10 Python Versioning Best Practices

Python is a programming language with many different versions and implementations. As a result, there are a few different ways to version your code. In this article, we will discuss 10 best practices for versioning your Python code.

1. Use the latest version of Python 3

Python 3 has been around for over a decade now, and the language has seen significant improvements during that time. Newer versions of Python 3 include features like type annotations, asyncio, and improved performance. In addition, many popular libraries and frameworks have dropped support for Python 2, so using the latest version of Python 3 is the best way to ensure you have access to all the latest features and libraries.

2. Don’t use Python 2 unless you have to

Python 2 is no longer supported by the Python community. What this means is that there are no more security updates or bug fixes being released for Python 2. So if you’re using Python 2, you’re essentially running on an unsupported version of the language which could leave you vulnerable to attack.

Additionally, many popular libraries and frameworks have also stopped supporting Python 2, so you may find yourself unable to use certain features or packages unless you upgrade to Python 3.

Ultimately, it’s in your best interest to migrate to Python 3 as soon as possible to take advantage of the latest features, security updates, and bug fixes.

3. Always specify a version in your requirements file

When you install a package from PyPI using pip, it will by default install the latest version of that package. However, what if a new version is released and it breaks your code? By specifying a particular version in your requirements file, you can ensure that your code will always be compatible with the packages you’re using.

To specify a version, simply add =={version} after the package name in your requirements file. For example, if you want to use version 3.4.1 of the requests package, you would add requests==3.4.1 to your requirements file.

4. Specify a range of versions in your requirements file

When you specify a single version number in your requirements file, like so:

Django==1.8
You’re saying that your code will only work with Django 1.8 – no other versions. But what happens when Django 1.9 is released? Your code will break, because it’s not compatible with Django 1.9.

On the other hand, if you specify a range of versions in your requirements file, like so:

Django>=1.8,<1.9
You’re saying that your code will work with any version of Django from 1.8 up to (but not including) 1.9. So when Django 1.9 is released, your code will still work, because it’s compatible with Django 1.9.

This is a much more flexible approach, and it’s the recommended way to specify dependencies in your requirements file.

5. Pin all dependencies, including Django and other libraries

When you’re working on a project, you might be tempted to use the latest and greatest versions of everything in order to get all the new features and bug fixes. However, this can lead to problems down the road because newer versions of dependencies might not be compatible with each other or with your code.

Pinning dependencies ensures that everyone working on a project is using the same versions, which makes it much easier to reproduce bugs and to know that a particular feature will work as expected. It also makes it easier to deploy projects because you don’t have to worry about whether the dependencies on the production server are the same as the ones on your development machine.

6. Run automated tests against multiple versions of Python

As a Python developer, you’re probably aware of the fact that there are multiple versions of Python in active use at any given time. What you might not be aware of is that each new version of Python introduces subtle changes that can break your code.

By running automated tests against multiple versions of Python, you can catch these breakages before they cause problems for your users. Not only will this save you time and effort in the long run, it will also give you the peace of mind that comes with knowing your code is robust and reliable.

7. Test on major operating systems

Different operating systems have different ways of handling Python code. For example, Windows uses a different set of instructions than Linux. As a result, your code might work on one operating system but not on another.

To avoid this problem, it’s important to test your code on as many major operating systems as possible. That way, you can be sure that your code will work on the majority of systems.

One easy way to do this is to use a tool like Docker. Docker allows you to create “containers” that contain all the necessary files and dependencies for your code to run. This means you can easily test your code on multiple operating systems without having to install each one separately.

8. Deploy with pinned versions

When you deploy your code, you want to make sure that the dependencies you’re using are going to be there when you need them. That way, you can avoid any potential issues that might arise from using different versions of dependencies in different environments.

Pinning dependencies ensures that everyone is using the same versions, which makes it much easier to track down and fix any potential issues.

9. Monitor for security vulnerabilities

As a Python developer, you’re probably aware of the fact that there are two main versions of Python in active use today: Python 2 and Python 3. While Python 3 is the newer and more feature-rich version of the language, many older applications still rely on Python 2 for compatibility reasons.

This can pose a security risk, as vulnerabilities that have been patched in Python 3 may still exist in Python 2. Therefore, it’s important to keep an eye on both versions of the language and make sure that you’re using the most up-to-date and secure version of Python for your application.

The best way to do this is to use a tool like pyup.io, which will automatically monitor for new versions of Python and notify you when an update is available.

10. Upgrade regularly

New versions of Python are released every few months, and each new release brings with it a host of new features, bug fixes, and performance improvements. By upgrading to the latest version of Python, you can take advantage of all these new features and improvements.

Additionally, new versions of Python often drop support for older versions. This means that if you’re using an older version of Python, you may not be able to take advantage of new features and improvements, and you may also be at risk of security vulnerabilities.

Therefore, it’s important to upgrade to the latest version of Python on a regular basis to ensure that you’re always up-to-date.