You can use the following basic syntax to count the occurrences of True and False values in a column of a pandas DataFrame:

df['my_boolean_column'].value_counts()

This will count the occurrences of both True and False values.

If you only want to count one of the specific values, you can use the following syntax:

#count occurrences of True
df['my_boolean_column'].values.sum()

#count occurrences of False
(~df['my_boolean_column']).values.sum()

The following example shows how to use this syntax in practice.

Example: Count Occurrences of True and False in Pandas

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'C', 'C'],
                   'points': [18, 22, 19, 14, 14, 28, 20],
                   'all_star': [True, False, False, True, False, True, True]})

#view DataFrame
print(df)

  team  points  all_star
0    A      18      True
1    A      22     False
2    A      19     False
3    B      14      True
4    B      14     False
5    C      28      True
6    C      20      True

We can use the value_counts() function to count the occurrences of both True and False values in the all_star column:

#count occurrences of True and False in all_star column
df['all_star'].value_counts()

True     4
False    3
Name: all_star, dtype: int64

From the output we can see:

• The value True occurs 4 times in the all_star column.
• The value False occurs 3 times in the all_star column.

You can also use the following syntax to only count the occurrences of True:

#count occurrences of True in all_star column
df['all_star'].values.sum()

4

And you can use the following syntax to only count the occurrences of False:

#count occurrences of False in all_star column
(~df['all_star']).values.sum()

3

The following tutorials explain how to perform other common tasks in pandas: