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This article mainly explains "how to use Cython to accelerate Python code". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's ideas to study and learn "how to use Cython to accelerate Python code".

If you've ever written code in Python, you may have spent more time waiting for some blocks of code to execute than you wanted. Although there are some ways to improve the efficiency of the code, it is still likely to be slower than C code. This mainly boils down to the fact that Python is a dynamic programming language and transfers much of what C is responsible for during compilation to running.

However, if you prefer to code in Python and still want to speed up your code, consider using Cython. Although Cython itself is an independent programming language, it can be easily incorporated into your workflow. When executed, Cython converts Python code to C, which is usually much faster.

Install Cython

To be able to use Cython, you need a C compiler. Therefore, the installation process varies depending on your current operating system. For Linux, there is usually a GNU C compiler (gncc). For Mac OS, you can download Xcode to get gncc. If you should use Windows, the installation process can be a bit complicated.

Once you have your C compiler, all you need to run on your terminal is:

Pip install Cython

How to use Cython

The easiest way to demonstrate the functionality of Cython is through Jupyter notebook. To use Cython in our notebook, we will use the IPython magic command. The Magic command starts with a percent sign and provides some additional features to enhance your workflow. Generally, there are two types of magic commands:

The line magic is represented by a "%" and operates on only one line of input

Cell magic is represented by two "%" and operates on multiple lines of input.

Let's start with:

First, in order to be able to use Cython, we must run:

% load_ext Cython

Now, whenever we want to run Cython in a unit of code, we must first put the following magic command in the cell:

Cython

Once you've done this, you can start coding using Cython.

How much faster is Cython?

How much faster Cython is than normal Python code really depends on the code itself. For example, if you are running a computationally expensive loop with many variables, Cython will perform much better than regular Python code. Recursive functions also make Cython much faster than Python.

Let's demonstrate this with the Fibonacci sequence. Simply put, this algorithm is to find the next number by adding the first two numbers. This is what it looks like in Python:

Def fibonacci (n): if n

< 0: print("1st fibonacci number = 0") elif n == 1: return 0 elif n == 2: return 1 else: return fibonacci(n-1) + fibonacci(n-2) 首先，为了能够使用Cython，我们必须运行： 如您所见，查找序列中的第39个数字花费了13.3秒。这里的Wall time指的是函数调用从开始到结束所花费的总时间。 让我们在Cython中定义相同的函数。 这是怎么回事?如你所见，我们在上面使用了一些细胞magic，允许我们在这个细胞中使用Cython。我稍后将解释"-a"选项的作用。然后，我们基本上采用与上面相同的代码，除了现在我们能够使用静态类型声明并将n定义为integer类型。 如您所见，通过在这个神奇的命令之后添加"-a"，我们收到了注释，这些注释显示了代码中有多少Python交互。这里的目标是去掉所有的黄线，用白色的背景代替。在这种情况下，没有Python交互，所有代码都将在C中运行。您还可以单击每一行旁边的"+"符号，查看Python代码的C语言翻译。 代码快了多少?让我们来看看：

In this case, Cython is about 6.75x faster than Python. This clearly demonstrates the time-saving power of taking advantage of Cython, and Cython provides better improvements than regular Python code in this respect.

Thank you for your reading, the above is the content of "how to use Cython to accelerate Python code". After the study of this article, I believe you have a deeper understanding of how to use Cython to accelerate Python code, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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