Pandas: Groups

Groups

•  We can group the data items in the data set and get locations of these data items in the data set by using the groups function. Following code returns the indices of each country in the data set.

import pandas as pd

spending =

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']) print(spendings.groupby(['Country']).groups)

Output

{'Canada': [5], 'France': [1], 'Germany': [0, 6, 10], 'Great_Britain': [2, 7], 'Japan': [3, 8, 11], 'USA': [4, 9, 12]}

Iterating through each group

• We can iterate through each group and display the record. Following code illustrates it

import pandas as pd

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'])

mygroup = spendings.groupby(['Country'])

for name,group in mygroup:

print(name)

print(group)

Output

Getting particular group

• Using get_group function we can select particular group from the data set and display the information of that group. Following code illustrates it

import pandas as pd

spending =

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'])

mygroup = spendings.groupby(['Country'])

print(mygroup.get_group('Germany'))

Output

Review Question

1. In Pandas, explain the functions that are used for making groups