How to count nan values in a pandas DataFrame?

To count just null values, you can use isnull():

In [11]:dfd.isnull().sum()Out[11]:a    2dtype: int64

Here a is the column name, and there are 2 occurrences of the null value in the column.

If you want to count only NaN values in column 'a' of a DataFrame df, use:

len(df) - df['a'].count()

Here count() tells us the number of non-NaN values, and this is subtracted from the total number of values (given by len(df)).

To count NaN values in every column of df, use:

len(df) - df.count()

If you want to use value_counts, tell it not to drop NaN values by setting dropna=False (added in 0.14.1):

dfv = dfd['a'].value_counts(dropna=False)

This allows the missing values in the column to be counted too:

 3     3NaN    2 1     1Name: a, dtype: int64

The rest of your code should then work as you expect (note that it's not necessary to call sum; just print("nan: %d" % dfv[np.nan]) suffices).

A good clean way to count all NaN's in all columns of your dataframe would be ...

import pandas as pd import numpy as npdf = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})print(df.isna().sum().sum())

Using a single sum, you get the count of NaN's for each column. The second sum, sums those column sums.