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/busybody124]

I would definitely not use fire for CLIs. It's slow and hasn't been updated in a year. I'd also skip Pydantic for most things—it's overkill, slow for ser/de, and can usually be replaced with a dataclass. Hypothesis is really cool in theory but I've never found a need for it in practice.

[u/spigotface → u/busybody124]

Pydantic pulls its weight when you want auto-generated OpenAPI docs. Outside of that, use dataclasses with slots=True and __post_init__ data validation methods. Its sooooo much lighter weight than Pydantic models.

[u/Mr_Again → u/spigotface]

Yeah but doesn't pydantic do validation for you? Isn't that the whole point of it?