From b03953e192dc3b4c8ce10f740108339fea122056 Mon Sep 17 00:00:00 2001 From: Miguel Talambas Date: Wed, 2 Mar 2022 12:20:46 +0000 Subject: [PATCH] lab done --- data/wnba_clean.csv | 286 ++-- your-code/1.-Data-Cleaning.ipynb | 892 +++++++++++- your-code/2.-Exploratory-Data-Analysis.ipynb | 1344 +++++++++++++++++- 3 files changed, 2323 insertions(+), 199 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 60a0517..f6d07b4 100644 --- a/your-code/1.-Data-Cleaning.ipynb +++ b/your-code/1.-Data-Cleaning.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,9 +49,281 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
" + ], + "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", + "data = pd.read_csv('/Users/migue/OneDrive/Ambiente de Trabalho/Ironhack/Labs/week 5/M2-mini-project2/data/wnba.csv')\n", + "data.head()" ] }, { @@ -64,11 +336,55 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name 0\n", + "Team 0\n", + "Pos 0\n", + "Height 0\n", + "Weight 1\n", + "BMI 1\n", + "Birth_Place 0\n", + "Birthdate 0\n", + "Age 0\n", + "College 0\n", + "Experience 0\n", + "Games Played 0\n", + "MIN 0\n", + "FGM 0\n", + "FGA 0\n", + "FG% 0\n", + "3PM 0\n", + "3PA 0\n", + "3P% 0\n", + "FTM 0\n", + "FTA 0\n", + "FT% 0\n", + "OREB 0\n", + "DREB 0\n", + "REB 0\n", + "AST 0\n", + "STL 0\n", + "BLK 0\n", + "TO 0\n", + "PTS 0\n", + "DD2 0\n", + "TD3 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "data.isnull().sum()" ] }, { @@ -80,11 +396,123 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
91Makayla EppsCHIG178NaNNaNUSJune 6, 199522KentuckyR145221414.3050.02540.02024104600
\n", + "
" + ], + "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", + "data[data.isnull().any(axis = 1)]" ] }, { @@ -96,11 +524,25 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "we would remove 0.6993006993006993 % of our dataset\n" + ] + } + ], "source": [ - "#your code here" + "#your code here\n", + "# this is the number of rows we have in the dataframe\n", + "len(data)\n", + "\n", + "percentage = len(data[data.isnull().any(axis = 1)])/len(data)\n", + "\n", + "print('we would remove', percentage * 100,'% of our dataset')" ] }, { @@ -114,11 +556,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "data.drop(data.index[91], inplace = True)" ] }, { @@ -130,11 +573,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#your answer here\n", + "# Yes because it was not even 1% of our dataset and both values were in the same row. We wouldn't drop a row if we had a \n", + "# limited number of rows or if we're actually studiyng something related with the missing values" ] }, { @@ -147,11 +592,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name object\n", + "Team object\n", + "Pos object\n", + "Height int64\n", + "Weight float64\n", + "BMI float64\n", + "Birth_Place object\n", + "Birthdate object\n", + "Age int64\n", + "College object\n", + "Experience object\n", + "Games Played int64\n", + "MIN int64\n", + "FGM int64\n", + "FGA int64\n", + "FG% float64\n", + "3PM int64\n", + "3PA int64\n", + "3P% float64\n", + "FTM int64\n", + "FTA int64\n", + "FT% float64\n", + "OREB int64\n", + "DREB int64\n", + "REB int64\n", + "AST int64\n", + "STL int64\n", + "BLK int64\n", + "TO int64\n", + "PTS int64\n", + "DD2 int64\n", + "TD3 int64\n", + "dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "data.dtypes" ] }, { @@ -170,11 +659,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "data = data.astype({'Weight' : int})" ] }, { @@ -186,11 +676,346 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
