From babaa47fdaebfe6fb802d10b599901cc78756658 Mon Sep 17 00:00:00 2001 From: Aliya Janmohamed Date: Mon, 15 May 2023 15:03:43 +0100 Subject: [PATCH] Lab Done --- data/wnba_clean.csv | 286 ++--- your-code/1.-Data-Cleaning.ipynb | 1052 +++++++++++++++++- your-code/2.-Exploratory-Data-Analysis.ipynb | 802 ++++++++++++- your-code/3.-Inferential-Analysis.ipynb | 681 +++++++++++- 4 files changed, 2600 insertions(+), 221 deletions(-) diff --git a/data/wnba_clean.csv b/data/wnba_clean.csv index 3d702f3..b365fcb 100644 --- a/data/wnba_clean.csv +++ b/data/wnba_clean.csv @@ -1,143 +1,143 @@ -Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 -Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 -Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 -Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 -Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 -Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 -Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0 -Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0 -Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 -Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 -Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0 -Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 -Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 -Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 -Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 -Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 -Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0 -Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 -Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 -Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 -Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 -Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 -Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 -Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 -Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 -Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 -Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 -Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 -Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 -Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 -Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 -Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 -Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 -Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 -Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 -Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 -Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0 -Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 -Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 -Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 -Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 -Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 -Danielle Adams,CON,F/C,185,108,31.555880199999997,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 -Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 -Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 -Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 -Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 -Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 -Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 -Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 -Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 -Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 -Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 -Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 -Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 -Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 -Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 -Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 -Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 -Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 -Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 -Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 -Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 -Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 -Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 -Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 -Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 -Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 -Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 -Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 -Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 -Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 -Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 -Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 -Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0 -Kayla Alexander,SAN,C,193,88,23.624795300000002,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 -Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 -Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 -Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 -Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 -Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 -Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 -Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 -Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 -Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 -Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 -Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 -Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 -Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 -Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 -Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 -Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 -Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 -Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 -Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 -Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 -Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 -Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 -Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 -Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 -Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 -Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 -Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 -Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 -Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 -Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 -Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 -Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 -Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 -Rebecca Allen,NY,G/F,188,74,20.937075600000004,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 -Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 -Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 -Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 -Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 -Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 -Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 -Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 -Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 -Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 -Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 -Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 -Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 -Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 -Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 -Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 -Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 -Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 -Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 -Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 -Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 -Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 -Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 -Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 -Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 -Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 -Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 -Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 -Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 -Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 -Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 -Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 -Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 -Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 +,Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 +0,Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 +1,Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 +2,Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 +3,Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 +4,Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 +5,Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0 +6,Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0 +7,Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 +8,Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 +9,Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0 +10,Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 +11,Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 +12,Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 +13,Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 +14,Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 +15,Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0 +16,Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 +17,Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 +18,Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 +19,Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 +20,Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 +21,Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 +22,Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 +23,Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 +24,Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 +25,Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 +26,Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 +27,Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 +28,Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 +29,Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 +30,Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 +31,Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 +32,Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 +33,Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 +34,Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 +35,Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0 +36,Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 +37,Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 +38,Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 +39,Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 +40,Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 +41,Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0 +42,Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 +43,Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 +44,Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 +45,Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 +46,Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 +47,Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 +48,Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 +49,Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 +50,Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 +51,Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 +52,Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 +53,Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 +54,Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 +55,Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 +56,Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 +57,Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 +58,Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 +59,Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 +60,Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 +61,Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 +62,Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 +63,Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 +64,Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 +65,Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 +66,Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 +67,Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 +68,Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 +69,Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 +70,Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 +71,Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 +72,Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 +73,Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0 +74,Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 +75,Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 +76,Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 +77,Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 +78,Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 +79,Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 +80,Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 +81,Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 +82,Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 +83,Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 +84,Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 +85,Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 +86,Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 +87,Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 +88,Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 +89,Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 +90,Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 +92,Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 +93,Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 +94,Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 +95,Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 +96,Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 +97,Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 +98,Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 +99,Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 +100,Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 +101,Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 +102,Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 +103,Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 +104,Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 +105,Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 +106,Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 +107,Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 +108,Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 +109,Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 +110,Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 +111,Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 +112,Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 +113,Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 +114,Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 +115,Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 +116,Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 +117,Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 +118,Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 +119,Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 +120,Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 +121,Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 +122,Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 +123,Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 +124,Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 +125,Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 +126,Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 +127,Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 +128,Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 +129,Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 +130,Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 +131,Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 +132,Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 +133,Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 +134,Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 +135,Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 +136,Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 +137,Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 +138,Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 +139,Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 +140,Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 +141,Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 +142,Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 diff --git a/your-code/1.-Data-Cleaning.ipynb b/your-code/1.-Data-Cleaning.ipynb index d1c8eea..d1c956f 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -49,9 +49,281 @@ "cell_type": "code", "execution_count": 3, "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wbna = pd.read_csv('../data/wnba.csv')\n", + "wbna.head()" ] }, { @@ -64,11 +336,60 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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\n", + "1\n", + "1\n", + "2\n" + ] + } + ], "source": [ - "#your code here" + "#your code here\n", + "print(wbna.isna().sum())\n", + "\n", + "print(wbna['Weight'].isna().sum())\n", + "print(wbna['BMI'].isna().sum())\n", + "\n", + "print(wbna.isna().sum().sum())" ] }, { @@ -80,11 +401,123 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
91Makayla EppsCHIG178NaNNaNUSJune 6, 199522KentuckyR145221414.3050.02540.02024104600
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" + ], + "text/plain": [ + " Name Team Pos Height Weight BMI Birth_Place Birthdate Age \\\n", + "91 Makayla Epps CHI G 178 NaN NaN US June 6, 1995 22 \n", + "\n", + " College Experience Games Played MIN FGM FGA FG% 3PM 3PA 3P% \\\n", + "91 Kentucky R 14 52 2 14 14.3 0 5 0.0 \n", + "\n", + " FTM FTA FT% OREB DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "91 2 5 40.0 2 0 2 4 1 0 4 6 0 0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wbna[wbna['Weight'].isna()]" ] }, { @@ -96,7 +529,127 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, + "metadata": {}, + "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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wbna[wbna['BMI'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -114,11 +667,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "wbna.drop(91, inplace=True)" ] }, { @@ -130,11 +684,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#your answer here\n", + "\n", + "#We may require information from that player and it may impact the average weight & BMI \n" ] }, { @@ -147,11 +703,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "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": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wbna.dtypes" ] }, { @@ -170,11 +770,64 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "wbna['Weight'] = wbna['Weight'].astype('int')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "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": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wbna.dtypes" ] }, { @@ -186,11 +839,346 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "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": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wbna.describe()" ] }, { @@ -202,11 +1190,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#your answer here\n", + "\n", + "#The range between height and BMI is not as large as the range between weight" ] }, { @@ -218,17 +1208,25 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "wbna.to_csv('../data/wnba_clean.csv')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1240,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" } }, "nbformat": 4, diff --git a/your-code/2.-Exploratory-Data-Analysis.ipynb b/your-code/2.-Exploratory-Data-Analysis.ipynb index 48b485c..81a5958 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -36,11 +36,290 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
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" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73 21.329438 \n", + "2 2 Alex Bentley CON G 170 69 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78 25.469388 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 6 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 19 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 4 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 35 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 3 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 22 28 12 3 6 12 93 0 0 \n", + "1 82 101 72 63 13 40 217 0 0 \n", + "2 36 40 78 22 3 24 218 0 0 \n", + "3 134 169 65 20 10 38 188 2 0 \n", + "4 9 12 12 7 0 14 50 0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "\n", + "wbna = pd.read_csv('../data/wnba_clean.csv')\n", + "wbna.head()" ] }, { @@ -52,11 +331,355 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0HeightWeightBMIAgeGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
count142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000142.000000
mean70.859155184.61267678.97887323.09121427.11267624.429577500.10563474.401408168.70422543.10281714.83098643.69718324.97816939.53521149.42253575.82887322.06338061.59154983.65493044.51408517.7253529.78169032.288732203.1690141.1408450.007042
std41.5368918.69812810.9961102.0736913.6671807.075477289.37339355.980754117.1658099.85519917.37282946.15530218.45907536.74305344.24469718.53615121.51964849.66985468.20058541.49079013.41331212.53766921.447141153.0325592.9090020.083918
min0.000000165.00000055.00000018.39067521.0000002.00000012.0000001.0000003.00000016.7000000.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000002.0000000.0000000.0000000.0000002.0000002.0000000.0000000.000000
25%35.250000175.75000071.50000021.78587624.00000022.000000242.25000027.00000069.00000037.1250000.0000003.0000000.00000013.00000017.25000071.5750007.00000026.00000034.25000011.2500007.0000002.00000014.00000077.2500000.0000000.000000
50%70.500000185.00000079.00000022.87331427.00000027.500000506.00000069.000000152.50000042.05000010.50000032.00000030.55000029.00000035.50000080.00000013.00000050.00000062.50000034.00000015.0000005.00000028.000000181.0000000.0000000.000000
75%106.750000191.00000086.00000024.18071530.00000029.000000752.500000105.000000244.75000048.62500022.00000065.50000036.17500053.25000066.50000085.92500031.00000084.000000116.50000066.75000027.50000012.00000048.000000277.7500001.0000000.000000
max142.000000206.000000113.00000031.55588036.00000032.0000001018.000000227.000000509.000000100.00000088.000000225.000000100.000000168.000000186.000000100.000000113.000000226.000000334.000000206.00000063.00000064.00000087.000000584.00000017.0000001.000000
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" + ], + "text/plain": [ + " Unnamed: 0 Height Weight BMI Age \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 70.859155 184.612676 78.978873 23.091214 27.112676 \n", + "std 41.536891 8.698128 10.996110 2.073691 3.667180 \n", + "min 0.000000 165.000000 55.000000 18.390675 21.000000 \n", + "25% 35.250000 175.750000 71.500000 21.785876 24.000000 \n", + "50% 70.500000 185.000000 79.000000 22.873314 27.000000 \n", + "75% 106.750000 191.000000 86.000000 24.180715 30.000000 \n", + "max 142.000000 206.000000 113.000000 31.555880 36.000000 \n", + "\n", + " Games Played MIN FGM FGA FG% \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 24.429577 500.105634 74.401408 168.704225 43.102817 \n", + "std 7.075477 289.373393 55.980754 117.165809 9.855199 \n", + "min 2.000000 12.000000 1.000000 3.000000 16.700000 \n", + "25% 22.000000 242.250000 27.000000 69.000000 37.125000 \n", + "50% 27.500000 506.000000 69.000000 152.500000 42.050000 \n", + "75% 29.000000 752.500000 105.000000 244.750000 48.625000 \n", + "max 32.000000 1018.000000 227.000000 509.000000 100.000000 \n", + "\n", + " 3PM 3PA 3P% FTM FTA FT% \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 14.830986 43.697183 24.978169 39.535211 49.422535 75.828873 \n", + "std 17.372829 46.155302 18.459075 36.743053 44.244697 18.536151 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 3.000000 0.000000 13.000000 17.250000 71.575000 \n", + "50% 10.500000 32.000000 30.550000 29.000000 35.500000 80.000000 \n", + "75% 22.000000 65.500000 36.175000 53.250000 66.500000 85.925000 \n", + "max 88.000000 225.000000 100.000000 168.000000 186.000000 100.000000 \n", + "\n", + " OREB DREB REB AST STL BLK \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 22.063380 61.591549 83.654930 44.514085 17.725352 9.781690 \n", + "std 21.519648 49.669854 68.200585 41.490790 13.413312 12.537669 \n", + "min 0.000000 2.000000 2.000000 0.000000 0.000000 0.000000 \n", + "25% 7.000000 26.000000 34.250000 11.250000 7.000000 2.000000 \n", + "50% 13.000000 50.000000 62.500000 34.000000 15.000000 5.000000 \n", + "75% 31.000000 84.000000 116.500000 66.750000 27.500000 12.000000 \n", + "max 113.000000 226.000000 334.000000 206.000000 63.000000 64.000000 \n", + "\n", + " TO PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 142.000000 \n", + "mean 32.288732 203.169014 1.140845 0.007042 \n", + "std 21.447141 153.032559 2.909002 0.083918 \n", + "min 2.000000 2.000000 0.000000 0.000000 \n", + "25% 14.000000 77.250000 0.000000 0.000000 \n", + "50% 28.000000 181.000000 0.000000 0.000000 \n", + "75% 48.000000 277.750000 1.000000 0.000000 \n", + "max 87.000000 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wbna.describe()" ] }, { @@ -89,11 +712,52 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/3531157689.