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.