


Extending and Overriding Django Admin Templates with Overriding Template Loader
The challenge of extending and overriding Django admin templates without replacing them has been a common concern among developers. Despite previous discussions on this topic, the issue remains prevalent for projects using the app_directories template loader.
One workaround involves duplicating admin templates and extending from the copies, but this adds complexity and maintenance overhead. To simplify this process, a custom template loader has been developed that enables developers to extend any template within a specific app.
Implementing the Overriding Template Loader
To extend an admin template, for instance, admin/index.html, developers can create their own template with the same name in their app's templates directory, and include the following line at the start of the template:
{% extends "admin:admin/index.html" %}
Within this extended template, developers can define blocks to override specific portions of the base template. For example, to add extra links to the sidebar, they can create the following block:
{% block sidebar %} {{block.super}} <div> <h1 id="Extra-links">Extra links</h1> <a href="/admin/extra/">My extra link</a> </div> {% endblock %}
Addressing the Issue in Django
As of the latest and previous LTS versions of Django (3.2, 2.2, 1.11), the overriding and extension of admin templates using the app_directories template loader remains a documented issue. However, there is no indication of whether this will be addressed in future versions of Django.
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

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Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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