CategoricalDtypes aka Explicit Sorting

 

The pd.api.types.CategoricalDtype is used to define a categorical data type for a pandas Series. It allows you to specify the categories and their order explicitly. This is a very zen-like application. Hence the local guru meditates with always properly sorted indexes! 

In a customer satisfaction survey, an organization collects feedback from customers regarding their experience with the company's products or services. To analyze the survey data effectively, they can leverage the CategoricalDtype feature in pandas to handle the satisfaction levels expressed by customers.

The satisfaction levels are categorized as "Low," "Medium," and "High," representing different levels of customer satisfaction. Let's explore how CategoricalDtype can be utilized in this scenario:

Defining the Categorical Data Type:

Using CategoricalDtype, the organization can define a categorical data type with the categories ["Low", "Medium", "High"] and the desired order. By defining the order, they can ensure that when sorting or analyzing the data, "Low" is considered the lowest level of satisfaction, followed by "Medium," and finally "High."

Converting Survey Data to Categorical Type:

Once the categorical data type is defined, the organization can convert the survey responses to the categorical type using astype() or pd.Categorical(). This conversion allows them to represent the satisfaction levels as categorical data, taking advantage of the benefits it provides.

Analyzing and Visualizing the Data:

With the categorical data, the organization can easily perform analysis tasks such as calculating the percentage distribution of satisfaction levels. Visualization techniques, like bar charts or pie charts, can be employed to represent the distribution of satisfaction levels effectively.

Sorting and Filtering Based on Satisfaction Levels:

The organization can sort the survey data based on the satisfaction levels using sort_values(). By sorting the data, they can gain insights into patterns, such as identifying customers with high satisfaction levels or finding areas where improvements are needed.

Aggregating and Summarizing Data:

The organization can use groupby operations to aggregate the data based on satisfaction levels.This allows them to calculate summary statistics, such as the average satisfaction level or the count of customers falling into each category.

By utilizing the CategoricalDtype feature in pandas, the organization can efficiently handle customer satisfaction data. They can ensure proper ordering, easily analyze and visualize the data, and derive valuable insights to improve customer satisfaction levels.

Remember, this is just one example of how CategoricalDtype can be applied. The concept can be extended to various other domains where categorical data plays a significant role, providing clarity and efficiency in data analysis and manipulation.

A world without CategoricalDTypes

Without applying CategoricalDtype, sorting the values of the "Low", "Medium", and "High" categories without explicit categorical ordering would follow the default sorting behavior of pandas, which is based on the lexicographical order of strings.

The sort has undesirable results! High is before Low and Medium. 

Here is the code to create the CategoricalDtypes

The categories are sorted correctly, and the print shows us the applied CategoryDtypes.  Very nice! 

Click on the link below to run the code live: 

Google Colab Space