You might have noticed it before you try to import a module from your favorite package and suddenly, boom! The file is marked as read-only.

Now, let me tell you something: this isn’t Python being mean or trying to mess with our heads. It’s actually just a quirk in the way Windows handles files that are opened for writing but have their attributes set to read-only. And it’s not like we can blame Microsoft for this they’ve been dealing with these issues since the early days of DOS, and let’s face it: Windows is still pretty finicky when it comes to file permissions.

So what happens exactly? Well, when you try to import a module that has its source code in a read-only file on Windows, Python will automatically create a .pyc bytecode cache for that file. This is great and all, but here’s the catch: if the original file is marked as read-only, then the bytecode cache will also be created with those same permissions!

This can cause some serious headaches when you try to modify or delete the source code later on because now, Python thinks that it needs to keep a copy of the original file in its own little corner. And if you’re working on a team project and someone else is also trying to edit that same module at the same time… well, let’s just say that things can get messy pretty quickly.

But don’t freak out, my fellow Pythonistas! There are ways around this issue although they might require some extra effort on your part. For starters, you could try running Python with administrator privileges (assuming you have them), which should allow you to modify the files as needed. Or you could try using a different IDE or text editor that doesn’t rely on .pyc caches at all although this can be tricky if you’re used to working with PyCharm, for example.

Another option is to simply disable bytecode caching altogether by adding the following line to your Python interpreter:

# Import the sys module
import sys

# Set the dont_write_bytecode attribute to True to disable bytecode caching
sys.dont_write_bytecode = True

# This line prevents the creation of .pyc files, which are used for bytecode caching
# This can be useful when working with an IDE or text editor that relies on .pyc caches
# However, it can be tricky if you're used to working with PyCharm, for example
# By setting dont_write_bytecode to True, we are disabling this feature altogether

# This line adds the dont_write_bytecode attribute to the sys module
# This allows us to access and modify the attribute directly using sys.dont_write_bytecode

# This line sets the attribute to True, effectively disabling bytecode caching
# This means that .pyc files will not be created, and any changes made to the code will be reflected immediately without the need to recompile

# This line is not necessary, but it is good practice to import the sys module before modifying its attributes
# This ensures that the module is available for use before attempting to modify it

This will prevent Python from creating those ***** .pyc files in the first place, which can be a huge relief if you’re working on multiple projects at once and don’t want to deal with conflicting bytecode caches.

Of course, there are some downsides to this approach as well for one thing, it will significantly slow down your Python startup times (since the interpreter won’t be able to load any of its built-in modules from cache). And if you’re working on a large project with lots of dependencies and third-party packages, then disabling bytecode caching might not even be an option since it can take forever for Python to import all those files every time you start your script.

So what’s the solution? Well, unfortunately, there isn’t one that works for everyone. Some people prefer to use a different IDE or text editor altogether (like Sublime Text or Atom), while others swear by using virtual environments and managing their dependencies with tools like pip or conda. And if you’re working on a team project, then it might be worth investing in some kind of version control system that can handle conflicts between different versions of the same file although this can be tricky to set up and maintain over time.

In any case, I hope this article has shed some light on this little-known issue with Python’s behavior on Windows. And if you have any other tips or tricks for dealing with read-only files in Python (or any other programming language), then please feel free to share them in the comments below!