作者彙整: Serdar Yegulalp

The current state of IDEs built for Python is an embarrassment of riches. Six products made the cut in our last rundown, but there were many more worth noting—sophisticated efforts that provide a full-blown development system for experts, as well as modest projects suited to a first-time Pythonista. 

Here are seven other Python IDEs, from the minimal to the maximal, that also should be on your radar.

Eric6

Like Python’s own native IDE IDLE, Eric6 is a Python IDE written in Python, although it uses the Qt5 UI framework to provide a far more sophisticated UI than IDLE.

To read this article in full, please click here

(Insider Story) 閱讀全文 →

The rise of RESTful APIs has been met by a rise in tools for creating, testing, and managing them. Whether you’re an API newbie or an expert on an intractable deadline, you have a gamut of services to help you bring your API from concept to production, and many of them won’t cost you a dime.

Following is a sampling of free services for working with APIs. Some are quick and dirty applications to ease the job of assembling or testing an API. Others are entry-level tiers for full-blown professional API management services, allowing you to get started on a trial basis and later graduate to a more professional level of (paid) service if and when you need it.

To read this article in full, please click here

(Insider Story) 閱讀全文 →

Most discussions of developers making use of machine learning revolve around creating AI-powered applications and the tools used to create them: TensorFlow, PyTorch, Scikit-learn, and so on.

But there is another way machine learning is impacting software development: by way of new development tools that use machine learning techniques to make programming easier and more productive. Here are five projects—three commercial, two experimental—that put machine learning to work for developers within the development process.

To read this article in full, please click here

(Insider Story) 閱讀全文 →

No technology sticks around for 50 years unless it does its job better than most anything else—especially a computer technology. The C programming language has been alive and kicking since 1972, and it still reigns as one of the fundamental building blocks of our software-defined world.

But sometimes a technology sticks around because people just haven’t gotten around to replacing it. Over the last few decades, dozens of other languages have appeared—some explicitly designed to challenge C’s dominance, some chipping away at C from the side as a byproduct of their popularity.

It isn’t hard to argue C needs replacing. Programming language research and software development practices all hint at how there are far better ways to do things than C’s way. But C persists all the same, with decades of research and development behind it. Few other languages can beat it for performance, for bare-metal compatibility, or for ubiquity. Still, it’s worth seeing how C stacks up against big-name language competition in 2018.

To read this article in full, please click here

(Insider Story) 閱讀全文 →

Python 3.8 is the latest version of the popular language for everything from scripting and automation to machine learning and web development. Now available in an official beta release, Python 3.8 brings a number of slick syntax changes, memory sharing, more efficient serialization and deserialization, revamped dictionaries, and much more.

Naturally, Python 3.8 ushers in all manner of performance improvements as well. The overall result is a faster, more concise, more consistent, and more modern Python. Here’s what’s new and most significant in Python 3.8.

Assignment expressions 

The single most visible change in Python 3.8 is assignment expressions, which use what is known as the walrus operator ( := ). Assignment expressions allow a value to be assigned to a variable, even a variable that doesn’t exist yet, in the context of an expression rather than as a stand-alone statement.

To read this article in full, please click here

(Insider Story) 閱讀全文 →

The Kubernetes container management system has been described as “the new Linux” because of how it transforms the way software is deployed. But there is another way Kubernetes is akin to Linux: the sheer number and variety of distributions, incarnations, and re-packagings of Kubernetes out there.

Many of the most well-known Kubernetes distributions come from companies—such as Canonical, Red Hat, and Suse—that also provide Linux distributions. But these aren’t the only game in town. A number of other Kubernetes-powered products offer useful ways to do container management in specific scenarios. Here are five significant products that offer Kubernetes with a twist.

To read this article in full, please click here

(Insider Story) 閱讀全文 →