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How to combine Groupby and Multiple Aggregate Functions in Pandas?

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Pandas is a Python package that offers various data structures and operations for manipulating numerical data and time series. It is mainly popular for importing and analyzing data much easier. It is an open-source library that is built on top of NumPy library.

Groupby()

Pandas dataframe.groupby() function is used to split the data in dataframe into groups based on a given condition. Example 1:
# import library
import pandas as pd
  
# import csv file
df = pd.read_csv("https://bit.ly/drinksbycountry")
  
df.head()

                    
Output: Example 2:
# Find the average of each continent
# by grouping the data  
# based on the "continent".
df.groupby(["continent"]).mean()

                    
Output:

Aggregate()

Pandas dataframe.agg() function is used to do one or more operations on data based on specified axis Example:
# here sum, minimum and maximum of column 
# beer_servings is calculatad
df.beer_servings.agg(["sum", "min", "max"])

                    
Output: Using These two functions together: We can find multiple aggregation functions of a particular column grouped by another column. Example:
# find an aggregation of column "beer_servings"
# by grouping the "continent" column.
df.groupby(df["continent"]).beer_servings.agg(["min",
                                               "max",
                                               "sum",
                                               "count",
                                               "mean"])

                    
Output:

Last Updated : 10 May, 2020
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