Adventures in Machine Learning

Exploring CPython Internals: Unleashing Python’s Full Potential

Understanding CPython Internals

Python has become one of the widely used programming languages today. It is considered one of the most popular languages for developers to use thanks to its simplicity and ease of use. However, if you’re a Pythonista, then understanding the internals of CPython is a must.

This knowledge will help you optimize the performance of your Python code and create more efficient software. In this article, we’ll be discussing the importance of understanding CPython internals and exploring how to get the CPython source code.

Why is understanding CPython internals important?

Before we delve into the details of CPython internals, let’s first understand why it’s important to understand them.

  • Firstly, by knowing how CPython works under the hood, you can easily optimize your code for peak performance. You can also more effectively debug your code by understanding how the interpreter performs the tasks necessary to execute it.
  • Secondly, understanding CPython internals is also essential when you need to extend Python’s functionality. Suppose you want to create a new module or library, or want to improve an existing one. In that case, you’ll need to have a deep understanding of the CPython internals.

Getting the CPython Source Code

To start exploring CPython internals, it’s essential to download the source code. The CPython source code is readily available, and you can download it from the official Python website.

You can find source distributions files from the Downloads page. The CPython source code comes with various tools that allow you to get the most out of its functionality.

Among the tools included are:


The interpreter executes Python code. It’s the essential component of the CPython source code, responsible for running Python code from source files and interactive shell.


CPython comes with a standard library that consists of numerous modules that you can use in your Python projects. You’ll find modules that enable you to work with regular expressions, manage file I/O, and perform various network operations.


Many modules are available to use with Python, such as NumPy or Pandas. These are known as third-party libraries.

You can create these extensions by writing custom C code that interacts with the Python interpreter.


The CPython codebase is divided into many components that perform various operations.

These components include:

  • Objects: Deal with data objects like lists, strings, and dictionaries
  • Modules: Implements the module loading and importing system
  • Parser: Compiles the code and creates a parse tree
  • Bytecode: Used to execute the compiled code
  • Compiler: Converts Python source code to bytecode
  • Interpreter: Interpret bytecode and execute code
  • Memory manager: The memory manager of CPython is responsible for managing memory for Python objects.

Exploring CPython tools, libraries, and components

The best way to understand the components of CPython is to explore them.

You can start by reading the source code of the interpreter, which you will find in the directory called “Python.” The compiler is found in the directory labeled “Python/compile.” Then, look at the “Python/Include” directory, which holds all the header files that Python uses. Modules can be found in their directories, such as “Python/moduel-stubs.” The Library folder holds all the Python library code.

Finally, it’s worth noting that CPython is an open-source project. Therefore, you can contribute to its development and extend its capabilities by submitting pull requests, bug reports, or feature requests.


In conclusion, understanding CPython internals is crucial for any Python developer as this knowledge will help you optimize the performance of your Python code and create more efficient software. To explore CPython internals, you need to download the source code, familiarize yourself with its tools, libraries, and components.

By doing so, you’ll be able to contribute to the development and improvement of Python’s functionality.

3) Setting Up Your Development Environment

As Python is an interpreted language, it may be easy to forget that it is still implemented in C. Although you don’t need to know C to program in Python, having a basic understanding can help when working with the Python interpreter’s C API.

When setting up the development environment for Python and C, there are different tools and configurations required. However, most of these are standard tools and inexpensive to set up.

For Python, all you need is an editor like Visual Studio Code and an interpreter that would come in handy to test out short code snippets and scripts. You can obtain Python from the official website.

You will find a stable release of Python that can be downloaded and installed or use the package manager of your operating system. Once Python is installed, you need to configure your environment variables for Python’s executable.

For C programming, you need a compiler, build tools, and a text editor to help you create and compile your code such as Visual C++, MinGW, Code::Blocks, and others. Once the compiler is installed, just like setting up Python, you want to configure environment variables that point to the location of the compiler executables.

With a basic understanding of C and Python, you can now configure your development environment to include both languages. Depending on the platform you are using, you may need to install specific components before proceeding.

The steps below will guide you on how to configure an environment for C and Python:

  1. First, download and install Python from the official website.
  2. Next, install a C compiler like Visual C++, GCC, Code::Blocks, MinGW, or any other compiler of your choice.
  3. After installing the C compiler, ensure that the development kit is included if required.
  4. To use the C compiler on the command line, add the path to the directory containing the executable to your environment variables.
  5. Finally, set up an Integrated Development Environment (IDE) or a code editor like Visual Studio Code to work with both languages in one environment.

4) Compiling CPython

Compiling CPython allows you to have a Python executable that you can use to run Python code. To obtain the CPython source code, go to the official website, locate the source distribution file, and download it to your local machine.

