Mistakes That Django Developers Make and How To Avoid Them

There is no doubt that Python is one of the most popular programming languages. Furthermore, it offers frameworks like Django and Flask. But Django is the most famous among Python developers as it helps them with a rapid development process and pragmatic design. For example, the Object Relational Mapping tool (ORM), routing, and templating features make things easier for developers. 

But despite these powerful features, there are many mistakes, such as bad application structure and incorrect resource placement, as well as writing fat views and skinny models, etc. These types of problems are not just faced by newbie Python developers, but they are also difficult ones for experienced Python developers as well.

In this article, I have listed the most common problems developers face and how to avoid them.

1. Developers Often Use Python’s Global Environment for Project Dependencies 

This mistake is often made by new Python developers who don’t know about the environment isolation features of Python. You must not use the global environment for project dependencies as it generates dependency issues. 

Moreover, Python will be unable to use various package versions simultaneously. Further, this will be a significant issue, as various other projects will need different, irreconcilable varieties of the same package. 

You can get rid of this problem by isolating Python’s environment:

Use Virtual Environment 

You can use a module named virtualenv since it is a tool that helps you build virtual environments in Python. These environments will be separated from those system environments. Using virtualenv will create a folder that will include all the important executable files that your Python project will require to use the packages. 

Virtualenvwrapper 

Virtualenvwrapper is a Python package that installs globally. When it comes to creating, deleting, and activating virtual environments, a Python package has a toolset for it. You can keep all the virtual environments in a single folder. 

Virtual Machines (VM)

This is one of the best ways to isolate your environment since an entire virtual machine is dedicated to your application. You can choose from a collection of tools that include VirtualBox, Parallels, and Proxmox. Furthermore, if you integrate it with a VM automation tool named Vagrant, it will deliver results beyond your expectations. 

Containers 

For container automation, you can use a Docker tool since it has a lot of third-party tools. Moreover, this tool includes a catching feature that helps to rebuild your containers at a rapid speed.

Further, this is very easy to implement, and when you have an idea of how Docker works, then you will find a lot of helpful images like Postgres, MongoDB, Redis, PySpark, etc.

So, these are the best ways you can use to master project dependency isolation as well as management. 

2: Developers Do Not Pin Project Dependencies in a requirements.txt File

Starting your Python project, you should start it with an isolated environment with a requirement.txt file. And when developers install packages via pip/easy_install, they must also add them to their requirement.txt file. Therefore, it will be way easier for you if you deploy a project on the server. 

There are various modules, parameters, and functions for different versions of packages. Even a small change in the dependency is capable of breaking your package. Hence, it is very important that you pin the particular version of your dependencies in your requirements.txt file. 

Moreover, there is a list of pip-tools available in Python, and with command-line tools, you will be able to manage those dependencies with ease. This tool is useful since it automatically produces a requirement.txt file that helps to pin all of those dependencies and even a complete dependency tree. 

Also, keep in mind to have a copy of your dependencies file in the file system, S3 folder, FTP, as well as SFTP. 

3: You Don’t Know the Advantages of Both Function-Based Views and Class-Based Views

If you use function-based views, then it is a traditional approach to implementing views. Being implemented as normal Python functions, these take an HTTP request as if it is an argument and, therefore, return an HTTP response.

Function-Based Views (FBVs)

Below, you can read the benefits of using function-based views (FBVs):

FBVs Offer Flexibility

FBVs facilitate a great level of flexibility that enables Python developers to utilize any of the Python functions as a view that can also include third-party libraries as well as custom functions. 

They Are Simple to Understand    

Function-based views are very easy to understand. Therefore, FBVs are a great choice for small projects as well as simple views.

Familiarity

Since FBVs utilize a function-based syntax, it will be very easy for Python developers to use them. Python developers are more likely to be comfortable with FBVs as they are already familiar with them. 

Class-Based Views

On the other hand, class-based views (CBVs) offer an abstract class that carries out general development tasks. Here are the benefits of CBVs:

Using Structured API

On top of object-oriented programming, you can also benefit from utilizing structured API. So as a result, your code will be clearer and readable. 

The Benefit of Code Reusability

CBVs are reusable, and you can extend and modify them with ease with the help of subclassing.     

It Offers Consistency

In order to manage various versions of HTTP requests, CBVs offer a consistent interface. 

CBVs Are Modular in Nature

Since these are modular in nature, you will divide those complicated views into smaller as well as reusable components.

4: Django Developers Write the Application Logic in Views Instead of Model

If you write the logic in views, then it will make your application view “fat” and not to mention your model “skinny.” So, it is important that you avoid this mistake and write the application logic in models rather than writing in views. 

Django developers can further work on breaking the logic into tiny methods, and then they can write them into the models. This will enable them to utilize it various times from multiple sources such as front-end UI, admin interface UI, API endpoints, etc. And they can do it in just a few lines of code. Thus, they will get rid of copying and pasting lots of code. 

Therefore, when you send an email to a user, you must extend the model with the help of an email function rather than writing the logic in views. 

Furthermore, this will enable your code to be easily unit-tested. This is because Python developers can test the email logic in one place. So, as a result, they will get rid of testing the email logic over and over again in each controller. 

Thus, next time you work on your project, write fat models as well as skinny views.