nlargest() and nsmallest() in Pandas - Mini Project: Build & Apply

Find Top and Bottom Sales Using nlargest() and nsmallest()

📖 Scenario: You work in a store and have a list of products with their sales numbers. You want to find which products sold the most and which sold the least.

🎯 Goal: Use pandas nlargest() and nsmallest() functions to find the top 3 and bottom 2 selling products from the sales data.

📋 What You'll Learn

Create a pandas DataFrame with product names and their sales numbers.

Create a variable to store the number of top and bottom products to find.

Use nlargest() to find the top 3 selling products.

Use nsmallest() to find the bottom 2 selling products.

Print the results clearly.

💡 Why This Matters

🌍 Real World

Stores and businesses often need to quickly find their best and worst selling products to make decisions about stock and promotions.

💼 Career

Data analysts and data scientists use functions like <code>nlargest()</code> and <code>nsmallest()</code> to summarize and report key insights from sales data.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Product' and 'Sales'. Use these exact entries: 'Apples' with sales 150, 'Bananas' with sales 120, 'Cherries' with sales 90, 'Dates' with sales 200, and 'Elderberries' with sales 50.

Pandas

import pandas as pd
# Create the sales_data DataFrame with Product and Sales columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the product names and sales numbers.

2

Set the number of top and bottom products

Create two variables: top_n and bottom_n. Set top_n to 3 and bottom_n to 2.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [150, 120, 90, 200, 50]
})
# Set top_n to 3 and bottom_n to 2
# Your code here

Need a hint?

Just create two variables and assign the numbers 3 and 2 to them.

3

Find top and bottom selling products

Use nlargest() on sales_data to find the top top_n products by 'Sales' and store it in top_sellers. Use nsmallest() on sales_data to find the bottom bottom_n products by 'Sales' and store it in bottom_sellers.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [150, 120, 90, 200, 50]
})
top_n = 3
bottom_n = 2
# Find top_sellers using nlargest
# Find bottom_sellers using nsmallest
# Your code here

Need a hint?

Use sales_data.nlargest(top_n, 'Sales') and sales_data.nsmallest(bottom_n, 'Sales').

4

Print the top and bottom sellers

Print the top_sellers DataFrame with the label 'Top Sellers:'. Then print the bottom_sellers DataFrame with the label 'Bottom Sellers:'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [150, 120, 90, 200, 50]
})
top_n = 3
bottom_n = 2
top_sellers = sales_data.nlargest(top_n, 'Sales')
bottom_sellers = sales_data.nsmallest(bottom_n, 'Sales')
# Print top_sellers with label
# Print bottom_sellers with label
# Your code here

Need a hint?

Use print() to show the labels and DataFrames.