python - Pandas :将列的值分配到字典值设置的限制

Question

如何删除 iterrows()？这可以用 numpy 或 pandas 更快地完成吗？

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                   'B': 'one one two three two two one three'.split(),
                   'C': np.arange(8)*0  })
print(df)
#      A      B  C
# 0  foo    one  0
# 1  bar    one  0
# 2  foo    two  0
# 3  bar  three  0
# 4  foo    two  0
# 5  bar    two  0
# 6  foo    one  0
# 7  foo  three  0

selDict = {"foo":2, "bar":3}

这个有效:

for i, r in df.iterrows():
    if selDict[r["A"]] > 0:
        selDict[r["A"]] -=1         
        df.set_value(i, 'C', 1)

   print df
#      A      B  C
# 0  foo    one  1
# 1  bar    one  1
# 2  foo    two  1
# 3  bar  three  1
# 4  foo    two  0
# 5  bar    two  1
# 6  foo    one  0
# 7  foo  three  0

Answer 1

如果我没理解错，你可以使用 cumcount:

df['C'] = (df.groupby('A').cumcount() < df['A'].map(selDict)).astype('int')

df
Out: 
     A      B  C
0  foo    one  1
1  bar    one  1
2  foo    two  1
3  bar  three  1
4  foo    two  0
5  bar    two  1
6  foo    one  0
7  foo  three  0