Skip to content
0

How Much of the World Has Access to the Internet?

# Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#Defining the size of the figures
plt.rcParams["figure.figsize"] = (11,4)

# Read the data
broadband = pd.read_csv('data/broadband.csv')
people = pd.read_csv('data/people.csv')
internet = pd.read_csv('data/internet.csv')
broadband.head()
people.head()
internet.head()

How Much of the World Has Access to the Internet?

Now let's now move on to the competition and challenge.

📖 Background

You work for a policy consulting firm. One of the firm's principals is preparing to give a presentation on the state of internet access in the world. She needs your help answering some questions about internet accessibility across the world.

💾 The data

The research team compiled the following tables (source):
internet
  • "Entity" - The name of the country, region, or group.
  • "Code" - Unique id for the country (null for other entities).
  • "Year" - Year from 1990 to 2019.
  • "Internet_usage" - The share of the entity's population who have used the internet in the last three months.
people
  • "Entity" - The name of the country, region, or group.
  • "Code" - Unique id for the country (null for other entities).
  • "Year" - Year from 1990 to 2020.
  • "Users" - The number of people who have used the internet in the last three months for that country, region, or group.
broadband
  • "Entity" - The name of the country, region, or group.
  • "Code" - Unique id for the country (null for other entities).
  • "Year" - Year from 1998 to 2020.
  • "Broadband_Subscriptions" - The number of fixed subscriptions to high-speed internet at downstream speeds >= 256 kbit/s for that country, region, or group.

Acknowledgments: Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina (2015) - "Internet." OurWorldInData.org.

len(broadband)

💪 Challenge

Create a report to answer the principal's questions. Include:

  1. What are the top 5 countries with the highest internet use (by population share)?
  2. How many people had internet access in those countries in 2019?
  3. What are the top 5 countries with the highest internet use for each of the following regions: 'Africa Eastern and Southern', 'Africa Western and Central', 'Latin America & Caribbean', 'East Asia & Pacific', 'South Asia', 'North America', 'European Union'?
  4. Create a visualization for those five regions' internet usage over time.
  5. What are the 5 countries with the most internet users?
  6. What is the correlation between internet usage (population share) and broadband subscriptions for 2019?
  7. Summarize your findings.
  1. What are the top 5 countries with the highest internet use (by population share)?
#Filtering only by country (with Code) and Year is 2019
val = (~internet['Code'].isin([np.nan,'OWID_WRL']))&(internet['Year']==2019)
users_by_country_top_5 = internet[val].sort_values('Internet_Usage', ascending=False)[:5]
users_by_country_top_5.set_index('Entity',drop=True, inplace=True)
users_by_country_top_5
#Filtering only by country (with Code) and Year is 2019
users_by_country_top_5['Internet_Usage'].plot(kind='bar')
#Defining the title
plt.title('Top 5 countries with the highest internet use')
#Defining the x and y labels
plt.xlabel('Country')
plt.ylabel('Internet_Usage (%)')
#Showing the graph
plt.show()
  1. How many people had internet access in those countries in 2019?
#Filtering only by country (with Code) and year is 2019
val = (~people['Code'].isin([np.nan,'OWID_WRL']))&(people['Year']==2019)&(people['Entity'].isin(users_by_country_top_5.index.tolist()))
users_by_country_top_5 = people[val].sort_values('Users', ascending=False)[:5]
users_by_country_top_5.set_index('Entity',drop=True, inplace=True)
users_by_country_top_5
#Filtering only by country (with Code) and year is 2019
users_by_country_top_5['Users'].plot(kind='bar')
#Defining the title
plt.title('People with internet access in Top 5-countries with the highest internet use')
#Defining the x and y labels
plt.xlabel('Country')
plt.ylabel('Number of users')
#Showing the graph
plt.show()
‌
‌
‌