Applying function based on condition on pandas dataframe series

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.

Solution 1:

If want missing values for no matched rows filter rows before apply for processing only rows with Trues:

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

Or if need empty strings for non matched values, but apply processing all columns:

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