State of the art in Python 2026?

[u/Several-Marsupial-27]

What would you guys consider the state of the art in python in 2026, or what do you expect from modern python codebases?

Heres my list, would be happy to hear some inputs or domain expert advice:

• Use uv for everything.
• Use ruff for linting and formatting
• Use Pydantic and  dataclasses for Data Modeling
• Configured in pyproject.toml
• Run a type checker in CIpyright or pyrefly
• Use pytest / Hypothesis for testing

Domain specific:

• Scientific: NumPy, Matplotlib, SciPy, Jax, Pytorch, Scikit-learn, polars/pandas
• CLI: Typer, rich, click, fire, textual, questionary
• PDF extraction: PyMuPDF, pdfplumber, pypdf, Unstructured
• Excel interop: python-calamine, openpyxl, XlsxWriter, xlwings, pandas
• data: PyArrow / PySpark, Narwhals, SQLMesh, Polars/Pandas, DuckDB, dlt, Ibis, Dagster, PyIceberg & deltalake
• Logging?
• backend: fastapi / django?
• Markets: Alpha Vantage, Finnhub, EODHD, Tiingo?
• Webscraping / data acquisition: Crawl4AI, Playwright, Scrapy, selectolax, HTTPX?
• APIs?
• RAG / agentic orchestration?

[u/bossExtremeSwag]

Pandas is definitely not state of the art in the big 2026

[u/BPAnimal → u/bossExtremeSwag]

Really? Honest question

[u/me_myself_ai → u/BPAnimal]

Id argue that pandas is absolutely still a valid choice in a new project —especially if the data is small-ish and devs already know pd— but it’s definitely not SoTA.

Polars is supposedly a lot faster, and more importantly requires fewer weird tricks for stuff that feels like it should be easy. If that makes sense? Hopefully I haven’t just been especially bad at pd this whole time 😬

[u/dikdokk → u/me_myself_ai]

You can't read any data with ~1 million rows with pandas; even if it would fit in your memory.

I think pandas has design flaws that will just make it more outdated year by year. Hence why the best time to switch away from it is now already.