Now, I know what you might be thinking Oh great, another article on how to properly version my code. But don’t freak out, because this time its different.
To kick things off: why bother with versioning at all? Well, for starters, it helps you keep track of changes over time. And lets face it as Python developers, we love making changes! But sometimes those changes can cause problems down the line. Thats where versioning comes in handy. By keeping a record of each and every change, you can easily roll back to an earlier version if something goes wrong.
Now, there are many different ways to approach versioning in Python. Some people prefer using semantic versioning (which is great for those who like to follow rules), while others prefer just sticking with plain old numbers (because who needs fancy labels anyway?). But no matter which method you choose, the most important thing is consistency.
Heres a quick rundown of some popular Python versioning best practices:
1) Use semantic versioning This involves using three digits separated by periods to represent major, minor, and patch versions (e.g., 3.6.2). The first digit represents the major version number, which should only be incremented when there are significant changes or new features added. The second digit represents the minor version number, which should be incremented whenever a backwards-compatible change is made to existing functionality. And finally, the third digit represents the patch version number, which should be incremented for bug fixes and other small changes that dont affect compatibility.
2) Use Git tags This involves creating a tag in your repository for each release (e.g., v3.6.2). Tags are useful because they allow you to easily reference specific versions of your code, which can be helpful when working with collaborators or maintaining multiple branches.
3) Use version control tools There are many different version control tools available that can help you manage your Python projects (e.g., GitHub, Bitbucket). These tools provide features like branching, merging, and pull requests, which can make it easier to collaborate with others and keep track of changes over time.
4) Use a release manager This involves appointing someone who is responsible for managing the release process (e.g., creating tags, publishing packages). A release manager can help ensure that releases are consistent and follow best practices, which can be helpful when working on large or complex projects with multiple contributors.
5) Test your code before releasing Before releasing a new version of your Python project, its important to test your code thoroughly to make sure there arent any bugs or other issues. This can involve running automated tests (e.g., using pytest), as well as manually testing the code in different environments and scenarios.