Pandas slicing FutureWarning with 0.21.0

Question

I'm trying to select a subset of a subset of a dataframe, selecting only some columns, and filtering on the rows.

df.loc[df.a.isin(['Apple', 'Pear', 'Mango']), ['a', 'b', 'f', 'g']] 

However, I'm getting the error:

Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative. 

What 's the correct way to slice and filter now?

Answer 1

TL;DR: There is likely a typo or spelling error in the column header names.

This is a change introduced in v0.21.1, and has been explained in the docs at length -

Previously, selecting with a list of labels, where one or more labels were missing would always succeed, returning NaN for missing labels. This will now show a FutureWarning. In the future this will raise a KeyError (GH15747). This warning will trigger on a DataFrame or a Series for using .loc[] or [[]] when passing a list-of-labels with at least 1 missing label.

For example,

df       A    B  C 0  7.0  NaN  8 1  3.0  3.0  5 2  8.0  1.0  7 3  NaN  0.0  3 4  8.0  2.0  7 

Try some kind of slicing as you're doing -

df.loc[df.A.gt(6), ['A', 'C']]       A  C 0  7.0  8 2  8.0  7 4  8.0  7 

No problem. Now, try replacing C with a non-existent column label -

df.loc[df.A.gt(6), ['A', 'D']] FutureWarning: Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative.            A   D 0  7.0 NaN 2  8.0 NaN 4  8.0 NaN 

So, in your case, the error is because of the column labels you pass to loc. Take another look at them.