This app ranks the popularity of dozens of programming languages. You can filter them by excluding sectors that aren’t relevant to you, such as “Web” or “Embedded.” (Which sectors a language can be found listed in is based on typical use patterns we’ve seen in the wild.) Rankings are created by weighting and combining 11 metrics from 9 sources. We have one less source this year, as the Dice job site shut down its API. However, the Dice metric is still available for previous years’ data. (Read more about our method and sources).
The default set of weights produces our IEEE Spectrum ranking—but there are preset weights for those more interested in what’s trending or most looked for by employers. Don’t like the presets? Create your own ranking by adjusting the weights yourself. To compare with a previous year’s data, click “Add a Comparison” and then click “Edit Ranking,” which will give you the option to compare with data from 2014 to 2017.
This app was originally developed in collaboration with IEEE Spectrum by data journalist Nick Diakopoulous.
It’s been a while since we last published a status update about React Native.
At Facebook, we’re using React Native more than ever and for many important projects. One of our most popular products is Marketplace, one of the top-level tabs in our app which is used by 800 million people each month. Since its creation in 2015, all of Marketplace has been built with React Native, including over a hundred full-screen views throughout different parts of the app.
Python, though opinionated on syntax and style, is surprisingly flexible when it comes to structuring your applications.
On the one hand, this flexibility is great: it allows different use cases to use structures that are necessary for those use cases. On the other hand, though, it can be very confusing to the new developer.
The Internet isn’t a lot of help either—there are as many opinions as there are Python blogs. In this article, I want to give you a dependable Python application layout reference guide that you can refer to for the vast majority of your use cases.
You’ll see examples of common Python application structures, including command-line applications (CLI apps), one-off scripts, installable packages, and web application layouts with popular frameworks like Flask and Django.
Choosing an AWS region is the first decision you have to make when you set up your AWS components. You can’t do anything in the AWS Management Console, SDK or CLI without choosing a region. Most AWS customers choose one based on proximity to themselves or to their end users, which sounds like a sensible thing to do.
However, proximity alone is not enough. There are a lot of other factors to consider when choosing a region. Since my goal is to make applications grow on AWS, I’m always looking for factors that will make a difference. For example, AWS cost, feature selection, as well as the speed and resiliency of your AWS components.
So let’s get started…