describe() for statistical summary in Pandas - Mini Project: Build & Apply

Using describe() for Statistical Summary in pandas

📖 Scenario: You work as a data analyst for a small online store. You have collected sales data for the last week, including the number of items sold and the price of each item. You want to quickly understand the basic statistics of this data to see how sales are doing.

🎯 Goal: Build a simple pandas DataFrame with sales data and use the describe() method to get a statistical summary of the data.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Add a variable to select numeric columns

Use the describe() method on the DataFrame

Print the statistical summary output

💡 Why This Matters

🌍 Real World

Statistical summaries help quickly understand data trends and spot unusual values in sales, finance, or any numeric data.

💼 Career

Data analysts and scientists use describe() to get fast insights before deeper analysis or reporting.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
'quantity': [10, 5, 8, 7, 3],
'price': [0.5, 0.3, 0.7, 0.5, 0.3]

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact data
# Your code here

Need a hint?

Use pd.DataFrame and pass a dictionary with keys as column names and values as lists of data.

2

Select numeric columns for summary

Create a variable called numeric_cols that selects only the numeric columns from sales_data using select_dtypes(include=['number'])

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.7, 0.5, 0.3]
})
# Create numeric_cols selecting numeric columns from sales_data
# Your code here

Need a hint?

Use select_dtypes(include=['number']) on the DataFrame to get numeric columns.

3

Get statistical summary using describe()

Use the describe() method on numeric_cols and save the result in a variable called summary

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.7, 0.5, 0.3]
})
numeric_cols = sales_data.select_dtypes(include=['number'])
# Use describe() on numeric_cols and save to summary
# Your code here

Need a hint?

Call describe() on the numeric columns DataFrame and assign it to summary.

4

Print the statistical summary

Print the variable summary to display the statistical summary of the numeric sales data

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.7, 0.5, 0.3]
})
numeric_cols = sales_data.select_dtypes(include=['number'])
summary = numeric_cols.describe()
# Print the summary variable
# Your code here

Need a hint?

Use print(summary) to show the statistical summary.