| name | dataframes |
| description | 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. |
Dataframes (pandas + Polars)
Quick start
- Pick one engine per pipeline: pandas when index semantics, mixed columns, and ecosystem (statsmodels, sklearn I/O) matter; Polars when large scans, lazy plans, and expression clarity win. See reference-when-which.md.
- pandas: be explicit with
loc / iloc, assign, and dtypes; understand views vs copies and Copy-on-Write (2.x). See reference-pandas-core.md.
- Polars: default to
scan_* + lazy() for big data; build with select / with_columns / filter and pl.col; collect at the boundary. See reference-polars.md.
- Joins and groups: validate row counts and duplicates; name suffixes; prefer Polars for parallel group-by on large tables when the project already uses it. See reference-pandas-group-join.md and reference-polars.md.
- I/O: Parquet (often PyArrow) for analytics interchange;
uv add pyarrow when the stack needs it. See reference-io-dtypes-nulls.md.
- Handoff: convert at module boundaries with
from_pandas / to_pandas or Arrow; avoid ping-pong in inner loops. See reference-interop-performance.md.
Stack synergy
| Resource | Role |
|---|
| Python spec | Explicit pandas index/columns; lazy Polars for heavy queries |
| numpy-scientific | to_numpy(), dtypes, contiguous buffers |
| matplotlib-scientific | df.plot(ax=ax) and Polars .to_pandas() for plotting when needed |
| numpy-docstrings | Public API docstrings for DataFrame-returning functions |
| general-python | uv, ty, python-reviewer |
Reference index
| Topic | File |
|---|
| pandas vs Polars, boundaries | reference-when-which.md |
Index, loc/iloc, dtypes, COW, assignment | reference-pandas-core.md |
| GroupBy, merge/join, concat, pivot | reference-pandas-group-join.md |
| LazyFrame, expressions, group, join | reference-polars.md |
| CSV/Parquet, nulls, dtypes, Arrow | reference-io-dtypes-nulls.md |
| Conversion, streaming, typing | reference-interop-performance.md |
Official documentation
Dependencies
Use uv add pandas, uv add polars, uv add pyarrow as needed; do not hand-edit version pins in pyproject.toml.
