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While working in Pandas in Python...

I'm working with a dataset that contains some missing values, and I'd like to return a dataframe which contains only those rows which have missing data. Is there a nice way to do this?

(My current method to do this is an inefficient "look to see what index isn't in the dataframe without the missing values, then make a df out of those indices.")

6 Answers6

105

You can use any axis=1 to check for least one True per row, then filter with boolean indexing:

null_data = df[df.isnull().any(axis=1)]
cs95
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meepl
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df.isnull().any(axis = 1).sum()

this gives you the total number of rows with at least one missing data

David Buck
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Collins Kelechi
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1

If you want to see only the rows that contains the NaN values you could do:

data_frame[data_frame.iloc[:, insert column number here]=='NaN']
João Vitor Gomes
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You Can Use the code in this way

sum(df.isnull().any(axis=1))
Ahmed Khater
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I just had this problem I assume you want to view a section of data frame made up of rows with missing values I used

````df.loc[df.isnull().any(axis=1)]```
agravaine
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If you are looking for a quicker way to find the total number of missing rows in the dataframe, you can use this:

sum(df.isnull().values.any(axis=1))

Ikay
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