Reputation: 5212
I've seen in this question how to drop columns with all nan
, but I'm looking for a way to remove all columns with all False
values.
Using the info in that question, I'm thinking of replacing False with nan, dropping them, and then replacing nan
back with False
, but I don't know if that is the best approach.
A working piece of code with my approach would be as follows:
df = pd.DataFrame(data={'A':[True, True, False], 'B': [False, True, False], 'C':[False, False, False], 'D': [True, True, True]})
df.replace(to_replace=False, value=np.nan, inplace=True)
df.dropna(axis=1, how='all', inplace=True)
df.fillna(False, inplace=True)
Upvotes: 0
Views: 46
Reputation: 3294
You could use:
df.loc[:,~df.eq(False).all()]
Output:
A B D
0 True False True
1 True True True
2 False False True
Upvotes: 1