Why nlargest() and nsmallest() in Pandas? - Purpose & Use Cases

The Big Idea

What if you could find the top or bottom values in your data with just one simple command?

The Scenario

Imagine you have a huge list of sales numbers for thousands of products, and you want to find the top 5 best sellers or the 3 products with the lowest sales.

Doing this by hand means scanning through every number, comparing each one, and keeping track of the highest or lowest values.

The Problem

Manually sorting or scanning large data is slow and tiring. It's easy to make mistakes, like missing a number or mixing up the order.

Also, if the list changes, you have to repeat the whole process again, which wastes time.

The Solution

The nlargest() and nsmallest() functions in pandas quickly find the top or bottom values for you.

They do all the hard work behind the scenes, so you get the results instantly and accurately.

Before vs After

✗ Before

top5 = sorted(sales_list, reverse=True)[:5]

✓ After

top5 = df['sales'].nlargest(5)

What It Enables

With these functions, you can instantly spot the best or worst performers in your data, making decisions faster and smarter.

Real Life Example

A store manager uses nlargest() to find the 10 products with the highest sales last month to plan restocking.

Or uses nsmallest() to identify slow sellers to run promotions.

Key Takeaways

Manually finding top or bottom values is slow and error-prone.

nlargest() and nsmallest() do this quickly and correctly.

They help you focus on important data points instantly.

Practice

1. What does the nlargest() function in pandas do?

easy

A. Returns rows with the highest values in a specified column

B. Returns rows with the lowest values in a specified column

C. Sorts the entire DataFrame in ascending order

D. Removes duplicate rows based on a column

Solution

1. Step 1: Understand the purpose of nlargest()

   The nlargest() function is designed to select rows with the highest values in a given column.

2. Step 2: Compare with other options

   Options B and C describe other functions or behaviors, and D is unrelated to nlargest().

3. Final Answer:

   Returns rows with the highest values in a specified column -> Option A

4. Quick Check:

   nlargest() = highest rows [OK]

Hint: Remember: 'n' largest means top values [OK]

Common Mistakes:

• Confusing nlargest() with nsmallest()
• Thinking it sorts the whole DataFrame
• Assuming it removes duplicates

2. Which of the following is the correct syntax to get the 3 smallest values from column 'score' in a DataFrame df?

easy

A. df.nlargest(3, 'score')

B. df.nsmallest(3, 'score')

C. df.nsmallest('score', 3)

D. df.nlargest('score', 3)

Solution

1. Step 1: Recall the correct method signature

   The correct syntax is df.nsmallest(n, column_name), where n is the number of rows.

2. Step 2: Match the options with the syntax

   df.nsmallest(3, 'score') matches the correct syntax: df.nsmallest(3, 'score'). Options A and D use nlargest incorrectly, and C has arguments reversed.

3. Final Answer:

   df.nsmallest(3, 'score') -> Option B

4. Quick Check:

   nsmallest(n, column) = df.nsmallest(3, 'score') [OK]

Hint: Use nsmallest(n, column) with number first [OK]

Common Mistakes:

• Swapping arguments order
• Using nlargest instead of nsmallest
• Passing column name before number

3. Given the DataFrame:

import pandas as pd
data = {'name': ['Anna', 'Bob', 'Cara', 'Dave'], 'age': [23, 45, 31, 22]}
df = pd.DataFrame(data)

What is the output of df.nlargest(2, 'age')?

medium

A. Rows for Dave and Bob with ages 22 and 45

B. Rows for Anna and Dave with ages 23 and 22

C. Rows for Bob and Cara with ages 45 and 31

D. Rows for Cara and Anna with ages 31 and 23

Solution

1. Step 1: Identify the ages in the DataFrame

   The ages are Anna: 23, Bob: 45, Cara: 31, Dave: 22.

2. Step 2: Select the 2 largest ages

   The two largest ages are 45 (Bob) and 31 (Cara).

3. Final Answer:

   Rows for Bob and Cara with ages 45 and 31 -> Option C

4. Quick Check:

   nlargest(2, 'age') = Bob, Cara [OK]

Hint: Pick top n values by column with nlargest() [OK]

Common Mistakes:

• Choosing smallest instead of largest
• Mixing up row order
• Confusing names with ages

4. What is wrong with this code snippet?

df.nsmallest('score', 5)

Assuming df is a DataFrame with a column named 'score'.

medium

A. The arguments are in the wrong order

B. The method name should be nlargest

C. The number 5 should be a string

D. The column name should be an integer

Solution

1. Step 1: Check the method signature for nsmallest()

   The correct order is nsmallest(n, columns), where n is an integer and columns is a string or list.

2. Step 2: Identify the error in the code

   The code passes the column name first and then the number, which is reversed.

3. Final Answer:

   The arguments are in the wrong order -> Option A

4. Quick Check:

   nsmallest(n, column) needs number first [OK]

Hint: Number first, column second in nsmallest() [OK]

Common Mistakes:

• Swapping argument order
• Using wrong method name
• Passing wrong data types

5. You have a DataFrame df with columns ['product', 'price', 'rating']. You want to find the top 3 products with the highest rating but lowest price among those top rated. Which pandas code snippet correctly achieves this?

hard

A. df.nlargest(3, 'rating').nsmallest(3, 'price')

B. df.nlargest(3, 'rating').sort_values('price').head(3)

C. df.nsmallest(3, 'price').nlargest(3, 'rating')

D. df.sort_values(['rating', 'price'], ascending=[False, True]).head(3)

Solution

1. Step 1: Understand the goal

   We want top 3 products by highest rating, but among those, pick the ones with lowest price.

2. Step 2: Analyze each option

   df.nlargest(3, 'rating').nsmallest(3, 'price') chains nlargest then nsmallest but nsmallest after nlargest may reduce rows incorrectly. df.nlargest(3, 'rating').sort_values('price').head(3) sorts price after selecting top rating but may not correctly filter lowest price among top rated. df.nsmallest(3, 'price').nlargest(3, 'rating') reverses the order, which is wrong. df.sort_values(['rating', 'price'], ascending=[False, True]).head(3) sorts by rating descending and price ascending, then picks top 3, which correctly prioritizes rating then price.

3. Final Answer:

   df.sort_values(['rating', 'price'], ascending=[False, True]).head(3) -> Option D

4. Quick Check:

   Sort by rating desc, price asc, then head(3) [OK]

Hint: Sort by rating desc, price asc, then take top 3 [OK]

Common Mistakes:

• Chaining nlargest and nsmallest incorrectly
• Not sorting by both columns properly
• Selecting wrong rows due to order