Series vs DataFrame relationship in Pandas - Hands-On Comparison

Understanding Series vs DataFrame Relationship

📖 Scenario: Imagine you work in a small bookstore. You have data about book sales and want to organize it to understand sales better.

🎯 Goal: You will create a pandas Series and a pandas DataFrame to see how they relate. You will extract a Series from a DataFrame and print both to compare.

📋 What You'll Learn

Create a pandas Series with book titles as index and number of copies sold as values.

Create a pandas DataFrame with book titles, copies sold, and price per book.

Extract the 'copies sold' column from the DataFrame as a Series.

Print the Series and DataFrame to observe their relationship.

💡 Why This Matters

🌍 Real World

Organizing and analyzing sales data helps bookstores track performance and make decisions.

💼 Career

Data scientists often work with Series and DataFrames to clean, analyze, and visualize data efficiently.

1

Create a pandas Series for book sales

Import pandas as pd. Create a pandas Series called book_sales with these entries: 'Book A': 120, 'Book B': 85, 'Book C': 60. Use the book titles as the index.

Pandas

# Your code here

Need a hint?

Use pd.Series() with a dictionary where keys are book titles and values are copies sold.

2

Create a pandas DataFrame with book details

Create a pandas DataFrame called book_data with columns 'Title', 'Copies Sold', and 'Price'. Use these rows: ['Book A', 120, 15.99], ['Book B', 85, 12.50], ['Book C', 60, 9.99].

Pandas

import pandas as pd

book_sales = pd.Series({'Book A': 120, 'Book B': 85, 'Book C': 60})
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary of lists for each column.

3

Extract the 'Copies Sold' column as a Series

Create a variable called copies_series by extracting the 'Copies Sold' column from the book_data DataFrame.

Pandas

import pandas as pd

book_sales = pd.Series({'Book A': 120, 'Book B': 85, 'Book C': 60})

book_data = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Copies Sold': [120, 85, 60],
    'Price': [15.99, 12.50, 9.99]
})
# Your code here

Need a hint?

Use bracket notation book_data['Copies Sold'] to get the column as a Series.

4

Print the Series and DataFrame

Print the book_sales Series, the book_data DataFrame, and the copies_series Series to see their contents.

Pandas

import pandas as pd

book_sales = pd.Series({'Book A': 120, 'Book B': 85, 'Book C': 60})

book_data = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Copies Sold': [120, 85, 60],
    'Price': [15.99, 12.50, 9.99]
})

copies_series = book_data['Copies Sold']
# Your code here

Need a hint?

Use print() for each variable to see their data.