Why combining DataFrames matters in Pandas - See It in Action

Why combining DataFrames matters

📖 Scenario: Imagine you work in a small bookstore. You have two lists of books: one list shows books currently in stock, and another list shows books that were sold last month. You want to combine these lists to understand your inventory better.

🎯 Goal: You will create two small tables (DataFrames) for books in stock and books sold, then combine them to see all books together. This helps you learn why combining data is important in real life.

📋 What You'll Learn

Create two DataFrames named stock and sold with exact book data

Create a variable combined_books that combines stock and sold vertically

Print the combined_books DataFrame to see the result

💡 Why This Matters

🌍 Real World

Combining data from different sources is common in business to get a full picture, like merging sales and inventory data.

💼 Career

Data scientists often combine multiple datasets to prepare data for analysis and decision-making.

1

Create the initial DataFrames

Create a DataFrame called stock with these exact entries: columns 'Book' and 'Quantity', and rows 'Python Basics', 10 and 'Data Science 101', 5. Also create a DataFrame called sold with columns 'Book' and 'Quantity', and rows 'Python Basics', 3 and 'Machine Learning', 7. Use pandas and the exact variable names.

Pandas

import pandas as pd
# Create the stock DataFrame and the sold DataFrame with the exact data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for columns and lists for rows.

2

Set up the combination method

Create a variable called combine_method and set it to the string 'vertical'. This will help us remember how we want to combine the DataFrames.

Pandas

import pandas as pd

stock = pd.DataFrame({
    'Book': ['Python Basics', 'Data Science 101'],
    'Quantity': [10, 5]
})

sold = pd.DataFrame({
    'Book': ['Python Basics', 'Machine Learning'],
    'Quantity': [3, 7]
})

# Create the combine_method variable and set it to 'vertical'
# Your code here

Need a hint?

Just create a variable with the exact name and value.

3

Combine the DataFrames vertically

Use pd.concat to combine the stock and sold DataFrames vertically (one below the other). Store the result in a variable called combined_books. Use the exact variable names.

Pandas

import pandas as pd

stock = pd.DataFrame({
    'Book': ['Python Basics', 'Data Science 101'],
    'Quantity': [10, 5]
})

sold = pd.DataFrame({
    'Book': ['Python Basics', 'Machine Learning'],
    'Quantity': [3, 7]
})

combine_method = 'vertical'

# Combine stock and sold DataFrames vertically using pd.concat
# Your code here

Need a hint?

Use pd.concat with a list of DataFrames and ignore_index=True to reset the row numbers.

4

Print the combined DataFrame

Print the combined_books DataFrame to see all books from stock and sold combined.

Pandas

import pandas as pd

stock = pd.DataFrame({
    'Book': ['Python Basics', 'Data Science 101'],
    'Quantity': [10, 5]
})

sold = pd.DataFrame({
    'Book': ['Python Basics', 'Machine Learning'],
    'Quantity': [3, 7]
})

combine_method = 'vertical'

combined_books = pd.concat([stock, sold], ignore_index=True)

# Print the combined_books DataFrame
# Your code here

Need a hint?

Use print(combined_books) to show the combined table.