Pandas Data Analysis taste

Checks CSV time series (metrics, sensor readings, sales by hour) for data problems before you chart or alert on them: missing intervals, duplicate or out-of-order timestamps, empty values, flatlines from stuck exporters, robust median/MAD spikes, and counter resets. Use when a dashboard looks wrong, before training a forecast, or when asking "is this data complete?". Includes a stdlib Python checker with --self-test. 来源：https://prompts.chat/prompts/cmvexglly000rjr088nxj0x8r

Compares two CSV exports of the same records (old vs new, billing vs ledger, source vs warehouse) by key after normalizing headers, delimiters, currency formats, accounting negatives, dates and case. Reports added, removed and duplicate keys, key format drift, changed values per column with tolerances, possible re-keys and a control-total bridge, then writes a reconciliation report. Includes a stdlib Python checker with --self-test. 来源：https://prompts.chat/prompts/cmvrsgiov0017jt07ksqlpubk

Profiles CSV and spreadsheet exports before you trust them: infers column types and measures missing values, duplicates, mixed types, outliers, date format chaos, and key uniqueness with a tested Python script, then writes a prioritized data quality report with safe fixes. 来源：https://prompts.chat/prompts/cmuz806nl000aid04oivgasj2

Explains every line of a .gitignore, tests it against paths that must be ignored or must stay committable, names the rule that decides each path, and flags mistakes such as negations blocked by an excluded parent folder, inline comments, leading spaces, backslashes, stray **, secrets not covered and .env.example ignored. Returns a fixed file. Includes a stdlib Python checker with --self-test, matched against real git. 来源：https://prompts.chat/prompts/cmvqd0g7l000ela08fer31y84