" + ], + "text/plain": [ + " Height Weight BMI Age Games Played \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 184.612676 78.978873 23.091214 27.112676 24.429577 \n", + "std 8.698128 10.996110 2.073691 3.667180 7.075477 \n", + "min 165.000000 55.000000 18.390675 21.000000 2.000000 \n", + "25% 175.750000 71.500000 21.785876 24.000000 22.000000 \n", + "50% 185.000000 79.000000 22.873314 27.000000 27.500000 \n", + "75% 191.000000 86.000000 24.180715 30.000000 29.000000 \n", + "max 206.000000 113.000000 31.555880 36.000000 32.000000 \n", + "\n", + " MIN FGM FGA FG% 3PM \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 500.105634 74.401408 168.704225 43.102817 14.830986 \n", + "std 289.373393 55.980754 117.165809 9.855199 17.372829 \n", + "min 12.000000 1.000000 3.000000 16.700000 0.000000 \n", + "25% 242.250000 27.000000 69.000000 37.125000 0.000000 \n", + "50% 506.000000 69.000000 152.500000 42.050000 10.500000 \n", + "75% 752.500000 105.000000 244.750000 48.625000 22.000000 \n", + "max 1018.000000 227.000000 509.000000 100.000000 88.000000 \n", + "\n", + " 3PA 3P% FTM FTA FT% OREB \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 43.697183 24.978169 39.535211 49.422535 75.828873 22.063380 \n", + "std 46.155302 18.459075 36.743053 44.244697 18.536151 21.519648 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 3.000000 0.000000 13.000000 17.250000 71.575000 7.000000 \n", + "50% 32.000000 30.550000 29.000000 35.500000 80.000000 13.000000 \n", + "75% 65.500000 36.175000 53.250000 66.500000 85.925000 31.000000 \n", + "max 225.000000 100.000000 168.000000 186.000000 100.000000 113.000000 \n", + "\n", + " DREB REB AST STL BLK TO \\\n", + "count 142.000000 142.000000 142.000000 142.000000 142.000000 142.000000 \n", + "mean 61.591549 83.654930 44.514085 17.725352 9.781690 32.288732 \n", + "std 49.669854 68.200585 41.490790 13.413312 12.537669 21.447141 \n", + "min 2.000000 2.000000 0.000000 0.000000 0.000000 2.000000 \n", + "25% 26.000000 34.250000 11.250000 7.000000 2.000000 14.000000 \n", + "50% 50.000000 62.500000 34.000000 15.000000 5.000000 28.000000 \n", + "75% 84.000000 116.500000 66.750000 27.500000 12.000000 48.000000 \n", + "max 226.000000 334.000000 206.000000 63.000000 64.000000 87.000000 \n", + "\n", + " PTS DD2 TD3 \n", + "count 142.000000 142.000000 142.000000 \n", + "mean 203.169014 1.140845 0.007042 \n", + "std 153.032559 2.909002 0.083918 \n", + "min 2.000000 0.000000 0.000000 \n", + "25% 77.250000 0.000000 0.000000 \n", + "50% 181.000000 0.000000 0.000000 \n", + "75% 277.750000 1.000000 0.000000 \n", + "max 584.000000 17.000000 1.000000 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "data.describe()" ] }, { @@ -202,11 +1027,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "#your answer here" + "#your answer here\n", + "# probably the Weight and the Age will have outliers, due to the std and min and max, but we have to check" ] }, { @@ -218,12 +1044,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "#your code here" + "#your code here\n", + "data.to_csv('/Users/migue/OneDrive/Ambiente de Trabalho/Ironhack/Labs/week 5/M2-mini-project2/data/wnba_clean.csv')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -242,7 +1076,7 @@ "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..dc8e71b 100644 --- a/your-code/2.-Exploratory-Data-Analysis.ipynb +++ b/your-code/2.-Exploratory-Data-Analysis.ipynb @@ -36,11 +36,289 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\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", + " 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", + "wnba = pd.read_csv('/Users/migue/OneDrive/Ambiente de Trabalho/Ironhack/Labs/week 5/M2-mini-project2/data/wnba_clean.csv')\n", + "wnba.head()" ] }, { @@ -52,11 +330,355 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
" + ], + "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", + "wnba.describe()" ] }, { @@ -70,11 +692,423 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#your code here\n", + "# checking the interquartile range for 'Age'\n", + "IQR = wnba['Age'].quantile(0.75) - wnba['Age'].quantile(0.25)\n", + "IQR" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# outliers in the lower part\n", + "lower = wnba['Age'].quantile(0.25) - IQR * 1.5\n", + "lower" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "39.