Filter out rows of panda-df by comparing to list [duplicate]

Question

The following problem occurs often. Say I have a dataframe, where one column can take a discrete value:

df = pd.DataFrame({'col1': [1, 2,3,4,5,6,7], 'col2': ["A", "B", "A", "C", "B", "A", "D"]})

In this case col2 can take values A, B or C. I only want to rows where col2 is not equal to A or B. I thought the following syntax would work,

df["col2"] not in ["A", "B"]

However, this gives me the error ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Is there a neat way to filter those rows out?

You want df[~df['col2'].isin(['A','B'])]

EdChum
– EdChum

2018-06-05 14:10:05 +00:00
Commented Jun 5, 2018 at 14:10 — EdChum
– EdChum, Commented Jun 5, 2018 at 14:10

Mihai Alexandru-Ionut · Accepted Answer · 2018-06-05 14:11:24Z

7

You can use isin method.

df = df[~df.col2.isin(['A', 'B'])]

Output

 col1 col2 3 4 C 6 7 D

edited Jun 5, 2018 at 14:11

answered Jun 5, 2018 at 14:10

Mihai Alexandru-Ionut

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1 Comment

EdChum Over a year ago

this is a dupe, not necessary to answer IMO, also didn't downvote

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