Categorical variable in Python

In Python, a categorical variable is a variable that can take on one of a limited number of possible values. These values are usually non-numeric and are used to represent data that is divided into categories or groups. Categorical variables are also called as nominal variables or factors.

One of the most common examples of a categorical variable is a variable that represents the color of an object. The possible values for this variable would be "red", "green", "blue", and so on. Another example of a categorical variable is a variable that represents the type of animal. The possible values for this variable would be "dog", "cat", "bird", and so on.

In Python, there are several ways to represent and manipulate categorical variables. One of the most common ways is to use the pandas library, which is a powerful data manipulation library for Python.

To create a categorical variable in pandas, you can use the pandas.Series() function. This function creates a new Series object that can be used to store the values of a categorical variable. The Series object can be created from a list of values, such as a list of strings or integers.

This code creates a new Series object called "color" that contains the values "red", "green", and "blue". The Series object can be used to manipulate and analyze the data in the same way as a DataFrame.

Another way to represent and manipulate categorical variables in Python is to use the category data type. The category data type is a new data type introduced in pandas version 0.15.0, which allows you to store categorical variables in a more efficient way.

To convert a Series object to a categorical variable, you can use the astype() function. The astype() function takes a single argument, which is the data type to convert the Series object to.

This code converts the "color" Series object to a categorical variable. The astype() function creates a new categorical variable that contains the same values as the original Series object, but it is stored in a more efficient way.

Categorical variables can also be used in various statistical analysis, by encoding them into numerical values. This process is called encoding and it can be done in two ways, either by ordinal encoding or one-hot encoding.

Ordinal encoding is used when the categorical variable has an inherent order. For example, the variable "Size" (small, medium, large) can be ordinal encoded into numerical values (1, 2, 3). While one-hot encoding is used to create a binary variable for each category in the variable.

Another way to work with categorical variable is by using the scikit-learn library, which is a popular machine learning library for Python. The scikit-learn library provides a preprocessing module that contains several functions for encoding categorical variables. One of the most commonly used functions is the LabelEncoder() function.

This code creates a new LabelEncoder object and applies it to the "color" Series object. The fit_transform() function encodes the values in the Series object and returns a new array of encoded values.

To work with categorical variables in Python, we can use the pandas library. Here is an example of how to create a categorical variable and perform some basic operations:

In this example, we first create a sample dataframe with a column named 'color' containing the values 'red', 'blue', 'green', 'red', 'blue'. Next, we use the "astype()" function to convert the 'color' column to a categorical variable. Finally, we print the dataframe to see the changes.

We can also use the "value_counts()" function to count the number of occurrences of each unique value in the categorical variable:

In this example, the output would be: