Pandas: Grouping

Grouping

•  Grouping involves creating subsets of data based on one or more columns. This is useful when you want to analyze and compare data within specific categories.

•  There are three important activities that take place during grouping operation ‒

1) Splitting data into groups based on some criteria.

2) Applying a function to each group independently.

3) Combining the result into a data structure.

•  For example ‒ Consider following data set

Demo example

We will consider the data set for health expenses spent by each country in the year 1970,1971, 1972. Also this data set shows the life expectancy according to the country. The code to read this data set is as follows ‒

import pandas as pd

spendings =

pd.DataFrame({'Year: [1970, 1970, 1970, 1970, 1970, 1971, 1971, 1971, 1971, 1971,1972,1972, 1972],

 'Country': ['Germany', 'France', 'Great_Britain', 'Japan', 'USA','Canada',

'Germany', 'Great_Britain', 'Japan', 'USA', 'Germany', 'Japan', 'USA'],

'Spending USD': [252,192,123,150,326,313,298,134,163,357,337,185,397],

 'Life Expectancy': [70,72,71,72,71,73,70,72,73,71,71,74,71]},

columns = ['Year', 'Country','Spending_USD','Life Expectancy'])

spendings

Output