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.
# importing Pandasimport pandas as pd # example dataframeexample = {'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.
total_goals = df['Goals'].groupby(df['Team']) # printing the means valueprint(total_goals.mean()) |
Output:
Example 2:
import pandas as pd # example dataframeexample = {'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 valueprint(total_salary.mean()) |
Output:
