nunique() for cardinality in Data Analysis Python - 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?

Data Analysis Python

import pandas as pd

data = {'fruit': ['apple', 'banana', 'apple', 'orange', 'banana', 'banana']}
df = pd.DataFrame(data)

result = df['fruit'].nunique()
print(result)

A 3

B 6

C 4

D 2

💡 Hint

Count how many unique fruit names appear in the list.

The column 'fruit' contains three unique values: 'apple', 'banana', and 'orange'. The nunique() function counts these unique values, so the output is 3.

❓ data_output

intermediate

Number of unique values per column

Given the DataFrame below, what is the output of df.nunique()?

Data Analysis Python

import pandas as pd

data = {'A': [1, 2, 2, 3], 'B': ['x', 'y', 'x', 'z'], 'C': [True, True, False, False]}
df = pd.DataFrame(data)

result = df.nunique()
print(result)

A

A    3
B    3
C    2
dtype: int64

B

A    4
B    3
C    2
dtype: int64

C

A    3
B    2
C    2
dtype: int64

D

A    3
B    3
C    3
dtype: int64

💡 Hint

Count unique values in each column separately.

Column A has values [1,2,2,3] with 3 unique values (1,2,3). Column B has ['x','y','x','z'] with 3 unique values (x,y,z). Column C has [True,True,False,False] with 2 unique values (True, False).

🔧 Debug

advanced

Identify the error in using nunique()

What error will this code produce?

Data Analysis Python

import pandas as pd

data = {'numbers': [1, 2, 3, 4]}
df = pd.DataFrame(data)

result = df.nunique(axis=2)
print(result)

A TypeError: axis must be 0 or 1

B ValueError: axis 2 is out of bounds for array of dimension 2

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

D No error, prints the number of unique values per row

💡 Hint

Check the valid axis values for DataFrame methods.

DataFrames have only two axes: 0 (rows) and 1 (columns). Using axis=2 is invalid and raises a ValueError about axis being out of bounds.

🚀 Application

advanced

Using nunique() to find columns with high cardinality

You have a DataFrame with many columns. Which code snippet correctly finds columns with more than 10 unique values?

Data Analysis Python

import pandas as pd

# Assume df is predefined

# Find columns with more than 10 unique values
high_card_cols = ?

A high_card_cols = df.columns[df.nunique() < 10].tolist()

B high_card_cols = df.columns[df.nunique(axis=1) > 10].tolist()

C high_card_cols = df.columns[df.nunique() > 10].tolist()

D high_card_cols = df.columns[df.nunique(axis=0) < 10].tolist()

💡 Hint

nunique() by default counts unique values per column.

df.nunique() returns a Series with unique counts per column. Filtering with > 10 selects columns with more than 10 unique values. axis=1 counts per row, which is not desired here.

🧠 Conceptual

expert

Understanding nunique() behavior with missing values

Consider a DataFrame column with values: [1, 2, 2, None, 3, None]. What is the output of df['col'].nunique(dropna=False)?

Data Analysis Python

import pandas as pd

data = {'col': [1, 2, 2, None, 3, None]}
df = pd.DataFrame(data)

result = df['col'].nunique(dropna=False)
print(result)

A 3

B 2

C 5

D 4

💡 Hint

dropna=False counts NaN as a unique value.

The unique values are 1, 2, 3, and None (NaN). With dropna=False, NaN is counted as a unique value, so total unique count is 4.

Practice

1. What does the nunique() function do when applied to a pandas DataFrame column?

easy

A. Finds the maximum value in that column

B. Calculates the sum of all values in that column

C. Returns the total number of rows in the DataFrame

D. Counts the number of unique values in that column

Solution

1. Step 1: Understand the purpose of nunique()

   The nunique() function counts how many distinct or unique values exist in a column.

2. Step 2: Compare with other functions

   Unlike sum or max, nunique() focuses on counting unique entries, not numerical calculations.

