nunique() for cardinality in Data Analysis Python - Mini Project: Build & Apply

Counting Unique Values with nunique() for Cardinality

📖 Scenario: You work in a small bookstore. You have a list of books sold each day. You want to find out how many different book titles were sold.

🎯 Goal: Build a small program that counts the number of unique book titles sold using the nunique() method.

📋 What You'll Learn

Create a pandas DataFrame with a column named book_title containing the exact book titles sold.

Create a variable called unique_books_count to store the number of unique book titles using nunique().

Print the value of unique_books_count.

💡 Why This Matters

🌍 Real World

Counting unique items sold helps businesses understand product variety and customer preferences.

💼 Career

Data analysts often count unique values to summarize data and find important insights.

1

Create the sales data

Create a pandas DataFrame called sales_data with one column named book_title. The column should have these exact values in this order: 'The Alchemist', '1984', 'The Alchemist', 'Brave New World', '1984'.

Data Analysis Python

# Create the sales_data DataFrame with the book_title column
# Your code here

Hint

Use pd.DataFrame with a dictionary where the key is 'book_title' and the value is the list of book titles.

2

Set up the variable for unique count

Create a variable called unique_books_count and set it to the number of unique book titles in sales_data['book_title'] using the nunique() method.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'book_title': ['The Alchemist', '1984', 'The Alchemist', 'Brave New World', '1984']
})

# Create unique_books_count using nunique()
# Your code here

Hint

Use sales_data['book_title'].nunique() to count unique titles.

3

Print the number of unique book titles

Write a print() statement to display the value of unique_books_count.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'book_title': ['The Alchemist', '1984', 'The Alchemist', 'Brave New World', '1984']
})

unique_books_count = sales_data['book_title'].nunique()

# Print unique_books_count
# Your code here

Hint

Use print(unique_books_count) to show the result.

4

Explain the result

Add a print statement that shows a friendly message: print(f"Number of unique books sold: {unique_books_count}").

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'book_title': ['The Alchemist', '1984', 'The Alchemist', 'Brave New World', '1984']
})

unique_books_count = sales_data['book_title'].nunique()

print(unique_books_count)

# Print a friendly message with the count
# Your code here

Hint

Use an f-string inside print() to show the message with the count.

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