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csv_metadata_quality
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__init__.py
__main__.py
app.py
check.py
experimental.py
fix.py
util.py
version.py
data
tests
.drone.yml
.flake8
.gitignore
CHANGELOG.md
CITATION.cff
LICENSE.txt
MANIFEST.in
README.md
poetry.lock
pyproject.toml
pytest.ini
renovate.json
requirements-dev.txt
requirements.txt
I suspect this undermines the PyArrow backend performance gains in recent Pandas 2.0.0, but we are dealing with messy data sometimes and we must rely on data being strings.