group by - Python：Groupy-by查找df行和列中出现的百分比

发布于 2020-03-27 16:02:35

我下面有一个数据框，该entryName列中有多个人。根据此列（假设groupby），我想检查另一个，rows以查看其他人C1,C2,etc是否再次使用了in中的entryName人。所以Player1有两个条目，他在两个条目中都使用了Zach Parise。Zachs的使用百分比为的100％Player1。Player2有3个条目，他在3个条目中的1个中使用了Patrick Kane。Patricks的使用百分比为33％Player2。在上面，我认为这将使用某种方式，groupby但是我不确定它将如何检查其余部分。任何提示将非常有帮助。

import pandas as pd
import numpy as np

df = pd.DataFrame(data=np.array([['Player1','Mark Scheifele','Pierre-Luc Dubois','Mats Zuccarello','Oliver Bjorkstrand','Nick Foligno','Ryan Suter','Seth Jones','Devan Dubnyk','Zach Parise'],['Player2','Kyle Connor','Pierre-Luc Dubois','Mats Zuccarello','Oliver Bjorkstrand','Nick Foligno','Ryan Suter','Seth Jones','Devan Dubnyk','Boone Jenner'],['Player2','Patrick Kane','Joseph Walter','Luke Kunin','Oliver Bjorkstrand','Mark Foligno','Ryan Suter','Matt Dumba','Alex Stalock','Eric Staal'],['Player2','Boone Jenner','Pierre-Luc Dubois','Mats Zuccarello','Oliver Bjorkstrand','Nick Foligno','Ryan Suter','Seth Jones','Devan Dubnyk','Kyle Connor'],['Player1','Patrick Kane','David Backes','Zach Parise','Oliver Bjorkstrand','Mark Foligno','Jonas Brodin','Matt Dumba','Alex Stalock','Eric Staal']]), columns=['entryName','C1','C2','W1','W2','W3','D1','D2','G','UTIL'])

df   

  entryName              C1  ...             G          UTIL
0   Player1  Mark Scheifele  ...  Devan Dubnyk   Zach Parise
1   Player2     Kyle Connor  ...  Devan Dubnyk  Boone Jenner
2   Player2    Patrick Kane  ...  Alex Stalock    Eric Staal
3   Player2    Boone Jenner  ...  Devan Dubnyk   Kyle Connor
4   Player1    Patrick Kane  ...  Alex Stalock    Eric Staal

[5 rows x 10 columns]



df_result = pd.DataFrame(data=np.array([['Player1','Mark Scheifele','50.00','Pierre-Luc Dubois','50.00','Mats Zuccarello','50.00','Oliver Bjorkstrand','100.00','Nick Foligno','50.00','Ryan Suter','50.00','Seth Jones','50.00','Devan Dubnyk','50.00','Zach Parise','100.00'],['Player2','Kyle Connor','66.66','Pierre-Luc Dubois','66.66','Mats Zuccarello','66.66','Oliver Bjorkstrand','100.00','Nick Foligno','66.66','Ryan Suter','100.00','Seth Jones','66.66','Devan Dubnyk','66.66','Boone Jenner','66.66'],['Player2','Patrick Kane','33.33','Joseph Walter','33.33','Luke Kunin','33.33','Oliver Bjorkstrand','100.00','Mark Foligno','33.33','Ryan Suter','100.00','Matt Dumba','33.33','Alex Stalock','33.33','Eric Staal','33.33'],['Player2','Boone Jenner','66.66','Pierre-Luc Dubois','66.66','Mats Zuccarello','66.66','Oliver Bjorkstrand','100.00','Nick Foligno','66.66','Ryan Suter','100.00','Seth Jones','66.66','Devan Dubnyk','66.66','Kyle Connor','66.66'],['Player1','Patrick Kane','50.00','David Backes','50.00','Zach Parise','100.00','Oliver Bjorkstrand','100.00','Mark Foligno','50.00','Jonas Brodin','50.00','Matt Dumba','50.00','Alex Stalock','50.00','Eric Staal','50.00']]), columns=['entryName','C1','C1_Spr','C2','C2_Spr','W1','W1_Spr','W2','W2_Spr','W3','W3_Spr','D1','D1_Spr','D2','D2_Spr','G','G_Spr','UTIL','UTIL_Spr'])

