From 9e11479639df52605a5f3b4d9f032c504e52b455 Mon Sep 17 00:00:00 2001 From: Maria Garcia Date: Wed, 25 May 2022 21:16:16 +0100 Subject: [PATCH] completed --- your-code/1.-Data-Cleaning.ipynb | 858 ++++++++++- your-code/2.-Exploratory-Data-Analysis.ipynb | 1352 +++++++++++++++++- your-code/3.-Inferential-Analysis.ipynb | 432 +++++- your-code/wnba_clean.csv | 143 ++ 4 files changed, 2700 insertions(+), 85 deletions(-) create mode 100644 your-code/wnba_clean.csv diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index 60a0517..7505b67 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": 15, "metadata": {}, "outputs": [], "source": [ @@ -47,11 +47,282 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba = pd.read_csv('/Users/mariagarcia/Desktop/labs/week5/M2-mini-project2/data/wnba.csv')\n", + "wnba.head(5)" ] }, { @@ -64,11 +335,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Weight 1\n", + "BMI 1\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "# Show columns where there are NaN:\n", + "wnba_null = wnba.isnull().sum()\n", + "wnba_null[wnba_null>0]\n", + "\n", + "# We see that there is 1 null value in Weight and 1 in BMI" ] }, { @@ -80,11 +368,124 @@ }, { "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" + "wnba[wnba.isnull().any(axis=1)]\n", + "\n", + "# rown 91 contains both null values" ] }, { @@ -96,11 +497,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.006993006993006993" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "1/len(wnba)" ] }, { @@ -114,11 +526,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba.dropna(inplace=True)" ] }, { @@ -130,11 +542,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "# There might be some cases in which, because of the business context, the lack of a value may be a \n", + "# meaningful insight. Also if we have a dataset that does not contain many records and the number of\n", + "# rows with at least one null value is relatively high, dropping all rows with nulls may have\n", + "# a significant impact." ] }, { @@ -147,11 +562,54 @@ }, { "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" + "wnba.dtypes" ] }, { @@ -170,11 +628,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba['Weight'] = wnba['Weight'].astype(int)" ] }, { @@ -186,11 +644,345 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std8.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
max206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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" + ], + "text/plain": [ + " Height Weight BMI Age Games Played \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 184.612676 78.978873 23.091214 27.112676 24.429577 \n", + "std 8.698128 10.996110 2.073691 3.667180 7.075477 \n", + "min 165.000000 55.000000 18.390675 21.000000 2.000000 \n", + "25% 175.750000 71.500000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.500000 \n", + "75% 191.000000 86.000000 24.180715 30.000000 29.000000 \n", + "max 206.000000 113.000000 31.555880 36.000000 32.000000 \n", + "\n", + " MIN FGM FGA FG% 3PM \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 500.105634 74.401408 168.704225 43.102817 14.830986 \n", + "std 289.373393 55.980754 117.165809 9.855199 17.372829 \n", + "min 12.000000 1.000000 3.000000 16.700000 0.000000 \n", + "25% 242.250000 27.000000 69.000000 37.125000 0.000000 \n", + "50% 506.000000 69.000000 152.500000 42.050000 10.500000 \n", + "75% 752.500000 105.000000 244.750000 48.625000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 43.697183 24.978169 39.535211 49.422535 75.828873 22.063380 \n", + "std 46.155302 18.459075 36.743053 44.244697 18.536151 21.519648 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 13.000000 17.250000 71.575000 7.000000 \n", + "50% 32.000000 30.550000 29.000000 35.500000 80.000000 13.000000 \n", + "75% 65.500000 36.175000 53.250000 66.500000 85.925000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 61.591549 83.654930 44.514085 17.725352 9.781690 32.288732 \n", + "std 49.669854 68.200585 41.490790 13.413312 12.537669 21.447141 \n", + "min 2.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 26.000000 34.250000 11.250000 7.000000 2.000000 14.000000 \n", + "50% 50.000000 62.500000 34.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.500000 66.750000 27.500000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 \n", + "mean 203.169014 1.140845 0.007042 \n", + "std 153.032559 2.909002 0.083918 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 77.250000 0.000000 0.000000 \n", + "50% 181.000000 0.000000 0.000000 \n", + "75% 277.750000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "wnba.describe()" ] }, { @@ -202,11 +994,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#There are significant differences between the extremes for many values \n", + "# (e.g. Weight goes from 55 to 113)" ] }, { @@ -218,17 +1011,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "wnba.to_csv('wnba_clean.csv')" ] } ], "metadata": { + "interpreter": { + "hash": "df1e530b06c038aca8dfbfe65ce8d1df3600bb82945a1ea01c8811dba597269e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -242,7 +1038,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 30de970..84fc2be 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -38,9 +38,280 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
0Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
1Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
2Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
3Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
4Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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23Brionna JonesCONF19110428.507990USDecember 18, 199521MarylandR19112142653.8000.0161984.211142527174400
126Sue BirdSEAG1756822.204082USOctober 16, 198036Connecticut152780610324442.25013437.3172470.8746531773135727310
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
12Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
36Courtney ParisDALC19311330.336385USSeptember 21, 198729Oklahoma716217325756.1000.061250.0283462568187000
96Moriah JeffersonSANG1685519.486961USAugust 3, 199423Connecticut1215148115552.392045.0202774.163137923324319100
