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csv-metadata-quality/csv_metadata_quality/fix.py

137 lines
3.5 KiB
Python
Executable File

import pandas as pd
import re
def whitespace(field):
"""Fix whitespace issues.
Return string with leading, trailing, and consecutive whitespace trimmed.
"""
# Skip fields with missing values
if pd.isna(field):
return
# Initialize an empty list to hold the cleaned values
values = list()
# Try to split multi-value field on "||" separator
for value in field.split('||'):
# Strip leading and trailing whitespace
value = value.strip()
# Replace excessive whitespace (>2) with one space
pattern = re.compile(r'\s{2,}')
match = re.findall(pattern, value)
if match:
print(f'Excessive whitespace: {value}')
value = re.sub(pattern, ' ', value)
# Save cleaned value
values.append(value)
# Create a new field consisting of all values joined with "||"
new_field = '||'.join(values)
return new_field
def separators(field):
"""Fix for invalid multi-value separators (ie "|")."""
# Skip fields with missing values
if pd.isna(field):
return
# Initialize an empty list to hold the cleaned values
values = list()
# Try to split multi-value field on "||" separator
for value in field.split('||'):
# After splitting, see if there are any remaining "|" characters
pattern = re.compile(r'\|')
match = re.findall(pattern, value)
if match:
print(f'Fixing invalid multi-value separator: {value}')
value = re.sub(pattern, '||', value)
# Save cleaned value
values.append(value)
# Create a new field consisting of all values joined with "||"
new_field = '||'.join(values)
return new_field
def unnecessary_unicode(field):
"""Remove unnecessary Unicode characters.
Removes unnecessary Unicode characters like:
- Zero-width space (U+200B)
- Replacement character (U+FFFD)
- No-break space (U+00A0)
Return string with characters removed.
"""
# Skip fields with missing values
if pd.isna(field):
return
# Check for zero-width space characters (U+200B)
pattern = re.compile(r'\u200B')
match = re.findall(pattern, field)
if match:
print(f'Removing unnecessary Unicode (U+200B): {field}')
field = re.sub(pattern, '', field)
# Check for replacement characters (U+FFFD)
pattern = re.compile(r'\uFFFD')
match = re.findall(pattern, field)
if match:
print(f'Removing unnecessary Unicode (U+FFFD): {field}')
field = re.sub(pattern, '', field)
# Check for no-break spaces (U+00A0)
pattern = re.compile(r'\u00A0')
match = re.findall(pattern, field)
if match:
print(f'Removing unnecessary Unicode (U+00A0): {field}')
field = re.sub(pattern, '', field)
return field
def duplicates(field):
"""Remove duplicate metadata values."""
# Skip fields with missing values
if pd.isna(field):
return
# Try to split multi-value field on "||" separator
values = field.split('||')
# Initialize an empty list to hold the de-duplicated values
new_values = list()
# Iterate over all values
for value in values:
# Check if each value exists in our list of values already
if value not in new_values:
new_values.append(value)
else:
print(f'Dropping duplicate value: {value}')
# Create a new field consisting of all values joined with "||"
new_field = '||'.join(new_values)
return new_field