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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 d7888d59a8
csv_metadata_quality/check.py: Return date even if it is invalid
Otherwise it is missing from the final CSV and then we can't even
fix it. :)
2019-07-29 17:40:14 +03:00
csv_metadata_quality csv_metadata_quality/check.py: Return date even if it is invalid 2019-07-29 17:40:14 +03:00
data data/test.csv: Use more descriptive tests 2019-07-29 17:38:46 +03:00
tests Add check for "suspicious" characters 2019-07-29 17:08:49 +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
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README.md README.md: Update note about unsafe options 2019-07-29 17:25:42 +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 ✓
  • Check for "suspicious" characters that could indicate encoding or copy/paste issues, for example "foreˆt" should be "forêt" ✓

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 to fix things like invalid multi-value separators ("|"). It's possible that a metadata value could legitimately use "|" but in my experience this is rather always an error where the user meant to enter multiple values for a field, for example Kenya||Tanzania.

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.