOAADACIodAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATFDgAAwAiKHQAAgBEUOwAAACModgAAAEZQ7AAAAIyg2AEAABhBsQMAADAiKdYDAABAkoYu3hJy/8CKKTEaCZC4OGMHAABgBMUOAADACIodAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGJFw17E7/zpHANAZbd87uD4aAKs4YwcAAGAExQ4AAMAIih0AAIARFDsAAAAjKHYAAABGUOwAAACMoNgBAAAYkXDXseuq869/x3WsgMTA3AWAzuOMHQAAgBEUOwAAACModgAAAEZQ7AAAAIyg2AEAABhBsQMAADCCYgcAAGCEZ65jB9vOv9ZZW1z3zJa2WZMtwsXxA+s4YwcAAGAExQ4AAMAIih0AAIARFDsAAAAjKHYAAABGUOwAAACMoNgBAAAYwXXskJAudt06eBfXKLPr/DlPvkDHOGMHAABgBMUOAADACIodAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGMF17OApXAsLSBw9fb1K3g9gEWfsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATFDgAAwAiKHQAAgBEUOwAAACM8ex27i10fiWsZAYnhYvO4p6+Bhthqmy/v2cD/4YwdAACAERQ7AAAAI8Iudr/5zW90xx13KDs7Wz6fT6+//nrI4845FRcXKzs7WykpKSosLFR1dXWkxosYIXdvInfvIntvIvfEF3axO3HihK677jqVlZV1+PjKlSu1atUqlZWVqaqqSn6/XxMnTlRzc3O3B4vYIXdvInfvIntvIvfEF/aHJyZPnqzJkyd3+JhzTk8//bSWLVum6dOnS5LWrVunzMxMrV+/Xg899FD3RouYIXdvInfvIntvIvfEF9HfsaupqVFdXZ2KioqCy5KTkzVhwgRVVlZG8kshjpC7N5G7d5G9N5F7Yojo5U7q6uokSZmZmSHLMzMzdfDgwQ6fEwgEFAgEgvebmpoiOSREQVdyl8g+0ZG7d5G9N5F7YuiRT8X6fL6Q+865dstalZaWKiMjI3jLycnpiSEhCsLJXSJ7K6zlPnTxlpAbLsxa9ugcco9vES12fr9f0v+1+lb19fXtGn6rJUuWqLGxMXirra2N5JAQBV3JXSL7REfu3kX23kTuiSGixS4vL09+v1/l5eXBZS0tLaqoqNC4ceM6fE5ycrLS09NDbkgsXcldIvtER+7eRfbeRO6JIezfsTt+/Lj++Mc/Bu/X1NRo9+7dGjBggIYMGaL58+erpKRE+fn5ys/PV0lJifr166dZs2ZFdOCILnL3JnL3LrL3JnJPfGEXu507d+rmm28O3l+wYIEkafbs2Vq7dq0WLlyoU6dOac6cOWpoaFBBQYG2bdumtLS0yI0aUUfu3kTu3kX23kTuiS/sYldYWCjn3AUf9/l8Ki4uVnFxcXfGhThD7t5E7t5F9t5E7omPvxULAABgBMUOAADAiIheoBgAgGg7/3qDB1ZMidFI0NPCubakV48DztgBAAAYQbEDAAAwgmIHAABgBMUOAADACIodAACAERQ7AAAAIyh2AAAARnAdOwCAKW2vdebVa5nBuzhjBwAAYATFDgAAwAiKHQAAgBEUOwAAACModgAAAEZQ7AAAAIyg2AEAABhh+jp2ba9lBAAAYB1n7AAAAIyg2AEAABhBsQMAADCCYgcAAGAExQ4AAMAIih0AAIARFDsAAAAjTF/HDpA6fz3D89c7sGJKTwwHhrQ9Zjhe4lOsr2fKMYJo44wdAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATXsQsT1ySyhWvceVM41zZjzntHZ7O+2PHDMYJY44wdAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATXsfscF7teEdc2sy2ca53BGzgmvCPWWXP9RHQVZ+wAAACMoNgBAAAYQbEDAAAwgmIHAABgBMUOAADACIodAACAERQ7AAAAI7iOXZRc7JpEXK8IAICe46XrznLGDgAAwAiKHQAAgBEUOwAAACModgAAAEZQ7AAAAIyg2AEAABhBsQMAADCC69gBXXD+NZHa6uz1kbx0XSUvCOeY4NqVdl3sOIjE+wY672L7+2LrdTWLSFyvNhJj4YwdAACAERQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATXsetAZ699c7HnReI6OOe/Dtc9SwxdPQ4icQ0kxKdIvKdIZG9ZV48RdE1PX28wlnlyxg4AAMAIih0AAIARPVbsnn32WeXl5alv374aPXq03nrrrZ76Uogj5O5dZO9N5O5N5B6/eqTYbdy4UfPnz9eyZcv0u9/9Tl/5ylc0efJkHTp0qCe+HOIEuXsX2XsTuXsTuce3Hil2q1at0v33368HHnhAf/EXf6Gnn35aOTk5eu6553riyyFOkLt3kb03kbs3kXt8i3ixa2lp0XvvvaeioqKQ5UVFRaqsrIz0l0OcIHfvIntvIndvIvf4F/HLnXz88cf69NNPlZmZGbI8MzNTdXV17dYPBAIKBALB+42NjZKkpqam4LJzgZORHmaPazt+KXQbLvbYxV7n/PVaH2v9r3Oua4ONgHBzlz4/+0TM/XznZ91WZ3M/f922jyVi9lbnfFeFc4ww570pnuZ8T+QuJU72XX1P7+prdGXO99h17Hw+X8h951y7ZZJUWlqq7373u+2W5+Tk9NTQoiLj6a491p3XaW5uVkZGRudfvAd0NnfJbvZthZN1Z5/X0WOJlL0Xcg9Hd94PEil3iey7Kh7nvFdz7+p7eldfoytzPuLF7rLLLlOvXr3aNff6+vp2DV+SlixZogULFgTvnzt3Tp988okGDhwon8+npqYm5eTkqLa2Vunp6ZEebtT1xPY459Tc3Kzs7OyIvF5XhJu7dPHsm5ubTeXeXRc6bhIxe6/N+bYitW2JmLvEnG+rq8dCrLOPdO6W53wktyuc3CNe7Pr06aPRo0ervLxcX//614PLy8vLNW3atHbrJycnKzk5OWTZF77whXbrpaenmwo80tsT6/9rDzd36eLZt/6fn7Xcu6uj/ZFo2Xt1zrcViW1LtNwl5nxHurK9scw+0rm3ZTX7SG1XZ3PvkR/FLliwQPfee6+uv/56jR07Vi+88IIOHTqkhx9+uCe+HOIEuXsX2XsTuXsTuce3Hil2M2bM0NGjR/W9731PR44c0ciRI/WrX/1Kubm5PfHlECfI3bvI3pvI3ZvIPc65OHf69Gm3fPlyd/r06VgPJSKsbU9PYT+F8tL+sLytlretu7y2b7y2vRdjdV/Eart8zsXw8/IAAACImB77W7EAAACILoodAACAERQ7AAAAI+K62D377LPKy8tT3759NXr0aL311luxHlKHSktLNWbMGKWlpenyyy/XnXfeqQ8//DBknfvuu08+ny/kduONN4asEwgENG/ePF122WVKTU3V1772Nf35z3+O5qbEjUTJvjs6c9w451RcXKzs7GylpKSosLBQ1dXVIetYOm4s5F5cXNxurvv9/uDjncnUayzkLkUme0vzuTMSPfu4nO9R/ahGGDZs2OB69+7tXnzxRbd371732GOPudTUVHfw4MFYD62dSZMmuTVr1rgPPvjA7d69202ZMsUNGTLEHT9+PLjO7Nmz3W233eaOHDkSvB09ejTkdR5++GH3xS9+0ZWXl7tdu3a5m2++2V133XXu7Nmz0d6kmEqk7LujM8fNihUrXFpamvv5z3/u9uzZ42bMmOGysrJcU1NTcB0rx42V3JcvX+5GjBgRMtfr6+uDj3cmUy+xkrtzkcneynzuDAvZx+N8j9tid8MNN7iHH344ZNk111zjFi9eHKMRdV59fb2T5CoqKoLLZs+e7aZNm3bB5xw7dsz17t3bbdiwIbjsf/7nf9wll1zitm7d2pPDjTuJnH13nH/cnDt3zvn9frdixYrgOqdPn3YZGRnu+eefd87ZOm6s5L58+XJ33XXXdfhYZzL1Giu5O9f97C3N586wkH08zve4/FFsS0uL3nvvPRUVFYUsLyoqUmVlZYxG1XmNjY2SpAEDBoQs37Fjhy6//HJdddVVevDBB1VfXx987L333tOZM2dCtjk7O1sjR45MiG2OlETPvjvOP25qampUV1cXsi+Sk5M1YcKE4L6wctxYy33fvn3Kzs5WXl6eZs6cqf3790vqXKZeYi13qXvZW5nPnWEp+3ib73FZ7D7++GN9+umn7f6gcGZmZrs/PBxvnHNasGCBbrrpJo0cOTK4fPLkyXrllVf05ptv6oc//KGqqqp0yy23KBAISJLq6urUp08fXXrppSGvlwjbHEmJnH13dHTctG7vxfaFlePGUu4FBQV6+eWX9V//9V968cUXVVdXp3Hjxuno0aOdytRLLOUudT97K/O5M6xkH4/zvUf+pFiktP5R6FbOuXbL4s3cuXP1/vvv6+233w5ZPmPGjOC/R44cqeuvv165ubnasmWLpk+ffsHXS4Rt7gmJmH13XOi4kbq2LxJ1f1nIffLkycF/jxo1SmPHjtWVV16pdevWBT8wZWE7I8nK/uip7BN1f3RGomcfj/M9Ls/YXXbZZerVq1e7RltfX9+u+caTefPmadOmTdq+fbsGDx580XWzsrKUm5urffv2SZL8fr9aWlrU0NAQsl68b3OkJWr23XGh46b1k1UX2xdWjhvLuaempmrUqFHat29fpzL1Esu5S+Fnb2U+d4bV7ONhvsdlsevTp49Gjx6t8vLykOXl5eUaN25cjEZ1Yc45zZ07V6+++qrefPNN5eXlfe5zjh49qtraWmVlZUmSRo8erd69e4ds85EjR/TBBx/E5Tb3lETLvjs+77jJy8uT3+8P2RctLS2qqKgI7gsrx43l3AOBgP7whz8oKyurU5l6ieXcpfCztzKfO8Nq9nEx33vsYxnd1Pox6Jdeesnt3bvXzZ8/36WmproDBw7EemjtPPLIIy4jI8Pt2LEj5CPPJ0+edM4519zc7B5//HFXWVnpampq3Pbt293YsWPdF7/4xXYfcx88eLD79a9/7Xbt2uVuueUWsx9zv5hEyr47Pu+4ce6zj8pnZGS4V1991e3Zs8fdfffdHV4ewcJxYyX3xx9/3O3YscPt37/fvfPOO27q1KkuLS0tuB2dydRLrOTuXGSytzKfO8NC9vE43+O22Dnn3E9+8hOXm5vr+vTp47785S+HXD4knkjq8LZmzRrnnHMnT550RUVFbtCgQa53795uyJAhbvbs2e7QoUMhr3Pq1Ck3d+5cN2DAAJeSkuKmTp3abh2vSJTsu+PzjhvnPvu4/PLly53f73fJyclu/Pjxbs+ePSGvY+m4sZB763