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
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