The Supreme Guide to Understand the Workings of CPython

CPython is the standard Python software implementation or the default Python interpreter. The main purpose of CPython is to execute the programming language Python. CPython has great compatibility with various Python packages and modules. In this tutorial, you will get a detailed look at CPython.

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History of CPython

The first version of CPython was released in 1994 by the Python developer community. The project was originally sponsored by Google and was headed by full-time Google employees Thomas Wouters, Jeffrey Yasskin, and Collin Winter; however, most project contributors were not Google employees. It is directly downloaded from and written in the programming language C.  



Python is a high-level general-purpose programming language.


Implementation is all about what the interpreter ends up doing.

Machine Code

High-level languages use compilers to translate the source code into executable machine code. Further, the machine code directly gets executed by the CPU. Each machine code instruction performs a unique task, and every processor or processor family has its own machine code instruction set.


Bytecode is a binary representation executed by a virtual machine and not by the CPU directly. Here, a virtual machine converts binary instruction into a specific machine instruction. Java is one example.

Machine Code is much faster than Bytecode, but Bytecode is portable and secure compared to machine code.

Source Code of CPython

The CPython source distribution comes with various tools, libraries, and components.

To compile CPython from the source code, you need a C compiler, and some build tools according to your OS.

In Windows, to download a copy of the CPython source code, you can use git to pull the latest version to a working copy locally:

$ git clone

$ cd cpython

$ git checkout v3.8.0b4

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Why Is CPython Written in C and Not Python?

CPython is written in C, following a source-to-source compiler model. There are two types of compiler models:

  • Self-hosted compilers are compilers written in the same language they go on compiling, such as the Go compiler.
  • Source-to-source compilers are first written in another language that already possesses a compiler.

If you are developing a new language, you must write their compilers in a more established language. You have a very good example when it comes to the Go language. The first Go compiler was based on the C programming language. As and when Go could be compiled, the compiler was rewritten in Go.

Is Python an Interpreted or Compiled Language?

Before concluding, it’s best to understand both concepts.


The compilation is a way that translates the source code into machine-readable code. It takes the whole code file as input. 

In compilation, the code is once translated into machine code and can be run many times. It will not execute the machine-readable code it produced.



Interpretation is the process that takes a single line of code at a time and executes it. The interpreter executes the instruction specified in the source file and parallelly the program gets executed.


You must have heard that Python is an Interpreted language. In reality, it is both a compiled and an interpreted language.


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Working of CPython

CPython produces Bytecode which is Python-specific and then executes it.


  • The Compiler receives the source code.
  • It then keeps a check on the syntax in the source code.
  • If the Compiler runs into an error, it halts the translation process and shows an error message (Syntax error).
  • And if the instruction is well-formatted, then it translates the Python source code into a special low-level intermediary language called Bytecode.
  • This Bytecode is stored in .pyc files in a hidden directory and cached for execution and only understood by CPython.
  • Bytecode is then sent to the Python Virtual Machine (PVM). Python Virtual Machine runs the python code in the bytecode format and is part of the Python system.
  • PVM takes the bytecode instructions, inputs, and library modules.
  • PVM executes the instructions and in case any error occurs, it displays an error message (Runtime error). Else it results in the output.
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With this, you have learned about CPython and saw how it works. CPython is one of the many Python runtimes other than PyPy, Cython, and Jython.

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If you have queries or would like to provide your inputs to our editorial team regarding this “The Supreme Guide to CPython” tutorial, feel free to use the comments section below. Our team of SMEs will get back to you soon!


1. How is CPython different from other Python implementations?

The default Python implementation, CPython, is well-known for its reliability and broad support. It is built in C. It makes it simple to integrate Python programming with C libraries by compiling and interpreting Python code to bytecode. Although other implementations are tailored to particular contexts, such as Jython (Java-based), PyPy (JIT-compiled for speed), and IronPython (.NET-based), CPython is still the most popular because of its standard solid library and extensive compatibility.

2. What is the difference between PyPy and CPython?

The primary difference between PyPy and CPython lies in their execution speed and memory usage. CPython is the standard Python implementation written in C, known for its stability and extensive library support. It interprets Python code directly, which can result in slower execution for some tasks. PyPy, on the other hand, is an alternative implementation that uses Just-In-Time (JIT) compilation to translate Python code into machine code at runtime, significantly speeding up execution. PyPy also tends to use memory more efficiently. While CPython is more compatible with third-party C extensions, PyPy excels in performance, particularly for long-running applications.

3. Where is CPython used?

CPython is used in a wide range of applications due to its versatility, stability, and extensive library support. It is commonly employed in the following areas:

  • Web Development: Frameworks like Django, Flask, and Pyramid are built on CPython, enabling the creation of robust web applications.
  • Data Science and Machine Learning: Popular libraries such as NumPy, Pandas, SciPy, and TensorFlow rely on CPython for data analysis, statistical computations, and machine learning tasks.
  • Automation and Scripting: CPython is widely used for automating tasks, writing scripts for system administration, and managing IT infrastructure.
  • Software Development: CPython serves as a general-purpose programming language for developing desktop applications, games, and other software solutions.
  • Education and Research: Its simplicity and readability make CPython a preferred choice for teaching programming and conducting research in various scientific fields.
  • Embedded Systems: CPython is used in microcontroller programming and IoT applications, thanks to its ease of use and extensive libraries.

4. Can CPython be used with other languages?

Yes, CPython can be used with other languages in several ways. CPython, being written in C, allows seamless integration with C and C++ libraries through the Python C API, enabling the development of Python extensions and embedding Python in C/C++ applications. Additionally, tools like ctypes, cffi, and SWIG facilitate calling functions from shared libraries written in other languages such as C, C++, or Fortran. Beyond C/C++, CPython can also interact with Java via Jython, an implementation of Python that runs on the Java platform, and with .NET languages through IronPython, which runs on the .NET framework. These integrations enhance CPython's versatility, allowing developers to leverage libraries and frameworks from multiple programming languages within their Python projects.

5. What are the advantages of using CPython?

CPython, the default and most widely-used implementation of Python, offers several key advantages. Its stability and reliability make it a trusted choice for production environments, while its comprehensive standard library reduces the need for external dependencies by providing modules for a wide range of tasks. CPython's compatibility with C and C++ extensions allows for high-performance extensions and embedding Python in C/C++ applications, enhancing functionality and performance. 

It benefits from a vast ecosystem of third-party libraries and strong community support, ensuring continuous development and ample resources for troubleshooting. Additionally, CPython is platform-independent, running on various operating systems like Windows, macOS, and Linux, and maintains Python's simplicity and readability, making it accessible for both beginners and experienced developers. These features make CPython a powerful and versatile choice for diverse applications, from web development and data science to automation and software development.

About the Author

Abhisar AhujaAbhisar Ahuja

Abhisar Ahuja is a computer science engineering graduate, he is well versed in multiple coding languages such as C/C++, Java, and Python.

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