python-pipeline

# Python data pipeline development Patterns for building production-quality data processing pipelines with Python. **Targeted at Python 3.11+** for `asyncio.TaskGroup` and exception groups; Python 3.12+ for the lighter `type X = ...` syntax. Pin a 3.13+ runtime if you want the JIT or experimental free-threading; the patterns here don't depend on either. ## Choosing a DataFrame engine: pandas vs polars vs DuckDB For a long time pandas was the default for any tabular work in Python. As of 2026 the default has shifted: **polars** is the right pick for multi-GB pipelines on a single machine, **DuckDB** is the right pick when SQL or larger-than-RAM scans are involved, and **pandas** stays useful for small data and the ML/notebook ecosystem (scikit-learn, statsmodels, plotnine all speak it natively). | Tool | When | Why | |---|---|---| | pandas | < ~1 GB data, ML interop, single-threaded familiarity | Mature, ubiquitous, eager DataFrame model. Slowest in benchmarks but most ecosystem support. | | polars | 1 GB - tens of GB on one box, performance-critical pipelines | Multithreaded by default, lazy query engine, Arrow-native. ~5x speedup over pandas on filter / aggregate at 100M rows. | | DuckDB | SQL workflows, larger-than-RAM, parquet/CSV scanning, joins across many files | Vectorized + pipelined execution, cost-based optimizer, streaming scans. Works great as a thin wrapper over a directory of parquet files. | All three speak Apache Arrow, so zero-copy interop between them ...