You can use the isna() method (or it’s alias isnull() which is also compatible with older pandas versions < 0.21.0) and then sum to count the NaN values. For one column: In [1]: s = pd.Series([1,2,3, np.nan, np.nan]) In [4]: s.isna().sum() # or s.isnull().sum() for older pandas versions Out[4]: 2 For several columns, it also works: In [5]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]}) In [6]: df.isna().sum() Out[6]: a 1 b 2 dtype: int64 Lets assume df is a pandas DataFrame. Then, df.isnull().sum(axis = 0) This will give number of NaN values in every column. If you need, NaN values in every row, df.isnull().sum(axis = 1)

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