Popular Python Shifts to Annual Updates

The latest version of the Python programming language incorporates a new parser used to convert data files to more usable formats. The update due out this week also marks the beginning of smaller, but more frequent releases, reflecting Python’s growing popularity among data scientists, machine learning developers and data engineers.

The parser upgrade addresses current development hurdles due to factors such as “limits on the expressiveness of grammar rules,” noted James Briggs in a blog post previewing Python 3.9. The latest version is scheduled for release on Wednesday (Oct. 7), and Briggs said the language “will have been released from its… shackles,” with the unveiling of Python 3.10.

That version is scheduled to be released about this time next year, Briggs said.

The improved parser would simplify the process of converting data files as they are ingested to a desired format based on how developers plan to use those data.

Briggs noted the current Python parser relies on “a lot of workarounds, overcomplicating the process.” Even with workarounds, “only so much is possible,” he added. “The rules can be bent, but not broken.”

Those limitations are overcome via a parsing expression grammar, or PEG, tool used to describe the programming language based on a set of rules for recognizing strings.

The results of what is described as a “shiny new PEG parser” won’t be immediately noticed by Python programmer as they upgrade to the 3.9 version, Briggs said. The parsing tool will be fully integrated in the following release.

Meanwhile, Python promoters said those version releases will be smaller but more frequent, with updates coming every 12 months from the previous 18 months. That will mean fewer new features but shorter waits for those that are released on an annual basis. The same goes for bug fixes.

“What we are seeing here is a focus on smaller, incremental changes in a 12-month cycle—rather than bigger changes every 18 months,” Briggs said. “At the same time, development velocity is expected to stay the same.”

Python updates are available here.

A recent survey of data scientists found that Python usage for AI, deep learning and natural language processing projects grew by 9 percent over 2018. Java ranked second in 2019, but usage actually declined slightly year-on-year.

Python’s popularity is attributed in part to its use in university computer science departments. Hence, college graduates tend to favor the programming language when they enter the workforce as data scientists.

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