Standalone FDD with pandas

Pandas is the open-source Python library for tabular and time-series data: DataFrames, indexing, aggregation, and a huge ecosystem of tools. It grew out of work by Wes McKinney at AQR (2008) and became a NumFOCUS project; it is now the de facto standard for data manipulation and analysis in Python and is so widely used in modern data science that it is hard to imagine doing data work without it. In Open-FDD, pandas is the heart of number crunching—all rule evaluation runs over pandas DataFrames—and it is a natural fit for FDD: time-aligned sensor columns, rolling windows, bounds and expression checks, and fault flags map directly onto DataFrame operations.

open-fdd 2.x on PyPI already ships open_fdd.engine.RuleRunner: load a CSV (or any DataFrame), point at the same YAML rules as the platform, get fault flag columns — no database required for that path. See Engine-only deployment and external IoT pipelines.

Typical uses:

• One-off and scripted FDD — Exported CSVs, heat pump dumps, ad‑hoc rule tuning.
• Vendor / cloud pipelines — pip install open-fdd in a job that already produces DataFrames.

The edge platform (TimescaleDB, Grafana, BACnet, API) remains repo + Docker; PyPI is the library slice.

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Archived workflows in the repo

For the current edge platform use the main docs below.

For the current edge platform, use the main docs: Home, Overview, Getting Started.