Grouping Rows in pandas

Last Updated : 14 Jan, 2019

Pandas is the most popular Python library that is used for data analysis. It provides highly optimized performance with back-end source code is purely written in C or Python. Let's see how to group rows in Pandas Dataframe with help of multiple examples. Example 1: For grouping rows in Pandas, we will start with creating a pandas dataframe first.

Python3

# importing Pandas
import pandas as pd

# example dataframe
example = {'Team':['Arsenal', 'Manchester United', 'Arsenal',
                   'Arsenal', 'Chelsea', 'Manchester United',
                   'Manchester United', 'Chelsea', 'Chelsea', 'Chelsea'],
                   
           'Player':['Ozil', 'Pogba', 'Lucas', 'Aubameyang',
                       'Hazard', 'Mata', 'Lukaku', 'Morata', 
                                         'Giroud', 'Kante'],
                                         
           'Goals':[5, 3, 6, 4, 9, 2, 0, 5, 2, 3] }

df = pd.DataFrame(example)

print(df)

Now, create a grouping object, means an object that represents that particular grouping.

Python3

total_goals = df['Goals'].groupby(df['Team'])

# printing the means value
print(total_goals.mean())

Output:

Example 2:

Python3 1==

import pandas as pd

# example dataframe
example = {'Team':['Australia', 'England', 'South Africa',
                   'Australia', 'England', 'India', 'India',
                        'South Africa', 'England', 'India'],
                        
           'Player':['Ricky Ponting', 'Joe Root', 'Hashim Amla',
                     'David Warner', 'Jos Buttler', 'Virat Kohli',
                     'Rohit Sharma', 'David Miller', 'Eoin Morgan',
                                                 'Dinesh Karthik'],
                                                 
          'Runs':[345, 336, 689, 490, 989, 672, 560, 455, 342, 376],
          
          'Salary':[34500, 33600, 68900, 49000, 98899,
                    67562, 56760, 45675, 34542, 31176] }

df = pd.DataFrame(example)

total_salary = df['Salary'].groupby(df['Team'])

# printing the means value
print(total_salary.mean())