You can use the following methods to calculate a cumulative count in a pandas DataFrame:

Method 1: Cumulative Count by Group

df['cum_count'] = df.groupby('col1').cumcount()

Method 2: Cumulative Count by Multiple Groups

df['cum_count'] = df.groupby(['col1', 'col2']).cumcount() 

The following examples shows how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'position': ['G', 'G', 'G', 'F', 'G','G', 'F', 'F'],
                   'points': [14, 22, 25, 34, 30, 12, 10, 18]})

#view DataFrame
print(df)

  team position  points
0    A        G      14
1    A        G      22
2    A        G      25
3    A        F      34
4    B        G      30
5    B        G      12
6    B        F      10
7    B        F      18

Example 1: Cumulative Count by Group in Pandas

We can use the following syntax to create a new column called team_cum_count that displays the cumulative count for each team in the DataFrame:

#calculate cumulative count by team
df['team_cum_count'] = df.groupby('team').cumcount()

#view updated DataFrame
print(df)

  team position  points  team_cum_count
0    A        G      14               0
1    A        G      22               1
2    A        G      25               2
3    A        F      34               3
4    B        G      30               0
5    B        G      12               1
6    B        F      10               2
7    B        F      18               3

The new column called team_cum_count contains the cumulative count of each team, starting with a value of zero.

If you’d like the count to start at one instead, simply add one to the end of the line:

#calculate cumulative count (starting at 1) by team
df['team_cum_count'] = df.groupby('team').cumcount() + 1

#view updated DataFrame
print(df)

  team position  points  team_cum_count
0    A        G      14               1
1    A        G      22               2
2    A        G      25               3
3    A        F      34               4
4    B        G      30               1
5    B        G      12               2
6    B        F      10               3
7    B        F      18               4

The new column called team_cum_count contains the cumulative count of each team, starting with a value of one.

Example 2: Calculate Cumulative Count by Group in Pandas

We can use the following syntax to create a new column called team_pos_cum_count that displays the cumulative count for each team and position in the DataFrame:

#calculate cumulative count by team
df['team_pos_cum_count'] = df.groupby(['team', 'position']).cumcount() 

#view updated DataFrame
print(df)

  team position  points  team_pos_cum_count
0    A        G      14                   0
1    A        G      22                   1
2    A        G      25                   2
3    A        F      34                   0
4    B        G      30                   0
5    B        G      12                   1
6    B        F      10                   0
7    B        F      18                   1

Note: You can find the complete documentation for the cumcount function in pandas .

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