How can I apply lambda functions to multiple columns in a DataFrame?

How can I apply lambda functions to multiple columns in a DataFrame?

You can use the apply method along with a lambda function to apply it to multiple columns. For example, you can do something like this: df[['col1', 'col2']].apply(lambda x: x['col1'] + x['col2'], axis=1).

Can I apply different lambda functions to different columns?

Yes, you can use the apply method with a dictionary to specify different functions for each column. For example: df['col1'].apply(lambda x: x * 2) and df['col2'].apply(lambda x: x + 1).

What if I want to apply a function that depends on multiple columns?

You can still use the apply method with axis=1 to access multiple columns. For instance: df.apply(lambda row: row['col1'] + row['col2'], axis=1).

Are there any performance considerations when using lambda functions?

Yes, using lambda functions can be slower than vectorized operations. If performance is critical, consider using built-in pandas functions or NumPy.