Using Complex Conditions To Form A Pandas Data Frame From The Existing One

I've got the following dataframe containing function names, their arguments, the default values of the arguments and argument types: FULL_NAME ARGUMENT DEF_VALS TYPE 'func

Solution 1:

You can do df = df[df['ARGUMENT'] != 'self'].copy(deep=True) to remove all the rows with ARGUMENT equal to "self" before apply the solution.

P.S. I am also guessing you only care about remove "self" if it's the first argument, in that case, the appropriate preprocessing step would be

df = df[
    ~(
        (df['ARGUMENT'] == 'self') &
        (df.groupby('FULL_NAME').cumcount() == 0)
    )
].copy(deep=True)