concat() for stacking DataFrames in Pandas - Mini Project: Build & Apply

Stacking DataFrames using concat()

📖 Scenario: You work in a small store that tracks sales in two separate lists for two days. You want to combine these lists into one to see all sales together.

🎯 Goal: Combine two sales DataFrames into one using concat() to stack them vertically.

📋 What You'll Learn

Create two DataFrames named sales_day1 and sales_day2 with given data

Create a variable all_sales that stacks the two DataFrames vertically using pd.concat()

Print the combined DataFrame all_sales

💡 Why This Matters

🌍 Real World

Combining sales data from multiple days or stores helps businesses analyze total sales easily.

💼 Career

Data scientists often combine data from different sources to prepare it for analysis or reporting.

1

Create two sales DataFrames

Create a DataFrame called sales_day1 with columns 'Product' and 'Quantity' and these rows: 'Apple', 10 and 'Banana', 5. Also create a DataFrame called sales_day2 with the same columns and these rows: 'Orange', 7 and 'Banana', 3.

Pandas

import pandas as pd
# Create sales_day1 DataFrame
# Create sales_day2 DataFrame

Need a hint?

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

2

Set concat axis for stacking

Create a variable called axis and set it to 0 to stack DataFrames vertically using pd.concat().

Pandas

import pandas as pd
sales_day1 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Quantity': [10, 5]})
sales_day2 = pd.DataFrame({'Product': ['Orange', 'Banana'], 'Quantity': [7, 3]})
# Set axis variable for vertical stacking

Need a hint?

Use axis = 0 because stacking means adding rows.

3

Stack the DataFrames using concat()

Use pd.concat() with the list [sales_day1, sales_day2] and the variable axis to create a new DataFrame called all_sales that stacks the two DataFrames vertically.

Pandas

import pandas as pd
sales_day1 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Quantity': [10, 5]})
sales_day2 = pd.DataFrame({'Product': ['Orange', 'Banana'], 'Quantity': [7, 3]})
axis = 0
# Use pd.concat() to stack sales_day1 and sales_day2 into all_sales

Need a hint?

Pass a list of DataFrames to pd.concat() and set axis=0 to stack rows.

4

Print the combined DataFrame

Print the DataFrame all_sales to see the stacked sales data.

Pandas

import pandas as pd
sales_day1 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Quantity': [10, 5]})
sales_day2 = pd.DataFrame({'Product': ['Orange', 'Banana'], 'Quantity': [7, 3]})
axis = 0
all_sales = pd.concat([sales_day1, sales_day2], axis=axis)
# Print all_sales DataFrame

Need a hint?

Use print(all_sales) to display the combined DataFrame.