data-analysis

# Data Analysis Deliver the requested result and artifacts. Do not turn the task into a proof exercise or add evidence, reports, explanations, or intermediate files that were not requested. ## Before you start Require a concrete data-analysis task, its available inputs, and the requested deliverable, location, and format. Ask only when missing information prevents a defensible result and would materially change the deliverable; otherwise proceed. ## Contract Read the task, supplied inputs, and relevant data documentation. Identify every required output path and format, plus only the definitions that can change the result: scope, observation grain, keys, units, operators, ordering, coverage, and explicit formatting rules. Treat examples as illustrative unless the task makes them normative. If information is incomplete or ambiguous, first resolve it from the supplied materials. Ask only when the missing choice prevents a defensible result and would materially change the deliverable. Otherwise choose the best-supported interpretation and proceed. ## Bounded inspection For large or unfamiliar inputs, begin with a bounded inventory, schema check, targeted sample, or narrow query. Expand inspection only when it can change a selection, transformation, calculation, or output. Do not exhaustively read or render data merely to increase confidence. ## Data semantics Compute at the correct row or entity grain. Evaluate conjunctive conditions on the same record or entity; do not...