You can use the following basic syntax to get the first row of each group in a pandas DataFrame:

df.groupby('column_name').nth(0)

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

Example: Get First Row of Each Group in Pandas

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],
                   'points': [18, 22, 19, 14, 14, 11, 20, 29],
                   'assists': [5, 19, 14, 8, 9, 12, 13, 8]})

#view DataFrame
df

	team	points	assists
0	A	18	5
1	A	22	19
2	B	19	14
3	B	14	8
4	B	14	9
5	C	11	12
6	C	20	13
7	C	29	8

We can use the following code to get the first row for each team:

#get first row for each team
df.groupby('team').nth(0)

	points	assists
team		
A	18	5
B	19	14
C	11	12

We can also specify as_index=False to keep the original index values:

#get first row for each team, keep original index values
df.groupby('team', as_index=False).nth(0)
team	points	assists
0	A	18	5
2	B	19	14
5	C	11	12

Also note that you can pass a list of values to the nth() function if you’d like to get the first n rows for each group.

For example, the following code shows how to get the first two rows for each group:

#get first two rows for each team, keep original index values
df.groupby('team', as_index=False).nth((0, 1))
        team	points	assists
0	A	18	5
1	A	22	19
2	B	19	14
3	B	14	8
5	C	11	12
6	C	20	13

Note: You can find the complete documentation for the nth() function .

Additional Resources

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