nlargest() and nsmallest() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of nlargest() on a DataFrame column

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [5, 2, 9, 1, 7], 'B': [10, 20, 30, 40, 50]})
result = df['A'].nlargest(3)
print(result)

A

0    5
1    2
3    1
Name: A, dtype: int64

B

4    7
2    9
0    5
Name: A, dtype: int64

C

2    9
4    7
0    5
Name: A, dtype: int64

D

3    1
1    2
0    5
Name: A, dtype: int64

💡 Hint

nlargest() returns the top n values sorted descending by default.

The nlargest(3) method returns the top 3 largest values from column 'A' sorted in descending order, preserving the original index.

❓ data_output

intermediate

Result of nsmallest() on multiple columns

Given the DataFrame below, what is the result of selecting the 2 rows with the smallest values in column 'B'?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [3, 1, 4, 2], 'B': [40, 10, 20, 30]})
result = df.nsmallest(2, 'B')
print(result)

A

   A   B
1  1  10
2  4  20

B

   A   B
0  3  40
3  2  30

C

   A   B
3  2  30
2  4  20

D

   A   B
1  1  10
3  2  30

💡 Hint

nsmallest() returns rows with smallest values in the specified column, sorted ascending.

The nsmallest(2, 'B') method returns the two rows with the smallest values in column 'B', sorted ascending by 'B'.

❓ visualization

advanced

Visualizing top 3 largest values using nlargest()

Which option correctly plots the top 3 largest values from column 'score' in descending order using pandas and matplotlib?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'name': ['Anna', 'Bob', 'Cara', 'Dan', 'Eli'], 'score': [88, 92, 79, 95, 85]})
top_scores = df.nlargest(3, 'score')

plt.bar(top_scores['name'], top_scores['score'])
plt.show()

A Plots bars for Dan (95), Bob (92), Anna (88) in that order from left to right.

B Plots bars for Eli (85), Cara (79), Bob (92) in that order from left to right.

C Plots bars for Cara (79), Eli (85), Anna (88) in that order from left to right.

D Plots bars for Anna (88), Bob (92), Dan (95) in that order from left to right.

💡 Hint

nlargest() returns rows sorted descending by the column specified.

The nlargest(3, 'score') returns the top 3 rows sorted descending by 'score'. The plot shows bars in that order from left to right.

🔧 Debug

advanced

Identify the error in nsmallest() usage

What error will this code raise and why?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]})
result = df.nsmallest(2, ['X', 'Z'])
print(result)

A No error, returns rows with smallest values in 'X' only.

B TypeError: nsmallest() expects a string, not a list.

C ValueError: nsmallest() cannot handle multiple columns.

D KeyError: 'Z' because column 'Z' does not exist in the DataFrame.

💡 Hint

Check if all columns specified exist in the DataFrame.

The column 'Z' does not exist in the DataFrame, so pandas raises a KeyError.

🚀 Application

expert

Using nlargest() to find top scoring students with ties

Given the DataFrame below, which option correctly returns the top 3 students by score including ties (students with the same score as the 3rd highest)?

Pandas

import pandas as pd

df = pd.DataFrame({'student': ['Amy', 'Ben', 'Cara', 'Dan', 'Eli'], 'score': [90, 85, 90, 80, 85]})

# Your task: get top 3 scores including ties

# Which code achieves this?

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

B

threshold = df['score'].nlargest(3).min()
top_scores = df[df['score'] >= threshold]

C top_scores = df[df['score'] > df['score'].nlargest(3).min()]

D top_scores = df.sort_values('score', ascending=False).head(3)

💡 Hint

nlargest(3) returns exactly 3 rows, but may exclude ties with the same score as the 3rd highest.

Option B finds the 3rd highest score, then selects all students with scores greater or equal to that, including ties.

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