concat() for stacking DataFrames in Data Analysis Python - Mini Project: Build & Apply

Stacking DataFrames using concat()

📖 Scenario: You work in a small store and keep sales records for two different months separately. Now, you want to combine these records to see all sales together.

🎯 Goal: Learn how to stack two sales records DataFrames vertically using concat() to get a combined sales record.

📋 What You'll Learn

Create two DataFrames named sales_jan and sales_feb with given sales data

Create a list named sales_list containing both DataFrames

Use pd.concat() with sales_list to stack the DataFrames vertically into all_sales

Print the all_sales DataFrame to see the combined sales

💡 Why This Matters

🌍 Real World

Combining monthly or weekly sales data into one table for easier analysis and reporting.

💼 Career

Data analysts often need to merge data from multiple sources or time periods to get a complete picture.

1

Create two sales DataFrames

Create a DataFrame called sales_jan with columns 'Product' and 'Quantity' and these rows: 'Apple', 10, 'Banana', 15. Also create a DataFrame called sales_feb with the same columns and these rows: 'Apple', 7, 'Orange', 12. Use pd.DataFrame() and pass a list of dictionaries.

Data Analysis Python

import pandas as pd
# Create sales_jan DataFrame
# Create sales_feb DataFrame

Hint

Use pd.DataFrame with a list of dictionaries. Each dictionary is a row with keys as column names.

2

Create a list of the two DataFrames

Create a list called sales_list that contains the two DataFrames sales_jan and sales_feb in that order.

Data Analysis Python

import pandas as pd
sales_jan = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 10},
    {'Product': 'Banana', 'Quantity': 15}
])
sales_feb = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 7},
    {'Product': 'Orange', 'Quantity': 12}
])
# Create sales_list containing sales_jan and sales_feb

Hint

Use square brackets to create a list with the two DataFrames inside.

3

Stack the DataFrames vertically using concat()

Use pd.concat() with the list sales_list to stack the two DataFrames vertically. Assign the result to a new variable called all_sales.

Data Analysis Python

import pandas as pd
sales_jan = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 10},
    {'Product': 'Banana', 'Quantity': 15}
])
sales_feb = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 7},
    {'Product': 'Orange', 'Quantity': 12}
])
sales_list = [sales_jan, sales_feb]
# Use pd.concat() to stack sales_list vertically into all_sales

Hint

Call pd.concat() with the list of DataFrames as the argument.

4

Print the combined DataFrame

Print the DataFrame all_sales to see the stacked sales records from January and February.

Data Analysis Python

import pandas as pd
sales_jan = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 10},
    {'Product': 'Banana', 'Quantity': 15}
])
sales_feb = pd.DataFrame([
    {'Product': 'Apple', 'Quantity': 7},
    {'Product': 'Orange', 'Quantity': 12}
])
sales_list = [sales_jan, sales_feb]
all_sales = pd.concat(sales_list)
# Print all_sales

Hint

Use print(all_sales) to display the combined DataFrame.