How Python Works Under the Hood: Understanding __pycache__ and the Python Execution Process

When writing Python programs, you might have noticed a mysterious folder named __pycache__ appear after importing modules. Ever wondered what it does and why it’s created? In this blog, we’ll dive deep into the inner workings of Python, exploring how your code is compiled, stored, and executed.

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What is __pycache__?

The __pycache__ folder stores compiled versions of the Python files you import. Instead of working directly with the .py source files every time, Python converts them into bytecode — a more efficient, lower-level representation of your code. This bytecode is saved as .pyc files inside the __pycache__ folder.

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Why does Python create __pycache__?

The purpose of __pycache__ is simple: performance optimization.
When Python runs, it doesn’t directly execute your .py files. Instead, it compiles them into bytecode. If it had to recompile every imported file each time you ran your program, it would slow things down. So, Python caches this compiled bytecode to quickly load it on subsequent runs, speeding up execution dramatically.

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How are files in __pycache__ named?

Inside the __pycache__ folder, you’ll find files named like this:

<file_name>.cpython-3.13.pyc

Let’s break that down:

• file_name — The name of the Python file you imported.

• cpython — The Python interpreter used. CPython is the most common implementation, but there are others:

  • Jython — Python running on the Java platform.

  • IronPython — Python for .NET frameworks.

  • Stackless Python — A Python variant for micro-threading.

  • PyPy — An efficient Python interpreter with Just-In-Time (JIT) compilation.

• 3.13 — The version of Python you’re using (for example, Python 3.13).

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How does Python handle changes to files?

Python is smart enough not to waste resources. If you modify the source file, Python uses a diffing algorithm to detect changes and automatically generates an updated .pyc file inside __pycache__. This prevents the interpreter from recompiling the entire file unnecessarily.

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What happens after bytecode compilation?

Here’s where things get interesting:

• The compiled .pyc file isn’t machine code; it’s bytecode — a portable, intermediate representation.

• This bytecode is fed into the Python Virtual Machine (PVM).

• The PVM is an execution engine that runs in a loop, interpreting bytecode instructions one by one and handling execution behind the scenes.

• Since bytecode isn’t native machine code, it can’t be directly processed by the CPU. The PVM acts as a translator that bridges this gap.

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Key details you should know:

• The __pycache__ folder is created only for imported modules, not for the top-level script you run.

• On most online compilers and cloud-based IDEs, the __pycache__ folder and .pyc files are hidden to reduce clutter.

• On your local machine, these files are visible and help speed up your program execution, especially in larger projects with multiple dependencies.

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In a nutshell

The __pycache__ folder and the entire bytecode compilation process are part of what makes Python efficient and easy to use. By caching compiled bytecode and using the Python Virtual Machine for execution, Python ensures that your programs run faster and more smoothly.

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Here’s a great explainer video on Python bytecode by Hitesh Choudhary