Validate and process tabular data in Python.
Python Makefile
Latest commit 3ea326e Feb 6, 2017 @roll roll committed on GitHub v1.0.0-alpha5

README.md

goodtables

Travis Coveralls PyPi SemVer Gitter

Goodtables is a framework to inspect tabular data.

Version v1.0 has renewed API introduced in NOT backward-compatibility manner. Previous version could be found here.

Features

  • tabular data inspection and validation
  • general, structure and schema checks
  • support for different input data presets
  • parallel computation for multitable datasets
  • builtin command-line interface

Getting Started

Installation

$ pip install goodtables --pre

Example

Let's start with the simple example:

from goodtables import Inspector

inspector = Inspector()
print(inspector.inspect('data/invalid.csv'))

# will print
#{'time': 0.029,
# 'valid': False',
# 'error-count': 2,
# 'table-count': 1,
# 'errors': [],
# 'tables': [
#    {'time': 0.027,
#     'valid': False',
#     'headers': ['id', 'name', ''],
#     'row-count': 4,
#     'source': 'data/invalid.csv'
#     'error-count': 2,
#     'errors': [
#        {'row': None,
#         'code': 'blank-header',
#         'message': 'Blank header',
#         'row-number': None,
#         'column-number': 2},
#        {'row': [],
#         'code': 'blank-row',
#         'message': 'Blank row',
#         'row-number': 3,
#         'column-number': None}]}]}

Inspection

Goodtables inspects your tabular data to find general, structure and schema errors. As presented in an example above to inspect data:

  • Inspector(**options) class should be instantiated
  • inspector.inspect(source, preset=<preset>, **options) should be called
  • a returning value will be a report dictionary

Dataset

Goodtables support different sources for an inspection. But it should be convertable to dataset presented on a figure 1. Details will be explained in the next sections:

Dataset

Report

As a result of inspection goodtables returns a report dictionary. It includes valid flag, count of errors, list of reports per table including errors etc. See example above for an instance. A report structure and all errors are standartised and described in data quality spec:

https://github.com/frictionlessdata/goodtables-py/blob/next-initial/goodtables/spec.json

Errors

Report errors are categorized by type:

  • general - data can't be loaded or parsed
  • structure - general tabular errors like duplicate headers
  • schema - error of checks against JSON Table Schema

Report errors are categorized by context:

  • any - generic errors like IO, HTTP error
  • dataset - the whole dataset errors like invalid datapackage
  • table - the whole table errors like bad encoding
  • head - headers errors
  • body - contents errors

Presets

Table is a main inspection object in goodtables. The simplest option is to pass to Inspector.inspect path and other options for one table (see example above). But when multitable parallized inspection is needed different presets could be used to process a dataset.

Let's see how to inspect a datapackage:

from goodtables import Inspector

inspector = Inspector()
inspector.inspect('datapackage.json', preset='datapackage')

A preset function proceses passed source and options and fills tables list for the following inspection. If any errors have happened a preset function should add them to errors list.

Builtin presets

Goodtables by default supports the following presets:

  • table
  • tables
  • datapackage

Custom presets

It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete goodtables version to your requirements file.

To register a custom preset user could use a preset decorator. This way the builtin preset could be overriden or could be added a custom preset.

from tabulator import Stream
from jsontableschema import Schema
from goodtables import Inspector, preset

@preset('custom-preset')
def custom_preset(source, **options):
    for table in source:
        try:
            tables.append({
                'source':  str(source),
                'stream':  Stream(...),
                'schema': Schema(...),
                'extra': {...},
            })
        except Exception:
            errors.append({
                'code': 'error-code',
                'message': 'Error message',
                'row-number': None,
                'column-number': None,
            })

inspector = Inspector(custom_presets=[custom_preset])
inspector.inspect(source, preset='custom-preset')

See builtin presets to learn more about the dataset extration protocol.

Checks

Check is a main inspection actor in goodtables. Every check is associated with a specification error. Checking order is the same as order of errors in the specification. List of checks could be customized using inspector's checks argument. Let's explore options on an example:

inspector = Inspector(checks='all/structure/schema') # type
inspector = Inspector(checks={'bad-headers': False}) # exclude
inspector = Inspector(checks={'bad-headers': True}) # cherry-pick

Check gets input data from framework based on context (e.g. columns, sample for head context) and update errors and columns lists in-place.

Buitin checks

Goodtables by default supports the following checks:

  • [check for every error from the specification]

Custom checks

It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete goodtables version to your requirements file.

To register a custom check user could use a check decorator. This way the builtin check could be overriden (use the spec error code like duplicate-row) or could be added a check for a custom error (use type, context and after/before arguments):

from goodtables import Inspector, check

@check('custom-error', type='structure', context='body', after='blank-row')
def custom_check(errors, columns, row_number,  state=None):
    for column in columns:
        errors.append({
            'code': 'custom-error',
            'message': 'Custom error',
            'row-number': row_number,
            'column-number': column['number'],
        })
        columns.remove(column)

inspector = Inspector(custom_checks=[custom_check])

See builtin checks to learn more about checking protocol.

CLI

It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete goodtables version to your requirements file.

All common goodtables tasks could be done using a command-line interface (command per preset excluding tables):

$ goodtables
Usage: cli.py [OPTIONS] COMMAND [ARGS]...

Options:
  --json
  --error-limit INTEGER
  --table-limit INTEGER
  --row-limit INTEGER
  --infer-schema
  --infer-fields
  --order-fields
  --help                 Show this message and exit.

Commands:
  datapackage
  table

For example write a following command to the shell:

$ goodtables table data/invalid.csv

And a report (the same as in the initial example) will be printed to the standard output.

FAQ

Is it an inspection or validation?

For now we use inspector word because we create reports as result of an inspection. One difference to validation - goodtables will not raise an exception if dataset is invalid. Final naming is under considiration and based on exposed methods (only inspect or like inspect/validate/stream).

Is it possible to stream reporting?

For now - it's not. But it's under considiration. Not for multitable datasets because of parallelizm but for one table it could be exposed to public API because internally it's how goodtables works. Question here is what should be streamed - errors or valid/invalid per row indication with errors etc. We would be happy to see a real world use case for this feature.

API Reference

Snapshot

Inspector(checks='all',
          table_limit=10,
          row_limit=1000,
          error_limit=1000,
          infer_schema=False,
          infer_fields=False,
          order_fields=False,
          custom_presets=[],
          custom_checks=[])
    inspect(source, preset='table', **options)
~@preset(name)
~@check(error)
exceptions
spec
~cli

Detailed

Contributing

Please read the contribution guideline:

How to Contribute

Thanks!