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I have a dataframe with a lot of columns using the suffix '_o'. Is there a way to drop all the columns that has '_o' in the end of its label?

In this post I've seen a way to drop the columns that start with something using the filter function. But how to drop the ones that end with something?

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3 Answers 3

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21

Pandonic

df = df.loc[:, ~df.columns.str.endswith('_o')]

df = df[df.columns[~df.columns.str.endswith('_o')]]

List comprehensions

df = df[[x for x in df if not x.endswith('_o')]]

df = df.drop([x for x in df if x.endswith('_o')], 1)
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2 Comments

List comprehensions are not correct, it should be df.columns not df. E.g., [x for x in df.columns...]
@Yasmin, No, it's fine.. for x in df loops through the column names of a dataframe.. test it yourself.
2

To use df.filter() properly here you could use it with a lookbehind:

>>> df = pd.DataFrame({'a': [1, 2], 'a_o': [2, 3], 'o_b': [4, 5]})

>>> df.filter(regex=r'.*(?<!_o)$')
   a  o_b
0  1    4
1  2    5
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0

This can be done by re-assigning the dataframe with only the needed columns

df = df.iloc[:, [not o.endswith('_o') for o in df.columns]]
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