tableschema 1.0.8

A utility library for working with Table Schema in Python

tableschema-py
==============

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A library for working with `Table
Schema <http://specs.frictionlessdata.io/table-schema/>`__ in Python.

Version v1.0 includes various important changes. Please read a
`migration guide <#v10>`__.

Features
--------

- ``Table`` to work with data tables described by Table Schema
- ``Schema`` representing Table Schema
- ``Field`` representing Table Schema field
- ``validate`` to validate Table Schema
- ``infer`` to infer Table Schema from data
- built-in command-line interface to validate and infer schemas
- storage/plugins system to connect tables to different storage
backends like SQL Database

Gettings Started
----------------

Installation
~~~~~~~~~~~~

The package use semantic versioning. It means that major versions could
include breaking changes. It's highly recommended to specify
``tableschema`` version range in your ``setup/requirements`` file e.g.
``tableschema>=1.0,<2.0``.

.. code:: bash

$ pip install tableschema

Examples
~~~~~~~~

Code examples in this readme requires Python 3.3+ interpreter. You could
see even more example in
`examples <https://github.com/frictionlessdata/tableschema-py/tree/master/examples>`__
directory.

.. code:: python

from tableschema import Table

# Create table
table = Table('path.csv', schema='schema.json')

# Print schema descriptor
print(table.schema.descriptor)

# Print cast rows in a dict form
for keyed_row in table.iter(keyed=True):
print(keyed_row)

Documentation
-------------

Table
~~~~~

A table is a core concept in a tabular data world. It represents a data
with a metadata (Table Schema). Let's see how we could use it in
practice.

Consider we have some local csv file. It could be inline data or remote
link - all supported by ``Table`` class (except local files for
in-brower usage of course). But say it's ``data.csv`` for now:

.. code:: csv

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,N/A

Let's create and read a table. We use static ``Table.load`` method and
``table.read`` method with a ``keyed`` option to get array of keyed
rows:

.. code:: python

table = Table('data.csv')
table.headers # ['city', 'location']
table.read(keyed=True)
# [
# {city: 'london', location: '51.50,-0.11'},
# {city: 'paris', location: '48.85,2.30'},
# {city: 'rome', location: 'N/A'},
# ]

As we could see our locations are just a strings. But it should be
geopoints. Also Rome's location is not available but it's also just a
``N/A`` string instead of JavaScript ``null``. First we have to infer
Table Schema:

.. code:: python

table.infer()
table.schema.descriptor
# { fields:
# [ { name: 'city', type: 'string', format: 'default' },
# { name: 'location', type: 'geopoint', format: 'default' } ],
# missingValues: [ '' ] }
table.read(keyed=True)
# Fails with a data validation error

Let's fix not available location. There is a ``missingValues`` property
in Table Schema specification. As a first try we set ``missingValues``
to ``N/A`` in ``table.schema.descriptor``. Schema descriptor could be
changed in-place but all changes sould be commited by
``table.schema.commit()``:

.. code:: python

table.schema.descriptor['missingValues'] = 'N/A'
table.schema.commit()
table.schema.valid # false
table.schema.errors
# [<ValidationError: "'N/A' is not of type 'array'">]

As a good citiziens we've decided to check out schema descriptor
validity. And it's not valid! We sould use an array for
``missingValues`` property. Also don't forget to have an empty string as
a missing value:

.. code:: python

table.schema.descriptor['missingValues'] = ['', 'N/A']
table.schema.commit()
table.schema.valid # true

All good. It looks like we're ready to read our data again:

.. code:: python

table.read(keyed=True)
# [
# {city: 'london', location: [51.50,-0.11]},
# {city: 'paris', location: [48.85,2.30]},
# {city: 'rome', location: null},
# ]

Now we see that:

- locations are arrays with numeric lattide and longitude
- Rome's location is a native Python ``None``

And because there are no errors on data reading we could be sure that
our data is valid againt our schema. Let's save it:

.. code:: python

table.schema.save('schema.json')
table.save('data.csv')

Our ``data.csv`` looks the same because it has been stringified back to
``csv`` format. But now we have ``schema.json``:

.. code:: json

{
"fields": [
{
"name": "city",
"type": "string",
"format": "default"
},
{
"name": "location",
"type": "geopoint",
"format": "default"
}
],
"missingValues": [
"",
"N/A"
]
}

If we decide to improve it even more we could update the schema file and
then open it again. But now providing a schema path:

.. code:: python

table = Table('data.csv', schema='schema.json')
