KDnuggets

How to Process Large JSON Files in Python With ijson

I loaded a large JSON file in Python and watched memory jump from 43 MB to 808 MB. Here’s what happened and how streaming changed everything.

Practical Concepts for 'Concurrency and Asynchronous Processing' Part 5: Why Processes Still Stop Even When Using async/await

In Python, you can write asynchronous processes using async/await.It is a convenient mechanism for efficiently executing ...
Ars Technica

Python: writing to file with parallel processing

I'm running some simulations using the joblib library. For that, I have some number of parameter combinations, each of which I run 100,000 times. I'd now like to write the result of each simulation to ...
Analytics Insight

Three Ways to Speed Up Python Code With Numba: A Practical Guide

Overview: Numba is a just-in-time (JIT) compiler that turns Python functions into fast machine code. In one tested example, a single decorator gave about a 50 t ...