CPython 3.11 has been released this week. The main change is an increase in performance, it's between 10% to 60% faster based on the CPython benchmarks.
I tested the #pandas benchmarks with pandas 3.10 and pandas 3.11, and they are less than 1% faster with the new version (all critical code in #Python data projects is in C, not in Python).
Exceptions got couple of improvement, and there are several additions to typing.
For the Python data community, in my opinion the main improvement to Python would be to be able to overwrite the
I tested the #pandas benchmarks with pandas 3.10 and pandas 3.11, and they are less than 1% faster with the new version (all critical code in #Python data projects is in C, not in Python).
Exceptions got couple of improvement, and there are several additions to typing.
For the Python data community, in my opinion the main improvement to Python would be to be able to overwrite the
and and or operators in our libraries (pandas and numpy mainly). I wrote about it in this post.It's great to see a new chapter of the #Python sprints group. This one in Zurich. Get started or help others get started in the free software world if you're in the Zurich area: https://python-sprints.github.io/chapters/zurich_python_sprints.html
python-sprints.github.io
Python Sprints - Zürich Python Sprints
Python Sprints is a non for profit group gathering coders who want to help improve open source projects using Python programming language.
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How fast can a CSV file be processed? I explain in detail comparing many options such as #pandas, #DuckDB, #Polars, #Python, #R, #rustlang and more in this new blog post: https://datapythonista.me/blog/how-fast-can-we-process-a-csv-file
datapythonista blog
How fast can we process a CSV file
Introduction Comma-separated values (CSV) are an extremely popular format to store tabular data because of their simplicity and how easy...
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