Competition - XP Competition 2022
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    How Much of the World Has Access to the Internet?

    selection = (broadband['Entity'] == 'European Union') & (broadband['Year'] == 2018)
broadband[selection]

    You can also use other visualization libraries like Matplotlib or Seaborn by running the cell below to import them into this workspace.

    import matplotlib.pyplot as plt
import seaborn as sns
    plt.plot()

    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.

    # Read the internet table
internet = pd.read_csv('data/internet.csv')

# Take a look at the first rows
internet
    # Read the people table
people = pd.read_csv('data/people.csv')
people

    💪 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: 'Middle East & North Africa', 'Latin America & Caribbean', 'East Asia & Pacific', 'South Asia', 'North America', 'Europe & Central Asia'?
    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.

    Note: This is how the World Bank defines the different regions.

    🧑‍⚖️ Judging criteria

    CATEGORYWEIGHTINGDETAILS
    Response quality85%
    • Accuracy (30%) - The response must be representative of the original data and free from errors.
    • Clarity (25%) - The response must be easy to understand and clearly expressed.
    • Completeness (30%) - The response must be a full report that responds to the question posed.
    Presentation15%
    • How legible/understandable the response is.
    • How well-formatted the response is.
    • Spelling and grammar.

    In the event of a tie, earlier submission time will be used as a tie-breaker.

    📘 Rules

    To be eligible to win, you must:

    • Submit your response to this problem before the deadline.

    All responses must be submitted in English.

    Entrants must be:

    • 18+ years old.
    • Allowed to take part in a skill-based competition from their country.

    Entrants can not:

    • Be in a country currently sanctioned by the U.S. government.

    XP will be awarded at the end of the competition. Therefore competition XP will not count towards any daily prizes.

    ✅ Checklist before submitting your workspace

    • Rename your workspace to make it descriptive of your work. N.B., you should leave the notebook name as notebook.ipynb.
    • Remove redundant cells like the introduction to data science notebooks, so the workbook is focused on your story.
    • Check that all the cells run without error.