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# outliers in the upper part\n", + "upper = wnba['Age'].quantile(0.75) + IQR * 1.5\n", + "upper" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Unnamed: 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]\n", + "Index: []" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we are checking for values outside of this range!\n", + "wnba.loc[(wnba['Age'] > upper) | (wnba['Age'] < lower)]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.5" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the interquartile range for 'Weight'\n", + "IQR = wnba['Weight'].quantile(0.75) - wnba['Weight'].quantile(0.25)\n", + "IQR" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "49.75" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# outliers in the lower part\n", + "lower_w = wnba['Weight'].quantile(0.25) - IQR * 1.5\n", + "lower_w" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "107.75" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "# outliers in the upper part\n", + "upper_w = wnba['Weight'].quantile(0.75) + IQR * 1.5\n", + "upper_w" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0NameTeamPosHeightWeightBMIBirth_PlaceBirthdateAgeCollegeExperienceGames PlayedMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OREBDREBREBASTSTLBLKTOPTSDD2TD3
1212Amanda Zahui B.NYC19611329.414827SEAugust 9, 199324Minnesota325133205337.72825.091275.051823745125100
3636Courtney ParisDALC19311330.336385USSeptember 21, 198729Oklahoma716217325756.1000.061250.0283462568187000
4141Danielle AdamsCONF/C18510831.555880USFebruary 19, 198928Texas A&M51881164337.2123040.055100.0641044474900
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Name Team Pos Height Weight BMI \\\n", + "12 12 Amanda Zahui B. NY C 196 113 29.414827 \n", + "36 36 Courtney Paris DAL C 193 113 30.336385 \n", + "41 41 Danielle Adams CON F/C 185 108 31.555880 \n", + "\n", + " Birth_Place Birthdate Age College Experience Games Played \\\n", + "12 SE August 9, 1993 24 Minnesota 3 25 \n", + "36 US September 21, 1987 29 Oklahoma 7 16 \n", + "41 US February 19, 1989 28 Texas A&M 5 18 \n", + "\n", + " MIN FGM FGA FG% 3PM 3PA 3P% FTM FTA FT% OREB DREB REB \\\n", + "12 133 20 53 37.7 2 8 25.0 9 12 75.0 5 18 23 \n", + "36 217 32 57 56.1 0 0 0.0 6 12 50.0 28 34 62 \n", + "41 81 16 43 37.2 12 30 40.0 5 5 100.0 6 4 10 \n", + "\n", + " AST STL BLK TO PTS DD2 TD3 \n", + "12 7 4 5 12 51 0 0 \n", + "36 5 6 8 18 70 0 0 \n", + "41 4 4 4 7 49 0 0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wnba.loc[(wnba['Weight'] > upper_w) | (wnba['Weight'] < lower_w)]\n", + "# we have three ouliers!!" ] }, { @@ -89,11 +1123,49 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "wnba[['Height', 'Weight', 'Age', 'BMI']].boxplot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "wnba[['Height', 'Weight', 'Age', 'BMI']].hist()\n", + "plt.show()" ] }, { @@ -105,11 +1177,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "\n", + "# we have outliers both on the BMI and Weight column. They seem to follow a normal standard distribution, but we need to check\n" ] }, { @@ -134,11 +1208,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#your code here\n", + "wnba[['REB', 'AST', 'STL', 'PTS', 'BLK']].boxplot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "#your code here" + "wnba[['REB', 'AST', 'STL', 'PTS', 'BLK']].hist()\n", + "plt.show()" ] }, { @@ -150,11 +1262,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "# We can see that all of the are positively skewed, meaning that there are more players having lower stats,\n", + "# and then, as the stats increase, and so the difficulty of achieving them, there starts to be a decrease in players achieving\n", + "# those stats. We can also see that all of this dimensions have outliers, menaing, players that exceed in a particular stat" ] }, { @@ -173,11 +1288,180 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
REBASTSTLPTSBLK
00.1618500.0693640.0173410.5375720.034682
10.1066530.0760300.0665260.2291450.013728
20.0648300.1264180.0356560.3533230.004862
30.2343970.0901530.0277390.2607490.013870
40.0875910.0875910.0510950.3649640.000000
..................
