goodtables-py

Goodtables is a framework to validate tabular data. It can check the structure of your data (e.g. all rows have the same number of columns), and its contents (e.g. all dates are valid).

Features

• Structural checks: Ensure that there are no empty rows, no blank headers, etc.
• Content checks: Ensure that the values have the correct types ("string", "number", "date", etc.), that their format is valid ("string must be an e-mail"), and that they respect the constraints ("age must be a number greater than 18").
• Support for multiple tabular formats: CSV, Excel files, LibreOffice, Data Package, etc.
• Parallelized validations for multi-table datasets
• Command line interface

Contents

• Getting Started
  • Installing
  • Running on CLI
  • Running on Python
• Documentation
  • Report
  • Checks
  • Presets
  • Data Quality Errors
  • Frequently Asked Questions
• API Reference
  • cli
  • validate
  • preset
  • check
  • Error
  • spec
  • GoodtablesException
• Contributing
• Changelog

Getting Started

Installing

If you have a building error on MacOS please try following this solution - https://github.com/google/jsonnet/issues/573#issuecomment-433201074

pip install goodtables
pip install goodtables[ods]  # If you need LibreOffice's ODS file support

Running on CLI

goodtables data.csv

Use goodtables --help to see the different options.

Running on Python

from goodtables import validate

report = validate('invalid.csv')
report['valid'] # false
report['table-count'] # 1
report['error-count'] # 3
report['tables'][0]['valid'] # false
report['tables'][0]['source'] # 'invalid.csv'
report['tables'][0]['errors'][0]['code'] # 'blank-header'

You can read a more in depth explanation on using goodtables with Python on the developer documentation section. Check also the examples folder for other examples.

Documentation

Goodtables validates your tabular dataset to find structural and content errors. Consider you have a file named invalid.csv. Let's validate it:

report = validate('invalid.csv')

We could also pass a remote URI instead of a local path. It supports CSV, XLS, XLSX, ODS, JSON, and all other formats supported by the tabulator library.

Report

The validation report follows the JSON Schema defined on goodtables/schemas/report.json.

The output of the validate() method is a report dictionary. It includes information if the data was valid, count of errors, list of table reports, which individual checks failed, etc. A report will be looking like this:

{
    "time": 0.009,
    "error-count": 1,
    "warnings": [
        "Table \"data/invalid.csv\" inspection has reached 1 error(s) limit"
    ],
    "preset": "table",
    "valid": false,
    "tables": [
        {
            "errors": [
                {
                    "row-number": null,
                    "message": "Header in column 3 is blank",
                    "row": null,
                    "column-number": 3,
                    "code": "blank-header"
                }
            ],
            "error-count": 1,
            "headers": [
                "id",
                "name",
                "",
                "name"
            ],
            "scheme": "file",
            "row-count": 2,
            "valid": false,
            "encoding": "utf-8",
            "time": 0.007,
            "schema": null,
            "format": "csv",
            "source": "data/invalid"
        }
    ],
    "table-count": 1
}

The errors are divided in one of the following categories:

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

Checks

Check is a main validation actor in goodtables. The list of enabled checks can be changed using checks and skip_checks arguments. Let's explore the options on an example:

report = validate('data.csv') # by default structure and schema (if available) checks
report = validate('data.csv', checks=['structure']) # only structure checks
report = validate('data.csv', checks=['schema']) # only schema (if available) checks
report = validate('data.csv', checks=['bad-headers']) # check only 'bad-headers'
report = validate('data.csv', skip_checks=['bad-headers']) # exclude 'bad-headers'

By default a dataset will be validated against all available Data Quality Spec errors. Some checks can be unavailable for validation. For example, if the schema isn't provided, only the structure checks will be done.

Presets

Goodtables support different formats of tabular datasets. They're called presets. A tabular dataset is some data that can be split in a list of data tables, as:

We can change the preset using the preset argument for validate(). By default, it'll be inferred from the source, falling back to table. To validate a data package, we can do:

report = validate('datapackage.json') # implicit preset
report = validate('datapackage.json', preset='datapackage') # explicit preset

This will validate all tabular resources in the datapackage.

