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LopezAi
Asked: 2022-08-31 07:13:13 +0800 CST 2022-08-31 07:13:13 +0800 CST

How to apply a lambda function on a column based on NaT values

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I have a column with dates, and I want to apply a conditional lambda function, which gives the value one to the 'Type' column in case the values ​​are NaTs. I'm having a lot of problems detecting these NaTs.

With unique I get the empty values, and it tells me that they are NaT

df['fechaActivacion'].unique()

The result is as follows:

array([ 'NaT', '2022-08-02T15:27:28.000000000', '2022-07-09T21:10:03.000000000', ..., '2022-06-27T17:17:50.000000000', '2022- 08-10T21:56:10.000000000', '2022-08-23T17:53:08.000000000'], dtype='datetime64[ns]')

I have tried the following:

df['type'] = df['fechaActivacion'].apply(lambda x: 1 if x == pd.NaT else 0)

I don't get any errors, but all the values ​​are generated as 0, when there are NaT values ​​and 1's should have been generated as well.

Thanks in advance!

python

1 Answers

  1. Best Answer

    According to the documentation , the function should be used isna, instead of a normal comparison, to check if a value is missing .

    df['type'] = df['fechaActivacion'].apply(lambda x: 1 if pd.isna(x) else 0)

    Or better yet:

    df['type'] = df['fechaActivacion'].apply(lambda x: int(pd.isna(x)))
    • 2