How to count the NaN values in a column in a Python Pandas DataFrame?

To count the NaN values in a column in a Pandas DataFrame, we can use the isna() method with sum.

Steps

• Create a series, s, one-dimensional ndarray with axis labels (including time series).

• Print the series, s.

• Count the number of NaN present in the series.

• Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.

• Print the input DataFrame.

• Find NaN count column wise.

• Print the count DataFrame.

Example

 Live Demo

import pandas as pd import numpy as np s = pd.Series([1, np.nan, 3, np.nan, 3, np.nan, 7, np.nan, 3]) print "Input series is:
", s count = s.isna().sum() print "NAN count in series: ", count df = pd.DataFrame(    {       "x": [5, np.nan, 1, np.nan],       "y": [np.nan, 1, np.nan, 10],       "z": [np.nan, 1, np.nan, np.nan]    } ) print "
Input DataFrame is:
", df count = df.isna().sum() print "
NAN count in DataFrame:
", count

Output

Input series is: 0  1.0 1  NaN 2  3.0 3  NaN 4  3.0 5  NaN 6  7.0 7  NaN 8  3.0 dtype: float64 NAN count in series: 4 Input DataFrame is:     x    y    z 0  5.0  NaN  NaN 1  NaN  1.0  1.0 2  1.0  NaN  NaN 3  NaN  10.0 NaN NAN count in DataFrame: x  2 y  2 z  3 dtype: int64