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https://github.com/ilri/csv-metadata-quality.git
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fix.py: Massive improvements
Use Python's str.strip() instead of kludgy regular expressions and use split/join to handle multi-value fields more cleanly.
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fix.py
63
fix.py
@ -2,7 +2,7 @@
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import pandas as pd
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def fix_whitespace(value):
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def fix_whitespace(field):
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"""Fix whitespace issues.
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Return string with leading, trailing, and consecutive whitespace trimmed.
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@ -10,62 +10,43 @@ def fix_whitespace(value):
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import re
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# Skip cells with missing values
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if pd.isna(value):
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# Skip fields with missing values
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if pd.isna(field):
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return
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# Try to split multi-value cells on "||" separator
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#for value in cell.split('||'):
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# Initialize an empty list to hold the cleaned values
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values = list()
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# Check for leading whitespace
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pattern = re.compile(r'^\s+')
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# Try to split multi-value field on "||" separator
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for value in field.split('||'):
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# Strip leading and trailing whitespace
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value = value.strip()
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# Replace excessive whitespace (>2) with one space
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pattern = re.compile(r'\s{2,}')
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match = re.findall(pattern, value)
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if len(match) > 0:
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print('DEBUG: Leading whitespace')
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print('DEBUG: Excessive whitespace')
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value = re.sub(pattern, ' ', value)
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# Check for leading whitespace in multi-value cells
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# SOME VALUE|| ANOTHER VALUE
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pattern = re.compile(r'\|\|\s+')
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match = re.findall(pattern, value)
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# Save cleaned value
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values.append(value)
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if len(match) > 0:
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print('DEBUG: Leading whitespace in multi-value cell')
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value = re.sub(pattern, '||', value)
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# Create a new field consisting of all values joined with "||"
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new_field = '||'.join(values)
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# Check for trailing whitespace
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pattern = re.compile(r'\s+$')
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match = re.findall(pattern, value)
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if len(match) > 0:
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print('DEBUG: Trailing whitespace')
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value = re.sub(pattern, '', value)
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# Check for trailing whitespace in multi-value cells
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# SOME VALUE ||ANOTHER VALUE
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pattern = re.compile(r'\s+\|\|')
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match = re.findall(pattern, value)
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if len(match) > 0:
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print('DEBUG: Trailing whitespace in multi-value cell')
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value = re.sub(pattern, '||', value)
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return value
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return new_field
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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('/home/aorth/Downloads/2019-07-26-Bioversity-Migration.csv', dtype=str)
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#df = pd.read_csv('/tmp/quality.csv')
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#df = pd.read_csv('/tmp/omg.csv')
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#df = pd.read_csv('/home/aorth/Downloads/2019-07-26-Bioversity-Migration.csv', dtype=str)
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#df = pd.read_csv('/tmp/quality.csv', dtype=str)
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df = pd.read_csv('/tmp/omg.csv', dtype=str)
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# Fix whitespace in all columns
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for column in df.columns.values.tolist():
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print(column)
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# Skip the id column
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#if column == 'id':
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# continue
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print(f'DEBUG: {column}')
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df[column] = df[column].apply(fix_whitespace)
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