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84 lines
4.2 KiB
Markdown
[#]: collector: (lujun9972)
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[#]: translator: ( )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Write faster C extensions for Python with Cython)
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[#]: via: (https://opensource.com/article/19/5/python-cython)
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[#]: author: (Moshe Zadka https://opensource.com/users/moshez/users/moshez/users/foundjem/users/jugmac00)
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Write faster C extensions for Python with Cython
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======
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Learn more about solving common Python problems in our series covering
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seven PyPI libraries.
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![Hand drawing out the word "code"][1]
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Python is one of the most [popular programming languages][2] in use today—and for good reasons: it's open source, it has a wide range of uses (such as web programming, business applications, games, scientific programming, and much more), and it has a vibrant and dedicated community supporting it. This community is the reason we have such a large, diverse range of software packages available in the [Python Package Index][3] (PyPI) to extend and improve Python and solve the inevitable glitches that crop up.
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In this series, we'll look at seven PyPI libraries that can help you solve common Python problems. First up: **[**Cython**][4]** , a language that simplifies writing C extensions for Python.
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### Cython
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Python is fun to use, but sometimes, programs written in it can be slow. All the runtime dynamic dispatching comes with a steep price: sometimes it's up to 10-times slower than equivalent code written in a systems language like C or Rust.
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Moving pieces of code to a completely new language can have a big cost in both effort and reliability: All that manual rewrite work will inevitably introduce bugs. Can we have our cake and eat it too?
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To have something to optimize for this exercise, we need something slow. What can be slower than an accidentally exponential implementation of the Fibonacci sequence?
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```
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def fib(n):
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if n < 2:
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return 1
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return fib(n-1) + fib(n-2)
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```
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Since a call to **fib** results in two calls, this beautifully inefficient algorithm takes a long time to execute. For example, on my new laptop, **fib(36)** takes about 4.5 seconds. These 4.5 seconds will be our baseline as we explore how Python's Cython extension can help.
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The proper way to use Cython is to integrate it into **setup.py**. However, a quick and easy way to try things out is with **pyximport**. Let's put the **fib** code above in **fib.pyx** and run it using Cython.
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```
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>>> import pyximport; pyximport.install()
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>>> import fib
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>>> fib.fib(36)
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```
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Just using Cython with _no_ code changes reduced the time the algorithm takes on my laptop to around 2.5 seconds. That's a reduction of almost 50% runtime with almost no effort; certainly, a scrumptious cake to eat and have!
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Putting in a little more effort, we can make things even faster.
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```
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cpdef int fib(int n):
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if n < 2:
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return 1
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return fib(n - 1) + fib(n - 2)
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```
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We moved the code in **fib** to a function defined with **cpdef** and added a couple of type annotations: it takes an integer and returns an integer.
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This makes it _much_ faster—around 0.05 seconds. It's so fast that I may start suspecting my measurement methods contain noise: previously, this noise was lost in the signal.
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So, the next time some of your Python code spends too long on the CPU, maybe spinning up some fans in the process, why not see if Cython can fix things?
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In the next article in this series, we'll look at **Black** , a project that automatically corrects format errors in your code.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/19/5/python-cython
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作者：[Moshe Zadka ][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/moshez/users/moshez/users/foundjem/users/jugmac00
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/code_hand_draw.png?itok=dpAf--Db (Hand drawing out the word "code")
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[2]: https://opensource.com/article/18/5/numbers-python-community-trends
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[3]: https://pypi.org/
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[4]: https://pypi.org/project/Cython/
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