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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(wnba[\"BMI\"])\n", + "plt.show()" ] }, { @@ -109,7 +1190,11 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# BMI (I'm assuming it refers to Body Mass Index?) seems to follow a normal distribution, \n", + "# and it is skewed to the right. \n", + "# Age also seems to follow a normal distribution, skewed to the right. However, given\n", + "# the relatively small size of the sample, it is harder to notice, because there are some peaks.\n", + "# In weight and height there seem to be 2 major peaks." ] }, { @@ -134,11 +1219,117 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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O8FfpE8Dx7nbDNpjvUbkXes6BPwX+tct3Evibbnxh5/sCmXubay9rIEkNW+TDNZKkKVnyktQwS16SGmbJS1LDLHlJapglL0kNs+QlqWH/C91SJ+zjel3BAAAAAElFTkSuQmCC", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(wnba[\"BLK\"])\n", + "plt.show()" ] }, { @@ -154,7 +1345,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# All distributions are clearly skewed to the right.\n", + "# I would assume this is because there are a few best players with very high statistics, and then\n", + "# a much larger number of players who do not achieve such high scores / statistics." ] }, { @@ -173,11 +1366,117 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "plt.hist((wnba[\"REB\"]/wnba[\"MIN\"]))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist((wnba[\"BLK\"]/wnba[\"MIN\"]))\n", + "plt.show()" ] }, { @@ -193,7 +1492,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "# With the exception of BLK, we see that when we take into account the number of minutes played \n", + "# all other statistics are less skewed to the right. The minutes played balance more the results\n", + "# between all players, and so it starts to look more like a normal distribution. " ] }, { @@ -222,13 +1523,18 @@ "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "# We have data about weight and BMI, so we can answer to the first point from our grandmother.\n", + "# We have statistics about the players, so we should also be able to check if what our siblings \n", + "# say about free throws and assists stands true." ] } ], "metadata": { + "interpreter": { + "hash": "df1e530b06c038aca8dfbfe65ce8d1df3600bb82945a1ea01c8811dba597269e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -242,7 +1548,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index edc1da0..23b235c 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": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,11 +46,282 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 2, + "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
\n", + "
" + ], + "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": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba = pd.read_csv('/Users/mariagarcia/Desktop/labs/week5/M2-mini-project2/data/wnba_clean.csv')\n", + "wnba.head(5)" ] }, { @@ -74,7 +345,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "# The data is a bit limited to fulfill all requirements (ideally we would need a bigger sample)\n", + "# , but we can try to infer it with a certain confidence interval." ] }, { @@ -88,9 +360,28 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "data": { + "text/plain": [ + "(77.17027122332428, 80.78747525554897)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = len(wnba)\n", + "\n", + "mean = wnba['Weight'].mean()\n", + "\n", + "std = wnba['Weight'].std(ddof=1)\n", + "\n", + "conf = stats.norm.interval(0.95, loc=mean, scale=std/np.sqrt(n))\n", + "\n", + "conf" ] }, { @@ -106,7 +397,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# With a confidence of 95% we can say that the average weight for female players in the WNBA\n", + "# is between 77 kg and 80 kg." ] }, { @@ -122,7 +414,12 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# We do not have enough data to confirm or reject our grandmother's hypothesis.\n", + "# Firstly, because our grandmother claims she cannot play in ANY league, and our sample is limited\n", + "# to the WNBA. Secondly, although it is true that the average weight is significantly\n", + "# higher than our sister's weight, we have seen in the weight distribution that there are players that\n", + "# weight as less as 55 kilograms (and we saw in the histogram there is a relatively high peak\n", + "# between 60-70 kg). " ] }, { @@ -154,11 +451,35 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here" + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.135762984208236e-15\n", + "0.49999999999999634\n" + ] + } + ], + "source": [ + "# As indicated above, we are going to check if it is true that female players fail MORE THAN 40%\n", + "# of their free throws (I am assuming free throws = FT in the dataframe, although it is not 100% clear)\n", + "# Therefore, our H0 is that their % of FT is equal or lower than 40% (which would validate our \n", + "# sister's hypothesis), and our H1 is that the % of FT is actually higher than 40%\n", + "\n", + "#HO: meanFT% =<40%\n", + "#H1: mean FT% >40%\n", + "\n", + "FT=wnba['FT%']\n", + "mu=np.mean(FT)\n", + "\n", + "alpha=0.05\n", + "\n", + "stat, p_value=stats.ttest_1samp(FT, mu)\n", + "print(stat)\n", + "print(p_value/2)" ] }, { @@ -174,7 +495,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# (done above)" ] }, { @@ -190,7 +511,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# Based on the results, we would reject H0 (i.e. we choose to keep believing that female players\n", + "# do not fail more than 40% of their free throws)." ] }, { @@ -231,7 +553,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "# As mentioned above, our sample is limited and we are missing data to confidently prove \n", + "# our hypotheses. However, we can try to make some inferences based on our sample. " ] }, { @@ -243,11 +566,34 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9833692293229463\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean=52\n", + "alpha=0.05\n", + "\n", + "stat, p_value = ttest_1samp(wnba['AST'], 52, alternative='greater')\n", + "print(p_value)\n", + "p_value < alpha" ] }, { @@ -255,9 +601,7 @@ "execution_count": 18, "metadata": {}, "outputs": [], - "source": [ - "#your-answer-here" - ] + "source": [] }, { "cell_type": "markdown", @@ -268,11 +612,37 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [ - "#your-answer-here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.1499947192482898\n", + "0.016630770677053583\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean=52\n", + "alpha=0.05\n", + "\n", + "stat, p_value=stats.ttest_1samp(wnba['AST'], 52)\n", + "print(stat)\n", + "print((p_value/2))\n", + "\n", + "p_value/2