Wqevfu7bKzs9306dNddXV18PHOZOo1FnJ3LjLZW5rPnZHo2cfjfPc551zPnQ8EAABAtMTl79gBAAAgfBQ7AAAAIyh2AAAARlDsAAAAjKDYAQAAGEGxAwAAMIJiBwAAYATFDgAAwAiKHQAAgBGeLnb33XeffD6ffD6fkpKSNGTIED3yyCNqaGgIrjN06NDgOm1vK1askCQdOHAgZHmfPn00bNgwPfXUU+KPesS/yspK9erVS7fddlu7x37+85+roKBAGRkZSktL04gRI/T4449LkgoLCzs8LlpvQ4cOjfKWIBz19fV66KGHNGTIECUnJ8vv92vSpEkqLS29aK4+n09r167Vjh075PP5dOzYsVhvCsLQ9j2/d+/euuKKK/Ttb39bTzzxxOfmfuDAAZ04cUKLFi3SFVdcob59+2rQoEEqLCzU5s2bY71puIi2uft8Pg0cOFC33Xab3n///eA6Pp9Pr7/+eofP72i+Hz58WCNHjtRNN90Ud+8DSbEeQKzddtttWrNmjc6ePau9e/fqW9/6lo4dO6af/exnwXW+973v6cEHHwx5XlpaWsj9X//61xoxYoQCgYDefvttPfDAA8rKytL9998fle1A16xevVrz5s3TT3/6Ux06dEhDhgyR9FmeM2fOVElJib72ta/J5/Np7969euONNyRJr776qlpaWiRJtbW1uuGGG4LHgCT16tUrNhuETrnrrrt05swZrVu3TldccYU++ugjvfHGGxo+fLiOHDkSXO+xxx5TU1OT1qxZE1yWkZGh3/72t7EYNiKg9T3/zJkzeuutt/TAAw9oxowZIbmPGTNGf//3fx/yvj9o0CDdd999evfdd1VWVqbhw4fr6NGjqqys1NGjR2OxKQhDa+6SVFdXpyeffFJTp07VoUOHwn6tP/3pT5o4caKuueYa/cd//If69esX6eF2i+eLXev/rUvS4MGDNWPGDK1duzZknbS0tOA6FzJw4MDgOrm5uVq9erV27dpFsYtjJ06c0L/927+pqqpKdXV1Wrt2rb7zne9IkjZv3qybbrpJTzzxRHD9q666SnfeeackacCAAcHlp0+flhR6DCB+HTt2TG+//bZ27NihCRMmSPpszt5www3t1k1JSVEgECBXQ9q+58+aNUvbt2/X5s2bQ8p7r169Onzf/+Uvf6lnnnlGt99+u6TPfqIzevTo6A0eXdY2d7/fr0WLFmn8+PH63//9Xw0aNKjTr/P+++9r0qRJKiws1Msvv6zevXv31JC7zNM/ij3f/v37tXXr1m4HtXPnTu3atUsFBQURGhl6wsaNG3X11Vfr6quv1j333KM1a9YEf3zu9/tVXV2tDz74IMajRKT1799f/fv31+uvv65AIBDr4SDGUlJSdObMmU6t6/f79atf/UrNzc09PCr0pOPHj+uVV17RsGHDNHDgwE4/r7KyUhMmTND06dP1yiuvxGWpkyh22rx5s/r376+UlBRdeeWV2rt3rxYtWhSyzqJFi4LfDFpvO3bsCFln3Lhx6t+/v/r06aMxY8bob//2b/WNb3wjiluCcL300ku65557JH12mv748ePBH7XOmzdPY8aM0ahRozR06FDNnDlTq1evpggYkJSUpLVr12rdunX6whe+oL/6q7/S0qVLQ37fBt7w7rvvav369frqV7/aqfVfeOEFVVZWauDAgRozZoz+4R/+Qf/93//dw6NEJLR+r+/fv7/S0tK0adMmbdy4UZdc0vka9PWvf1133HGHfvKTn4T1vGiL35FFyc0336zdu3frt7/9rebNm6dJkyZp3rx5Ies88cQT2r17d8jt/LNxGzdu1O7du/X73/9eGzdu1C9+8QstXrw4mpuCMHz44Yd69913NXPmTEmffbOfMWOGVq9eLUlKTU3Vli1b9Mc//lFPPvmk+vfvr8cff1w33HCDTp48GcuhIwLuuusuHT58WJs2bdKkSZO0Y8cOffnLX273axiwp/UbfN++fTV27FiNHz9e//zP/9yp544fP1779+/XG2+8obvuukvV1dX6yle+ou9///s9PGp0V+v3+tbv90VFRZo8ebIOHjzY6deYNm2aXnvtNb311ls9ONIIcB42e/ZsN23atJBlhYWF7sknnwzez83NdT/60Y8u+Bo1NTVOkvvd734Xsry0tNQlJSW5U6dORXDEiJQnnnjCSXK9evUK3i655BKXnJzsPvnkkw6fs3//fpeUlORWr14dsvxCxwASy/333++GDBkSsqyj9wjnnNu+fbuT5BoaGqIzOETE7Nmz3a233ur27dvnDhw44FpaWjpc7/Pe99v6/ve/73r37u0CgUAER4pI6mgenz171qWmprply5Y555yT5F577bUOn9863z/55BP34IMPutTUVLdjx44eHnXXef6M3fmWL1+uf/qnf9Lhw4e79Tq9evXS2bNng5+cRPw4e/asXn75Zf3whz8MOQv7+9//Xrm5uXrllVc6fN7QoUPVr18/nThxIsojRjQMHz6cbD0gNTVVw4YNU25ubkR+R2r48OE6e/Zs8ENUSAw+n0+XXHKJTp06FdZz/uVf/kX33nuvbr/99na/khUvPP+p2PMVFhZqxIgRKikpUVlZmSSpublZdXV1Iev169dP6enpwftHjx5VXV2dzp49qz179uiZZ57RzTffHLIO4sPmzZvV0NCg+++/XxkZGSGP/fVf/7Veeuklffzxxzp58qRuv/125ebm6tixY/rxj3+sM2fOaOLEiTEaOSLh6NGj+pu/+Rt961vf0rXXXqu0tDTt3LlTK1eu1LRp08J6rT179rS79NFf/uVfRnC0iCeFhYW6++67df3112vgwIHau3evli5dynt9AggEAsHv4w0NDSorK9Px48d1xx13BNepqanR7t27Q543bNiwkPs+n0/PPvusevXqpSlTpuiXv/ylbrnllh4ffzgodh1YsGCBvvnNbwY/RPGd73wneBmMVg899JCef/754P1bb71V0mdn6rKysnT77bfrBz/4QfQGjU576aWXdOutt7YrddJnv3tVUlKie+65Rx988IG+8Y1v6KOPPtKll16qL33pS9q2bZuuvvrqGIwakdK/f38VFBToRz/6kf70pz/pzJkzysnJ0YMPPqilS5eG9Vrjx49vt8xxYXKzJk2apHXr1mnp0qU6efKksrOzNXXq1HbfHxB/tm7dqqysLEmfXcLsmmuu0b//+7+rsLAwuM6CBQvaPW/79u3tlvl8PpWVlalXr16aOnWqNm3aFOwA8cDneBcCAAAwgd+xAwAAMIJiBwAAYATFDgAAwAiKHQAAgBEUOwAAACModgAAAEZQ7AAAAIyg2AEAABhBsQMAADCCYgcAAGAExQ4AAMAIih0AAIAR/w8upTwmInyHDwAAAABJRU5ErkJggg==\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 +1175,13 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\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" ] }, { @@ -173,11 +1200,49 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "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 +1258,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\n", + "# every stat except blocks get less skewed when normalized on playing time" ] }, { @@ -222,13 +1289,20 @@ "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "#your comments here\n", + "\n", + "# population = all female professional basketball players (1 & 2) or all professional basketball players (3)\n", + "# sample = WNBA players\n", + "\n", + "# Hypothesis 1: BMI = BMI_average\n", + "# Hypothesis 2: FT% > x%\n", + "# Hypothesis 3: AST > 52" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1316,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..5db7ebe 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": 13, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,9 @@ "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 scipy.stats as st" ] }, { @@ -46,11 +48,294 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(142, 32)\n" + ] + }, + { + "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": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "\n", + "wnba = pd.read_csv(\"../data/wnba_clean.csv\")\n", + "\n", + "print(wnba.shape)\n", + "\n", + "wnba.head()" ] }, { @@ -70,11 +355,33 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 7, + "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 +393,37 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "78.75735294117646\n", + "9.578909180555195\n", + "136\n", + "0.95\n", + "(77.14746853948338, 80.36723734286954)\n" + ] + } + ], + "source": [ + "# your code here\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 +439,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "# with 95% confidence, the mean weight of a female basketball player is between 77.1 and 80.4 kg" ] }, { @@ -122,7 +457,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "# my sister's weight of 67 kg is quite below the ci lower limit as calculated above (67 < 77.1 kg)\n", + "# i would tell my grandmother that her assumption is most probably right" ] }, { @@ -154,11 +492,26 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(137, 32)\n" + ] + } + ], + "source": [ + "# your answer here\n", + "\n", + "# i need to reload my dataset as I have exluded some values in Q1\n", + "# similar to Q1, i would like to exclude outliers, such as players that never shot a free throw\n", + "\n", + "wnba = wnba[(wnba[\"FTA\"] > 0)]\n", + "\n", + "print(wnba.shape)" ] }, { @@ -170,11 +523,73 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can reject the null hypothesis\n", + "\n", + "population: 137\n", + "hypothesis: 40\n", + "confidence interval tail: greater\n", + "significance level: 0.05\n", + "\n", + "sample population: 137\n", + "sample mean: 78.5963503649635\n", + "sample std: 11.709306308628321\n", + "\n", + "statistic: 38.581166703103584\n", + "p-value: 2.0120042321359712e-75\n", + "df: 136\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "# population is all female professional basketball players that have shot a free throw\n", + "# sub-population is all wnba players that have shot a free throw\n", + "p = (wnba[\"FTA\"] > 0).count()\n", + "\n", + "# 1. Set the hypothesis\n", + "# H0: mu <= 40 (the mean FT% is less (or equal) than 40%)\n", + "# H1: mu > 40 (the mean FT% is more than 40%)\n", + "mu = 40\n", + "cit = \"greater\"\n", + "\n", + "# 2. Choose significance level\n", + "alpha = 0.05\n", + "\n", + "# 3. Sample (take the whole sub-population)\n", + "n = 137\n", + "sample = wnba[\"FT%\"].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 (for \"greater\" choose \">\", for \"less\" choose \"<\")\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\")\n", + " \n", + "# Give results\n", + "print()\n", + "print(\"population:\", p)\n", + "print(\"hypothesis:\", mu)\n", + "print(\"confidence interval tail:\", cit)\n", + "print(\"significance level:\", alpha)\n", + "print()\n", + "print(\"sample population:\", n)\n", + "print(\"sample mean:\", sample.mean())\n", + "print(\"sample std:\", sample.std(ddof = 1))\n", + "print()\n", + "print(\"statistic:\", t_test_result.statistic)\n", + "print(\"p-value:\", t_test_result.pvalue)\n", + "print(\"df:\", t_test_result.df)" ] }, { @@ -190,7 +605,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "# with 95% confidence I can say that the average of free throws is more than 40%\n", + "# under the definition of failure I gave (40%) most female players do not fail their free throws" ] }, { @@ -231,7 +649,12 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "# There