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whyqd provides an intuitive method for restructuring messy data to conform to a standardised metadata schema. It supports data managers and researchers looking to rapidly, and continuously, normalise any messy spreadsheets using a simple series of steps. Once complete, you can import wrangled data into more complex analytical systems or full-feature wrangling tools.

Read the docs and there are two worked tutorials to demonstrate
how you can use whyqd to support source data curation transparency:

Install using pip:

pip install whyqd

Version 0.5.0 introduced a new, simplified, API, along with script-based transformation actions. You can import and
transform any saved method.json files with:

SCHEMA = whyqd.Schema(source=SCHEMA_SOURCE)
schema_scripts = whyqd.parsers.LegacyScript().parse_legacy_method(
            version="1", schema=SCHEMA, source_path=METHOD_SOURCE_V1
        )

Where SCHEMA_SOURCE is a path to your schema. Existing schema.json files should still work.

Compare
Choose a tag to compare

whyqd provides an intuitive method for restructuring messy data to conform to a standardised metadata schema. It supports data managers and researchers looking to rapidly, and continuously, normalise any messy spreadsheets using a simple series of steps. Once complete, you can import wrangled data into more complex analytical systems or full-feature wrangling tools.

Read the docs and a full tutorial.

Install using pip:

pip install whyqd