3. Final Answer:

   Counts the number of unique values in that column -> Option D

4. Quick Check:

   nunique() = unique count [OK]

Hint: Remember: nunique() counts unique items only [OK]

Common Mistakes:

• Confusing nunique() with count()
• Thinking it sums values instead of counting unique ones
• Assuming it returns total rows instead of unique values

2. Which of the following is the correct syntax to count unique values in the 'Age' column of a pandas DataFrame named df?

easy

A. df.nunique('Age')

B. df['Age'].nunique()

C. df.nunique['Age']

D. df.nunique.Age()

Solution

1. Step 1: Access the column correctly

   To apply nunique() on a column, first select the column using df['Age'].

2. Step 2: Apply the nunique() method

   Then call nunique() as a method: df['Age'].nunique().

3. Final Answer:

   df['Age'].nunique() -> Option B

4. Quick Check:

   Correct syntax = df['column'].nunique() [OK]

Hint: Use bracket notation for columns before nunique() [OK]

Common Mistakes:

• Passing column name as argument to nunique()
• Using dot notation with parentheses on column name
• Trying to index nunique like a dictionary

3. Given the DataFrame below, what is the output of df['City'].nunique()?

import pandas as pd
data = {'City': ['Paris', 'London', 'Paris', 'Berlin', 'London', 'Rome']}
df = pd.DataFrame(data)

medium

A. 4

B. 3

C. 5

D. 6

Solution

1. Step 1: Identify unique cities in the list

   The cities are: 'Paris', 'London', 'Paris', 'Berlin', 'London', 'Rome'. Unique cities are 'Paris', 'London', 'Berlin', and 'Rome'.

2. Step 2: Count unique cities

   There are 4 unique cities in total.

3. Final Answer:

   4 -> Option A

4. Quick Check:

   Unique cities count = 4 [OK]

Hint: Count distinct values ignoring duplicates [OK]

Common Mistakes:

• Counting all entries instead of unique ones
• Miscounting duplicates as unique
• Confusing number of rows with unique count

4. What is wrong with this code snippet if we want to count unique values in the 'Product' column?

import pandas as pd
data = {'Product': ['A', 'B', 'A', 'C']}
df = pd.DataFrame(data)
unique_count = df.nunique('Product')

medium

A. The nunique() method does not take column names as arguments

B. The DataFrame is missing the 'Product' column

C. The nunique() method should be called without parentheses

D. The DataFrame constructor syntax is incorrect

Solution

1. Step 1: Check how nunique() works on DataFrames

   When called on a DataFrame, nunique() counts unique values for each column and does not accept column names as arguments.

2. Step 2: Correct way to count unique values in one column

   To count unique values in 'Product', select the column first: df['Product'].nunique().

3. Final Answer:

   The nunique() method does not take column names as arguments -> Option A

4. Quick Check:

   nunique() on DataFrame has no column argument [OK]

Hint: Call nunique() on column, not DataFrame with argument [OK]

Common Mistakes:

• Passing column name as argument to nunique() on DataFrame
• Calling nunique without parentheses
• Misunderstanding DataFrame constructor syntax

5. You have a DataFrame df with a column 'Score' containing some missing values (NaN). How do you count unique scores including the missing values as a unique category?

hard

A. df['Score'].count()

B. df['Score'].nunique(dropna=True)

C. df['Score'].nunique(dropna=False)

D. df['Score'].unique()

Solution

1. Step 1: Understand dropna parameter in nunique()

   By default, nunique() ignores missing values (NaN). Setting dropna=False counts NaN as a unique value.

2. Step 2: Apply nunique() with dropna=False

   Use df['Score'].nunique(dropna=False) to include NaN in the unique count.

3. Final Answer:

   df['Score'].nunique(dropna=False) -> Option C

4. Quick Check:

   Include NaN by setting dropna=False [OK]

Hint: Use dropna=False to count NaN as unique [OK]

Common Mistakes:

• Ignoring dropna and missing NaN count
• Using count() which excludes NaN
• Using unique() without counting length