dropping rows from dataframe based on a "not in" condition

Question

I want to drop rows from a pandas dataframe when the value of the date column is in a list of dates. The following code doesn’t work:

a=['2015-01-01' , '2015-02-01']

df=df[df.datecolumn not in a]

I get the following error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Asked By: gaurav gurnani

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Source

Answer 1

You can use Series.isin:

df = df[~df.datecolumn.isin(a)]

While the error message suggests that all() or any() can be used, they are useful only when you want to reduce the result into a single Boolean value. That is however not what you are trying to do now, which is to test the membership of every values in the Series against the external list, and keep the results intact (i.e., a Boolean Series which will then be used to slice the original DataFrame).

You can read more about this in the Gotchas.

Answered By: YS-L

Answer 2

You can use pandas.Dataframe.isin.

pandas.Dateframe.isin will return boolean values depending on whether each element is inside the list a or not. You then invert this with the ~ to convert True to False and vice versa.

import pandas as pd

a = ['2015-01-01' , '2015-02-01']

df = pd.DataFrame(data={'date':['2015-01-01' , '2015-02-01', '2015-03-01' , '2015-04-01', '2015-05-01' , '2015-06-01']})

print(df)
#         date
#0  2015-01-01
#1  2015-02-01
#2  2015-03-01
#3  2015-04-01
#4  2015-05-01
#5  2015-06-01

df = df[~df['date'].isin(a)]

print(df)
#         date
#2  2015-03-01
#3  2015-04-01
#4  2015-05-01
#5  2015-06-01

Answered By: Ffisegydd

dropping rows from dataframe based on a "not in" condition

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