# Continue the work

It was onle basic introduction to the ``Table`` class. To learn more
let's take a look on ``Table`` class API reference.

``Table(source, schema=None, strict=False, post_cast=[], storage=None, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Constructor to instantiate ``Table`` class. If ``references`` argument
is provided foreign keys will be checked on any reading operation.

- ``source (str/list[])`` - data source (one of):
- local file (path)
- remote file (url)
- array of arrays representing the rows
- ``schema (any)`` - data schema in all forms supported by ``Schema``
class
- ``strict (bool)`` - strictness option to pass to ``Schema``
constructor
- ``post_cast (function[])`` - list of post cast processors
- ``storage (None/str)`` - storage name like ``sql`` or ``bigquery``
- ``options (dict)`` - ``tabulator`` or storage options
- ``(exceptions.TableSchemaException)`` - raises any error occured in
table creation process
- ``(Table)`` - returns data table class instance

``table.headers``
^^^^^^^^^^^^^^^^^

- ``(str[])`` - returns data source headers

``table.schema``
^^^^^^^^^^^^^^^^

- ``(Schema)`` - returns schema class instance

``table.iter(keyed=Fase, extended=False, cast=True, relations=False)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Iter through the table data and emits rows cast based on table schema.
Data casting could be disabled.

- ``keyed (bool)`` - iter keyed rows
- ``extended (bool)`` - iter extended rows
- ``cast (bool)`` - disable data casting if false
- ``relations (dict)`` - dict of foreign key references in a form of
``{resource1: [{field1: value1, field2: value2}, ...], ...}``. If
provided foreign key fields will checked and resolved to its
references
- ``(exceptions.TableSchemaException)`` - raises any error occured in
this process
- ``(any[]/any{})`` - yields rows:
- ``[value1, value2]`` - base
- ``{header1: value1, header2: value2}`` - keyed
- ``[rowNumber, [header1, header2], [value1, value2]]`` - extended

``table.read(keyed=False, extended=False, cast=True, relations=False, limit=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Read the whole table and returns as array of rows. Count of rows could
be limited.

- ``keyed (bool)`` - flag to emit keyed rows
- ``extended (bool)`` - flag to emit extended rows
- ``cast (bool)`` - flag to disable data casting if false
- ``relations (dict)`` - dict of foreign key references in a form of
``{resource1: [{field1: value1, field2: value2}, ...], ...}``. If
provided foreign key fields will checked and resolved to its
references
- ``limit (int)`` - integer limit of rows to return
- ``(exceptions.TableSchemaException)`` - raises any error occured in
this process
- ``(list[])`` - returns array of rows (see ``table.iter``)

``table.infer(limit=100)``
^^^^^^^^^^^^^^^^^^^^^^^^^^

Infer a schema for the table. It will infer and set Table Schema to
``table.schema`` based on table data.

- ``limit (int)`` - limit rows samle size
- ``(dict)`` - returns Table Schema descriptor

``table.save(target, storage=None, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

To save schema use ``table.schema.save()``

Save data source to file locally in CSV format with ``,`` (comma)
delimiter

- ``target (str)`` - saving target (e.g. file path)
- ``storage (None/str)`` - storage name like ``sql`` or ``bigquery``
- ``options (dict)`` - ``tabulator`` or storage options
- ``(exceptions.TableSchemaException)`` - raises an error if there is
saving problem
- ``(True/Storage)`` - returns true or storage instance

Schema
~~~~~~

A model of a schema with helpful methods for working with the schema and
supported data. Schema instances can be initialized with a schema source
as a url to a JSON file or a JSON object. The schema is initially
validated (see `validate <#validate>`__ below). By default validation
errors will be stored in ``schema.errors`` but in a strict mode it will
be instantly raised.

Let's create a blank schema. It's not valid because
``descriptor.fields`` property is required by the `Table
Schema <http://specs.frictionlessdata.io/table-schema/>`__
specification:

.. code:: python

schema = Schema()
schema.valid # false
schema.errors
# [<ValidationError: "'fields' is a required property">]

To do not create a schema descriptor by hands we will use a
``schema.infer`` method to infer the descriptor from given data:

.. code:: python

schema.infer([
['id', 'age', 'name'],
['1','39','Paul'],
['2','23','Jimmy'],
['3','36','Jane'],
['4','28','Judy'],
])
schema.valid # true
schema.descriptor
#{ fields:
# [ { name: 'id', type: 'integer', format: 'default' },
# { name: 'age', type: 'integer', format: 'default' },
# { name: 'name', type: 'string', format: 'default' } ],
# missingValues: [ '' ] }