1370.1358890.0801390.0429730.5423930.009292
1380.1811020.0236220.0078740.2204720.023622
1390.1281670.0581220.0462000.4128170.007452
1400.2815130.0787820.0220590.6113450.023109
1410.0674160.0842700.0505620.4241570.002809
\n", + "

142 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " REB AST STL PTS BLK\n", + "0 0.161850 0.069364 0.017341 0.537572 0.034682\n", + "1 0.106653 0.076030 0.066526 0.229145 0.013728\n", + "2 0.064830 0.126418 0.035656 0.353323 0.004862\n", + "3 0.234397 0.090153 0.027739 0.260749 0.013870\n", + "4 0.087591 0.087591 0.051095 0.364964 0.000000\n", + ".. ... ... ... ... ...\n", + "137 0.135889 0.080139 0.042973 0.542393 0.009292\n", + "138 0.181102 0.023622 0.007874 0.220472 0.023622\n", + "139 0.128167 0.058122 0.046200 0.412817 0.007452\n", + "140 0.281513 0.078782 0.022059 0.611345 0.023109\n", + "141 0.067416 0.084270 0.050562 0.424157 0.002809\n", + "\n", + "[142 rows x 5 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#your code here" + "#your code here\n", + "wnba[['REB', 'AST', 'STL', 'PTS', 'BLK']].div(wnba.MIN, axis=0)\n", + "# here we have a dataframe of all the stats above per minute played" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbK0lEQVR4nO3dfZBcV3nn8e8vI2vLkYwpcJg4kmWpQMGjGl6MZu0lOwmanbVXZollap1CY14MNKUyFRntVrFruXorQEETA3ES4hdUKo9js+DWZos3xRaWKLmbMLuwkQEZW5o1JWwjCycBQ0phhAp5Rs/+0S1Nz6hn+o66Z27P7d+nqst97z195+nj7qePzr3nHEUEZma2+P1G2gGYmVlrOKGbmWWEE7qZWUY4oZuZZYQTuplZRixJ6w9fcsklsXr16rT+/FknTpxg2bJlaYfRFlwXk1wXk1wXk9qhLr773e++GBG/Ve9Yagl99erVPP7442n9+bPK5TIbNmxIO4y24LqY5LqY5LqY1A51IenHMx1zl4uZWUY4oZuZZYQTuplZRjihm5llhBO6mVlGOKGbWV3FYpHe3l4GBwfp7e2lWCymHZI1kNpti2bWvorFIvl8nuHhYSYmJujq6iKXywEwNDSUcnQ2E7fQzewchUKB4eFhBgYGWLJkCQMDAwwPD1MoFNIOzWaRKKFL2ijpaUlHJG2vc/xiSX8r6QlJhyS9r/WhmtlCGR0dpb+/f8q+/v5+RkdHU4rIkmiY0CV1AfcA1wHrgCFJ66YV+2PgcES8AdgA3ClpaYtjNbMF0tPTw8jIyJR9IyMj9PT0pBSRJZGkhX4VcCQinomIU8AuYNO0MgFcJEnAcuAXwHhLIzWzBZPP58nlcpRKJcbHxymVSuRyOfL5fNqh2SzUaAk6STcCGyPiA9XtdwNXR8TWmjIXAbuBK4CLgHdExCN1zrUF2ALQ3d29fteuXa16H+dtbGyM5cuXpx1GW3BdTHJdwP79+/nCF77A0aNHWbVqFe9617sYHBxMO6xUtcPnYmBg4LsR0Vf3YETM+gD+CLivZvvdwF3TytwI/AUg4DXAs8DLZjvv+vXrox2USqW0Q2gbrotJrotJrotJ7VAXwOMxQ15N0uVyDLisZnsl8MK0Mu8Dvlz9e0eqCf2KRD83ZmbWEkkS+gFgraQ11Qudm6l0r9Q6CgwCSOoGXgs808pAzcxsdg0HFkXEuKStwF6gC7g/Ig5JuqV6fAfwceABSU9S6Xa5LSJenMe4zcxsmkQjRSNiD7Bn2r4dNc9fAK5tbWhmZjYXHilqZpYRTuhmZhnhhG5mlhFO6GZmGeGEbmaWEU7oZmYZ4YRuZpYRTuhmZhnhhG5mlhFO6GZmGeGEbmaWEU7oZmYZ4YRuZpYRTuhmZhnhhG5mlhGJErqkjZKelnRE0vY6x/+rpIPVx1OSJiS9ovXhmpnZTBomdEldwD3AdcA6YEjSutoyEfGZiHhjRLwRuB34ZkT8Yh7iNTOzGSRpoV8FHImIZyLiFLAL2DRL+SGg2IrgzMwsuSQJfQXwfM32seq+c0j6TWAj8KXmQzMzs7lIsqao6uyLGcr+IfC/Z+pukbQF2ALQ3d1NuVxOEuO8Ghsba4s42oHrYpLrYpLrYlK710WShH4MuKxmeyXwwgxlNzNLd0tE7AR2AvT19cWGDRuSRTmPyuUy7RBHO3BdTHJdTHJdTGr3ukjS5XIAWCtpjaSlVJL27umFJF0MvAX4WmtDNLM0FItFent7GRwcpLe3l2LRl8baXcMWekSMS9oK7AW6gPsj4pCkW6rHd1SLvh3YFxEn5i1aM1sQxWKRfD7P8PAwExMTdHV1kcvlABgaGko5OptJovvQI2JPRPxuRLw6IgrVfTtqkjkR8UBEbJ6vQM1s4RQKBYaHhxkYGGDJkiUMDAwwPDxMoVBIOzSbhUeKmtk5RkdH6e/vn7Kvv7+f0dHRlCKyJJzQzewcPT09jIyMTNk3MjJCT09PShFZEk7oZnaOfD5PLpejVCoxPj5OqVQil8uRz+fTDs1mkeS2RTPrMGcufN56662Mjo7S09NDoVDwBdE254RuZnUNDQ0xNDTU9vde2yR3uZiZZYQTuplZRjihm5llhPvQzTqYVG/uvbmLmGm+PltIbqGbdbCIaPi4/LaHG5ax9uCEbmaWEU7oZmYZ4YRuZpYRTuhmZhnhhG5mlhFO6GZmGZEooUvaKOlpSUckbZ+hzAZJByUdkvTN1oZpZmaNNBxYJKkLuAe4hsqC0Qck7Y6IwzVlXg7cC2yMiKOSXjVP8ZqZ2QyStNCvAo5ExDMRcQrYBWyaVuYm4MsRcRQgIn7a2jDNzKyRJEP/VwDP12wfA66eVuZ3gQsklYGLgM9GxOenn0jSFmALQHd3N+Vy+TxCbq2xsbG2iKMduC4muS6mcl1UtPvnIklCrzfZw/SxvkuA9cAgcCHwbUnfiYgfTnlRxE5gJ0BfX1+0wxzLnut5kutikuuixqOPuC6q2v1zkSShHwMuq9leCbxQp8yLEXECOCHp74A3AD/EzMwWRJI+9APAWklrJC0FNgO7p5X5GvD7kpZI+k0qXTJeHtzMbAE1bKFHxLikrcBeoAu4PyIOSbqlenxHRIxKehT4AXAauC8inprPwM3MbKpE86FHxB5gz7R9O6Ztfwb4TOtCMzOzufBIUTOzjHBCNzPLCCd0M7OMcEI3M8sIJ3Qzs4xwQjczywgndDOzjHBCNzPLCCd0M7OMcEI3M8sIJ3Qzs4xwQjczywgndDOzjHBCNzPLCCd0M7OMSJTQJW2U9LSkI5K21zm+QdJxSQerjz9pfahmZjabhgtcSOoC7gGuobJ26AFJuyPi8LSi34qIt81DjGZmlkCSFvpVwJGIeCYiTgG7gE3zG5aZmc1VkiXoVgDP12wfo7II9HRvlvQE8ALw4Yg4NL2ApC3AFoDu7m7K5fKcA261sbGxtoijHbguJrkupnJdVLT75yJJQledfTFt+3vA5RExJumtwFeBtee8KGInsBOgr68vNmzYMKdg50O5XKYd4mgHrotJrosajz7iuqhq989Fki6XY8BlNdsrqbTCz4qIf4mIserzPcAFki5pWZRmZtZQkoR+AFgraY2kpcBmYHdtAUm/LUnV51dVz/vzVgdrZmYza9jlEhHjkrYCe4Eu4P6IOCTplurxHcCNwAcljQMngc0RMb1bxszM5lGSPvQz3Sh7pu3bUfP8buDu1oZmZmZz4ZGiZmYZ4YRuZpYRTuhmZhnhhG5mlhFO6GZmGeGEbmaWER2b0IvFIr29vQwODtLb20uxWEw7JDOzpiS6Dz1risUi+Xye4eFhJiYm6OrqIpfLATA0NJRydGZm56cjW+iFQoHh4WEGBgZYsmQJAwMDDA8PUygU0g7NzOy8dWQLfXR0lP7+/in7+vv7GR0dTSkis/nxho/t4/jJl5o+z+rtj5z3ay++8AKe+Mi1TcdgjXVkQu/p6WFkZISBgYGz+0ZGRujp6UkxKrPWO37yJZ674z82dY5mp4xt5sfA5qYju1zy+Ty5XI5SqcT4+DilUolcLkc+n087NDOz89aRLfQzFz5vvfVWRkdH6enpoVAo+IKomS1qHZnQoZLUh4aG2n4FEjOzpDqyy8XMLIsSJXRJGyU9LemIpO2zlPvXkiYk3di6EOeHBxaZWdY07HKR1AXcA1xDZX3RA5J2R8ThOuU+RWVlo7bmgUVmlkVJWuhXAUci4pmIOAXsAjbVKXcr8CXgpy2Mb154YJGZZVGShL4CeL5m+1h131mSVgBvB3awCHhgkZllUZK7XFRn3/QFoP8SuC0iJqR6xasnkrYAWwC6u7spl8vJomyxVatWcffdd3PllVcyNjZGuVzm+9//PqtWrUotpnZwpi4sW3XR7PtoRV1kpS7b/nMREbM+gDcDe2u2bwdun1bmWeC56mOMSrfLDbOdd/369ZGWhx56KNasWROPPfZYfOMb34jHHnss1qxZEw899FBqMbWDUqmUdghtIyt1cfltDzd9jmbrohUxtIt2+FwAj8cMeTVJC/0AsFbSGuAnwGbgpmk/CmvOPJf0APBwRHz1/H9m5pcHFplZFjVM6BExLmkrlbtXuoD7I+KQpFuqxxdFv/l0HlhkZlmTaKRoROwB9kzbVzeRR8R7mw/LzMzmyiNFzcwywgndzCwjnNDNzDIi07MtznZP/FxU7hQyW3wu6tnO6x6ccfql5B5sJgaA5hbZsGQyndCTJOLV2x9pekUXs3b1y9E7vGJRB3GXi5lZRjihm5llhBO6mVlGOKGbmWWEE7qZWUY4oZuZZYQTuplZRjihm5llhBO6mVlGOKGbmWWEE7qZWUYkSuiSNkp6WtIRSefM9CNpk6QfSDoo6XFJ/a0P1czMZtNwci5JXcA9wDXAMeCApN0Rcbim2H5gd0SEpNcDfwNcMR8Bm5lZfUla6FcBRyLimYg4BewCNtUWiIixmJzacBng+WbNzBZYkulzVwDP12wfA66eXkjS24E/BV7FDJMfS9oCbAHo7u6mXC7PMdz50S5xpG1sbMx1UZWlumj2fbSiLrJSl+3+uUiS0OutEnFOCzwivgJ8RdIfAB8H/n2dMjuBnQB9fX3RzBzLLfPoI03N9Zwlzc57nSWZqYsWfL6brosMfcfa/XORpMvlGHBZzfZK4IWZCkfE3wGvlnRJk7GZmdkcJGmhHwDWSloD/ATYDNxUW0DSa4AfVS+KvglYCvy81cGa2dy1ZMWgR8//HBdfeEHzf98SaZjQI2Jc0lZgL9AF3B8RhyTdUj2+A/hPwHskvQScBN4RXojTLHWtWF7RyzQuHonWFI2IPcCeaft21Dz/FPCp1oZmZmZz4ZGiZmYZ4YRuFItFent7GRwcpLe3l2KxmHZIZnYeEnW5WHYVi0Xy+TzDw8NMTEzQ1dVFLpcDYGhoKOXozGwu3ELvcIVCgeHhYQYGBliyZAkDAwMMDw9TKBTSDs3M5sgJvcONjo7S3z91LrX+/n5GR0dTisjMzpcTeofr6elhZGRkyr6RkRF6enpSisjMzpcTeofL5/PkcjlKpRLj4+OUSiVyuRz5fD7t0MxsjnxRtMOdufB56623Mjo6Sk9PD4VCwRdEzRYht9DNzDLCLfQO59sWzbLDCb3DFQoFbrrppildLjfddJO7XcwWISf0Dnf48GF+9atfndNCf+6559IOzczmyH3oHW7p0qVs3bp1ysCirVu3snTp0rRDM7M5WtQt9Dd8bB/HT77U9HmamS/64gsv4ImPXNt0DGk5deoUd911F1deeSUTExOUSiXuuusuTp06lXZoZjZHizqhHz/5UtPzNDe7pFRLFg9I0bp167jhhhum9KG/853v5Ktf/WraoZnZHCVK6JI2Ap+lssDFfRFxx7Tj7wRuq26OAR+MiCdaGajNj3w+X/cuF8/lYrb4NEzokrqAe4BrqKwvekDS7og4XFPsWeAtEfHPkq6jshD01fMRsLWWBxaZZUeSFvpVwJGIeAZA0i5gE3A2oUfE/6kp/x0qC0nbIjE0NMTQ0FDbr2huZrNLktBXAM/XbB9j9tZ3Dvh6vQOStgBbALq7uymXy8minEWz5xgbG2v6HK14H+2gFXWRFa6LqVwXFe3+uUiS0FVnX90FoCUNUEno/fWOR8ROKt0x9PX1RdOtwUcfabpF2XSrtAUxtAu30Ce5Lmpk6DPerHb/XCRJ6MeAy2q2VwIvTC8k6fXAfcB1EfHz1oRnZmZJJRlYdABYK2mNpKXAZmB3bQFJq4AvA++OiB+2PkwzM2ukYQs9IsYlbQX2Urlt8f6IOCTplurxHcCfAK8E7pUEMB4RffMXtpmZTZfoPvSI2APsmbZvR83zDwAfaG1o1krVH9qmRdS9fGJmbcBzuXSIiGj4uPy2hxuWMbP25YRuZpYRTuhmZhnhhG5mlhFO6GZmGbGop8+9qGc7r3twe/MnerCZGACam8LXzNpbsVikUCicncAun8+35QR2izqh/3L0Ds+HbmbzajEtpO4uFzOzWRQKBYaHh6cs0zg8PNyWawY4oZuZzWJ0dJT+/qnzDfb39zM6OppSRDNzQjfrYJIaPn78qbc1LJNlPT09jIyMTNk3MjJCT09PShHNzAndrIMlGUFcKpU6egRxPp8nl8tRKpUYHx+nVCqRy+XI5/Nph3aORX1R1Mxsvi2mZRrdQjczywi30M3MZuHbFs3MMiJzty1K2ijpaUlHJJ0zNFPSFZK+LenXkj7c+jDNzNKRqdsWJXUB9wDXAeuAIUnrphX7BfAh4M9aHqGZWYoW022LSfrQrwKORMQzAJJ2AZuAw2cKRMRPgZ9KWvBJTVoy9P7R8z/HxRde0Pzft7axWObssIVz5rbFM33oZ25bbMculyQJfQXwfM32MeDq+QlnbpqdxwUqPwitOE/a3vCxfRw/+VLT52nmB/LiCy/giY9c23QMaSkWi2zbto1ly5YREZw4cYJt27YB7XfxyxbOYrptMUlCrzcM7LxGEkjaAmwB6O7uplwun89pWq5d4mjG8ZMv8cDGZU2dY2xsjOXLl5/369/76IlFXZfbtm1jYmKCD33oQ6xZs4Znn32WT3ziE2zbto1LL7007fBSMzY2tqj/v7bCpZdeyt133z3lO9KWdZJgBNibgb0127cDt89Q9qPAh5OMPlu/fn20g8tvezjtEFqiFe+jVCqlHkOagNi3b19ETNbFvn37ovI16VzNfi6ypB3qAng8ZsirSe5yOQCslbRG0lJgM7C75b8sZmZtqlgs0tvby+DgIL29vRSLxbRDqqthl0tEjEvaCuwFuoD7I+KQpFuqx3dI+m3gceBlwGlJ/xlYFxH/Mn+hm7XWypUrufnmm/niF7949uLXzTffzMqVK9MOzVK0mAYWJRopGhF7gD3T9u2oef6PgD/1tqh9+tOfZtu2bbz//e/n6NGjrFq1ivHxce688860Q7MUFQoFXvnKVzI4OEhEIIn169e35YVRD/03qzrz5TxzO9qyZcv45Cc/2XZfWltYhw4dAuCDH/wgb33rW9mzZw+f+9znUo6qPg/9N6sxNDTEU089xf79+3nqqaeczA2A66+/nnvvvZfly5dz7733cv3116cdUl1uoZuZNXDw4EFKpdLZaysHDx5MO6S6nNAz4qKe7bzuwXOm2Zm7B5uJAaD9B2m1aoWdyPjCDlYhibVr104ZWLR27Vqef/75xi9eYE7oGfHL0TuaHvFaLpfZsGHDeb++JdMwLIAkiTgrI4gtudl+6Pfv33/2+aFDh872q9d7TZo/9O5DNzNj9kGW11577dnkLYlrr722LZfjc0I3M2tg7969nD59mstve5jTp0+zd+/etEOqK9NdLkn7SvWp2Y+n/atrZpZEphN6kkTcbL+xtR/PPGmdKtMJvdN4bviK4ydf8gVi60hO6BnhueHNzAndMsf35FunckK3zPE9+VZPJ1xbcUK3TPL1BJuuE66tOKFb5vh6gnUqDywyM8uIRC10SRuBz1JZsei+iLhj2nFVj78V+BXw3oj4XotjNTM7b51wsbxhQpfUBdwDXAMcAw5I2h0Rh2uKXQesrT6uBj5X/a+ZWVvohIvlSVroVwFHIuIZAEm7gE1AbULfBHy+uiL1dyS9XNKlEfEPLY/YzOw8Zf1ieZKEvgKonfj3GOe2vuuVWQFMSeiStgBbALq7uymXy3MMt/XGxsbaIo75NjAwkKhco3ltSqVSC6JJl+tibrLyHXlg47JZjyf9XDTS6HMxn3WZJKHXm+Fq+iQpScoQETuBnQB9fX3RDnOodMpcLp7XZpLrYm46pS6y8LlIcpfLMeCymu2VwAvnUcbMzOZRkoR+AFgraY2kpcBmYPe0MruB96ji3wDH3X9uZrawGna5RMS4pK3AXiq3Ld4fEYck3VI9vgPYQ+WWxSNUblt83/yFbGZm9SS6Dz0i9lBJ2rX7dtQ8D+CPWxuamZnNhUeKmpllhBO6mVlGOKGbmWWEE7qZWUYorRXtJf0M+HEqf3yqS4AX0w6iTbguJrkuJrkuJrVDXVweEb9V70BqCb1dSHo8IvrSjqMduC4muS4muS4mtXtduMvFzCwjnNDNzDLCCb06WZgBrotarotJrotJbV0XHd+HbmaWFW6hm5llhBO6mVlGZDqhS5qQdFDSU5L+VtLLq/tXSzpZPXbm8Z7qseckPVnd96SkTam+iRaS9HZJIemK6vZvSPqrav08KelAdZrk/1t9/0cl/aymjlan/BZaRlJe0iFJP6i+t1L1v0ckHa95z78nqSypbW9Va8a078j/krSi5r3/o6Sf1GwvrVNvmVk7uKYunpD0PUm/V92/WtJTdco/IOnG6vNXSPq+pFRnmk002+IidjIi3ggg6UEqM0IWqsd+dOZYHQMR8aKk1wL7gK/Nd6ALZAgYoTKn/UeBdwC/A7w+Ik5LWgmciIirASS9F+iLiK3phDs/JL0ZeBvwpoj4taRLgKUR8YKkDcCHI+JtNeXTCXRh1H5Hvgi8o2b7o8BYRPxZdbtuvaUR9DyprYv/APwp8JZGL5J0MZXpxXdGxF/Pa4QNZLqFPs23qaxzOhcvA/55HmJZcJKWA/8WyFFJ6ACXAv8QEacBIuJYRGTi/TZwKfBiRPwaICJejAivsAXfAl4zy/FOqrek3/3lwNeBhyLic/MbUmMdkdAldQGDTF1p6dXTulx+v+ZYqfpPrG8C/30hY51HNwCPRsQPgV9IehPwN8AfVt//nZKuTDXChbMPuEzSDyXdK6lhKyzrJC0BrgOenKVY1uvtwup34f8B9wEfT/CaPwdGIuIv5je0ZLKe0C+UdBD4OfAK4Bs1x34UEW+seXyr5thARPQCrwPurrZuF7shYFf1+S5gKCKOAa8FbgdOA/slDaYU34KJiDFgPbAF+BnwP6vdS53ozHfkceAoMDxTwQ6ot5PVXHAFsBH4vBr3tz0GbJL0qvkPr7GO6EOv9nE9TKUP/a+SvjgifiTpn4B1wN/PU4zzTtIrgX8H9EoKKksJhqT/Vv3n89eBr1ff6w3A/tSCXSARMQGUgbKkJ4GbgQfSjCklZ/uNk+iUeouIb1evEdSdBKvGLirXpfZIGoiIX85/dDPLegsdgIg4DnwI+LCkC5K+rvqru4b2mBWyGTcCn4+IyyNidURcBjwL/IGk34HKHS/A61n877UhSa+VtLZm1xvpgPfdrE6qt+qdYF1U/nU/q4j4SyqNoK9ISvUicdZb6GdFxPclPUHlguC3qPah1xS5PyLOtN5LkiaAC4DtEfFPCxttyw0Bd0zb9yUqLatfSPpX1X1/D9y9gHGlZTlwlyq3sY5TWdx8S4PXPCLpperzb0fEH81jfO3qfOptMbmwJicIuDkiJqq9Lq+VdKym7H+pfWFE3Cbpr4H/IWnozI0GC81D/83MMqIjulzMzDqBE7qZWUY4oZuZZYQTuplZRjihm5llhBO6mVlGOKGbmWXE/wfTJ/AG3g/79AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "wnba[['REB', 'AST', 'STL', 'PTS', 'BLK']].div(wnba.MIN, axis=0).boxplot()\n", + "plt.show()" ] }, { @@ -189,11 +1473,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "#your conclusions here" + "#your conclusions here\n", + "# that the stats are still positively skewed, but not that much!" ] }, { @@ -218,11 +1503,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ - "#your comments here" + "#your comments here\n", + "# For the first two no for sure. We only have data from the WNBA and not from the major leagues -> basketball in the US\n", + "# is different than in Europe. Nevertheless we can infer and use this as a sample\n", + "\n", + "# for the third statement we would need the data from the NBA. We can check the avg assist from the WNBA (44.5 from the\n", + "# describe function above), but we can't compare it!" ] } ], @@ -242,7 +1532,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.7" } }, "nbformat": 4,