append equivalent with concat in Pandas - Mini Project: Build & Apply

Append Equivalent with concat in pandas

📖 Scenario: You work in a small store and keep track of daily sales in tables. Sometimes you get new sales data and want to add it to your existing records.

🎯 Goal: Learn how to combine two sales tables using pandas.concat(), which works like append() but is more flexible and recommended.

📋 What You'll Learn

Create two pandas DataFrames with sales data

Create a list containing these DataFrames

Use pandas.concat() to combine the DataFrames

Print the combined DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often get new sales data daily and need to combine it with old data to analyze total sales.

💼 Career

Data analysts and scientists frequently combine multiple datasets to prepare data for reports and insights.

1

Create two sales DataFrames

Import pandas as pd. Create a DataFrame called sales_jan with columns 'Product' and 'Quantity' and these rows: 'Apple', 10, 'Banana', 15. Create another DataFrame called sales_feb with the same columns and these rows: 'Apple', 7, 'Orange', 12.

Pandas

# Your code here

Need a hint?

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

2

Create a list of DataFrames

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

Pandas

import pandas as pd

sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Quantity': [10, 15]
})
sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Orange'],
    'Quantity': [7, 12]
})

# Your code here

Need a hint?

Put the two DataFrames inside square brackets to make a list.

3

Combine DataFrames using concat

Use pd.concat() with the list sales_list to create a new DataFrame called all_sales that stacks the rows from both DataFrames.

Pandas

import pandas as pd

sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Quantity': [10, 15]
})
sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Orange'],
    'Quantity': [7, 12]
})

sales_list = [sales_jan, sales_feb]

# Your code here

Need a hint?

Use pd.concat() with the list of DataFrames to combine them.

4

Print the combined DataFrame

Print the DataFrame all_sales to see the combined sales data.

Pandas

import pandas as pd

sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Quantity': [10, 15]
})
sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Orange'],
    'Quantity': [7, 12]
})

sales_list = [sales_jan, sales_feb]

all_sales = pd.concat(sales_list)

# Your code here

Need a hint?

Use print(all_sales) to show the combined table.