Global EV adoption trends in EV Technology - Mini Project: Build & Apply

Global EV Adoption Trends

📖 Scenario: You are working with a global energy research team. They want to understand how electric vehicle (EV) adoption is growing worldwide. You will create a simple data structure to hold EV adoption numbers for different countries, set a threshold to identify leading countries, filter the data, and finalize the list of top adopters.

🎯 Goal: Build a data structure with EV adoption numbers by country, set a threshold for high adoption, filter countries exceeding that threshold, and finalize the list of top EV adopters.

📋 What You'll Learn

Create a dictionary named ev_adoption with exact country names and EV adoption numbers

Create a variable named high_adoption_threshold with the value 500000

Use a dictionary comprehension named top_ev_countries to select countries with EV adoption above the threshold

Add a final step to include a summary string variable summary describing the number of top EV adopter countries

💡 Why This Matters

🌍 Real World

Understanding EV adoption trends helps governments and companies plan infrastructure and policies to support electric vehicles.

💼 Career

Data analysts and energy researchers use similar data filtering and summarizing techniques to report on technology adoption and market trends.

1

Create EV adoption data

Create a dictionary called ev_adoption with these exact entries: 'China': 3500000, 'United States': 1200000, 'Germany': 700000, 'Norway': 450000, 'France': 300000.

EV Technology

# Create the ev_adoption dictionary with exact country and adoption numbers
# Your code here

Need a hint?

Use curly braces to create a dictionary. Each entry has a country name as a string key and an integer value for EV adoption.

2

Set high adoption threshold

Create a variable called high_adoption_threshold and set it to 500000.

EV Technology

ev_adoption = {
    'China': 3500000,
    'United States': 1200000,
    'Germany': 700000,
    'Norway': 450000,
    'France': 300000
}
# Create high_adoption_threshold variable
# Your code here

Need a hint?

Assign the number 500000 to the variable named high_adoption_threshold.

3

Filter top EV adopter countries

Use a dictionary comprehension named top_ev_countries to select countries from ev_adoption where the adoption number is greater than high_adoption_threshold.

EV Technology

ev_adoption = {
    'China': 3500000,
    'United States': 1200000,
    'Germany': 700000,
    'Norway': 450000,
    'France': 300000
}
high_adoption_threshold = 500000
# Create top_ev_countries dictionary comprehension
# Your code here

Need a hint?

Use a dictionary comprehension with for country, count in ev_adoption.items() and an if condition comparing count to high_adoption_threshold.

4

Add summary of top EV adopters

Create a string variable called summary that says: "Number of top EV adopter countries: X" where X is the number of countries in top_ev_countries.

EV Technology

ev_adoption = {
    'China': 3500000,
    'United States': 1200000,
    'Germany': 700000,
    'Norway': 450000,
    'France': 300000
}
high_adoption_threshold = 500000
top_ev_countries = {country: count for country, count in ev_adoption.items() if count > high_adoption_threshold}
# Create summary string with count of top_ev_countries
# Your code here

Need a hint?

Use an f-string to include the length of top_ev_countries inside the summary text.