How to Count Missing Values in a Pandas DataFrame


Often you may be interested in counting the number of missing values in a pandas DataFrame.

This tutorial shows several examples of how to count missing values using the following DataFrame:

import pandas as pd
import numpy as np

#create DataFrame with some missing values
df = pd.DataFrame({'a': [4, np.nan, np.nan, 7, 8, 12],
                   'b': [np.nan, 6, 8, 14, 29, np.nan],
                   'c': [11, 8, 10, 6, 6, np.nan]})

#view DataFrame
print(df)

      a     b     c
0   4.0   NaN  11.0
1   NaN   6.0   8.0
2   NaN   8.0  10.0
3   7.0  14.0   6.0
4   8.0  29.0   6.0
5  12.0   NaN   NaN

Count the Total Missing Values in Entire DataFrame

The following code shows how to calculate the total number of missing values in the entire DataFrame:

df.isnull().sum().sum()

5

This tells us that there are 5 total missing values.

Count the Total Missing Values per Column

The following code shows how to calculate the total number of missing values in each column of the DataFrame:

df.isnull().sum()

a    2
b    2
c    1

This tells us:

  • Column ‘a’ has missing values.
  • Column ‘b’ has missing values.
  • Column ‘c’ has 1 missing value.

You can also display the number of missing values as a percentage of the entire column:

df.isnull().sum()/len(df)*100

a    33.333333
b    33.333333
c    16.666667

This tells us:

  • 33.33% of values in Column ‘a’ are missing.
  • 33.33% of values in Column ‘b’ are missing.
  • 16.67% of values in Column ‘c’ are missing.

Count the Total Missing Values per Row

The following code shows how to calculate the total number of missing values in each row of the DataFrame:

df.isnull().sum(axis=1)

0    1
1    1
2    1
3    0
4    0
5    2

This tells us:

  • Row 1 has 1 missing value.
  • Row 2 has 1 missing value.
  • Row 3 has 1 missing value.
  • Row 4 has 0 missing values.
  • Row 5 has 0 missing values.
  • Row 6 has 2 missing values.

Additional Resources

How to Find Unique Values in Multiple Columns in Pandas
How to Create a New Column Based on a Condition in Pandas

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