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mirror of https://github.com/ilri/csv-metadata-quality.git synced 2024-11-17 19:47:03 +01:00
csv-metadata-quality/csv_metadata_quality/app.py
Alan Orth 62fea95087
Improve suspicious character detection
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.
2019-08-09 01:25:40 +03:00

97 lines
3.4 KiB
Python

from csv_metadata_quality.version import VERSION
import argparse
import csv_metadata_quality.check as check
import csv_metadata_quality.fix as fix
import pandas as pd
import re
import signal
import sys
def parse_args(argv):
parser = argparse.ArgumentParser(description='Metadata quality checker and fixer.')
parser.add_argument('--agrovoc-fields', '-a', help='Comma-separated list of fields to validate against AGROVOC, for example: dc.subject,cg.coverage.country')
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'))
parser.add_argument('--output-file', '-o', help='Path to output file (always CSV).', required=True, type=argparse.FileType('w', encoding='UTF-8'))
parser.add_argument('--unsafe-fixes', '-u', help='Perform unsafe fixes.', action='store_true')
parser.add_argument('--version', '-V', action='version', version=f'CSV Metadata Quality v{VERSION}')
args = parser.parse_args()
return args
def signal_handler(signal, frame):
sys.exit(1)
def run(argv):
args = parse_args(argv)
# set the signal handler for SIGINT (^C)
signal.signal(signal.SIGINT, signal_handler)
# Read all fields as strings so dates don't get converted from 1998 to 1998.0
df = pd.read_csv(args.input_file, dtype=str)
for column in df.columns.values.tolist():
# Fix: whitespace
df[column] = df[column].apply(fix.whitespace)
# Fix: newlines
if args.unsafe_fixes:
df[column] = df[column].apply(fix.newlines)
# Fix: unnecessary Unicode
df[column] = df[column].apply(fix.unnecessary_unicode)
# Check: invalid multi-value separator
df[column] = df[column].apply(check.separators)
# Check: suspicious characters
df[column] = df[column].apply(check.suspicious_characters, field_name=column)
# Fix: invalid multi-value separator
if args.unsafe_fixes:
df[column] = df[column].apply(fix.separators)
# Run whitespace fix again after fixing invalid separators
df[column] = df[column].apply(fix.whitespace)
# Fix: duplicate metadata values
df[column] = df[column].apply(fix.duplicates)
# Check: invalid AGROVOC subject
if args.agrovoc_fields:
# Identify fields the user wants to validate against AGROVOC
for field in args.agrovoc_fields.split(','):
if column == field:
df[column] = df[column].apply(check.agrovoc, field_name=column)
# Check: invalid language
match = re.match(r'^.*?language.*$', column)
if match is not None:
df[column] = df[column].apply(check.language)
# Check: invalid ISSN
match = re.match(r'^.*?issn.*$', column)
if match is not None:
df[column] = df[column].apply(check.issn)
# Check: invalid ISBN
match = re.match(r'^.*?isbn.*$', column)
if match is not None:
df[column] = df[column].apply(check.isbn)
# Check: invalid date
match = re.match(r'^.*?date.*$', column)
if match is not None:
df[column] = df[column].apply(check.date)
# Write
df.to_csv(args.output_file, index=False)
# Close the input and output files before exiting
args.input_file.close()
args.output_file.close()
sys.exit(0)