你也可以使用np.where函数，它的工作原理与MS-Excel的if函数完全相似。

import numpy as np
( df['Sentiment_Score']= 
np.where(df.Sentiment_Label='Disappointed',df['Sentiment_Score']-1,
df['Sentiment_Score']
)

您可以尝试以下操作：

df.loc[df['Sentiment_Label'] == 'Disappointed', 'Sentiment_Score'] -= 1

通过应用此方法，您将获得：
| 情感_标签|情绪_分数|
| --------------|--------------|
| 满意了|0.882621|
| 失望| -0.173587 |
| 满意了|0.893627|
| 满意了|0.863469|
| 失望| -0.017553 |

你可以在每一行上使用apply()，然后定义一个函数，如果Sentiment_Label的值是Disappointed，则该函数执行1 - Sentiment_Score：

# Function to apply to each row of dataframe
def sub_1(x):
    if x['Sentiment_Label'] == 'Disappointed':
        x["Sentiment_Score"] = 1-x["Sentiment_Score"]
    return x

# Dataframe definition
df = pd.DataFrame({
    'Sentiment_Label': ["Satisfied", "Disappointed", "Satisfied", "Satisfied", "Disappointed"],
    'Sentiment_Score': [0.882621, 0.826413, 0.893627, 0.863469, 0.982447],
})

# Apply function to dataframe by rows 
df = df.apply(sub_1, axis=1)

apply()之后的df值：

Sentiment_Label  Sentiment_Score
0       Satisfied         0.882621
1    Disappointed         0.173587
2       Satisfied         0.893627
3       Satisfied         0.863469
4    Disappointed         0.017553

你可以使用df.iterrows()循环df。然后你比较列值，并使用df.at()访问值和替换。

for i, v in df.iterrows():
    if(v["Sentiment_Label"] == 'Disappointed'):
        df.at[i, 'Sentiment_Score'] = 1 - v["Sentiment_Score"]

print(df)

输出：

Sentiment_Label  Sentiment_Score
0       Satisfied         0.882621
1    Disappointed         0.173587
2       Satisfied         0.893627
3       Satisfied         0.863469
4    Disappointed         0.017553

可以使用rsub：

m = df['Sentiment_Label'].eq('Disappointed')
df.loc[m, 'Sentiment_Score'] = df['Sentiment_Score'].rsub(1)

输出：

Sentiment_Label  Sentiment_Score
0       Satisfied         0.882621
1    Disappointed         0.173587
2       Satisfied         0.893627
3       Satisfied         0.863469
4    Disappointed         0.017553

df.update( df[df.iloc[:,0].eq("Disappointed")].iloc[:,1].sub(1).abs() )

print(df)

Sentiment_Label  Sentiment_Score
0       Satisfied         0.882621
1    Disappointed         0.173587
2       Satisfied         0.893627
3       Satisfied         0.863469
4    Disappointed         0.017553