Count NaN Values in a Column in 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