Writing a Custom Python Library Based on Open Source Ones

What will you learn?

Explore the art of crafting a custom Python library by leveraging existing open-source libraries to enhance functionality and tailor solutions to specific needs.

Introduction to the Problem and Solution

When faced with the need to extend functionalities of existing open-source Python libraries, creating a custom library that builds upon them emerges as an elegant solution. This approach allows for seamless integration of new features or alterations in behavior without directly modifying the original source code. By embracing inheritance and composition in object-oriented programming, we can create a custom library that not only meets our unique requirements but also maintains a clean separation from the base library.

To accomplish this, we utilize the principles of inheritance and composition: – Inheritance: Extending classes from open-source libraries to inherit their functionalities. – Composition: Using components from open-source libraries within our custom classes for enhanced capabilities.

By combining these concepts, we can develop a specialized Python library that caters specifically to our needs while benefiting from the reliability and robustness of established open-source libraries.


# Import necessary modules from open-source library
from external_library import BaseClass

# Define a custom class extending functionality
class CustomClass(BaseClass):
    def __init__(self, custom_param):
        self.custom_param = custom_param

    def custom_method(self):
        # Implement your customization here

# Example usage of our custom class
custom_obj = CustomClass(custom_param_value)

# Copyright PHD

Note: Replace external_library, BaseClass, CustomClass, custom_param, and custom_method with relevant names based on your implementation.

Credits: PythonHelpDesk.com


In the provided code snippet: – We import essential modules/classes from the open-source library. – Define a new class (CustomClass) that inherits behaviors from an existing class (BaseClass) in the open-source library. – Customize methods or attributes within our custom class as per specific requirements. – Employ instances of our customized class for extended functionalities while ensuring compatibility with the base library.

This approach promotes modularity, reusability, and maintainability within our codebase. It facilitates easy updates to the underlying open-source library without compromising our customized features.

    How do I decide which functions/methods to customize in my custom Python library?

    Tailor functions/methods based on project requirements; focus on areas needing modification or additional features.

    Can I distribute my custom Python library as an independent package?

    Yes, utilize tools like PyPI for packaging your custom library, enabling easy installation by others.

    Is it possible to contribute back enhancements to the original open-source project?

    Certainly! Submit your generic enhancements as contributions via pull requests if they benefit others.

    How do I handle conflicts with my customization when upstream changes occur in the open-source project?

    Review update release notes carefully; adjust your customization accordingly during updates triggered by upstream changes.

    Should I thoroughly test my custom Python library before deploying it in production environments?

    Absolutely! Comprehensive testing is vital before deploying any software into production´┐Żincluding customized Python libraries.

    Can multiple developers collaborate on building a single extension for a custom Python library?

    Yes, version control systems like Git facilitate collaboration among multiple developers working on extending/customizing shared codebases.


    Crafting a tailored Python Library by extending functionalities offered by reputable Open Source Libraries empowers flexibility while upholding robust solutions rooted in tested codes. This practice ensures reusable components, fostering modular development practices that lead to leaner codes ready for swift deployment across diverse applications relying on complex dependencies under varying scenarios necessitating dynamic adjustments.

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