Introduction To Cython

Introduction

In the world of programming, where there is a demand for better and faster solutions to problems, the demand for languages that provide programmers with a high degree of performance has risen. In this blog post, you will learn about Cython programming. Cython is a combination of Python and C programming language that enables us to create fast and small Python programs. This article covers everything about Cython Programming that you need to know, including its history, advantages, installation instructions, and much more.

What is Cython?

Cython is a hybrid programming language that combines the dynamism and readability of Python with the performance and static typing capabilities of compiled languages like C or C++. This makes it an excellent option for developers who want to build fast, reliable Python packages that have access to low-level system libraries.

Cython’s syntax is very similar to standard Python, so you should be able to learn its basics in just a few minutes. The syntax may feel familiar, but under the hood, it’s nothing like standard Python. Cython is not dynamically typed and has a very different type system than standard Python. You can think of it as ‘restricted’ Python, which has some limitations on what and how you can do.

Why use Cython?

The main reason to use Cython is to increase performance in Python code. Cython does this by taking the Python source code and compiling it down to equivalent C code. This allows for fast execution of Python code by using the speed of C code.

These are some other advantages of using Cython:

It reduces the size of the code.
It enables you to call C libraries from Python.
It increases the parallelism of code.
It enables Python code to run on other platforms.
It reduces the dependency on other languages.
It provides access to more features of Python.
It provides access to debugging and profiling tools.

Install Cython

To get started with Cython, you’ll first need to install it. Cython is currently included in the standard library of some Linux distributions, so you may be able to install it with your package manager. If not, you can download Cython directly from the official website. Cython is compatible with Python 2 and Python 3.

Commands for installing Cython:

pip install cython

Ways to compile Cython:

Compiled and run by an IPython interpreter
Automatically at import time
By building tools like Python’s distutils
Integrated into standard build systems

The installation of Cython on various operating systems, such as Windows, Linux, or Mac OS, is covered in detail in this video. Click here for more details.

Working with C Libraries in Cython

Cython’s ability to access low-level system libraries makes it a great option for building Python packages that rely on C. For example, the NumPy and SciPy scientific computing libraries are written in Cython and rely on low-level system libraries.Cython is a superset of Python, which means you can run standard Python code in a Cython module. This means that you can also use Python’s built-in libraries from within your Cython code. You can also use Python’s “cimport” syntax to import and call functions and classes from C libraries. In Cython, you would write this with the “cdef” syntax.

Python Vs Cython

Compatibility with Python: First and foremost, Cython is compatible with Python. This means that you can use the two languages together to build powerful applications and programs. You can also use Cython to extend the functionality and syntax of Python.

Access to C Libraries: Cython allows you to call C libraries from Python. This allows you to use the functionality of C libraries in your Python code. This is helpful because many high-performance libraries exist in C.
Access to C Compiler: Another advantage of Cython is that you don’t need to use an interpreter to execute the code. Instead, you can use a C compiler, which makes the code execution faster.

Parallel code execution: Another advantage of using Cython is that it allows you to execute parallel code. Cython allows you to take advantage of thread-level parallelism.

Other Platforms: Cython enables you to write code that can run on other platforms. This means that you can use Python to write code that can run on Windows, macOS, Linux, and even other operating systems.

Reduced dependency on other languages: Cython allows you to access Python features that are not Python-specific. This reduces the dependency on other languages and allows greater interoperability.

Access to debugging tools: Cython allows you to take advantage of debugging and profiling tools that are available only to compiled code.

Access to Python features: Cython lets you access Python features such as introspection and metaclass.

Cython Limitations

Compilation time: One of the main limitations of Cython is that the compilation process can be slow. This is because the compilation process is done for each Python file one at a time.
Code size: Another limitation of Cython is that the compiled code is larger than the equivalent Python code. This can be problematic if you are dealing with large projects or files.
Error handling: Another limitation of Cython is that it does not support exceptions in the compiled code. This means that you cannot use the try/catch block to handle errors. Instead, you have to use the error-handling process of your programming language.
Documentation: Another limitation of Cython is that the documentation is limited. This means that it can be difficult to find information about the language.
Parallelism: One of the limitations of Cython is that it does not support parallelism by default. You have to take special steps to take advantage of parallelism.

A case study demonstrating the speed of Cython over Python

Step 1: Create a python file having .py extension

We have created a file name fact.py

def factorial(x):
    f=1
    for i in range(x):
        f*=(x-i)
    return f

This is a simple python program that returns the factorial of a given number.

Step 2: Create a cython file having a .pyx extension

We have created a file name cfact.py

#function defining using cpdef keyword 
cpdef int factorial(int x):

    # variables are declared using cdef keyword	
    cdef int f = 1
    cdef int i 

    for i in range(x):
        f *=(x-i)

    return f

Cython supports a Const Keyword instead of a C Static keyword because a C Static keyword is used to declare a variable whose lifetime extends with the entire lifetime of the program.

Step 3: Create a setup file name setup.py to compile the code

from setuptools import setup
from Cython.Build import cythonize

setup(ext_modules=cythonize('cfact.pyx')
)

You need to run this file using the following command:

python setup.py build_ext --inplace

After successfully running this file, a file with the extension .c will be created automatically. In this example, a file called cfact.c will be created.

Step 4: Create a python file name benchmark.py to check the performance of our code

#importing a library to check the execution speed of code
from timeit import timeit

# code for measuring speed of Python code
t1 = timeit(
    "factorial(100)",
    setup="from fact import factorial",
    number=10_000,
)

# code for measuring speed of Cython code
t2 = timeit(
    "factorial(100)",
    setup="from cfact import factorial",
    number=10_000,
)

print(f"Python: {t1: .3f}")
print(f"Cython: {t2: .3f}")
print(f"Cython is {t1 / t2: .3f} x faster!")

Output:

When we will run this file for the first time it will give the following output

But when we run this file for the second time it will generate an amazing result that will blow your mind

Conclusion

Cython is a powerful programming language that can be used for many different things. It’s a great way to optimize Python code and call C libraries from Python. If you want to make your Python code faster and smaller, Cython is the way to go.

In this article, we’ve talked about why you should use Cython, what the differences between Cython and Python are, and we’ve also talked about the limitations of Cython.

We also demonstrated how Cython is quicker than python using a factorial program, where we witnessed an unbelievable speed of code execution, and how we can use this to develop amazing applications that can be performed in less time and space.

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