nlargest() and nsmallest() in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas function nlargest() do?

The nlargest() function returns the top n rows from a DataFrame or Series based on the largest values in a specified column.

beginner

How does nsmallest() differ from nlargest() in pandas?

nsmallest() returns the bottom n rows with the smallest values in a specified column, while nlargest() returns the top n rows with the largest values.

beginner

Can nlargest() and nsmallest() be used on both Series and DataFrames?

Yes, both functions work on pandas Series and DataFrames. For DataFrames, you specify the column to sort by.

beginner

What parameter do you need to provide to nlargest() and nsmallest() to specify how many rows to return?

You provide the parameter n, which is the number of rows you want to get from the top or bottom.

beginner

Example: How to get the 3 largest values from a DataFrame column named 'score'?

df.nlargest(3, 'score')

This returns the 3 rows with the highest 'score' values.

What does df.nlargest(5, 'age') return?

A The first 5 rows of the DataFrame

B The 5 rows with the smallest 'age' values

C The 5 rows with the largest 'age' values

D The last 5 rows of the DataFrame

nlargest() returns rows with the largest values in the specified column.

Which function returns the rows with the smallest values in a column?

A nlargest()

B head()

C sort_values()

D nsmallest()

nsmallest() returns rows with the smallest values in a column.

Can nlargest() be used on a pandas Series?

A Yes, it works on Series and DataFrames

B Only on DataFrames with numeric columns

C Only on Series with numeric data

D No, only DataFrames

nlargest() works on both Series and DataFrames.

What parameter do you need to specify in nlargest() to choose the column to sort by?

A columns or column name

B columns

C subset

D n

You specify the column name as the second argument to nlargest().

If you want the 10 smallest values from a column 'price', which code is correct?

A df.nsmallest(10, 'price')

B Both B and C

C df.sort_values('price').head(10)

D df.nlargest(10, 'price')

Both nsmallest() and sorting then taking head(10) give the 10 smallest values.

Explain how you would use nlargest() to find the top 5 highest scores in a DataFrame.

Think about the parameters needed for nlargest.

Describe the difference between nlargest() and nsmallest() in pandas.

Focus on what values each function returns.

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