nunique() for cardinality in Data Analysis Python

Introduction

We use nunique() to count how many unique values are in a list or column. It helps us understand the variety or diversity in data.

To find out how many different products customers bought in a store.

To check how many unique cities appear in a list of addresses.

To count distinct users who visited a website in a day.

To see how many unique categories exist in a dataset.

To identify the number of unique dates in a sales record.

Syntax

Data Analysis Python

DataFrame['column_name'].nunique(dropna=True)

# or for a whole DataFrame
DataFrame.nunique(dropna=True)

dropna=True means it ignores empty or missing values when counting.

You can use nunique() on a single column or on the whole DataFrame to get counts for each column.

Examples

Counts unique cities in the 'City' column.

Data Analysis Python

df['City'].nunique()

Counts unique values for every column in the DataFrame.

Data Analysis Python

df.nunique()

Counts unique products including missing values as a unique category.

Data Analysis Python

df['Product'].nunique(dropna=False)

Sample Program

This program creates a small table with names, cities, and products. It then counts how many unique names, cities, and products there are. For products, it counts missing values as unique if any.

Data Analysis Python

import pandas as pd

# Create a simple data table
 data = {
     'Name': ['Alice', 'Bob', 'Alice', 'David', 'Bob', None],
     'City': ['NY', 'LA', 'NY', 'Chicago', 'LA', 'NY'],
     'Product': ['Book', 'Pen', 'Book', 'Pen', 'Notebook', 'Pen']
 }

df = pd.DataFrame(data)

# Count unique names ignoring missing values
unique_names = df['Name'].nunique()

# Count unique cities
unique_cities = df['City'].nunique()

# Count unique products including missing values
unique_products_including_na = df['Product'].nunique(dropna=False)

print(f"Unique Names: {unique_names}")
print(f"Unique Cities: {unique_cities}")
print(f"Unique Products (including NA): {unique_products_including_na}")

Important Notes

If you want to count missing values as unique, set dropna=False.

nunique() is very useful to quickly check data diversity or cardinality.

It works well with pandas DataFrames and Series.

Summary

nunique() counts unique values in data.

Use it to understand how many different items or categories exist.

It can ignore or include missing values based on dropna setting.

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