Pivot tables with pivot_table() in Data Analysis Python - Mini Project: Build & Apply

Pivot tables with pivot_table()

📖 Scenario: You work in a small store that sells different types of fruits. You have a list of sales data showing how many fruits were sold each day. You want to summarize this data to see the total sales for each fruit and each day.

🎯 Goal: Build a pivot table using pandas to summarize total fruit sales by fruit type and day.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Create a variable for the pivot table configuration

Use pivot_table() to summarize total sales by fruit and day

Print the resulting pivot table

💡 Why This Matters

🌍 Real World

Pivot tables help summarize and analyze sales data quickly, making it easier to understand trends and totals.

💼 Career

Data analysts and business intelligence professionals use pivot tables to create reports and insights from raw data.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and rows:
{'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]}

Data Analysis Python

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame() with a dictionary to create the data.

2

Set up the pivot table configuration

Create a variable called pivot_config that is a dictionary with keys index set to 'Fruit', columns set to 'Day', and values set to 'Quantity'.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]})
# Create the pivot_config dictionary
# Your code here

Hint

Use a dictionary with keys index, columns, and values.

3

Create the pivot table

Use pd.pivot_table() with sales_data and the pivot_config dictionary to create a pivot table called sales_pivot. Use the aggfunc parameter set to sum to add quantities.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]})
pivot_config = {'index': 'Fruit', 'columns': 'Day', 'values': 'Quantity'}
# Create the sales_pivot pivot table using pd.pivot_table
# Your code here

Hint

Use pd.pivot_table() with the keys from pivot_config and aggfunc=sum.

4

Print the pivot table

Print the sales_pivot pivot table to see the total quantities sold by fruit and day.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]})
pivot_config = {'index': 'Fruit', 'columns': 'Day', 'values': 'Quantity'}
sales_pivot = pd.pivot_table(sales_data, index=pivot_config['index'], columns=pivot_config['columns'], values=pivot_config['values'], aggfunc=sum)
# Print the sales_pivot DataFrame
# Your code here

Hint

Use print(sales_pivot) to display the pivot table.