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Combining Multiple Columns in Pandas groupby with Dictionary
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Grouping Rows in pandas

Last Updated : 14 Jan, 2019
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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())
Output:

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Combining Multiple Columns in Pandas groupby with Dictionary

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