In this note, I use df for DataFrame, s for Series.

Libraries

import pandas as pd # import pandas package
import numpy as np

Dataframe

dataquest_aio = '<https://raw.githubusercontent.com/dinhanhthi/dataquest-aio/master/step-2-data-analysis-and-visualization/>'
dataset_url = dataquest_aio + 'course-4-data-cleaning-and-analysis/data/World_Happiness_2015.csv'
df = pd.read_csv(dataset_url) # read the data set
df.head()
Country Region Happiness Rank Happiness Score Standard Error
0 Switzerland Western Europe 1 7.587 0.03411
1 Iceland Western Europe 2 7.561 0.04884
2 Denmark Western Europe 3 7.527 0.03328
3 Norway Western Europe 4 7.522 0.03880
4 Canada North America 5 7.427 0.03553

Group dataset using groupby()

Group df by column Region and then selct the column Western Europe,

df.groupby('Region').get_group('Western Europe') # returns a df
Country Region Happiness Rank Happiness Score Standard Error
0 Switzerland Western Europe 1 7.587 0.03411
1 Iceland Western Europe 2 7.561 0.04884
2 Denmark Western Europe 3 7.527 0.03328
3 Norway Western Europe 4 7.522 0.03880
5 Finland Western Europe 6 7.406 0.03140

Select just the Happiness Score column and then find the mean,

df.groupby('Region')['Happiness Score'].mean()
# other methods: size, max, min, count

Region
Australia and New Zealand          7.285000
Central and Eastern Europe         5.332931
Eastern Asia                       5.626167
Latin America and Caribbean        6.144682
Middle East and Northern Africa    5.406900
North America                      7.273000
Southeastern Asia                  5.317444
Southern Asia                      4.580857
Sub-Saharan Africa                 4.202800
Western Europe                     6.689619
Name: Happiness Score, dtype: float64

Apply multiple/custom functions,

def max_min(group):
  return group.max() - group.min()

df.groupby(['Country', 'Region']).agg([np.mean, np.max, max_min]).head()
Happiness Rank Happiness Score
mean amax max_min mean amax max_min
Country Region
Afghanistan Southern Asia 153 153 0 3.575 3.575 0.0
Albania Central Europe 95 95 0 4.959 4.959 0.0
Algeria Middle Africa 68 68 0 5.605 5.605 0.0

If you wanna apply different functions on different columns,

df.groupby(['Country', 'Region']).agg({
    'Happiness Rank': max_min,
    'Happiness Score': ['min', 'max'],
    'Standard Error': 'count'
}).head(3)
Happiness Rank Happiness Score
max_min min max
Country Region
Afghanistan Southern Asia 0 3.575 3.575
Albania Central Europe 0 4.959 4.959
Algeria Middle Africa 0 5.605 5.605

Or using apply and lambda function,