df_result

  entryName              C1 C1_Spr  ...  G_Spr          UTIL UTIL_Spr
0   Player1  Mark Scheifele  50.00  ...  50.00   Zach Parise   100.00
1   Player2     Kyle Connor  66.66  ...  66.66  Boone Jenner    66.66
2   Player2    Patrick Kane  33.33  ...  33.33    Eric Staal    33.33
3   Player2    Boone Jenner  66.66  ...  66.66   Kyle Connor    66.66
4   Player1    Patrick Kane  50.00  ...  50.00    Eric Staal    50.00

[5 rows x 19 columns]

提问者

Mike.J

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df_melt = df.melt(id_vars='entryName') df_cross = pd.crosstab(df_melt['entryName'], df_melt['value']) df_pct = df_cross / df.groupby('entryName').size().values[:, None] * 100 print(df_pct.T.sort_index())

entryName Player1 Player2 value Alex Stalock 50.0 33.333333 Boone Jenner 0.0 66.666667 David Backes 50.0 0.000000 Devan Dubnyk 50.0 66.666667 Eric Staal 50.0 33.333333 Jonas Brodin 50.0 0.000000 Joseph Walter 0.0 33.333333 Kyle Connor 0.0 66.666667 Luke Kunin 0.0 33.333333 Mark Foligno 50.0 33.333333 Mark Scheifele 50.0 0.000000 Mats Zuccarello 50.0 66.666667 Matt Dumba 50.0 33.333333 Nick Foligno 50.0 66.666667 Oliver Bjorkstrand 100.0 100.000000 Patrick Kane 50.0 33.333333 Pierre-Luc Dubois 50.0 66.666667 Ryan Suter 50.0 100.000000 Seth Jones 50.0 66.666667 Zach Parise 100.0 0.000000

# append percents to main dataframe df_stack = df_pct.stack().round(2) for col in df.columns[1:]: df = df.merge(df_stack.to_frame(f'{col}_Spr'), left_on=['entryName', col], right_index=True) # sort columns in desired order cols = [df.columns[0]] for col in df.columns[1:10]: cols.append(col) cols.append(f'{col}_Spr') df = df.reindex(cols, axis=1).sort_index()

entryName C1 C1_Spr C2 C2_Spr \ 0 Player1 Mark Scheifele 50.00 Pierre-Luc Dubois 50.00 1 Player2 Kyle Connor 66.67 Pierre-Luc Dubois 66.67 2 Player2 Patrick Kane 33.33 Joseph Walter 33.33 3 Player2 Boone Jenner 66.67 Pierre-Luc Dubois 66.67 4 Player1 Patrick Kane 50.00 David Backes 50.00 W1 W1_Spr W2 W2_Spr W3 W3_Spr \ 0 Mats Zuccarello 50.00 Oliver Bjorkstrand 100.0 Nick Foligno 50.00 1 Mats Zuccarello 66.67 Oliver Bjorkstrand 100.0 Nick Foligno 66.67 2 Luke Kunin 33.33 Oliver Bjorkstrand 100.0 Mark Foligno 33.33 3 Mats Zuccarello 66.67 Oliver Bjorkstrand 100.0 Nick Foligno 66.67 4 Zach Parise 100.00 Oliver Bjorkstrand 100.0 Mark Foligno 50.00 D1 D1_Spr D2 D2_Spr G G_Spr \ 0 Ryan Suter 50.0 Seth Jones 50.00 Devan Dubnyk 50.00 1 Ryan Suter 100.0 Seth Jones 66.67 Devan Dubnyk 66.67 2 Ryan Suter 100.0 Matt Dumba 33.33 Alex Stalock 33.33 3 Ryan Suter 100.0 Seth Jones 66.67 Devan Dubnyk 66.67 4 Jonas Brodin 50.0 Matt Dumba 50.00 Alex Stalock 50.00 UTIL UTIL_Spr 0 Zach Parise 100.00 1 Boone Jenner 66.67 2 Eric Staal 33.33 3 Kyle Connor 66.67 4 Eric Staal 50.00

group by - Python：Groupy-by查找df行和列中出现的百分比

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