Using a real-world example to demonstrate a map-reduce program — Introduction Thanks to GIL, using multiple threads to perform CPU-bound tasks has never been an option. With the popularity of multicore CPUs, Python offers a multiprocessing solution to perform CPU-bound tasks. But until now, there were still some problems with using multiprocess-related APIs directly. Before we start, we still have a…

Use best practices and real-world examples to demonstrate the powerful text parser library — This article introduces a Python library called parse for quickly and conveniently parsing and extracting data from text, serving as a great alternative to Python regular expressions. And which covers the best practices with the parse library and a real-world example of parsing nginx log text. Introduction

Enhancing your data analysis performance with Python’s Numexpr and Pandas’ eval/query functions — This article will introduce you to the Python library Numexpr, a tool that boosts the computational performance of Numpy Arrays. The eval and query methods of Pandas are also based on this library. This article also includes a hands-on weather data analysis project. By reading this article, you will understand…

Leveraging NumPy’s broadcasting, fancy Indexing, and sorting for performance computing — Introduction I have a friend who is a city planner. One day, he was tasked with reassessing the location suitability of thousands of gas stations in the city, needing to find the positions of the k-nearest gas stations to each one. How can we find the nearest k stations with little…

Exploring allocation differences and efficiency gains — In this article, we will delve into the memory design differences between native Python lists and NumPy arrays, revealing why NumPy can provide better performance in many cases. We will compare data structures, memory allocation, and access methods, showcasing the power of NumPy arrays. Introduction Imagine you are preparing to go…

Harness the power of asyncio and multiprocessing to turbocharge your applications — In this article, I will take you into the world of aiomultiprocess, a library that combines the powerful capabilities of Python asyncio and multiprocessing. This article will explain through rich code examples and best practices. By the end of this article, you will understand how to leverage the powerful features…

Enhancing your Python projects with robust retry mechanisms and error-handling techniques — This article will discuss Tenacity’s basic usage and customization capabilities. This instrumental Python library provides a retry mechanism. We will also explore Tenacity’s retry and exception-handling capabilities through a practical example. Introduction Imagine you’re managing hundreds of web services, some located overseas (with high latency) and others pretty old (and not…

Even no need to know much about asyncio and multiprocessing — In this article, I will introduce how to integrate multiprocessing and asyncio using the aiomultiprocess library easily. The article includes a web scraping project example and the best practices for using this library. Introduction My colleague Wang came to me today and said that his boss assigned him a new task…

Mastering the producer-consumer pattern with asyncio through real-life examples — In this article, I will explain the API usage and application scenarios of various queues in Python asyncio relaxedly. At the end of the article, I will demonstrate the practical usage of asyncio.Queue in a classic shopping scenario. Introduction Why do we need asyncio.Queue As readers who have read my previous articles know, I love asyncio…