mirror of
https://github.com/ilri/csv-metadata-quality.git
synced 2024-09-27 20:34:17 +02:00
Alan Orth
62fea95087
Now it will print just the part of the metadata value that contains the suspicious character (up to 80 characters, so we don't make the line break on terminals that use 80 character width by default). Also, print the name of the field in which the metadata value is so that it is easier for the user to locate.
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
from csv_metadata_quality.version import VERSION
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import argparse
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import csv_metadata_quality.check as check
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import csv_metadata_quality.fix as fix
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import pandas as pd
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import re
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import signal
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import sys
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def parse_args(argv):
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parser = argparse.ArgumentParser(description='Metadata quality checker and fixer.')
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parser.add_argument('--agrovoc-fields', '-a', help='Comma-separated list of fields to validate against AGROVOC, for example: dc.subject,cg.coverage.country')
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parser.add_argument('--input-file', '-i', help='Path to input file. Can be UTF-8 CSV or Excel XLSX.', required=True, type=argparse.FileType('r', encoding='UTF-8'))
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parser.add_argument('--output-file', '-o', help='Path to output file (always CSV).', required=True, type=argparse.FileType('w', encoding='UTF-8'))
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parser.add_argument('--unsafe-fixes', '-u', help='Perform unsafe fixes.', action='store_true')
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parser.add_argument('--version', '-V', action='version', version=f'CSV Metadata Quality v{VERSION}')
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args = parser.parse_args()
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return args
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def signal_handler(signal, frame):
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sys.exit(1)
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def run(argv):
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args = parse_args(argv)
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# set the signal handler for SIGINT (^C)
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signal.signal(signal.SIGINT, signal_handler)
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# Read all fields as strings so dates don't get converted from 1998 to 1998.0
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df = pd.read_csv(args.input_file, dtype=str)
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for column in df.columns.values.tolist():
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# Fix: whitespace
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df[column] = df[column].apply(fix.whitespace)
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# Fix: newlines
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if args.unsafe_fixes:
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df[column] = df[column].apply(fix.newlines)
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# Fix: unnecessary Unicode
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df[column] = df[column].apply(fix.unnecessary_unicode)
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# Check: invalid multi-value separator
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df[column] = df[column].apply(check.separators)
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# Check: suspicious characters
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df[column] = df[column].apply(check.suspicious_characters, field_name=column)
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# Fix: invalid multi-value separator
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if args.unsafe_fixes:
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df[column] = df[column].apply(fix.separators)
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# Run whitespace fix again after fixing invalid separators
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df[column] = df[column].apply(fix.whitespace)
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# Fix: duplicate metadata values
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df[column] = df[column].apply(fix.duplicates)
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# Check: invalid AGROVOC subject
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if args.agrovoc_fields:
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# Identify fields the user wants to validate against AGROVOC
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for field in args.agrovoc_fields.split(','):
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if column == field:
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df[column] = df[column].apply(check.agrovoc, field_name=column)
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# Check: invalid language
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match = re.match(r'^.*?language.*$', column)
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if match is not None:
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df[column] = df[column].apply(check.language)
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# Check: invalid ISSN
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match = re.match(r'^.*?issn.*$', column)
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if match is not None:
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df[column] = df[column].apply(check.issn)
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# Check: invalid ISBN
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match = re.match(r'^.*?isbn.*$', column)
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if match is not None:
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df[column] = df[column].apply(check.isbn)
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# Check: invalid date
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match = re.match(r'^.*?date.*$', column)
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if match is not None:
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df[column] = df[column].apply(check.date)
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# Write
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df.to_csv(args.output_file, index=False)
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# Close the input and output files before exiting
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args.input_file.close()
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args.output_file.close()
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sys.exit(0)
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