As part of ongoing data quality-assurance work for MalariaGEN’s P. falciparum Genome Variation project, I’ve written a small Python library called csvvalidator for validating data in CSV files or similar row-oriented data sources.
The source code for csvvalidator is on github, and you call find csvvalidator on the Python package index (so you can do easy_install csvvalidator
).
Here’s a simple example:
import sys import csv from csvvalidator import * field_names = ( 'study_id', 'patient_id', 'gender', 'age_years', 'age_months', 'date_inclusion' ) validator = CSVValidator(field_names) # basic header and record length checks validator.add_header_check('EX1', 'bad header') validator.add_record_length_check('EX2', 'unexpected record length') # some simple value checks validator.add_value_check('study_id', int, 'EX3', 'study id must be an integer') validator.add_value_check('patient_id', int, 'EX4', 'patient id must be an integer') validator.add_value_check('gender', enumeration('M', 'F'), 'EX5', 'invalid gender') validator.add_value_check('age_years', number_range_inclusive(0, 120, int), 'EX6', 'invalid age in years') validator.add_value_check('date_inclusion', datetime_string('%Y-%m-%d'), 'EX7', 'invalid date') # a more complicated record check def check_age_variables(r): age_years = int(r['age_years']) age_months = int(r['age_months']) valid = (age_months >= age_years * 12 and age_months % age_years < 12) if not valid: raise ValueError(age_years, age_months) validator.add_record_check(check_age_variables, 'EX8', 'invalid age variables') # validate the data and write problems to stdout data = csv.reader('/path/to/data.csv', delimiter='\t') problems = validator.validate(data) write_problems(problems, sys.stdout)
For more involved examples, see example.py and tests.py … or use the source, Luke 🙂