How to count nan values in a pandas DataFrame?

Question

What is the best way to account for (not a number) nan values in a pandas DataFrame?

The following code:

import numpy as np import pandas as pd dfd = pd.DataFrame([1, np.nan, 3, 3, 3, np.nan], columns=['a']) dfv = dfd.a.value_counts().sort_index() print("nan: %d" % dfv[np.nan].sum()) print("1: %d" % dfv[1].sum()) print("3: %d" % dfv[3].sum()) print("total: %d" % dfv[:].sum()) 

Outputs:

nan: 0 1: 1 3: 3 total: 4 

While the desired output is:

nan: 2 1: 1 3: 3 total: 6 

I am using pandas 0.17 with Python 3.5.0 with Anaconda 2.4.0.

Answer 1

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

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

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