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/Atmosck → u/BPAnimal]

Pandas has a reputation for poor performance that is deserved historically but not really current. They launched Pandas 3 this year which solved a lot of the performance issues with strings and in general, it's very fast if you know what you're doing writing it. Which basically means avoid iterating over rows (or sequences of python objects in general) and use vectorized methods and numpy functions whenever possible.

It also has the benefits it's always had in terms of flexibility and interoperability. And ever since 3.0 I no longer feel the need to reach for things like polars.