By Philip Herron
- Learn the best way to expand C purposes with natural Python code
- Get extra from Python – you will not just research Cython, you are going to additionally liberate a better knowing of ways to harness Python
- Packed with guidance and methods that make Cython glance effortless, dive into this available programming consultant and discover what occurs in case you convey C and Python together!
Cython is a hybrid programming language used to jot down C extensions for Python language. Combining the practicality of Python and velocity and simplicity of the c programming language it truly is a thrilling language worthy studying that will construct quick functions with ease.
This re-creation of studying Cython Programming indicates you the way to start, taking you thru the basics so that you can start to event its distinct powers.
You'll how you can get organize, sooner than exploring the connection among Python and Cython. you are going to additionally examine debugging Cython, earlier than relocating directly to C++ constructs, Caveat on C++ utilization, Python threading and GIL in Cython. eventually, you are going to research item initialization and assemble time, and achieve a deeper perception into Python three, in an effort to assist you not just turn into a convinced Cython developer, yet a way more fluent Python developer too.
What you'll learn
- Reuse Python logging in C
- Make an IRC bot from your C application
- Extend an software so that you have an online server for relaxation calls
- Practice Cython opposed to your C++ code
- Discover tips to paintings with Python ConfigParser in C
- Create Python bindings for local libraries
- Find out approximately threading and concurrency concerning GIL
- Expand Terminal Multiplexer Tmux with Cython
About the Author
Philip Herron is a developer who focuses his ardour towards compilers and digital desktop implementations. whilst he used to be first accredited to Google summer season of Code 2010, he used thought from Paul Biggar's PhD at the optimization of dynamic languages to enhance an explanation of the concept that GCC frontend to assemble Python. This venture sparked his deep curiosity in how Python works.
After finishing a consecutive yr at the comparable venture in 2011, Philip utilized to Cython less than the Python origin to achieve a deeper appreciation of the normal Python implementation. via this he began leveraging the benefits of Python to manage the common sense in platforms or maybe upload extra high-level interfaces, akin to embedding Flask internet servers in a relaxation API to a system-level piece of software program, with out writing any C code.
Philip at the moment works as a software program advisor for Instil software program dependent in Northern eire. He develops cellular functions with embedded local code for video streaming. Instil has given him loads of aid in turning into a greater engineer.
He has written a number of tutorials for the UK-based Linux layout journal on Python and likes to proportion his ardour for the Python programming language.
Table of Contents
- Cython will not Bite
- Understanding Cython
- Extending Applications
- Debugging Cython
- Advanced Cython
- Further Reading
Read Online or Download Learning Cython Programming - Second Edition PDF
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Cython lets us write and work with the native code without having to know anything about the Python runtime. We can write almost pure Python code that just so happens to let us manipulate C/C++ types and classes. We can call back and forth from the native code and into the Python code. More importantly, Cython removes the complexity and intrinsicity and lets the programmer focus on solving problems. What this book covers Chapter 1, Cython Won't Bite, introduces core concepts and demonstrates Cython "Hello World".
So, let's get right to doing that. Cython was originally designed to make raw Python computation faster. So, the initial proof of concept for Cython was to enable programmers to take existing Python code and use Cython's cdef keyword to require native typing to bypass the Python runtime for heavy computation. The culmination of this is increased performance in the time it takes to perform calculations and lower memory usage. It's even possible to write type-safe wrappers to existing Python libraries for fully typed Python code.