GroupBy pandas DataFrame and select most common value

Given a Pandas DataFrame, we have to make its column headers all lowercase.
Submitted by Pranit Sharma, on June 22, 2022

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 the data.

GroupBy pandas DataFrame and select most common value

To groupby and select the most common value of a column from a pandas DataFrame, we will use the groupby() method. 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 split the object, apply some operations, and then combines them to create a group hence a large amount of data and computations can be performed on these groups. After groupby(), we will use agg() method to select the most common value.



To work with pandas, we need to import pandas package first, below is the syntax:

import pandas as pd

Let us understand with the help of an example,

Python program to GroupBy pandas DataFrame and select most common value

# Importing pandas package
import pandas as pd

# Creating a dictionary
d = {
    'Nation' : ['India', 'India', 'SriLanka','Sri Lanka','South-Africa'],
        'Sport' : ['Cricket', 'Cricket','Cricket','Cricket','Cricket',],
        'Short name' : ['Ind','Ind','SL','SL','SA']

# Creating a DataFrame
df = pd.DataFrame(d)

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

# Selecting most common value
result = df.groupby(['Nation','Sport'])['Short name'].agg(pd.Series.mode).to_frame()

# Display result


The output of the above program is:

Example: GroupBy pandas DataFrame

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