Creating Categorical Variables in Pandas: A Guide for Beginners

Have you ever wanted to create a categorical variable in Pandas but didn’t know where to start? Look no further, as this article will guide you through the process of creating both scratch-made and existing numerical variable-based categorical variables.

Let’s get started!

## Introducing Categorical Variables

Categorical variables are a type of data variable that can take on one of a finite, defined set of values. These values are usually non-numeric and represent a type of category.

Examples include gender, color, occupation, and geographic location. In Pandas, categorical variable data type is used to represent these variables.

The categorical data type in Pandas is used to reduce memory usage and improve performance. It also enables easy and fast manipulation of data with categorical columns.

Method 1: Creating a Categorical Variable from Scratch

To create a categorical variable from scratch, you will need to leverage Pandas’ DataFrame. A DataFrame is a tabular data structure that allows you to store and manipulate data in a two-dimensional table.

Here’s an example:

## import pandas as pd

data = pd.DataFrame({

‘gender’: [‘male’, ‘female’, ‘female’, ‘male’, ‘male’, ‘male’, ‘female’, ‘female’],

‘score’: [50, 80, 70, 90, 60, 70, 80, 50]

})

In this example, we are creating a DataFrame with two columns, gender, and score. The gender column contains categorical data, and the score column contains numerical data.

To create a categorical variable, we will convert the gender column from an object data type to a category data type. Here’s how:

data[‘gender’] = pd.Categorical(data[‘gender’])

We used the pd.Categorical method to convert the gender column data type to a categorical type.

Method 2: Creating a Categorical Variable from an Existing Numerical Variable

You can also create a categorical variable from an existing numerical variable using the cut method. The cut method is used to segment and sort data values into bins.

The result is a categorical variable with labels for the bins. Here’s how:

## import pandas as pd

data = pd.DataFrame({

‘score’: [50, 80, 70, 90, 60, 70, 80, 50]

})

In this example, we are creating a DataFrame with one column, score, containing numerical data. Now, let’s create a categorical variable by categorizing the scores into bins of 10 points each.

Here’s how:

bins = [0, 59, 69, 79, 89, 100]

labels = [‘F’, ‘D’, ‘C’, ‘B’, ‘A’]

data[‘grade’] = pd.cut(data[‘score’], bins=bins, labels=labels)

We used the pd.cut method to create a categorical variable, grade, with bins for each score range, and then labeled each bin based on a corresponding grade value.

## Conclusion

Congratulations! You have successfully learned how to create categorical variables from scratch and an existing numerical variable using Pandas. These skills will be valuable for data analysis and manipulation.

While we only covered basic examples, you can apply these methods to more complex datasets and create categorical variables to suit your data analysis needs. Keep practicing, and remember that practice makes perfect!

Method 1: Creating a Categorical Variable from Scratch

Let’s dive deeper into the first method of creating a categorical variable.

In the previous example, we created a DataFrame with two columns, one containing categorical data, and the other containing numerical data. However, in reality, you may have a dataset that requires you to create a categorical variable from scratch.

Here’s an example:

## import pandas as pd

colors = [‘red’, ‘green’, ‘yellow’, ‘blue’, ‘green’, ‘blue’, ‘red’, ‘yellow’, ‘red’, ‘blue’]

data = pd.DataFrame({‘color’: colors})

In this example, we created a list of categorical values, colors, containing strings that represent four color categories. We then created a DataFrame with one column, color, and assigned the list of categorical values as its data.

We can convert the color column data type to a categorical type using the pd.Categorical method, as we did before:

data[‘color’] = pd.Categorical(data[‘color’])

At this point, we have successfully created and converted a categorical variable from scratch. Method 2: Creating a Categorical Variable from an Existing Numerical Variable

Let’s take a closer look at the second method of creating a categorical variable.

In the previous example, we created a DataFrame with one column containing numerical data. However, you may have data represented as numerical values that require you to create a categorical variable.

Let’s continue with the previous example and demonstrate how we would use the cut method to create a categorical variable:

## import pandas as pd

scores = [50, 80, 70, 90, 60, 70, 80, 50]

data = pd.DataFrame({‘score’: scores})

In the previous example, we created a list of numerical values, scores, that represent the scores of eight students. We then created a DataFrame with one column, score, and assigned the list of numerical values as its data.

We can now create a categorical variable, grade, by categorizing the scores into bins of ten points each. Here’s how:

bins = [0, 59, 69, 79, 89, 100]

labels = [‘F’, ‘D’, ‘C’, ‘B’, ‘A’]

data[‘grade’] = pd.cut(data[‘score’], bins=bins, labels=labels)

In this example, we used the pd.cut method to create a categorical variable, grade, with bins specifying score ranges and labels corresponding to each score range.

One of the advantages of using categorical variables in Pandas is that you can reduce data storage size and increase computation efficiency. This is important when working with large datasets because it reduces the time and memory needed to process data, sometimes resulting in significantly reduced processing time.

When working with categorical variables, it is also good practice to manage the categories. For example, you may want to rename or remove categories altogether.

You can use the rename_categories and remove_categories methods to achieve this. Here’s an example:

## import pandas as pd

data = pd.DataFrame({‘color’: [‘red’, ‘green’, ‘yellow’, ‘blue’, ‘green’, ‘blue’, ‘red’, ‘yellow’, ‘red’, ‘blue’]})

data[‘color’] = pd.Categorical(data[‘color’])

print(data[‘color’].cat.categories)

# Rename the Category

data[‘color’].cat.rename_categories([‘R’, ‘G’, ‘Y’, ‘B’, ‘G’])

# Remove a Category

data[‘color’].cat.remove_categories([‘yellow’])

In this example, we created a DataFrame with a column containing categorical data, color. We then converted the data type of the column to a categorical type using the pd.Categorical method, resulting in a categories object of [‘blue’, ‘green’, ‘red’, ‘yellow’].

We used the cat.rename_categories method to rename the category ‘green’ to ‘G’ and the cat.remove_categories method to remove the entire ‘yellow’ category from the data.

## Conclusion

Creating categorical variables is an important skill for data analysis and manipulation. In this article, we have demonstrated how to create categorical variables from both scratch and existing numerical variables using Pandas DataFrames.

We also touched on how to manage categories, such as renaming or removing them. By using categorical variables, you can improve the efficiency and memory management of your data analysis workflow.

We hope this article has provided you with a solid foundation to start working with categorical variables in Pandas. Remember, practice makes perfect! Keep exploring this topic, and you’ll be well on your way to becoming a data analysis and manipulation pro.

In summary, creating categorical variables in Pandas is an essential skill for data analysis and manipulation. In this article, we covered two methods of creating categorical data: creating from scratch and creating from an existing numerical variable.

We also explored how to manage categories, including how to rename or remove them. By utilizing categorical variables, you can improve your data analysis workflow’s efficiency and memory management.

Takeaway the importance of understanding how to create categorical variables to become a data analysis and manipulation pro. Keep exploring this topic to develop and enhance skills to meet the needs of working with complex datasets in a user-friendly, efficient manner.