is the column AST which counts assists. \n", + "So I can check, if 52 is an average that would be reached with only the \n", + "# WNBA players, and if that is not true,\n", + "if only WNBA players are probable to show a higher average of assists." ] }, { @@ -243,11 +666,70 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can reject the null hypothesis\n", + "\n", + "population: 142\n", + "hypothesis: 52\n", + "confidence interval tail: two-sided\n", + "significance level: 0.05\n", + "\n", + "sample population: 142\n", + "sample mean: 44.514084507042256\n", + "sample std: 41.49078952999805\n", + "\n", + "statistic: -2.1499947192482898\n", + "p-value: 0.033261541354107166\n", + "df: 141\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", + " \n", + "print()\n", + "print(\"population:\", p)\n", + "print(\"hypothesis:\", mu)\n", + "print(\"confidence interval tail:\", cit)\n", + "print(\"significance level:\", alpha)\n", + "print()\n", + "print(\"sample population:\", n)\n", + "print(\"sample mean:\", sample.mean())\n", + "print(\"sample std:\", sample.std(ddof = 1))\n", + "print()\n", + "print(\"statistic:\", t_test_result.statistic)\n", + "print(\"p-value:\", t_test_result.pvalue)\n", + "print(\"df:\", t_test_result.df)" ] }, { @@ -256,7 +738,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "\n", + "# The average number of assists for WNBA players is not 52" ] }, { @@ -268,11 +752,80 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can not reject the null hypothesis\n", + "\n", + "population: 142\n", + "hypothesis: 52\n", + "confidence interval tail: greater\n", + "significance level: 0.05\n", + "\n", + "sample population: 142\n", + "sample mean: 44.514084507042256\n", + "sample std: 41.49078952999803\n", + "\n", + "statistic: -2.14999471924829\n", + "p-value: 0.9833692293229465\n", + "df: 141\n" + ] + } + ], + "source": [ + "#your-answer-here\n", + "\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\")\n", + " \n", + "print()\n", + "print(\"population:\", p)\n", + "print(\"hypothesis:\", mu)\n", + "print(\"confidence interval tail:\", cit)\n", + "print(\"significance level:\", alpha)\n", + "print()\n", + "print(\"sample population:\", n)\n", + "print(\"sample mean:\", sample.mean())\n", + "print(\"sample std:\", sample.std(ddof = 1))\n", + "print()\n", + "print(\"statistic:\", t_test_result.statistic)\n", + "print(\"p-value:\", t_test_result.pvalue)\n", + "print(\"df:\", t_test_result.df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# The average number of assists for WNBA players is less than 52" ] }, { @@ -343,7 +896,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +910,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.10" } }, "nbformat": 4,