问题

我是Pandas的新手

我的数据框架：

**df**

A B
first True
second False
third False
fourth True
fifth False

**期望的输出**

A B C
first True en
second False
third False
fourth True en
fifth False

我试图仅在`B`列为`True`时对`C`列应用函数。

**我使用的代码**

```python
if (df['B'] == True)):
    df['C'] = df['A'].apply(
        lambda x: TextBlob(x).detect_language())

但是我遇到了一个错误：

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我尝试过的

df['B'].bool()
df['B'] is True
df['B'] == 'True'

但是错误仍然存在，不确定如何构建一个说'仅在B列为True时'的语句。

感谢您的建议。

<details>
<summary>英文:</summary>

I am new to Pandas

My dataframe:

**df**

A B
first True
second False
third False
fourth True
fifth False

**Desired output**

A B C
first True en
second False
third False
fourth True en
fifth False

I am trying to apply a function to column `C` only when the `B` column is `True`.

**What I use**

if (df['B'] == True)):
df['C'] = df['A'].apply(
lambda x: TextBlob(x).detect_language())

But I get an error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

**What I've tried**

df['B'].bool()
df['B'] is True
df['B'] == 'True'

But the error persists, not sure how I would form a statement saying 'only where column B is True'.

Thank you for your suggestions.


</details>


# 答案1
**得分**: 3

如果希望在没有匹配行的情况下获取缺失值，请在“apply”之前过滤行，仅处理具有“True”的行：

```python
df['C'] = df.loc[df['B'], 'A'].apply(lambda x: TextBlob(x).detect_language())
print(df)
        A      B    C
0   first   True   en
1  second  False  NaN
2   third  False  NaN
3  fourth   True   en
4   fifth  False  NaN

或者，如果需要空字符串来表示非匹配的值，但要处理所有列，请使用以下代码：

df['C'] = np.where(df['B'], df['A'].apply(lambda x: TextBlob(x).detect_language()), '')
print(df)
        A      B   C
0   first   True  en
1  second  False    
2   third  False    
3  fourth   True  en
4   fifth  False    

df['C'] = df.loc[df['B'], 'A'].apply(lambda x: TextBlob(x).detect_language())
print (df)
        A      B    C
0   first   True   en
1  second  False  NaN
2   third  False  NaN
3  fourth   True   en
4   fifth  False  NaN

df['C'] = np.where(df['B'], df['A'].apply(lambda x: TextBlob(x).detect_language()), '')
print (df)
        A      B   C
0   first   True  en
1  second  False    
2   third  False    
3  fourth   True  en
4   fifth  False