77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
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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def parse_args(argv):
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parser = argparse.ArgumentParser(description='Metadata quality checker and fixer.')
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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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args = parser.parse_args()
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return args
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def run(argv):
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args = parse_args(argv)
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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)
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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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match = re.match(r'.*?dc\.subject.*$', column)
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if match is not None:
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df[column] = df[column].apply(check.agrovoc)
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# Check: invalid language
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match = re.match(r'^.*?language.*$', 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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