Home » Python » Python Programs

What does the group_keys argument to pandas.groupby actually do?

Learn, what does the group_keys argument to pandas.groupby actually do? By Pranit Sharma Last updated : October 06, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Usages of group_keys argument to pandas.groupby

In Pandas dataframe groupby() method, there is an argument group_keys which is supposed to do something related to how group keys are included in dataframe subsets. According to the documentation, group keys accept a Boolean type value and the default value is True. We are given a data frame and we need to understand how to use groupby and pass an argument group_keys so that we can understand what it actually do.

The groupby() is a simple but very useful concept in pandas. By using groupby, we can create a grouping of certain values and perform some operations on those values.

The groupby() method splits the object, applies some operations, and then combines them to create a group hence large amounts of data and computations can be performed on these groups.

Another important point about the group keys argument is that it can be used with the apply operations that create an additional index column corresponding to the group column.

Let us understand with the help of an example,

Python program to demonstrate the usages of group_keys argument to pandas.groupby

# Importing pandas package
import pandas as pd

# Importing numpy package
import numpy as np

# Creating DataFrames
df = pd.DataFrame([[0, 1, 3],[3, 1, 1],[3, 0, 0],[2, 3, 3],[2, 1, 0]], columns=list('ABC'))

# Display original DataFrame
print("Original DataFrame:\n",df,"\n")

# Using groupby and group_keys arguement
res = df.groupby('A',group_keys=True).apply(lambda row: row['A'])

# Display result
print("Result:\n",res)

Output

The output of the above program is:

Python Pandas Programs »