After that, follow the steps below to compile CPython:

  1. Extract the contents of the file you downloaded to a local directory on your machine.
  2. Navigate to the extracted directory which should contain the ‘configure’ script.
  3. Open a terminal or command prompt in the directory, and run the ‘configure’ script.
  4. This will configure the build for CPython. After the configure script runs successfully, run the ‘make’ command. This will create the Python interpreter executable.
  5. Finally, run the ‘make install’ command to install the Python executable to your local machine.

The ‘configure’ step can take some time since it configures the build for your local system. In case you encounter any issues during this process, check the configure output for errors to help you resolve them.

Once the ‘make install’ command runs successfully, the Python interpreter should be installed onto your system. You can also specify where to install the Python interpreter with the ‘–prefix’ option like `./configure –prefix=/usr/local/python`.

As you compile CPython, you will encounter some components like “ceval”, “Objects” and “Modules,” which directly relate to specific parts of Python’s functionality. The “ceval” module is responsible for running each bytecode instruction of compiled Python code, “Objects” module holds several basic object types, and “Modules” implements the importing and module lookup subsystem of Python.

Creating an executable interpreter

After you have successfully compiled CPython, you will have an executable interpreter ready for use. The executable interpreter will read Python source code files, compile them into bytecode, and run the bytecode.

The creation of an executable interpreter makes it possible to deploy Python applications across systems, as you no longer need to have Python installed to run an application. To run a Python script using the compiled interpreter, you can navigate to the directory containing the script and execute the script using the executable interpreter.

The syntax to run a Python script in the terminal or command prompt is as follows, `python`. In conclusion, setting up a development environment for both C and Python requires installing specific tools and configurations.

Compiling CPython involves downloading CPython source code, configuring the build using the configure script, and creating an executable interpreter by running the make commands. With a basic understanding of C and Python, compiling CPython will allow you to create an executable interpreter that can be used to run Python source code files.

5) The Python Language and Grammar

Python is a high-level programming language developed by Guido van Rossum in the late 1980s. It is designed to be easy to learn and use, while also being powerful and expressive.

While Python is an interpreted language, CPython, the standard implementation of Python, is written in C. This is because C is a low-level language that offers the type of control that Python requires for efficient execution.

One of the benefits of CPython being written in C is that it makes it easy to use Python with other languages that are also written in C, such as C++, Java, and Ruby. Additionally, because CPython is the most widely used implementation of Python, and because it is written in C, it has strong support for the majority of operating systems and platforms.

Python’s grammar and syntax are documented in its Language Specification. The specification defines the structure of Python’s code and provides a set of rules for the Python interpreter to follow when executing code.

It is the go-to reference for Python developers who want to understand how Python works and how to write Python code. The specification defines the syntax of Python, which includes rules about how to construct literals, variables, operators, expressions, statements, and code blocks.

It also defines the rules for white space and comments, as well as the recommended naming conventions for Python code. To interpret Python code, the interpreter makes use of a Parser Generator.

A Parser Generator is a tool that creates a parser, which is a program that analyzes the structure of a piece of code, determines its syntax, and produces an abstract syntax tree that can be executed by the interpreter. Python’s Parser Generator is called Ply.

Ply reads the grammar of the Python language specification and generates a parser that implements the rules. The parser then reads Python code, checks its syntax against the language specification, and generates an abstract syntax tree.

The abstract syntax tree is then executed by the Python interpreter.

6) Configuration and Input

To execute Python code, you need to put it into a state that can be executed. This process involves two steps: first, configuring the Python environment, and second, inputting the code.

Python configuration involves setting up various options to ensure that Python behaves the way you want it to. This can include setting system-wide environment variables, Python-specific environment variables, or configuring Python on a per-project basis.

Configuration can also include setting default directories, configuring third-party modules, installing libraries, and updating the Python environment. One of the most crucial elements of Python configuration is setting up a virtual environment.

A virtual environment creates an isolated Python environment that is separate from the global environment. This is particularly important when working on multiple projects simultaneously, as different projects may require different packages or versions of libraries.

To input Python code, you use a text editor or an Integrated Development Environment (IDE). A text editor is a simple tool for writing and editing text files, including Python code.

An IDE is a more advanced tool that provides features like syntax highlighting, code completion, code navigation, and debugging. Once you have written your Python code, you can execute it in different ways.

One way is to use the Python interpreter to execute the code in the terminal or command line. Another way is to use an IDE or text editor that has built-in support for executing Python scripts.

Finally, you can also package and distribute your code as an executable or as a library. In conclusion, understanding the Python language specification and how inputting code into a Python environment works is essential for Python developers.

CPython being written in C provides Python with the control and efficiency necessary for efficient execution. The Ply Parser Generator, which reads the Python language specification and generates a parser, is critical to interpreting Python code.

Configuring Python, including setting up a virtual environment, a vital step to ensure that Python behaves how you want. The input of Python code can be executed in different ways, including using the Python interpreter in the command line, executing Python scripts in text editors or IDEs, and packaging and distributing the code as an executable or a library.

