nlargest() and nsmallest() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the 3 largest values from the 'score' column.

Pandas

top_scores = df['score'].[1](3)

Drag options to blanks, or click blank then click option'

Ansmallest

Bsort_values

Cnlargest

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using nsmallest() instead of nlargest()

Using sort_values() without specifying ascending=False

The nlargest() function returns the top n largest values from a Series.

2fill in blank

medium

Complete the code to get the 2 smallest values from the 'age' column.

Pandas

youngest = df['age'].[1](2)

Drag options to blanks, or click blank then click option'

Ansmallest

Bnlargest

Csort_values

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using nlargest() instead of nsmallest()

Using tail() which returns last rows, not smallest values

The nsmallest() function returns the smallest n values from a Series.

3fill in blank

hard

Fix the error in the code to get the 4 largest values from the 'height' column.

Pandas

tallest = df.[1]('height', 4)

Drag options to blanks, or click blank then click option'

Ahead

Bnsmallest

Csort_values

Dnlargest

Attempts:

3 left

💡 Hint

Common Mistakes

Calling nlargest() on a Series instead of DataFrame with column name

Using sort_values() without specifying ascending=False

The nlargest() method is called on the DataFrame with the column name and number of values.

4fill in blank

hard

Fill both blanks to get the 5 smallest values from the 'weight' column.

Pandas

lightest = df.[1](5, '[2]')

Drag options to blanks, or click blank then click option'

Ansmallest

Bnlargest

Cweight

Dheight

Attempts:

3 left

💡 Hint

Common Mistakes

Using nlargest() instead of nsmallest()

Using wrong column name

Use nsmallest() on the DataFrame with the column name 'weight' to get smallest values.

5fill in blank

hard

Fill all three blanks to create a dictionary of top 3 tallest people's names and heights.

Pandas

top_tallest = {person: height for person, height in zip(df['[1]'][df['[2]'].[3](3).index], df['[2]'].[3](3))}

Drag options to blanks, or click blank then click option'

Aname

Bheight

Cnlargest

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column names

Using nsmallest() instead of nlargest()

We use the 'name' column for people, 'height' column for heights, and nlargest(3) to get top 3 heights.

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