Interfacing Python and C: The CFFI Module
How to use Python’s built-in CFFI module for interfacing Python with native libraries as an alternative to the “ctypes” approach.
Articles with tag „optimization“
Speed up your Python programs with a powerful, yet convenient, caching technique called “memoization.”
Learn advanced patterns for interfacing Python with native libraries, like dealing with C structs from Python and pass-by-value versus pass-by-reference semantics.
If your Python programs are slower than you’d like you can often speed them up by parallelizing them. In this short primer you’ll learn the basics of parallel processing in Python 2 and 3.
An end-to-end tutorial of how to extend your Python programs with libraries written in C, using the built-in “ctypes” module.
In this article series we’ll take a tour of some fundamental data structures and implementations of abstract data types (ADTs) available in Python’s standard library.
I worked on a Python web app a while ago that was struggling with using too much memory in production. A helpful technique for debugging this issue was adding a simple API endpoint that exposed memory stats while the app was running.
Memoization in Python: How to Cache Function Results
Interfacing Python and C: Advanced “ctypes” Features
Python Parallel Computing (in 60 Seconds or less)
Extending Python With C Libraries and the “ctypes” Module
Fundamental Data Structures in Python
Debugging memory usage in a live Python web app
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