Merging on different column names in Pandas - Mini Project: Build & Apply

Merging DataFrames on Different Column Names

📖 Scenario: You work in a small store. You have two lists of information. One list has product IDs and names. The other list has product codes and prices. You want to combine these lists to see the product name with its price.

🎯 Goal: Build a program that merges two pandas DataFrames using columns with different names to get a combined table showing product names and prices.

📋 What You'll Learn

Create two pandas DataFrames with given data

Use a merge operation on columns with different names

Display the merged DataFrame with product names and prices

💡 Why This Matters

🌍 Real World

Stores and businesses often have data in separate lists or tables with different column names. Merging on different column names helps combine this data for better analysis.

💼 Career

Data analysts and scientists frequently merge datasets from different sources. Knowing how to merge on different column names is a key skill for data cleaning and preparation.

1

Create the first DataFrame

Create a pandas DataFrame called df_products with these exact columns and values:
product_id: [101, 102, 103]
product_name: ['Pen', 'Notebook', 'Eraser']

Pandas

import pandas as pd
# Create the DataFrame df_products with product_id and product_name columns
# 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 second DataFrame

Create a pandas DataFrame called df_prices with these exact columns and values:
code: [101, 102, 103]
price: [1.5, 2.0, 0.5]

Pandas

import pandas as pd

df_products = pd.DataFrame({
    'product_id': [101, 102, 103],
    'product_name': ['Pen', 'Notebook', 'Eraser']
})
# Create the DataFrame df_prices with code and price columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for the second table, matching the exact column names and values.

3

Merge the DataFrames on different column names

Use pd.merge to merge df_products and df_prices where df_products.product_id matches df_prices.code. Save the result in a variable called df_merged.

Pandas

import pandas as pd

df_products = pd.DataFrame({
    'product_id': [101, 102, 103],
    'product_name': ['Pen', 'Notebook', 'Eraser']
})

df_prices = pd.DataFrame({
    'code': [101, 102, 103],
    'price': [1.5, 2.0, 0.5]
})
# Merge df_products and df_prices where product_id matches code
# Your code here

Need a hint?

Use pd.merge with left_on and right_on parameters to join on columns with different names.

4

Display the merged DataFrame

Print the df_merged DataFrame to show the combined product names and prices.

Pandas

import pandas as pd

df_products = pd.DataFrame({
    'product_id': [101, 102, 103],
    'product_name': ['Pen', 'Notebook', 'Eraser']
})

df_prices = pd.DataFrame({
    'code': [101, 102, 103],
    'price': [1.5, 2.0, 0.5]
})

df_merged = pd.merge(df_products, df_prices, left_on='product_id', right_on='code')
# Print the merged DataFrame
# Your code here

Need a hint?

Use print(df_merged) to show the combined table.