Question

我有一个数据框：

import pandas as pd

df=pd.DataFrame({
'Player': ['John','John','John','Steve','Steve','Ted', 'James','Smitty','SmittyJr','DJ'],
'Name': ['A','B', 'A','B','B','C', 'A','D','D','D'],
'Group':['2A','1B','2A','2A','1B','1C','2A','1C','1C','2A'],
'Medal':['G', '?', '?', 'S', 'B','?','?','?','G','?']
})

df =  df[['Player','Group', 'Name', 'Medal']]
print(df)

我想更新所有'？'在Medal列中，其中包含匹配Name＆amp;的任何行的值已填写的Group列。

例如，因为第一个row 0是Name:A, Group:2A, Medal:G，所以'？'在row 6和2上将是'G'

结果应该如下：

res=pd.DataFrame({
'Player': ['John','John','John','Steve','Steve','Ted', 'James','Smitty','SmittyJr','DJ'],
'Name': ['A','B', 'A','B','B','C', 'A','D','D','D'],
'Group':['2A','1B','2A','2A','1B','1C','2A','1C','1C','2A'],
'Medal':['G', 'B', 'G', 'S', 'B','?','G','G','G','?']
})

res =  res[['Player','Group', 'Name', 'Medal']]
print(res)

最有效的方法是什么？

Answer 1

另一个解决方案replace ?的最后一个值（iloc}的排序Medal（sort_values}在每个组中：

df['Medal'] = df.groupby(['Group','Name'])['Medal']
                .apply(lambda x: x.replace('?', x.sort_values().iloc[-1]))

print(df)
     Player Group Name Medal
0      John    2A    A     G
1      John    1B    B     B
2      John    2A    A     G
3     Steve    2A    B     S
4     Steve    1B    B     B
5       Ted    1C    C     ?
6     James    2A    A     G
7    Smitty    1C    D     G
8  SmittyJr    1C    D     G
9        DJ    2A    D     ?

<强>计时：

In [81]: %timeit (df.groupby(['Group','Name'])['Medal'].apply(lambda x: x.replace('?', x.sort_values().iloc[-1])))
100 loops, best of 3: 4.13 ms per loop

In [82]: %timeit (df.replace('?', np.nan).groupby(['Name', 'Group']).apply(lambda df: df.ffill().bfill()).fillna('?'))
100 loops, best of 3: 11.3 ms per loop

Answer 2

尝试：

import pandas as pd
import numpy as np

myfill = lambda df: df.ffill().bfill()
df.replace('?', np.nan).groupby(['Name', 'Group']).apply(myfill).fillna('?')

     Player Group Name Medal
0      John    2A    A     G
1      John    1B    B     B
2      John    2A    A     G
3     Steve    2A    B     S
4     Steve    1B    B     B
5       Ted    1C    C     ?
6     James    2A    A     G
7    Smitty    1C    D     G
8  SmittyJr    1C    D     G
9        DJ    2A    D     ?

Pandas python根据预先存在的值和条件更新表中的值

2 个答案: