Metadata-Version: 1.1
Name: goodtables
Version: 1.0.0a9
Summary: Goodtables is a framework to inspect tabular data.
Home-page: https://github.com/frictionlessdata/goodtables
Author: Open Knowledge International
Author-email: info@okfn.org
License: MIT
Description: # goodtables
        
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        Goodtables is a framework to inspect tabular data.
        
        > [BREAKING] Version `v1.0.0-alpha8` has merged preset `tables` and preset `datapackages` into universal `nested` preset.
        
        ---
        
        > [BREAKING] Version `v1.0` has renewed API introduced in NOT backward-compatibility manner. Previous version could be found [here](https://github.com/frictionlessdata/goodtables-py/tree/4b85254cc0358c0caf85bbd41d0c2023df99fb9b).
        
        ## 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
        
        ```bash
        $ pip install goodtables --pre
        ```
        
        ### Example
        
        Let's start with the simple example:
        
        ```python
        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](data/dataset.png)
        
        #### 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:
        
        ```python
        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
        - datapackage
        - nested (a special preset allows to nest `inspect` calls - [example](https://github.com/frictionlessdata/goodtables-py/blob/master/examples/nested.py))
        
        #### 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.
        
        ```python
        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:
        
        ```python
        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):
        
        ```python
        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 the word `inspector` because we create reports as the result of an inspection. One difference to validation - goodtables will not raise an exception if the dataset is invalid.  Final naming is under consideration 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 consideration. Not for multitable datasets because of parallelizm, but for one table it could be exposed to public via API because internally that's how goodtables works.  The 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
        
        - [Docstrings](https://github.com/frictionlessdata/goodtables-py/tree/master/goodtables)
        - [Changelog](https://github.com/frictionlessdata/goodtables/commits/master)
        
        ## Contributing
        
        Please read the contribution guideline:
        
        [How to Contribute](CONTRIBUTING.md)
        
        Thanks!
Keywords: data validation,frictionless data,open data,json schema,json table schema,data package,tabular data package
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
Classifier: Topic :: Software Development :: Libraries :: Python Modules
