[Python] How to modify mulitple columns applying if else to multiple pandas dataframe columns

I have a dataFrame with columns Age, Salary and others, if I used:

df['Age'] = df['Age'].apply(lambda x : x+100 if x>30 else 0)

Then I can modify the Age column with the if else condition. Also, if I used:

df[['Age', 'Salary']] = df[['Age', 'Salary']].apply(lambda x : x+100)

Then, I can apply the lambda equation to each column independently. But as soon as I use an if else condition on both columns as:

df[['Age', 'Salary']] = df[['Age', 'Salary']].apply(lambda x : x+100 if x>30 else 0)

Then I get the following error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

So, how can I modify the Age, Salary and n columns applying the same if else or other lambda condition to each column independently?

I know two possible solution are:

1. To use a for to call each column:

cols = ['Age', 'Salary'] for i in cols: df = df.apply(lambda x : x+100 if x>30 else 0)

1. To use the apply on each column:

df['Age'] = df['Age'].apply(lambda x : x+100 if x>30 else 0) df['Salary'] = df['Salary'].apply(lambda x : x+100 if x>30 else 0)

Is there a way to do the same but only on one line (such as the code that I tried) using apply or other function?

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