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Pandas is a library in Python that provides a way to group data and select the top N rows within each group. This can be done by using the groupby() function, followed by the head() or tail() methods to select the top or bottom N rows. This is useful when you need to analyze the data for each group separately and quickly identify the top values within each group.
You can use the following basic syntax to get the top N rows by group in a pandas DataFrame:
df.groupby('group_column').head(2).reset_index(drop=True)
This particular syntax will return the top 2 rows by group.
Simply change the value inside the head() function to return a different number of top rows.
The following examples show how to use this syntax with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'], 'position': ['G', 'G', 'G', 'F', 'F', 'G', 'G', 'F', 'F', 'F'], 'points': [5, 7, 7, 9, 12, 9, 9, 4, 7, 7]}) #view DataFrame print(df) team position points 0 A G 5 1 A G 7 2 A G 7 3 A F 9 4 A F 12 5 B G 9 6 B G 9 7 B F 4 8 B F 7 9 B F 7
Example 1: Get Top N Rows Grouped by One Column
The following code shows how to return the top 2 rows, grouped by the team variable:
#get top 2 rows grouped by team
df.groupby('team').head(2).reset_index(drop=True)
team position points
0 A G 5
1 A G 7
2 B G 9
3 B G 9
The output displays the top 2 rows, grouped by the team variable.
Example 2: Get Top N Rows Grouped by Multiple Columns
The following code shows how to return the top 2 rows, grouped by the team and position variables:
#get top 2 rows grouped by team and position
df.groupby(['team', 'position']).head(2).reset_index(drop=True)
team position points
0 A G 5
1 A G 7
2 A F 9
3 A F 12
4 B G 9
5 B G 9
6 B F 4
7 B F 7 The output displays the top 2 rows, grouped by the team and position variables.