Current State of Documentation

Oct 13, 2025 by wbrum.org 52 views

Introduction

The px_classification function in Plotly Express is a powerful tool for creating interactive classification plots. However, with great power comes great complexity, and it can be challenging for users to navigate the various options and parameters available. In this proposal, we outline a comprehensive documentation plan for px_classification to ensure that users can effectively utilize this function to create high-quality visualizations.

Current State of Documentation

While Plotly Express provides a wealth of documentation for its various functions, the documentation for px_classification is limited. The current documentation primarily focuses on the basic usage of the function, without delving into the more advanced options and parameters available. This lack of comprehensive documentation can lead to confusion and frustration for users, particularly those who are new to Plotly Express or classification plots in general.

Proposed Documentation Plan

To address the current limitations in documentation, we propose the following comprehensive documentation plan for px_classification:

1. Overview of px_classification

Description: A brief overview of the px_classification function, including its purpose, usage, and key features.
Syntax: The syntax for calling the px_classification function, including the required and optional parameters.
Example Use Cases: Real-world examples of how to use the px_classification function to create interactive classification plots.

2. Customizing the Plot

Column Selection: A detailed explanation of how to select the column(s) to be used for classification, including the importance of data types and aggregation.
Aggregation: A discussion on the different aggregation methods available, including their usage and limitations.
Data Types: A description of the data types supported by px_classification, including the importance of using the correct data type for optimal results.
Customizing the Plot: A guide on how to customize the appearance of the plot, including options for changing the layout, colors, and other visual elements.

3. Advanced Options

Sorting: A detailed explanation of how to sort the plot by different criteria, including the importance of using the correct data type for optimal results.
Grouping: A discussion on how to group the plot by different categories, including the usage and limitations of this feature.
Customizing the Legend: A guide on how to customize the legend, including options for changing the layout, colors, and other visual elements.

4. Troubleshooting

Common Issues: A list of common issues that users may encounter when using px_classification, including solutions and workarounds.
Error Messages: A description of the error messages that may be generated by px_classification, including their meaning and how to resolve them.

5. API Documentation

Function Signature: The function signature for px_classification, including the required and optional parameters.
Parameter Descriptions: Detailed descriptions of each parameter, including their usage, limitations, and default values.
Return Values: A description of the return values of px_classification, including their format and usage.

Implementation Plan

To implement the proposed documentation plan, we will follow these steps:

Conduct a thorough review of the current documentation for px_classification to identify areas for improvement.
Develop a comprehensive outline for the new documentation, including the proposed sections and content.
Create high-quality content for each section, including examples, code snippets, and visualizations.
Review and revise the content to ensure accuracy, clarity, and consistency.
Publish the new documentation on the Plotly Express website, including a clear and concise introduction to the function.

Conclusion

The proposed documentation plan for px_classification aims to provide a comprehensive and user-friendly resource for users to effectively utilize this function to create high-quality visualizations. By following the implementation plan outlined above, we can ensure that the new documentation meets the needs of users and provides a valuable resource for the Plotly Express community.

Future Work

In addition to the proposed documentation plan, we also propose the following future work:

Regularly update the documentation to reflect changes in the px_classification function and Plotly Express.
Conduct user testing to ensure that the documentation meets the needs of users and is easy to use.
Develop additional resources, such as tutorials, videos, and example code, to support users in their use of px_classification.

Introduction

The px_classification function in Plotly Express is a powerful tool for creating interactive classification plots. However, with great power comes great complexity, and it can be challenging for users to navigate the various options and parameters available. In this Q&A article, we answer some of the most frequently asked questions about px_classification to help users get the most out of this function.

Q: What is px_classification?

A: px_classification is a function in Plotly Express that creates interactive classification plots. It allows users to visualize the relationship between a categorical variable and a continuous variable, making it easier to understand and interpret the data.

Q: What are the key features of px_classification?

A: The key features of px_classification include:

Interactive plots that allow users to hover over data points to see more information
Customizable plot appearance, including colors, layout, and more
Support for multiple classification variables
Ability to sort and group data by different criteria

Q: How do I use px_classification?

A: To use px_classification, you need to have a Pandas DataFrame with a categorical variable and a continuous variable. You can then call the px_classification function, passing in the DataFrame and the column names for the categorical and continuous variables.

Q: What are the different aggregation methods available in px_classification?

A: The px_classification function supports several aggregation methods, including:

mean: calculates the mean of the continuous variable for each category
median: calculates the median of the continuous variable for each category
sum: calculates the sum of the continuous variable for each category
count: counts the number of data points for each category

Q: How do I customize the plot appearance in px_classification?

A: To customize the plot appearance in px_classification, you can use various options, including:

color: specifies the color of the plot
layout: specifies the layout of the plot, including the title, axis labels, and more
hover_name: specifies the name of the hover-over text
hover_data: specifies the data to be displayed in the hover-over text

Q: How do I sort and group data in px_classification?

A: To sort and group data in px_classification, you can use the sort_by and group_by options. The sort_by option allows you to sort the data by a specific column, while the group_by option allows you to group the data by a specific column.

Q: What are some common issues that users may encounter when using px_classification?

A: Some common issues that users may encounter when using px_classification include:

Error messages: users may encounter error messages if they pass in the wrong data type or if the data is not in the correct format.
Plot not displaying: users may encounter issues if the plot is not displaying correctly, such as if the data is not being sorted or grouped correctly.
Customization issues: users may encounter issues if they are trying to customize the plot appearance but are not using the correct options.

Q: How do I troubleshoot issues with px_classification?

A: To troubleshoot issues with px_classification, you can try the following:

Check the data: make sure that the data is in the correct format and that the column names are correct.
Check the options: make sure that you are using the correct options to customize the plot appearance.
Check the documentation: make sure that you are using the correct function and options as described in the documentation.

Conclusion

The px_classification function in Plotly Express is a powerful tool for creating interactive classification plots. By understanding the key features and options available in px_classification, users can create high-quality visualizations that help them understand and interpret their data. We hope that this Q&A article has been helpful in answering some of the most frequently asked questions about px_classification. If you have any further questions or need additional help, please don't hesitate to contact us.