lemma-eda

name	lemma-eda
description	Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

• Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
• Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
• Explore only patterns that could change a decision or next step.
• Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
• State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.