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A simple but opinionated metadata quality checker and fixer designed to work with CSVs in the DSpace ecosystem.
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Alan Orth 8047a57cc5
Add support for fixing "unnecessary" Unicode
These are things like non-breaking spaces, "replacement" characters,
etc that add nothing to the metadata and often cause errors during
parsing or displaying in a UI.
2019-07-29 16:38:10 +03:00
csv_metadata_quality Add support for fixing "unnecessary" Unicode 2019-07-29 16:38:10 +03:00
data Add support for fixing "unnecessary" Unicode 2019-07-29 16:38:10 +03:00
tests Add support for fixing "unnecessary" Unicode 2019-07-29 16:38:10 +03:00
.build.yml .build.yml: Fix setup script 2019-07-27 00:41:57 +03:00
.flake8 Add flake8 to pipenv dev environment 2019-07-28 17:46:30 +03:00
.travis.yml .travis.yml: Only test Python 3.6 and 3.7 2019-07-29 12:13:55 +03:00
LICENSE.txt Add GPLv3 license 2019-07-26 22:16:16 +03:00
Pipfile Add flake8 to pipenv dev environment 2019-07-28 17:46:30 +03:00
Pipfile.lock Add flake8 to pipenv dev environment 2019-07-28 17:46:30 +03:00
README.md README.md: Add note about unnecessary Unicode fixes 2019-07-29 16:34:39 +03:00
requirements-dev.txt Update python requirements 2019-07-28 17:12:20 +03:00
requirements.txt Update python requirements 2019-07-28 17:12:20 +03:00

CSV Metadata Quality Build Status builds.sr.ht status

A simple but opinionated metadata quality checker and fixer designed to work with CSVs in the DSpace ecosystem. Supports multi-value fields using the standard DSpace value separator ("||"). Despite the name it does support reading Excel files.

Requires Python 3.6 or greater. CSV and Excel support comes from the Pandas library.

Functionality

  • Read/write CSV files ✓
  • Read Excel files ✓
  • Validate dates, ISSNs, ISBNs, and multi-value separators ("||") ✓
  • Fix leading, trailing, and excessive whitespace ✓
  • Fix invalid multi-value separators ("|") using --unsafe-fixes
  • Remove unnecessary Unicode like non-breaking spaces, replacement characters, etc ✓

Installation

The easiest way to install CSV Metadata Quality is with pipenv:

$ git clone https://git.sr.ht/~alanorth/csv-metadata-quality
$ cd csv-metadata-quality
$ pipenv install
$ pipenv shell

Otherwise, if you don't have pipenv, you can use a vanilla Python virtual environment:

$ git clone https://git.sr.ht/~alanorth/csv-metadata-quality
$ cd csv-metadata-quality
$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -r requirements.txt

Usage

Run CSV Metadata Quality with the --help flag to see available options:

$ python -m csv_metadata_quality --help

To validate and clean a CSV file you must specify input and output files using the -i and -o options. For example, using the included test file:

$ python -m csv_metadata_quality -i data/test.csv -o /tmp/test.csv

You can enable "unsafe fixes" with the --unsafe-fixes option. This will attempt

Todo

  • Reporting / summary
  • Real logging
  • Detect and fix duplicate values like "Alan||Alan"

License

This work is licensed under the GPLv3.

The license allows you to use and modify the work for personal and commercial purposes, but if you distribute the work you must provide users with a means to access the source code for the version you are distributing. Read more about the GPLv3 at TL;DR Legal.