---

name: "data-preparation" description: "Route CSV, LIBSVM, TFRecords, and iris fixture questions." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Data Preparation

Use this sub-skill for:

• dense CSV conversion with a configurable label position and label type
• sparse LIBSVM conversion with id:value pairs
• TFRecords inspection for dense or sparse tf.train.Example schemas
• tiny iris CSV fixtures generated from sklearn
• DICOM conversion caveats when the external labels file is missing

Route elsewhere

• Model training, export, inference, checkpoints, or TensorBoard -> ../training-and-export/SKILL.md
• Serving requests or client payloads -> ../serving-and-clients/SKILL.md

Bundled helpers

• scripts/convert_csv_to_tfrecords.py
• scripts/convert_libsvm_to_tfrecords.py
• scripts/inspect_tfrecords.py
• scripts/create_iris_csv_fixture.py

Canonical rules

• Dense CSV defaults to features first and label last. Use --label-position first only for legacy label-first files.
• Sparse LIBSVM keeps the label first, followed by id:value tokens.
• Dense TFRecords store label plus a float list named features.
• Sparse TFRecords store label plus ids and values.
• If a TFRecord does not match the chosen schema, treat that as a schema error instead of printing empty fields.
• The iris fixture comes from local sklearn.datasets.load_iris(); it does not download anything.
• DICOM conversion is reference-only here because the source workflow depends on pydicom and an external labels CSV that is not present in this checkout.

Read next

• references/data-formats.md
• references/workflows.md
• references/troubleshooting.md