describe() for statistics in Data Analysis Python - Mini Project: Build & Apply

Using describe() for Statistics

📖 Scenario: You work in a small bakery. You have sales data for different types of bread sold each day. You want to understand the basic statistics of daily sales to plan better.

🎯 Goal: Learn how to use the describe() method in pandas to get quick statistics like mean, median, min, max, and quartiles for your sales data.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data

Use the describe() method to get summary statistics

Print the summary statistics

💡 Why This Matters

🌍 Real World

Businesses often need to quickly understand their sales or production data to make decisions. Using describe() helps get a quick overview of key statistics.

💼 Career

Data analysts and scientists use describe() to summarize datasets before deeper analysis or reporting.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
'Bread': [30, 45, 50, 40, 60],
'Pastry': [20, 25, 30, 22, 28]

Data Analysis Python

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

Need a hint?

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

2

Set the Day column as index

Create a new DataFrame called sales_indexed by setting the 'Day' column as the index of sales_data using the set_index() method.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread': [30, 45, 50, 40, 60],
    'Pastry': [20, 25, 30, 22, 28]
})
# Set 'Day' as index and save to sales_indexed
# Your code here

Need a hint?

Use set_index('Day') on the DataFrame to make 'Day' the index.

3

Get summary statistics with describe()

Use the describe() method on sales_indexed and save the result to a variable called summary_stats.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread': [30, 45, 50, 40, 60],
    'Pastry': [20, 25, 30, 22, 28]
})
sales_indexed = sales_data.set_index('Day')
# Use describe() on sales_indexed and save to summary_stats
# Your code here

Need a hint?

Call describe() on the DataFrame to get statistics like mean, min, max, and quartiles.

4

Print the summary statistics

Print the variable summary_stats to display the statistics of the sales data.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread': [30, 45, 50, 40, 60],
    'Pastry': [20, 25, 30, 22, 28]
})
sales_indexed = sales_data.set_index('Day')
summary_stats = sales_indexed.describe()
# Print summary_stats
# Your code here

Need a hint?

Use print(summary_stats) to show the statistics.