Why policy drives EV adoption in EV Technology - See It in Action

Why Policy Drives EV Adoption

📖 Scenario: You are working with a team that wants to understand how government policies influence the adoption of electric vehicles (EVs) in different countries. You will build a simple data structure to represent EV adoption rates and related policies, then analyze how policies affect adoption.

🎯 Goal: Build a small data set of countries with their EV adoption rates and policies, add a threshold for high adoption, filter countries with strong policies and high adoption, and finally mark those countries as policy-driven EV adopters.

📋 What You'll Learn

Create a dictionary with countries as keys and their EV adoption rates as values

Create a dictionary with countries as keys and their policy strength as values

Add a threshold variable for high EV adoption rate

Filter countries with policy strength 'strong' and adoption rate above threshold

Create a final dictionary marking these countries as 'policy-driven EV adopters'

💡 Why This Matters

🌍 Real World

Understanding how policies impact EV adoption helps governments and companies plan better strategies to promote clean transportation.

💼 Career

Data analysts, policy advisors, and environmental researchers use such data to make informed decisions and recommendations.

1

Create EV adoption data

Create a dictionary called ev_adoption with these exact entries: 'Norway': 75, 'Germany': 25, 'USA': 15, 'China': 20, 'India': 5. These numbers represent the percentage of new car sales that are electric vehicles.

EV Technology

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

Need a hint?

Use curly braces to create a dictionary with country names as keys and numbers as values.

2

Add policy strength data

Create a dictionary called policy_strength with these exact entries: 'Norway': 'strong', 'Germany': 'moderate', 'USA': 'weak', 'China': 'strong', 'India': 'weak'. These describe how strong the government policies are to support EV adoption.

EV Technology

ev_adoption = {'Norway': 75, 'Germany': 25, 'USA': 15, 'China': 20, 'India': 5}
# Create the policy_strength dictionary with exact country and policy strength entries
# Your code here

Need a hint?

Use a dictionary with the same countries as keys and policy strength as string values.

3

Set high adoption threshold and filter countries

Create a variable called high_adoption_threshold and set it to 20. Then create a list called strong_policy_high_adoption that contains countries where policy_strength is 'strong' and ev_adoption is greater than high_adoption_threshold. Use a list comprehension with for country in ev_adoption.

EV Technology

ev_adoption = {'Norway': 75, 'Germany': 25, 'USA': 15, 'China': 20, 'India': 5}
policy_strength = {'Norway': 'strong', 'Germany': 'moderate', 'USA': 'weak', 'China': 'strong', 'India': 'weak'}
# Set high_adoption_threshold to 20
# Create strong_policy_high_adoption list with countries having 'strong' policy and adoption > threshold
# Your code here

Need a hint?

Use a list comprehension with an if condition checking both policy strength and adoption rate.

4

Mark policy-driven EV adopters

Create a dictionary called policy_driven_adopters where keys are countries from strong_policy_high_adoption and values are the string 'Yes'. Use a dictionary comprehension with for country in strong_policy_high_adoption.

EV Technology

ev_adoption = {'Norway': 75, 'Germany': 25, 'USA': 15, 'China': 20, 'India': 5}
policy_strength = {'Norway': 'strong', 'Germany': 'moderate', 'USA': 'weak', 'China': 'strong', 'India': 'weak'}
high_adoption_threshold = 20
strong_policy_high_adoption = [country for country in ev_adoption if policy_strength[country] == 'strong' and ev_adoption[country] > high_adoption_threshold]
# Create policy_driven_adopters dictionary marking these countries with 'Yes'
# Your code here

Need a hint?

Use a dictionary comprehension to assign 'Yes' to each country in the filtered list.