Exercise_with_solutions.ipynb

Kernel: Python 3 (ipykernel)

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

import pandas as pd

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called drinks.(Watch the values of Column continent NA (North America), and how Pandas interprets it!

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

Step 4. Which continent drinks more beer on average?

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

continent
AF     61.471698
AS     37.045455
EU    193.777778
NA    145.434783
OC     89.687500
SA    175.083333
Name: beer_servings, dtype: float64

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

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

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

drinks.groupby('continent').mean(numeric_only=True)

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

drinks.groupby('continent').median(numeric_only=True)

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

This time output a DataFrame

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