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_1.set_xticklabels(wbna['Height'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/3531157689.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_2.set_xticklabels(wbna['Weight'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/3531157689.py:11: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_3.set_xticklabels(wbna['Age'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/3531157689.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_4.set_xticklabels(wbna['BMI'])\n" + ] + }, + { + "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=4, ncols=1, figsize=(18,15))\n", + "\n", + "chart_1.hist(wbna['Height'])\n", + "chart_2.hist(wbna['Weight'])\n", + "chart_3.hist(wbna['Age'])\n", + "chart_4.hist(wbna['BMI'])\n", + "\n", + "chart_1.set_xticklabels(wbna['Height'])\n", + "chart_2.set_xticklabels(wbna['Weight'])\n", + "chart_3.set_xticklabels(wbna['Age'])\n", + "chart_4.set_xticklabels(wbna['BMI'])\n", + "\n", + "chart_1.title.set_text('Height')\n", + "chart_2.title.set_text('Weight')\n", + "chart_3.title.set_text('Age')\n", + "chart_4.title.set_text('BMI')" ] }, { @@ -109,7 +773,10 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "#Height and Weight seem to be a fairly normal distribution\n", + "#Age is quite varied \n", + "#BMI is quite skewed to the right" ] }, { @@ -134,11 +801,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/1699988611.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_1.set_xticklabels(wbna['REB'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/1699988611.py:11: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_2.set_xticklabels(wbna['AST'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/1699988611.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_3.set_xticklabels(wbna['STL'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/1699988611.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_4.set_xticklabels(wbna['PTS'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/1699988611.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_5.set_xticklabels(wbna['BLK'])\n" + ] + }, + { + "data": { + "image/png": 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+ "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, chart_5) = plt.subplots(nrows=5, ncols=1, figsize=(20,18))\n", + "\n", + "chart_1.hist(wbna['REB'])\n", + "chart_2.hist(wbna['AST'])\n", + "chart_3.hist(wbna['STL'])\n", + "chart_4.hist(wbna['PTS'])\n", + "chart_5.hist(wbna['BLK'])\n", + "\n", + "chart_1.set_xticklabels(wbna['REB'])\n", + "chart_2.set_xticklabels(wbna['AST'])\n", + "chart_3.set_xticklabels(wbna['STL'])\n", + "chart_4.set_xticklabels(wbna['PTS'])\n", + "chart_5.set_xticklabels(wbna['BLK'])\n", + "\n", + "chart_1.title.set_text('REB')\n", + "chart_2.title.set_text('AST')\n", + "chart_3.title.set_text('STL')\n", + "chart_4.title.set_text('PTS')\n", + "chart_5.title.set_text('BLK')" ] }, { @@ -154,7 +867,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "#All the game stats, rebounds, assists, steals, points annd blocks, are skewed to the right" ] }, { @@ -173,11 +887,57 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/2991681023.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_1.set_xticklabels(wbna['REB'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/2991681023.py:11: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_2.set_xticklabels(wbna['AST'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/2991681023.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_3.set_xticklabels(wbna['STL'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/2991681023.py:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_4.set_xticklabels(wbna['PTS'])\n", + "/var/folders/vh/7qkb52p54kq6xrbpd6rqjwsm0000gp/T/ipykernel_20368/2991681023.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " chart_5.set_xticklabels(wbna['BLK'])\n" + ] + }, + { + "data": { + "image/png": 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+ "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, chart_5) = plt.subplots(nrows=5, ncols=1, figsize=(18,18))\n", + "\n", + "chart_1.hist((wbna['REB']/wbna['MIN']))\n", + "chart_2.hist((wbna['AST']/wbna['MIN']))\n", + "chart_3.hist((wbna['STL']/wbna['MIN']))\n", + "chart_4.hist((wbna['PTS']/wbna['MIN']))\n", + "chart_5.hist((wbna['BLK']/wbna['MIN']))\n", + "\n", + "chart_1.set_xticklabels(wbna['REB'])\n", + "chart_2.set_xticklabels(wbna['AST'])\n", + "chart_3.set_xticklabels(wbna['STL'])\n", + "chart_4.set_xticklabels(wbna['PTS'])\n", + "chart_5.set_xticklabels(wbna['BLK'])\n", + "\n", + "chart_1.title.set_text('REB/MIN')\n", + "chart_2.title.set_text('AST/MIN')\n", + "chart_3.title.set_text('STL/MIN')\n", + "chart_4.title.set_text('PTS/MIN')\n", + "chart_5.title.set_text('BLK/MIN')" ] }, { @@ -193,7 +953,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "#The rebounds, assists, steals and points are now more normally distributed, however, blocks still remain skewed\n", + "#to the right" ] }, { @@ -228,7 +990,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -242,7 +1004,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" } }, "nbformat": 4, diff --git a/your-code/3.-Inferential-Analysis.ipynb b/your-code/3.-Inferential-Analysis.ipynb index 366765b..95aeec3 100644 --- a/your-code/3.-Inferential-Analysis.ipynb +++ b/your-code/3.-Inferential-Analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,8 @@ "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", + "import scipy.stats as st" ] }, { @@ -46,11 +47,532 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
00Aerial PowersDALF1837121.200991USJanuary 17, 199423Michigan State28173308535.3123237.5212680.8622281236129300
