Internet: A Global Phenomenon
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    Internet: A Global Phenomenon

    This dataset contains information on internet access around the world.

    The workspace is set up with two CSV files containing information on global internet access for years ranging from 1990 to 2020.

    • internet_users.csv
      • users - The number of people who have used the internet in the last three months
      • share - The share of the entity's population who have used the internet in the last three months
    • adoption.csv
      • fixed_telephone_subs - The number of people who have a telephone landline connection
      • fixed_telephone_subs_share - The share of the entity's population who have a telephone landline connection
      • fixed_broadband_subs - The number of people who have a broadband internet landline connection
      • fixed_broadband_subs_share - The share of the entity's population who have a broadband internet landline connection
      • mobile_cell_subs - The number of people who have a mobile subscription
      • mobile_cell_subs_share - The share of the entity's population who have a mobile subscription

    Both data files are indexed on the following 3 attributes:

    • 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.

    Check out the guiding questions or the scenario described below to get started with this dataset! Feel free to make this workspace yours by adding and removing cells, or editing any of the existing cells.

    Source: Our World In Data

    🌎 Some guiding questions to help you explore this data:

    1. What are the top 5 countries with the highest internet use (by population share)?
    2. What are the top 5 countries with the highest internet use for some large regions?
    3. What are the top 5 countries with the highest internet use for some large regions?

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

    📊 Visualization ideas

    • Line chart: Display internet usage over time of the top 5 countries.
    • What are the top 5 countries with the highest internet use for some large regions??. Leveraging, for example, GeoPandas or Folium.

    🔍 Scenario: Identify emerging markets for a global internet provider

    This scenario helps you develop an end-to-end project for your portfolio.

    Background: You work for a global internet provider on a mission to provide affordable Internet access to everybody around the world using satellites. You are tasked with identifying which markets (regions or countries) are most worthwhile to focus efforts on.

    Objective: Construct a top 5 list of countries where there is a big opportunity to roll out our services. Try to consider the amount of people not having access to (good) wired or mobile internet and their spending power.

    You can query the pre-loaded CSV files using SQL directly. Here’s a sample query:

    Unknown integration
    DataFrameavailable as
    df
    variable
    SELECT *
FROM 'internet_users.csv'
LIMIT 10
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from plotly import express
from datetime import datetime

internet_users = pd.read_csv('internet_users.csv')
internet_users.head()
    adoption = pd.read_csv('adoption.csv')
adoption.head()

    First we explor our data before Analyize.

    To explor our data we need to know:

    1. Numbers of rows and columns
    2. Data taype
    3. Statistic summary
    4. Missing values
    # Numbers of rows
print("Numbers of rows", internet_users.shape)
    # Types of data
print("Data Types\n", internet_users.dtypes )
    print("Statistic summary\n", internet_users.describe())
    # Missing values
print('Missing Values\n', internet_users.isna().sum())

    After we exaplor our data. The secont step we have to process the data by:

    1. Clean the missing
    2. Converting the data that needs to be converted
    # Count the numbers of missing values in each columns
internet_users.isna().sum()
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