It's also possible to validate a list of files using the "nested" preset. To do so, the first argument to validate() should be a list of dictionaries, where each key in the dictionary is named after a parameter on validate(). For example:

report = validate([{'source': 'data1.csv'}, {'source': 'data2.csv'}]) # implicit preset
report = validate([{'source': 'data1.csv'}, {'source': 'data2.csv'}], preset='nested') # explicit preset

Is similar to:

report_data1 = validate('data1.csv')
report_data2 = validate('data2.csv')

The difference is that goodtables validates multiple tables in parallel, so calling using the "nested" preset should run faster.

Data Quality Errors

Base report errors are standardized and described in Data Quality Spec.

Source errors

The basic checks can't be disabled, as they deal with goodtables being able to read the files.

Structure errors

These checks validate that the structure of the file are valid.

Schema errors

These checks validate the contents of the file. To use them, you need to pass a Table Schema. If you don't have a schema, goodtables can infer it if you use the infer_schema option.

If your schema only covers part of the data, you can use the infer_fields to infer the remaining fields.

Lastly, if the order of the fields in the data is different than in your schema, enable the order_fields option.

Custom errors

blacklisted-value

Sometimes we have to check for some values we don't want to have in out dataset. It accepts following options:

Consider the following CSV file:

id,name
1,John
2,bug
3,bad
5,Alex

Let's check that the name column doesn't contain rows with bug or bad:

from goodtables import validate

report = validate('data.csv', checks=[
    {'blacklisted-value': {'column': 'name', 'blacklist': ['bug', 'bad']}},
])
# error on row 3 with code "blacklisted-value"
# error on row 4 with code "blacklisted-value"

deviated-value

This check helps to find outlines in a column containing positive numbers. It accepts following options:

Consider the following CSV file:

temperature
1
-2
7
0
1
2
5
-4
100
8
3

We use median to get an average of the column values and allow interval of 3 standard deviations. For our case median is 2.0 and standard deviation is 29.73 so all valid values must be inside the [-87.19, 91.19] interval.

report = validate('data.csv', checks=[
    {'deviated-value': {'column': 'temperature', 'average': 'median', 'interval': 3}},
])
# error on row 10 with code "deviated-value"

foreign-key

We support here relative paths. It MUST be used only for trusted data sources.

This check validate foreign keys within a data package. Consider we have a data package defined below:

DESCRIPTOR = {
  'resources': [
    {
      'name': 'cities',
      'data': [
        ['id', 'name', 'next_id'],
        [1, 'london', 2],
        [2, 'paris', 3],
        [3, 'rome', 4],
        # [4, 'rio', None],
      ],
      'schema': {
        'fields': [
          {'name': 'id', 'type': 'integer'},
          {'name': 'name', 'type': 'string'},
          {'name': 'next_id', 'type': 'integer'},
        ],
        'foreignKeys': [
          {
            'fields': 'next_id',
            'reference': {'resource': '', 'fields': 'id'},
          },
          {
            'fields': 'id',
            'reference': {'resource': 'people', 'fields': 'label'},
          },
        ],
      },
    }, {
      'name': 'people',
      'data': [
        ['label', 'population'],
        [1, 8],
        [2, 2],
        # [3, 3],
        # [4, 6],
      ],
    },
  ],
}

Running goodtables on it will raise a few foreign-key errors because we have commented some rows in the data package's data:

report = validate(DESCRIPTOR, checks=['structure', 'schema', 'foreign-key'])
print(report)

{'error-count': 2,
 'preset': 'datapackage',
 'table-count': 2,
 'tables': [{'datapackage': '...',
             'error-count': 2,
             'errors': [{'code': 'foreign-key',
                         'message': 'Foreign key "[\'next_id\']" violation in '
                                    'row 4',
                         'message-data': {'fields': ['next_id']},
                         'row-number': 4},
                        {'code': 'foreign-key',
                         'message': 'Foreign key "[\'id\']" violation in row 4',
                         'message-data': {'fields': ['id']},
                         'row-number': 4}],
             'format': 'inline',
             'headers': ['id', 'name', 'next_id'],
             'resource-name': 'cities',
             'row-count': 4,
             'schema': 'table-schema',
             'source': 'inline',
             'time': 0.031,
             'valid': False},
            {'datapackage': '...',
             'error-count': 0,
             'errors': [],
             'format': 'inline',
             'headers': ['label', 'population'],
             'resource-name': 'people',
             'row-count': 3,
             'source': 'inline',
             'time': 0.038,
             'valid': True}],
 'time': 0.117,
 'valid': False,
 'warnings': []}

It experimetally supports external resource checks, for example, for a foreignKey definition like these:

{"package": "../people/datapackage.json", "resource": "people", "fields": "label"}
{"package": "http:/example.com/datapackage.json", "resource": "people", "fields": "label"}

sequential-value

This checks is for pretty common case when a column should have integers that sequentially increment. It accepts following options:

Consider the following CSV file:

id,name
1,one
2,two
3,three
5,five

Let's check if the id column contains sequential integers:

from goodtables import validate

report = validate('data.csv', checks=[
    {'sequential-value': {'column': 'id'}},
])
# error on row 5 with code "sequential-value"

truncated-value

Some database or spreadsheet software (like MySQL or Excel) could cutoff values on saving. There are some well-known heuristics to find this bad values. See https://github.com/propublica/guides/blob/master/data-bulletproofing.md for more detailed information.

Consider the following CSV file:

id,amount,comment
1,14000000,good
2,2147483647,bad
3,32767,bad
4,234234234,bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbad

To detect all probably truncated values we could use truncated-value check:

report = validate('data.csv', checks=[
    'truncated-value',
])
# error on row 3 with code "truncated-value"
# error on row 4 with code "truncated-value"
# error on row 5 with code "truncated-value"

custom-constraint

With Table Schema we could create constraints for an individual field but sometimes it's not enough. With a custom constraint check every row could be checked against given limited python expression in which variable names resolve to column values. See list of available operators. It accepts following options:

constraint (str)

Constraint definition (e.g. col1 + col2 == col3)

Consider csv file like this:

id,name,salary,bonus
1,Alex,1000,200
2,Sam,2500,500
3,Ray,1350,500
4,John,5000,1000

Let's say our business rule is to be shy on bonuses:

report = validate('data.csv', checks=[
    {'custom-constraint': {'constraint': 'salary > bonus * 4'}},
])
# error on row 4 with code "custom-constraint"

Frequently Asked Questions

How can I add a new custom check?

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

from goodtables import validate, check, Error

@check('custom-check', type='custom', context='body')
def custom_check(cells):
    errors = []
    for cell in cells:
        message = 'Custom error on column {column_number} and row {row_number}'
        error = Error(
            'custom-error',
            cell,
            message
        )
        errors.append(error)
    return errors

report = validate('data.csv', checks=['custom-check'])

For now this documentation section is incomplete. Please see builtin checks to learn more about checking protocol.

How can I add support for a new tabular file type?

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

from tabulator import Stream
from tableschema import Schema
from goodtables import validate

@preset('custom-preset')
def custom_preset(source, **options):
    warnings = []
    tables = []
    for table in source:
        try:
            tables.append({
                'source':  str(source),
                'stream':  Stream(...),
                'schema': Schema(...),
                'extra': {...},
            })
        except Exception:
            warnings.append('Warning message')
    return warnings, tables

report = validate(source, preset='custom-preset')

For now this documentation section is incomplete. Please see builtin presets to learn more about the dataset extraction protocol.

API Reference

cli

cli()

Command-line interface

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

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  validate*  Validate tabular files (default).
  init       Init data package from list of files.

validate

validate(source, **options)

Validates a source file and returns a report.

Arguments

• source (Union[str, Dict, List[Dict], IO]): The source to be validated. It can be a local file path, URL, dict, list of dicts, or a file-like object. If it's a list of dicts and the preset is "nested", each of the dict key's will be used as if it was passed as a keyword argument to this method.

