dft-learn

Pandas and Polars for tabular data: when to use which, indexing and dtypes, joins and reshaping, lazy Polars, I/O and Parquet, nulls, Arrow interop, and performance. Use for DataFrame/Series/LazyFrame code. Triggers on pandas, polars, DataFrame, LazyFrame, parquet, groupby, merge, join.

Python stack hub: uv/ruff/ty workflows, builtins and collections, functions and classes, dataclasses, typing boundaries, pytest and NumPy-style docs. Use for any Python task in repos that follow this stack; pairs with python-reviewer, python-types, and python-refactor. Related skills: numpy-scientific, dataframes, numpy-docstrings, matplotlib-scientific, lab-instrumentation. Triggers on Python, uv, ruff, ty, pytest, dataclass, typing.

Applies cross-language defaults for scientific/engineering work, documentation discipline, non-lazy execution, and effective use of rules, skills, and documentation tools.

PyVISA and lab instrument control in Python: VISA resource lifecycle, timeouts and terminations, raw TCP/UDP sockets vs VISA, when to introduce hardware abstraction layers, validating user and config input before hardware, testing without hardware, and extracting tables or text from instrument manuals and datasheets (PDF). Triggers on pyvisa, VISA, GPIB, USB-TMC, serial instrument, socket lab, SCPI, datasheet PDF, instrument driver.

Build publication-quality Matplotlib figures for scientific Python. Use when plotting experiment or analysis results, multi-panel figures, journal-sized layouts, tick formatting, legends (fancy/plain, placement, compact handles), point annotations, inset zooms, twin axes, colored spines/ticks, colorblind-safe palettes, PNG/PDF export, or SciencePlots styling. Triggers on matplotlib, pyplot, subplots, savefig, legend, annotate, inset, twinx, scientific figures, panel labels.

Write and review NumPy-style (numpydoc) docstrings for Python public APIs. Covers section order, semantics (what belongs in docstrings vs types vs tests), anti-patterns (comment soup, stub docs, wrong style), Parameters, Returns, Yields, Raises, See Also, Notes, References, Examples, and class/module docs. Use when authoring or auditing docstrings. Triggers on docstring, numpydoc, Parameters, Returns, Examples, Sphinx, TODO, comment.

NumPy for scientific Python: dtypes and casting, creation, reshaping, broadcasting, indexing vs views, ufuncs and reductions, linear algebra and einsum, random Generator, I/O, structured arrays, performance and pandas boundaries. Use when writing or reviewing ndarray code. Triggers on numpy, ndarray, broadcasting, dtype, ufunc, einsum, vectorization.