map() for element-wise transformation in Pandas - Mini Project: Build & Apply

Using map() for Element-wise Transformation in pandas

📖 Scenario: You work in a small bookstore. You have a list of books with their genres. You want to create a new column that shows a short code for each genre to make your data easier to read.

🎯 Goal: Build a pandas DataFrame with book titles and genres, then use map() to create a new column with genre codes.

📋 What You'll Learn

Create a pandas DataFrame called books with columns 'Title' and 'Genre' using the exact data provided.

Create a dictionary called genre_codes that maps each genre to its short code.

Use the map() function on the 'Genre' column to create a new column called 'GenreCode'.

Print the books DataFrame to show the new column.

💡 Why This Matters

🌍 Real World

Mapping long or complex category names to short codes helps in reports, dashboards, and data storage.

💼 Career

Data analysts and scientists often use map() to clean and prepare data for analysis or visualization.

1

Create the books DataFrame

Create a pandas DataFrame called books with two columns: 'Title' and 'Genre'. Use these exact entries: 'The Hobbit' with 'Fantasy', '1984' with 'Dystopian', 'To Kill a Mockingbird' with 'Classic', 'The Great Gatsby' with 'Classic', and 'Brave New World' with 'Dystopian'.

Pandas

import pandas as pd
# Create the books DataFrame with the exact titles and genres
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Create the genre_codes dictionary

Create a dictionary called genre_codes that maps the genres to short codes exactly as follows: 'Fantasy' to 'FAN', 'Dystopian' to 'DYS', and 'Classic' to 'CLA'.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Hobbit', '1984', 'To Kill a Mockingbird', 'The Great Gatsby', 'Brave New World'],
    'Genre': ['Fantasy', 'Dystopian', 'Classic', 'Classic', 'Dystopian']
})
# Create the genre_codes dictionary mapping genres to codes
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with the exact key-value pairs.

3

Use map() to create the GenreCode column

Use the map() function on the 'Genre' column of the books DataFrame with the genre_codes dictionary to create a new column called 'GenreCode'.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Hobbit', '1984', 'To Kill a Mockingbird', 'The Great Gatsby', 'Brave New World'],
    'Genre': ['Fantasy', 'Dystopian', 'Classic', 'Classic', 'Dystopian']
})
genre_codes = {
    'Fantasy': 'FAN',
    'Dystopian': 'DYS',
    'Classic': 'CLA'
}
# Use map() on the 'Genre' column to create 'GenreCode'
# Your code here

Need a hint?

Use books['Genre'].map(genre_codes) to transform the genres into codes.

4

Print the books DataFrame

Print the books DataFrame to display the new 'GenreCode' column alongside the original data.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Hobbit', '1984', 'To Kill a Mockingbird', 'The Great Gatsby', 'Brave New World'],
    'Genre': ['Fantasy', 'Dystopian', 'Classic', 'Classic', 'Dystopian']
})
genre_codes = {
    'Fantasy': 'FAN',
    'Dystopian': 'DYS',
    'Classic': 'CLA'
}
books['GenreCode'] = books['Genre'].map(genre_codes)
# Print the books DataFrame
# Your code here

Need a hint?

Use print(books) to show the DataFrame with the new column.