Why use pandas.assign rather than simply initialize new column?

Question

I just discovered the assign method for pandas dataframes, and it looks nice and very similar to dplyr\'s mutate in R. However, I\'ve always gotten

Answer 1

The premise on assign is that it returns:

A new DataFrame with the new columns in addition to all the existing columns.

And also you cannot do anything in-place to change the original dataframe.

The callable must not change input DataFrame (though pandas doesn't check it).

On the other hand df['ln_A'] = np.log(df['A']) will do things inplace.

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So is there a reason I should stop using my old method in favour of df.assign?

I think you can try df.assign but if you do memory intensive stuff, better to work what you did before or operations with inplace=True.