Pandas best way to subset a dataframe inplace, using a mask

Question

I have a pandas dataset that I want to downsize (remove all values under x).

The mask is df[my_column] > 50

I would typically just use df = df[mask], but want to avoid making a copy every time, particularly because it gets error prone when used in functions (as it only gets altered in the function scope).

What is the best way to subset a dataset inplace?

I was thinking of something along the lines of
df.drop(df.loc[mask].index, inplace = True)

Is there a better way to do this, or any situation where this won't work at all?

Answer 1

You are missing the inplace parameter :

df.drop(df[df.my_column < 50].index, inplace = True)

Answer 2

you can use df.query()

like:

bool_series = df[my_column] > 50
df.query("@bool_series",inplace=True)