One-hot encoding in Data Analysis Python - Mini Project: Build & Apply

One-hot Encoding with Python

📖 Scenario: Imagine you work in a company that collects customer data. One of the columns in your data is favorite_fruit, which contains names of fruits customers like. You want to prepare this data for a machine learning model, which needs numbers instead of words.

🎯 Goal: You will create a simple one-hot encoding for the favorite_fruit column. This means you will turn each fruit name into a new column with 1 or 0, showing if the customer likes that fruit or not.

📋 What You'll Learn

Create a list called favorite_fruits with exact values: 'apple', 'banana', 'apple', 'orange', 'banana'

Create a list called unique_fruits that contains the unique fruits from favorite_fruits

Create a list of dictionaries called one_hot_encoded where each dictionary represents one customer's one-hot encoding

Print the one_hot_encoded list to show the final result

💡 Why This Matters

🌍 Real World

One-hot encoding is used to convert text categories into numbers so computers can understand and learn from data.

💼 Career

Data scientists and analysts use one-hot encoding to prepare data for machine learning models.

1

Create the favorite fruits list

Create a list called favorite_fruits with these exact values in order: 'apple', 'banana', 'apple', 'orange', 'banana'.

Data Analysis Python

# Create the favorite_fruits list with the exact fruits
# Your code here

Hint

Use square brackets [] to create a list and separate items with commas.

2

Find unique fruits

Create a list called unique_fruits that contains the unique fruits from the favorite_fruits list. Use the set() function and convert it back to a list.

Data Analysis Python

favorite_fruits = ['apple', 'banana', 'apple', 'orange', 'banana']
# Create unique_fruits list from favorite_fruits
# Your code here

Hint

Use set(favorite_fruits) to get unique items, then list() to convert back to a list.

3

Create one-hot encoding

Create a list called one_hot_encoded. Use a for loop with variable fruit to go through favorite_fruits. For each fruit, create a dictionary where keys are fruits from unique_fruits and values are 1 if the key matches fruit, else 0. Append each dictionary to one_hot_encoded.

Data Analysis Python

favorite_fruits = ['apple', 'banana', 'apple', 'orange', 'banana']
unique_fruits = list(set(favorite_fruits))
# Create one_hot_encoded list with one-hot dictionaries
# Your code here

Hint

Use a dictionary comprehension inside the loop to create the encoding for each fruit.

4

Print the one-hot encoded list

Print the one_hot_encoded list to display the final one-hot encoding result.

Data Analysis Python

favorite_fruits = ['apple', 'banana', 'apple', 'orange', 'banana']
unique_fruits = list(set(favorite_fruits))
one_hot_encoded = []
for fruit in favorite_fruits:
    encoding = {key: 1 if key == fruit else 0 for key in unique_fruits}
    one_hot_encoded.append(encoding)
# Print the one_hot_encoded list
# Your code here

Hint

Use print(one_hot_encoded) to show the final list.