Programming languages ranking

The more a language tutorial is searched, the more popular the language is assumed to be. The raw data comes from Google Trends. If you believe in collective wisdom, programming languages ranking PYPL Popularity of Programming Language index can help you decide which language to study, or which one to use in a new software project.

This chart uses a logarithmic scale. The PYPL PopularitY of Programming Language Index is created by analyzing how often language tutorials are searched on Google : the more a language tutorial is searched, the more popular the language is assumed to be. Why is PYPL so different from TIOBE ? The TIOBE Index is a lagging indicator. It counts the number of web pages with the language name. Why do you use tutorial as Google Trends keyword ? What is a python tutorial, if not a tutorial on the programming language ?

My favorite language is not in the index ! The index is currently limited to 22 languages. You can still analyze the popularity of your favorite language and compare it to others, using Google Trends. C on Google trends: to avoid duplication, it is not included in the PYPL index. How do you compute the share of web searches ?

We first calculate the interest of each language tutorials relative to java tutorials every month. Why is Java so flat in PYPL’s diagram, while it is going down in Google Trends over 5 years ? That’s because Google Trends diagrams show how the total number of Java tutorial searches varies over time. Instead, PYPL’s diagram shows the share of Java tutorial searches in all language tutorial searches. That share has been fairly stable for Java since 2004.

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Can I copy material from this page ? This work is licensed under a Creative Commons Attribution 3. Sorry, you need Javascript on to email me. Instead, our interactive app lets you choose how these metrics are weighted when they are combined, so you can put an emphasis on what matters to you. There’s a detailed description of our methods and sources available. We do include a default weighting, tuned to the interests of a typical IEEE member, and we offer some other presets that focus on things like what’s au courant for open source projects. So what are the Top Ten Languages of 2018, as ranked for the typical IEEE member and Spectrum reader?

Python has maintained its grip on the No. Last year it came out on top by just barely beating out C, with Python’s score of 100 to C’s 99. 7 score, while C moves down to fourth place at 96. Why is Python continuing to have such a hold on programmer mindshare? Two other changes in the Top Programming Languages may give a hint as to why. First, Python is now listed as an embedded language. Previously, writing for embedded applications tilted heavily toward compiled languages, to avoid the overhead of evaluating code on the fly on machines with limited processing power and memory.

But while Moore’s Law may be fading, it’s not dead yet. Many modern microcontrollers now have more than enough power to host a Python interpreter. Another reason for Python’s increasing popularity may be seen in R’s small decline. 5 in 2016, dropped to No.

6 last year, and is now in seventh place. R is a language specialized for handling statistics and big data. Looking at the Trending preset, designed to weight the metrics to emphasize languages that are growing quickly, we see that Google’s Go has risen from No. But perhaps the biggest mover is Scala, rising from No. Scala was created to be an improvement over Java, so perhaps Java’s drop in the default ranking owes something to the upward trend for Scala. Last year it came in dead last with a ranking of 0. 0, so we were all set to eliminate it.

However, it’s managed to come back into second-to-last place with a score of 1. 6, while Forth, once a workhorse of the embedded world takes the goose eggs. I would be sad to see Forth go, as it’s one of my personal favorites, but if it comes in a zero again next year, it’ll be axed. Editor’s note: This article was updated on 23 August to reflect changes made in the interactive app to enforce the consistency of ranking calculations across browser platforms. As a result, most languages have small changes in their weighted scores. In some cases, with closely separated languages, this has caused languages to swap positions in the rankings.