Python Packaging and Execution Demystified

[Naval Kishor Upadhyay]

Python has become one of the most widely used programming languages in the world. It powers everything from automation scripts to large-scale web services and cutting-edge machine learning.

On the surface, Python feels easy:

python myscript.py

That single command seems straightforward, but under the hood Python is performing multiple complex steps. And when you want to share your Python project with others, you don’t just copy-paste your scripts—you rely on a sophisticated packaging ecosystem that makes software portable and installable anywhere.

This article will guide you through how Python executes your code and how Python applications are packaged and distributed.

1. The Python Execution Model​

When you run a Python program, you’re not directly executing the .py file. Instead, Python goes through a series of stages.

1. 
   Writing the code
   
   Everything begins with your source file, usually ending in .py. This is human-readable text written in Python syntax.
   
   
2. 
   Compilation to bytecode
   
   Before the code can run, Python compiles it into an intermediate form called bytecode. This bytecode is not machine code, but a lower-level set of instructions that is easier for the interpreter to execute. Python stores this in .pyc files inside the __pycache__ folder for reuse, so future runs of the same script can skip this compilation step.
   
   
3. 
   Execution inside the Python Virtual Machine (PVM)
   
   The bytecode is then handed over to the Python Virtual Machine (PVM), which executes it line by line. The PVM is the core engine of Python—it is what makes your code run on any system where Python is installed.
   

Unlike languages like Java, Python’s most common implementation (CPython) does not use Just-In-Time (JIT) compilation. This means execution is usually slower compared to compiled languages, but the trade-off is flexibility and portability.

Example: When you run python hello.py, Python first compiles it into bytecode, then the PVM reads and executes that bytecode step by step until your program finishes.

2. Bytecode and the Python Virtual Machine (PVM)​

Think of bytecode as a middle layer between your source code and the hardware. It is not tied to any specific machine or operating system, which is why the same .py file can run on Windows, macOS, or Linux without modification.

The PVM is the interpreter that understands this bytecode. Its job is to translate the instructions into actual machine operations at runtime. Because the PVM does this on the fly, Python is slower than languages that compile directly to machine code, but it gains tremendous portability.

To make an analogy:

• Writing Python code is like writing a recipe.
• Bytecode is like translating that recipe into a set of universal cooking steps.
• The PVM is the chef who reads the steps and cooks the meal in your kitchen.

No matter which kitchen you’re in (Windows, macOS, Linux), the chef (PVM) can make the same dish, as long as the bytecode is present.

3. Why Packaging Matters​

Running your own script is one thing. But what if you want to share your Python project with others? You could send them your .py files, but that creates several problems:

• Different machines may have different versions of Python installed.
• Your code may depend on external libraries, which others would need to install manually.
• Some packages include compiled components, which won’t work unless they’re built correctly on the target system.

This is why packaging exists. Packaging ensures that your Python code, dependencies, and metadata are bundled together in a consistent way so anyone can install it reliably.

Python mainly uses two distribution formats:

• 
  Source Distribution (.tar.gz)
  
  This format includes your raw source code. When someone installs it, their system needs to build it locally, which means compiling and resolving dependencies at install time. If their environment isn’t set up correctly, installation can fail.
  
  
• 
  Built Distribution (.whl, Wheel file)
  
  A wheel is a pre-built package. It’s already compiled and structured in the exact format that Python needs. Installing from a wheel requires no building—it’s essentially just extracting files. This makes installations much faster and more reliable.
  

Analogy:

A source distribution is like sharing a recipe along with all the ingredients—you still need to cook it yourself.

A wheel is like receiving a ready-made meal: you just need to unpack and eat it.

4. Wheels (.whl) in Detail​

The wheel format was introduced to solve the pain of installing Python packages that required compilation. Defined in PEP 427, it has become the standard format for distributing Python software.

Why wheels are important​

• Speed: Installing a wheel is just unzipping and copying files. No compilation needed.
• Stability: Wheels avoid installation failures that occur when compilers or system libraries are missing.
• Cross-platform compatibility: Developers can publish wheels targeted at specific platforms, ensuring they “just work” on those systems.
• Preferred by pip: Tools like pip will always try to install a wheel before attempting to build from source.

What’s inside a wheel?​

Although a wheel file looks like a single .whl, it is actually a ZIP archive with:

• The compiled or pure Python package code.
• A WHEEL file with format information.
• A METADATA file describing the package name, version, author, and dependencies.
• A RECORD file listing all contents and checksums.

This structure ensures consistency and makes it easy for pip to install.

Understanding wheel file names​

Consider this example:

numpy-1.26.4-cp311-cp311-win_amd64.whl

Breaking it down:

• numpy → The package name.
• 1.26.4 → The version.
• cp311 → Compatible with CPython 3.11.
• win_amd64 → Built for Windows, 64-bit.

With this naming scheme, pip can automatically determine whether a wheel is compatible with your system.

5. How pip Uses Wheels​

When you type:

pip install numpy

pip follows a clear process:

1. It queries the Python Package Index (PyPI) for available versions.
2. It looks for a wheel (.whl) matching your Python version and platform.
3. If a match is found, it downloads and installs the wheel immediately.
4. If no wheel is available, it falls back to a source distribution (.tar.gz), which must be built locally.

This fallback is the reason some installations are lightning-fast while others feel painfully slow. If you’ve ever watched TensorFlow compile for 30 minutes, it’s because pip couldn’t find a wheel for your environment.

The popularity of wheels has made such painful installs less common. Today, most major libraries publish wheels for multiple platforms, ensuring smooth installation experiences.

6. Conclusion​

Running Python code may seem trivial, but beneath the surface lies a powerful and well-structured system:

• Python compiles your code into bytecode, then executes it with the PVM.
• Packaging ensures your code can be shared and installed anywhere.
• Source distributions provide flexibility, but wheels have become the modern standard for speed and reliability.
• Tools like pip make the entire installation process seamless by preferring wheels and falling back to source only when necessary.

By understanding how execution and packaging work, you gain the ability to debug tricky installation errors, package your own libraries, and appreciate the infrastructure that makes Python development so productive.

The next time you run pip install, remember: you’re not just grabbing some files—you’re benefiting from years of work to make Python fast, portable, and developer-friendly.

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