Category: python

Some interesting findings:

Though Python 2 reached “end-of-life” status in April of 2020, last year’s survey found 7% of respondents were still using Python 2. This year’s survey found that number has finally dropped… to 6%.
“Almost half of Python 2 holdouts are under 21 years old,” the survey results point out, “and a third are students. Perhaps courses are still using Python 2?”

Meanwhile, 73% are using one of the last three versions of Python (3.10, 3.11, or 3.12)
“The share of developers using Linux as their development environment has decreased through the years: compared with 2021, it’s dropped by 8 percentage points.” [The graphic is a little confusing, showing 55% using Linux, 55% using Windows, 29% on MacOS, 2% on BSD, and 1% on “Other.”]
Visual Studio Code is the most popular IDE (22%), followed by Jupyter Notebook (20%) and Vim (17%). The next-most popular IDEs were PyCharm Community Edition (13%), JupyterLab (12%), NotePad++ (11%) and Sublime Text (9%). Interestingly, just 23% of the 25,000 respondents said they only used one IDE, with 38% saying they used two, 21% using three, and 19% using four or more. [The annual survey is a collaboration between the Python Software Foundation and JetBrains.]
37% said they’d contributed to open-source projects within the last year. (77% of those contributed code, while 38% contributed documentation, 35% contributed governance/leadership/maintainer duties, and 33% contributed tests…)

For “age range,” nearly one-third (32%) said 21-29 (with another 8% choosing 18-20). Another 33% said 30-39, while 16% said 40-49, 7% said 50-59, and 3% chose “60 or older.”

49% of respondents said they had less than two years of programming experience, with 33% saying “less than 1 year” and 16% saying “1-2 years.” (34% of developers also said they practiced collaborative development.)

And here’s how the 25,000 developers answered the question: how long have you been programming in Python?

Less than 1 year: 25%
1-2 years: 16%
3-5 years: 26%
6-10 years: 19%
11+ years: 13%

So what are they doing with Python? Among those who’d said Python was their main language:

Data analysis: 44%
Web development: 44%
Machine learning: 34%
Data engineering: 28%
Academic research: 26%
DevOps / Systems administration / Writing automation scripts 26%
Programming of web parsers / scrapers / crawlers: 25%

62% were “fully employed by a company,” while the next-largest category was “student” (12%) with another 5% in “working student”. There were also categories for “self-employed” (6%), “freelancer” (another 6%), and “partially employed by a company” (4%). Another 4% said they were unemployed.

In other news, the Python Software Foundation board has also “decided to invest more in connecting and serving the global Python community” by hosting monthly “office hours” on their Discord channel.

“We do type checking during the compilation process, which lets us avoid all of that expensive type manipulation at runtime,” says Ariya Shajii, an MIT CSAIL graduate student and lead author on a recent paper about Codon.

Without any unnecessary data or type checking during runtime, Codon results in zero overhead, according to Shajii. And when it comes to performance, “Codon is typically on par with C++. Versus Python, what we usually see is 10 to 100x improvement,” he says. But Codon’s approach comes with its trade-offs. “We do this static type checking, and we disallow some of the dynamic features of Python, like changing types at runtime dynamically,” says Shajii. “There are also some Python libraries we haven’t implemented yet….”

Codon was initially designed for use in genomics and bioinformatics. “Data sets are getting really big in these fields, and high-level languages like Python and R are too slow to handle terabytes per set of sequencing data,” says Shajii. “That was the gap we wanted to fill — to give domain experts who are not necessarily computer scientists or programmers by training a way to tackle large data without having to write C or C++ code.” Aside from genomics, Codon could also be applied to similar applications that process massive data sets, as well as areas such as GPU programming and parallel programming, which the Python-based compiler supports. In fact, Codon is now being used commercially in the bioinformatics, deep learning, and quantitative finance sectors through the startup Exaloop, which Shajii founded to shift Codon from an academic project to an industry application.

To enable Codon to work with these different domains, the team developed a plug-in system. “It’s like an extensible compiler,” Shajii says. “You can write a plug-in for genomics or another domain, and those plug-ins can have new libraries and new compiler optimizations….” In terms of what’s next for Codon, Shajii and his team are currently working on native implementations of widely used Python libraries, as well as library-specific optimizations to get much better performance out of these libraries. They also plan to create a widely requested feature: a WebAssembly back end for Codon to enable running code on a Web browser.

The Python Docs cover some of the significant performance improvements made for this upcoming release. The formal Python 3.11.0 release isn’t expected until October while multiple betas will come through July and then at least two release candidates in the following months before early October.