Categorical Data

Lecture 20:- Categorical Data

Categorical data, also known as nominal data, is a type of data that represents categories or groups. Unlike numerical data, which consists of numerical values that can be ordered and have meaningful differences between them, categorical data only represents groupings or labels without any inherent order or numerical meaning. Categorical data is discrete and can be represented using labels, words, or symbols.

Examples of categorical data include:

1. Gender: Categories may include "Male" and "Female."
2. Color: Categories may include "Red," "Blue," "Green," etc.
3. Marital Status: Categories may include "Single," "Married," "Divorced," etc.
4. Education Level: Categories may include "High School," "Bachelor's Degree," "Master's Degree," etc.
5. Types of Fruit: Categories may include "Apple," "Orange," "Banana," etc.

Categorical data is essential in data analysis and is often used for grouping and summarizing data. When working with categorical data, it is common to use bar charts, pie charts, or frequency tables to visualize and understand the distribution of the categories.

In data analysis, categorical data is typically encoded using numerical values or one-hot encoding for use in machine learning algorithms. One-hot encoding converts categorical data into a binary format, where each category is represented as a separate binary column, and a 1 indicates the presence of that category, while a 0 indicates its absence.

Pandas, a popular data manipulation library in Python, provides specific data types for handling categorical data, which can be useful for efficient data storage and processing. It is essential to handle categorical data appropriately to avoid incorrect interpretations or biases in analysis and modeling tasks.