pandas: Boolean indexing with multi index

Question

There are many questions here with similar titles, but I couldn\'t find one that\'s addressing this issue.

I have dataframes from many different origins, and I want to f

Answer 1

Simply:

df.where(
    filt.rename_axis('a').rename('c').to_frame()
).dropna().astype(int)

Explanation:

• .rename_axis('a') renames the index as a (the index we want to filter by)
• .rename('c') renames the column as c (the column that stores the values)
• .to_frame() converts this Series into a DataFrame, for compatibility with df
• df.where(...) filters the rows, leaving missing values (NaN) where filter is False
• .drop_na() removes the rows with missing values (in our case where a == 2)
• .astype(int) converts from float back to int (not sure why float to begin with)

By the way, it seems that df.where(...) and df[...] behave similarly here, so take your pick.