用熊猫的列条件更改特定行的值

Question

我有一个数据框df，其中有两列gender, score。

|---------------------|------------------|
|      gender         |     score        |
|---------------------|------------------|
|          male       |         34       |
|---------------------|------------------|
|          female     |         34       |
|---------------------|------------------|
|          male       |         34       |
|---------------------|------------------|
|          female     |         34       |
|---------------------|------------------|
|          male       |         34       |
|---------------------|------------------|

我想将男性（gender == 'male'）的得分从第3行更改为第5行，以达到预期输出：

|---------------------|------------------|
|      gender         |     score        |
|---------------------|------------------|
|          male       |         34       |
|---------------------|------------------|
|          female     |         34       |
|---------------------|------------------|
|          male       |         0        |
|---------------------|------------------|
|          female     |         34       |
|---------------------|------------------|
|          male       |         0        |
|---------------------|------------------|

如何将iloc与该条件结合起来？

Answer 1

Alt1：

您可以使用两个遮罩（条件）来完成此操作。这应该是可读且有意义的。

m1 = (df.gender == 'male')
m2 = (df.gender.duplicated())

df.loc[m1&m2, 'score'] = 0

Alt2：

切掉非零掩码的第一个真值（需要import numpy as np）。这应该更快。

m = np.nonzero(df.gender=='male')[0][1:]
df.loc[m, 'score'] = 0

---

完整示例：

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'gender': ['male','female','male','female','male'],
    'score': 34
})

m1 = (df.gender == 'male')
m2 = (df.gender.duplicated())

m = np.nonzero(df.gender=='male')[0][1:]
df.loc[m, 'score'] = 0

print(df)

返回：

   gender  score
0    male     34
1  female     34
2    male      0
3  female     34
4    male      0

Answer 2

我认为您需要

m=df.loc[2:5,:].loc[df['gender']=='male']
df.loc[m.index,'score']=0
print(df)
    gender  score
0   male    34
1   female  34
2   male    0
3   female  34
4   male    0