nunique() for unique counts in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of nunique() on a DataFrame column

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 2, 3, 3, 3], 'B': ['x', 'y', 'y', 'z', 'z', 'z']})
result = df['A'].nunique()
print(result)

A 2

B 3

C 6

D 1

💡 Hint

Count how many different numbers appear in column 'A'.

The column 'A' has values [1, 2, 2, 3, 3, 3]. The unique values are 1, 2, and 3, so the count is 3.

❓ data_output

intermediate

Unique counts per column in a DataFrame

What is the output of the following code?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 1, 2, 2], 'Y': ['a', 'b', 'a', 'b'], 'Z': [5, 5, 5, 5]})
result = df.nunique()
print(result)

A

X    1
Y    1
Z    1
dtype: int64

B

X    4
Y    4
Z    4
dtype: int64

C

X    2
Y    2
Z    1
dtype: int64

D

X    2
Y    1
Z    1
dtype: int64

💡 Hint

Count unique values in each column separately.

Column 'X' has unique values 1 and 2 (2 unique), 'Y' has 'a' and 'b' (2 unique), 'Z' has only 5 (1 unique).

🔧 Debug

advanced

Error raised by incorrect use of nunique()

What error does the following code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
result = df.nunique(axis=2)
print(result)

A ValueError: No axis named 2 for object type DataFrame

B TypeError: nunique() got an unexpected keyword argument 'axis'

C AttributeError: 'DataFrame' object has no attribute 'nunique'

D No error, prints the unique counts

💡 Hint

Check valid axis values for DataFrame methods.

DataFrames only support axis=0 (rows) or axis=1 (columns). axis=2 is invalid and raises ValueError.

🚀 Application

advanced

Counting unique values after filtering

Given the DataFrame below, what is the output of the code?

Pandas

import pandas as pd

df = pd.DataFrame({'Category': ['A', 'B', 'A', 'C', 'B', 'C'], 'Value': [10, 20, 10, 30, 20, 40]})
filtered = df[df['Value'] > 15]
result = filtered['Category'].nunique()
print(result)

A 1

B 3

C 4

D 2

💡 Hint

Filter rows where Value > 15, then count unique categories.

Filtered rows have Values 20, 30, 20, 40 with Categories B, C, B, C. Unique categories are B and C, count is 2.

🧠 Conceptual

expert

Understanding nunique() with dropna parameter

What is the output of this code snippet?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2, 2, np.nan, np.nan]})
result = df['A'].nunique(dropna=False)
print(result)

A 3

B 2

C 4

D 5

💡 Hint

dropna=False counts NaN as a unique value.

The column 'A' has values [1, 2, 2, NaN, NaN]. The unique values are 1, 2, and NaN. Since dropna=False, NaN is counted as a unique value, so the count is 3.