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.