7) Lexing and Parsing with Syntax Trees

When you write code in a programming language, the computer needs to translate that code into something it can understand and execute. This process involves two critical steps: lexing and parsing.

Lexing involves breaking down the code into a series of tokens, which represent the fundamental building blocks of the code, such as keywords, identifiers, and literals. The resulting stream of tokens is then passed to the parser, which uses the grammar of the language to construct an abstract syntax tree (AST).

The AST is a logical structure that represents the syntactic structure of the code, and it is this structure that the interpreter or compiler operates on when executing the code. In Python, lexing and parsing are handled by the built-in “ast” module.

The ast module works with Python source code and converts it into an abstract syntax tree that can be used by the interpreter. The ast module provides interfaces for parsing Python source code and walking the resulting AST tree.

The ast module comes with several functions that can parse Python code, such as parse() and parseString(). These functions create an AST from Python source code or a string containing Python source code.

You can also access the AST by importing specific classes such as Attribute, Call, Load, and Store, and more.

8) The Python Compiler

Once the AST has been created, the next step is to compile it into a code object that can be executed by the Python interpreter. Compilation involves translating the logical structure of the AST into instructions that the Python virtual machine can execute.

These instructions are called bytecode, and they are represented as a sequence of integers. Bytecode is a low-level representation of the code that the interpreter uses to execute Python programs.

CPython is the reference implementation of Python, and its bytecode is stored in a “.pyc” file. When you run Python code, the interpreter first checks if there is a corresponding “.pyc” file.

If there is, the interpreter will load the bytecode from the “.pyc” file instead of recompiling the AST. If there is no “.pyc” file, the interpreter will compile the AST into bytecode and then execute it.

The CPython bytecode makes use of an internal stack to execute instructions. Most bytecode instructions use the stack to pass arguments to a function or return values from a function.

Some of the most common instructions in CPython bytecode include LOAD_CONST, LOAD_NAME, and CALL_FUNCTION. The LOAD_CONST instruction pushes a constant value onto the internal stack.

The constant value is typically a literal value, such as a number, string, or boolean. The LOAD_NAME instruction looks up the value of a name in the current namespace and pushes it onto the stack.

The CALL_FUNCTION instruction calls a function with the arguments passed on the stack. In conclusion, understanding the lexing and parsing processes in Python is crucial for developers who want to build software that integrates with Python.

The “ast” module in Python provides interfaces for parsing Python source code and creating an AST from it. Once the AST is created, the next step is to compile it into bytecode, which is represented as a sequence of integers that the Python interpreter can execute.

The bytecode used by the CPython interpreter makes use of an internal stack to execute instructions. By understanding these essential concepts, you can create and optimize Python code to make the most out of its potential.

9) The Evaluation Loop

The Evaluation Loop, also known as the execution loop, is the central component of the CPython interpreter. It is responsible for reading and executing Python code. The evaluation loop is a simple process that continues to run until a Python program is finished.

The evaluation loop works by repeatedly reading the next instruction from the bytecode and then executing it. The execution of a bytecode instruction can involve a variety of actions, such as loading a value onto the stack, calling a function, or performing an arithmetic operation.

The evaluation loop continues to run until it encounters a special instruction that signals the end of the program. The evaluation loop is a fundamental part of how the CPython interpreter works, and it is essential for understanding how Python code is executed.

10) Object Model and Garbage Collection

One of the key features of Python is its object-oriented nature. Python uses an object model where everything is an object, including variables, functions, and classes.

In CPython, objects are represented as C structures that hold the object’s data and its type information. The CPython interpreter uses a reference counting garbage collector to manage memory for Python objects. This means that the interpreter keeps track of how many references there are to each object, and when an object’s reference count drops to zero, the object is automatically destroyed, and its memory is reclaimed.

Reference counting is a simple and efficient garbage collection strategy, but it can sometimes be inefficient for circular references. This is why the CPython interpreter also uses a cycle detection algorithm to break circular references and reclaim the memory they occupy. Understanding Python’s object model and how garbage collection works is essential for developers who want to write efficient and performant Python code.

11) Python’s C API

CPython exposes a C API that allows developers to write custom C extensions that interact with the CPython interpreter. This API is essential for extending Python’s functionality and integrating with other languages that are written in C.

The C API provides a set of functions that allows developers to access Python’s internal data structures and objects, call Python functions, and interact with Python’s built-in modules. The C API is well-documented and provides a wealth of functionality for developers who want to extend Python’s capabilities.

12) Conclusion

In conclusion, understanding the internals of CPython is essential for any Python developer who wants to write efficient, performant, and robust code. By understanding how Python works under the hood, you can optimize your code for peak performance, extend Python’s functionality, and write custom extensions that interact with the CPython interpreter. The knowledge gained from understanding CPython internals will help you become a better and more versatile Python developer.

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