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How to flag anomalous fields in data files with a Jev-based CLI with Awesome Jev

Find unusual values in selected fields of a data file and review them in local previews.

Who it is forAn analyst or data engineer checking a CSV or other supported file for odd values before using it.

The problemManual review of many rows for anomalies is slow and misses patterns in selected fields.

The outcomeA set of flagged anomalies in chosen fields, with local previews and cached assessments for review.

What you need first

  • Access to Jev through TypeSafe, which the SDK and integration entries rely on
  • Git and Python, since most picks in this list are Python or run from a repo
  • Review of each project's permissions and data handling before running it, as the README asks

Steps

  1. Open the jevotron repository at https://github.com/cmungall/jevotron and read its README to confirm which file formats it supports.
  2. Choose the file and the fields you want checked.
  3. Run the jevotron CLI as its README describes on your file and fields.
  4. Open the local previews and review each flagged value.
  5. Keep or discard each flag, and rerun the CLI to reuse cached assessments for unchanged values.

Prompt for your agent

Use the jevotron repo (https://github.com/cmungall/jevotron) to flag anomalies in the fields <field-names> of the file <path-to-file>. Return the flagged values with their row references and any local preview paths. Verify that every flagged value matches a field you selected.

Caveat. The README lists local previews and cached assessments but does not say whether field values are sent to Jev, so check before using sensitive data.

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