Hey guys, in this blog we will see Python Programs to use groupby. We will see how to groupby using one or more columns.
So without any further due, let’s do it…
Syntax: DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)
Returns: GroupBy object
Reading the Dataframe
import pandas as pd # Read the dataframe df = pd.read_csv('nba.csv') # Let's see the dataframe df
- Here we are simply reading our ‘nba.csv’ dataset using pd.read_csv() method.
- Download this file from here.
Example 1: Groupby one column
# applying groupby() function to group the data on team value. team = df.groupby('Team') # Let's print the first entries in all the groups formed. team.first()
- In this step we are using dataframe.groupby(column) to group our dataframe.
- And then we are using group.first() to print the first entries of all the groups which are formed from our dataframe.
Extract a full group
# Finding the values contained in the "Brooklyn Nets" group team.get_group('Brooklyn Nets')
- We can also use group.get_group(group_name) to get the whole group.
Example 2: Groupby multiple columns
# importing pandas as pd import pandas as pd # Creating the dataframe df = pd.read_csv("nba.csv") # First grouping based on "Team" # Within each team we are grouping based on "Position" team_pos = df.groupby(['Team', 'Position']) # Print the first value in each group team_pos.first()
- We can also create groups using multiple columns.
- Here in this example, we used 2 columns [‘Team’, ‘Position’] to create multilevel index-type groups.
Check out our other python programming examples…