Single-Machine Analytics Engine
Parallel Dataframes. Purposeful UX. Zero-Nonsense Workflows.
FrameX combines Pandas-like tables, NumPy-compatible arrays, and Arrow-native memory so teams can process medium-to-large datasets without jumping straight to a cluster.
- Arrow-firstColumnar internals
- Eager + LazyChoose execution style
- NDArray InteropNumPy protocol support
pip install pyframe-xpyimport framex as fxArrow-Native Core
Tables are backed by Arrow for efficient columnar operations, cleaner interoperability, and better memory behavior.
DataFrame + NDArray
Use `DataFrame`, `Series`, and chunked `NDArray` in one workflow, with NumPy protocol support included.
Eager and Lazy Modes
Keep day-to-day work intuitive with eager execution, then switch to lazy pipelines when transformations become complex.
Real-World File IO
Ship data across parquet, ORC, SQLite, JSON, CSV, fixed-width text, and export-ready HTML/XML with one `read_file` / `write_file` surface.
Guides That Ship
Full docs now include onboarding, practical tutorials, use cases, architecture details, and API reference material.