Ex - GroupBy

Check out Alcohol Consumption Exercises Video Tutorial to watch a data scientist go through the exercises

Introduction:

GroupBy can be summarized as Split-Apply-Combine.

Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.

Check out this Diagram

Step 1. Import the necessary libraries

In [1]:

import pandas as pd

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called drinks.

In [4]:

drinks = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv')
drinks.head()

Out[4]:

Step 4. Which continent drinks more beer on average?

In [6]:

drinks.groupby('continent').beer_servings.mean()

Out[6]:

continent
AF     61.471698
AS     37.045455
EU    193.777778
OC     89.687500
SA    175.083333
Name: beer_servings, dtype: float64

Step 5. For each continent print the statistics for wine consumption.

In [9]:

drinks.groupby('continent').wine_servings.describe()

Out[9]:

continent       
AF         count     53.000000
           mean      16.264151
           std       38.846419
           min        0.000000
           25%        1.000000
           50%        2.000000
           75%       13.000000
           max      233.000000
AS         count     44.000000
           mean       9.068182
           std       21.667034
           min        0.000000
           25%        0.000000
           50%        1.000000
           75%        8.000000
           max      123.000000
EU         count     45.000000
           mean     142.222222
           std       97.421738
           min        0.000000
           25%       59.000000
           50%      128.000000
           75%      195.000000
           max      370.000000
OC         count     16.000000
           mean      35.625000
           std       64.555790
           min        0.000000
           25%        1.000000
           50%        8.500000
           75%       23.250000
           max      212.000000
SA         count     12.000000
           mean      62.416667
           std       88.620189
           min        1.000000
           25%        3.000000
           50%       12.000000
           75%       98.500000
           max      221.000000
dtype: float64

Step 6. Print the mean alcohol consumption per continent for every column

In [10]:

drinks.groupby('continent').mean()

Out[10]:

Step 7. Print the median alcohol consumption per continent for every column

In [14]:

drinks.groupby('continent').median()

Out[14]:

Step 8. Print the mean, min and max values for spirit consumption.

This time output a DataFrame

In [15]:

drinks.groupby('continent').spirit_servings.agg(['mean', 'min', 'max'])

Out[15]: