python - 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.