Skip to main content

A simple library for validating data contained in CSV files or similar row-oriented data sources.

Project description

This module provides some simple utilities for validating data contained in CSV files, or other similar data sources.

The source code for this module lives at:

https://github.com/alimanfoo/csvvalidator

Please report any bugs or feature requests via the issue tracker there.

Installation

This module is registered with the Python package index, so you can do:

$ easy_install csvvalidator

… or download from http://pypi.python.org/pypi/csvvalidator and install in the usual way:

$ python setup.py install

If you want the bleeding edge, clone the source code repository:

$ git clone git://github.com/alimanfoo/csvvalidator.git
$ cd csvvalidator
$ python setup.py install

Usage

The CSVValidator class is the foundation for all validator objects that are capable of validating CSV data.

You can use the CSVValidator class to dynamically construct a validator, e.g.:

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 RecordError('EX8', 'invalid age variables')
validator.add_record_check(check_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 complex use cases you can also sub-class CSVValidator to define re-usable validator classes for specific data sources.

For a complete account of all of the functionality available from this module, see the example.py and tests.py modules in the source code repository.

Notes

Note that the csvvalidator module is intended to be used in combination with the standard Python csv module. The csvvalidator module will not validate the syntax of a CSV file. Rather, the csvvalidator module can be used to validate any source of row-oriented data, such as is provided by a csv.reader object.

I.e., if you want to validate data from a CSV file, you have to first construct a CSV reader using the standard Python csv module, specifying the appropriate dialect, and then pass the CSV reader as the source of data to either the CSVValidator.validate or the CSVValidator.ivalidate method.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

csvvalidator-1.2.tar.gz (9.9 kB view details)

Uploaded Source

File details

Details for the file csvvalidator-1.2.tar.gz.

File metadata

  • Download URL: csvvalidator-1.2.tar.gz
  • Upload date:
  • Size: 9.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for csvvalidator-1.2.tar.gz
Algorithm Hash digest
SHA256 f1f222993589f9d8f8d83088d0bf80475462e5fa848baf920d0c03c03690c37e
MD5 71f9466c06111c7248509c8c48386b2e
BLAKE2b-256 22cd0b1a28226ed8cefb15bb0024a2adb6b6018b7248e0a75320ffeac316e6fb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page