    The file can be a CSV, XLS, JSON, and any other format supported by
    `tabulator`_.
  

• checks (List[str]): List of checks names to be enabled. They can be individual check names (e.g. blank-headers), or check types (e.g. structure).

• skip_checks (List[str]): List of checks names to be skipped. They can be individual check names (e.g. blank-headers), or check types (e.g. structure).

• infer_schema (bool): Infer schema if one wasn't passed as an argument.

• infer_fields (bool): Infer schema for columns not present in the received schema.

• order_fields (bool): Order source columns based on schema fields order. This is useful when you don't want to validate that the data columns' order is the same as the schema's.

• error_limit (int): Stop validation if the number of errors per table exceeds this value.

• table_limit (int): Maximum number of tables to validate.

• row_limit (int): Maximum number of rows to validate.

• preset (str): Dataset type could be table (default), datapackage, nested or custom. Usually, the preset can be inferred from the source, so you don't need to define it.

• Any (Any): Any additional arguments not defined here will be passed on, depending on the chosen preset. If the preset is table, the extra arguments will be passed on to tabulator, if it is datapackage, they will be passed on to the datapackage constructor.

Raises

• GoodtablesException: Raised on any non-tabular error.

Returns

dict: The validation report.

preset

preset(name)

Register a custom preset (decorator)

Example

@preset('custom-preset')
def custom_preset(source, **options):
    # ...

Arguments

• name (str): preset name

check

check(name, type=None, context=None, position=None)

Register a custom check (decorator)

Example

@check('custom-check', type='custom', context='body')
def custom_check(cells):
    # ...

Arguments

• name (str): preset name
• type (str): has to be custom
• context (str): has to be head or body
• position (str): has to be before:<check-name> or after:<check-name>

Error

Error(self, code, cell=None, row_number=None, message=None, message_substitutions=None)

Describes a validation check error

Arguments

• code (str): The error code. Must be one in the spec.
• cell (dict, optional): The cell where the error occurred.
• row_number (int, optional): The row number where the error occurs.
• message (str, optional): The error message. Defaults to the message from the Data Quality Spec.
• message_substitutions (dict, optional): Dictionary with substitutions to be used when generating the error message and description.

Raises

• KeyError: Raised if the error code isn't known.

spec

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)

GoodtablesException

GoodtablesException(self, /, *args, **kwargs)

Base goodtables exception

Contributing

The project follows the Open Knowledge International coding standards.

Recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into active environment:

$ make install

To run tests with linting and coverage:

$ make test

Changelog

Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.

v2.4

• Added integrity checks for data packages. If resource.bytes or resource.hash (sha256) is provided it will be verified against actual values

v2.3

• Added a foreign keys check

v2.2

• Improved missing/non-matching-headers detection (#298)

v2.1

• A new key added to the error.to_dict return: message-data

v2.0

Breaking changes:

• Checks method signature now only receives the current row's cells list
• Checks raise errors by returning an array of Error objects
• Cells have the row number in the row-number key
• Files with ZIP extension are presumed to be datapackages, so goodtables mydatapackage.zip works
• Improvements to goodtables CLI (#233)
• New goodtables init <data paths> command to create a new datapackage.json with the files passed as parameters and their inferred schemas.

Bug fixes:

• Fix bug with truncated-values check on date fields (#250)

v1.5

New API added:

• Validation source now could be a pathlib.Path

v1.4

Improved behaviour:

• rebased on Data Quality Spec v1
• rebased on Data Package Spec v1
• rebased on Table Schema Spec v1
• treat primary key as required/unique field

v1.3

New advanced checks added:

• blacklisted-value
• custom-constraint
• deviated-value
• sequential-value
• truncated-value

v1.2

New API added:

• report.preset
• report.tables[].schema

v1.1

New API added:

• report.tables[].scheme
• report.tables[].format
• report.tables[].encoding

v1.0

This version includes various big changes. A migration guide is under development and will be published here.

v0.6

First version of goodtables.