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Author SHA1 Message Date
d3880a9dfa
Remove Python 3.6 support
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continuous-integration/drone/push Build is passing
Pandas 1.2.0 apparently requires Python 3.7.1+.
2021-01-03 15:51:53 +02:00
7edb8b19d7
tests/test_check.py: Reformat with black 2021-01-03 15:50:21 +02:00
4 changed files with 5 additions and 18 deletions

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@ -46,20 +46,4 @@ steps:
- python setup.py install
- csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dc.subject,cg.coverage.country
---
kind: pipeline
type: docker
name: python36
steps:
- name: test
image: python:3.6-slim
commands:
- id
- python -V
- pip install -r requirements-dev.txt
- pytest
- python setup.py install
- csv-metadata-quality -i data/test.csv -o /tmp/test.csv -e -u --agrovoc-fields dc.subject,cg.coverage.country
# vim: ts=2 sw=2 et

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@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## Unreleased
### Changed
- Reformat with black
- Requires Python 3.7+ for pandas 1.2.0
### Updated
- Run `poetry update`

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@ -1,7 +1,7 @@
# CSV Metadata Quality ![GitHub Actions](https://github.com/ilri/csv-metadata-quality/workflows/Build%20and%20Test/badge.svg) [![builds.sr.ht status](https://builds.sr.ht/~alanorth/csv-metadata-quality.svg)](https://builds.sr.ht/~alanorth/csv-metadata-quality?)
A simple, but opinionated metadata quality checker and fixer designed to work with CSVs in the DSpace ecosystem (though it could theoretically work on any CSV that uses Dublin Core fields as columns). The implementation is essentially a pipeline of checks and fixes that begins with splitting multi-value fields on the standard DSpace "||" separator, trimming leading/trailing whitespace, and then proceeding to more specialized cases like ISSNs, ISBNs, languages, etc.
Requires Python 3.6 or greater (3.8 recommended). CSV and Excel support comes from the [Pandas](https://pandas.pydata.org/) library, though your mileage may vary with Excel because this is much less tested.
Requires Python 3.7 or greater (3.8 recommended). CSV and Excel support comes from the [Pandas](https://pandas.pydata.org/) library, though your mileage may vary with Excel because this is much less tested.
## Functionality

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@ -69,7 +69,9 @@ def test_check_unnecessary_separators(capsys):
check.separators(field, field_name)
captured = capsys.readouterr()
assert captured.out == f"Unnecessary multi-value separator ({field_name}): {field}\n"
assert (
captured.out == f"Unnecessary multi-value separator ({field_name}): {field}\n"
)
def test_check_valid_separators():