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:
>>> s = pd.Series([1,2,3, np.nan, np.nan])
>>> s.isna().sum() # or s.isnull().sum() for older pandas versions
2
For several columns, this also works:
>>> df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})
>>> df.isna().sum()
a 1
b 2
dtype: int64