# Comparison and Other Common Operations

Discover the comparison and common operations that can be performed on categorical data.

## We'll cover the following

## Introduction

While categorical data may appear less flexible than numerical data in terms of the availability of defined `pandas`

operations, there are still several operations we can apply to them once they are encoded accordingly with `dtype='category'`

. In particular, let’s explore the comparison and common operations that can be implemented on categorical data.

## Comparison

Recall that `pandas`

comes with a set of logical comparison operations for us to compare values in a DataFrame. In the case of categorical data, there are two scenarios where we can apply comparison operations on them. We’ll reuse the credit card dataset to demonstrate these two scenarios.

### Unordered categories

When we have a set of unordered categories, we can compare their equality with a list-like object (e.g., `Series`

, list, or `NumPy`

array) of the same length as the categorical data. For example, we can compare the first five values of the `Gender`

categorical column with a `Series`

object, comprising five `Male`

values, as shown below:

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