11Alana BeardLAG/F1857321.329438USMay 14, 198235Duke12309479017750.851827.8324178.019821017263134021700
22Alex BentleyCONG1706923.875433USOctober 27, 199026Penn State4266178221837.6196429.7354283.343640782232421800
33Alex MontgomerySANG/F1858424.543462USDecember 11, 198828Georgia Tech6317217519538.5216830.9172181.0351341696520103818820
44Alexis JonesMING1757825.469388USAugust 5, 199423BaylorR24137165032.072035.0111291.739121270145000
......................................................................................................
137138Tiffany HayesATLG1787022.093170USSeptember 20, 198927Connecticut62986114433143.54311238.413616184.52889117693785046700
138139Tiffany JacksonLAF1918423.025685USApril 26, 198532Texas922127122548.0010.04666.75182331382800
139140Tiffany MitchellINDG1756922.530612USSeptember 23, 198432South Carolina2276718323834.9176924.69410292.2167086393154027700
140141Tina CharlesNYF/C1938422.550941USMay 12, 198829Connecticut82995222750944.6185632.111013581.55621226875212271582110
141142Yvonne TurnerPHOG1755919.265306USOctober 13, 198729Nebraska2303565914042.1114723.4222878.6111324301813215100
\n", + "

142 rows × 33 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "0 0 Aerial Powers DAL F 183 71 21.200991 \n", + "1 1 Alana Beard LA G/F 185 73 21.329438 \n", + "2 2 Alex Bentley CON G 170 69 23.875433 \n", + "3 3 Alex Montgomery SAN G/F 185 84 24.543462 \n", + "4 4 Alexis Jones MIN G 175 78 25.469388 \n", + ".. ... ... ... ... ... ... ... \n", + "137 138 Tiffany Hayes ATL G 178 70 22.093170 \n", + "138 139 Tiffany Jackson LA F 191 84 23.025685 \n", + "139 140 Tiffany Mitchell IND G 175 69 22.530612 \n", + "140 141 Tina Charles NY F/C 193 84 22.550941 \n", + "141 142 Yvonne Turner PHO G 175 59 19.265306 \n", + "\n", + " Birth_Place Birthdate Age College Experience \\\n", + "0 US January 17, 1994 23 Michigan State 2 \n", + "1 US May 14, 1982 35 Duke 12 \n", + "2 US October 27, 1990 26 Penn State 4 \n", + "3 US December 11, 1988 28 Georgia Tech 6 \n", + "4 US August 5, 1994 23 Baylor R \n", + ".. ... ... ... ... ... \n", + "137 US September 20, 1989 27 Connecticut 6 \n", + "138 US April 26, 1985 32 Texas 9 \n", + "139 US September 23, 1984 32 South Carolina 2 \n", + "140 US May 12, 1988 29 Connecticut 8 \n", + "141 US October 13, 1987 29 Nebraska 2 \n", + "\n", + " Games Played MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB \\\n", + "0 8 173 30 85 35.3 12 32 37.5 21 26 80.8 6 \n", + "1 30 947 90 177 50.8 5 18 27.8 32 41 78.0 19 \n", + "2 26 617 82 218 37.6 19 64 29.7 35 42 83.3 4 \n", + "3 31 721 75 195 38.5 21 68 30.9 17 21 81.0 35 \n", + "4 24 137 16 50 32.0 7 20 35.0 11 12 91.7 3 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... ... \n", + "137 29 861 144 331 43.5 43 112 38.4 136 161 84.5 28 \n", + "138 22 127 12 25 48.0 0 1 0.0 4 6 66.7 5 \n", + "139 27 671 83 238 34.9 17 69 24.6 94 102 92.2 16 \n", + "140 29 952 227 509 44.6 18 56 32.1 110 135 81.5 56 \n", + "141 30 356 59 140 42.1 11 47 23.4 22 28 78.6 11 \n", + "\n", + " DREB REB AST STL BLK TO PTS DD2 TD3 \n", + "0 22 28 12 3 6 12 93 0 0 \n", + "1 82 101 72 63 13 40 217 0 0 \n", + "2 36 40 78 22 3 24 218 0 0 \n", + "3 134 169 65 20 10 38 188 2 0 \n", + "4 9 12 12 7 0 14 50 0 0 \n", + ".. ... ... ... ... ... .. ... ... ... \n", + "137 89 117 69 37 8 50 467 0 0 \n", + "138 18 23 3 1 3 8 28 0 0 \n", + "139 70 86 39 31 5 40 277 0 0 \n", + "140 212 268 75 21 22 71 582 11 0 \n", + "141 13 24 30 18 1 32 151 0 0 \n", + "\n", + "[142 rows x 33 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "wbna = pd.read_csv('../data/wnba_clean.csv')\n", + "wbna" ] }, { @@ -86,11 +608,32 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "data": { + "text/plain": [ + "(77.16386907227387, 80.79387740659938)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "alpha = 0.05\n", + "\n", + "sample = wbna\n", + "\n", + "std = wbna['Weight'].std(ddof=1)\n", + "\n", + "mean = wbna['Weight'].mean()\n", + "\n", + "stats.norm.interval(0.95, loc=mean, scale = std/np.sqrt(141)) " ] }, { @@ -106,7 +649,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#95% confident the average weight lies between 77.2 - 80.8" ] }, { @@ -122,7 +666,9 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#Although we are 95% sure her weight falls under the average weight, the min weight is 55kg so it should not\n", + "#be the only factor when considering her ability to play " ] }, { @@ -154,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -170,11 +716,33 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=9.135762984208236e-15, pvalue=0.49999999999999634, df=141)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "#H0: mean_FT% <= 40\n", + "#HA: mean_FT%>40\n", + "\n", + "\n", + "sample=wbna['FT%']\n", + "mean = np.mean(sample)\n", + "\n", + "alpha=0.05\n", + "\n", + "st.ttest_1samp(sample, mean, alternative = 'greater')" ] }, { @@ -190,7 +758,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#The p-value is less than alpha, so we can reject the H0" ] }, { @@ -227,11 +796,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n" ] }, { @@ -243,11 +812,31 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [ - "#your code here" + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=-2.1499947192482898, pvalue=0.033261541354107166, df=141)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "\n", + "#H0: mf_ast =52\n", + "#HA: mf_ast !=52\n", + "\n", + "sample = wbna['AST']\n", + "alpha = 0.05\n", + "mean = 52\n", + "\n", + "st.ttest_1samp(sample, mean, alternative='two-sided')\n" ] }, { @@ -256,7 +845,8 @@ "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#your-answer-here\n", + "#We can reject the H0 " ] }, { @@ -268,11 +858,40 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=-2.1499947192482898, pvalue=0.016630770677053583, df=141)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your-answer-here\n", + "\n", + "#H0: mf_ast >=52\n", + "#HA: mf_ast <52\n", + "\n", + "sample = wbna['AST']\n", + "alpha = 0.05\n", + "mean = 52\n", + "\n", + "st.ttest_1samp(sample, mean, alternative='less')" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#your-answer-here" + "#We can reject the H0" ] }, { @@ -343,7 +962,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -357,7 +976,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" } }, "nbformat": 4,