Now we have an inferred schema and it's valid. We could cast data row
against our schema. We provide a string input by an output will be cast
correspondingly:

.. code:: python

schema.cast_row(['5', '66', 'Sam'])
# [ 5, 66, 'Sam' ]

But if we try provide some missing value to ``age`` field cast will fail
because for now only one possible missing value is an empty string.
Let's update our schema:

.. code:: python

schema.cast_row(['6', 'N/A', 'Walt'])
# Cast error
schema.descriptor['missingValues'] = ['', 'N/A']
schema.commit()
schema.cast_row(['6', 'N/A', 'Walt'])
# [ 6, None, 'Walt' ]

We could save the schema to a local file. And we could continue the work
in any time just loading it from the local file:

.. code:: python

schema.save('schema.json')
schema = Schema('schema.json')

It was onle basic introduction to the ``Schema`` class. To learn more
let's take a look on ``Schema`` class API reference.

``Schema(descriptor, strict=False)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Constructor to instantiate ``Schema`` class.

- ``descriptor (str/dict)`` - schema descriptor:
- local path
- remote url
- dictionary
- ``strict (bool)`` - flag to alter validation behaviour:
- if false error will not be raised and all error will be collected in
``schema.errors``
- if strict is true any validation error will be raised immediately
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(Schema)`` - returns schema class instance

``schema.valid``
^^^^^^^^^^^^^^^^

- ``(bool)`` - returns validation status. It always true in strict
mode.

``schema.errors``
^^^^^^^^^^^^^^^^^

- ``(Exception[])`` - returns validation errors. It always empty in
strict mode.

``schema.descriptor``
^^^^^^^^^^^^^^^^^^^^^

- ``(dict)`` - returns schema descriptor

``schema.primary_key``
^^^^^^^^^^^^^^^^^^^^^^

- ``(str[])`` - returns schema primary key

``schema.foreign_keys``
^^^^^^^^^^^^^^^^^^^^^^^

- ``(dict[])`` - returns schema foreign keys

``schema.fields``
^^^^^^^^^^^^^^^^^

- ``(Field[])`` - returns an array of ``Field`` instances

``schema.field_names``
^^^^^^^^^^^^^^^^^^^^^^

- ``(str[])`` - returns an array of field names.

``schema.get_field(name)``
^^^^^^^^^^^^^^^^^^^^^^^^^^

Get schema field by name.

- ``name (str)`` - schema field name
- ``(Field/None)`` - returns ``Field`` instance or null if not found

``schema.add_field(descriptor)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Add new field to schema. The schema descriptor will be validated with
newly added field descriptor.

- ``descriptor (dict)`` - field descriptor
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(Field/None)`` - returns added ``Field`` instance or null if not
added

``schema.remove_field(name)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Remove field resource by name. The schema descriptor will be validated
after field descriptor removal.

- ``name (str)`` - schema field name
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(Field/None)`` - returns removed ``Field`` instances or null if not
found

``schema.cast_row(row)``
^^^^^^^^^^^^^^^^^^^^^^^^

Cast row based on field types and formats.

- ``row (any[])`` - data row as an array of values
- ``(any[])`` - returns cast data row

``schema.infer(rows, headers=1)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Infer and set ``schema.descriptor`` based on data sample.

- ``rows (list[])`` - array of arrays representing rows.
- ``headers (int/str[])`` - data sample headers (one of):
- row number containing headers (``rows`` should contain headers rows)
- array of headers (``rows`` should NOT contain headers rows)
- ``{dict}`` - returns Table Schema descriptor

``schema.commit(strict=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Update schema instance if there are in-place changes in the descriptor.

- ``strict (bool)`` - alter ``strict`` mode for further work
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(bool)`` - returns true on success and false if not modified

.. code:: python

descriptor = {'fields': [{'name': 'field', 'type': 'string'}]}
schema = Schema(descriptor)

schema.getField('name')['type'] # string
schema.descriptor.fields[0]['type'] = 'number'
schema.getField('name')['type'] # string
schema.commit()
schema.getField('name')['type'] # number

``schema.save(target)``
^^^^^^^^^^^^^^^^^^^^^^^

Save schema descriptor to target destination.

- ``target (str)`` - path where to save a descriptor
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(bool)`` - returns true on success

Field
~~~~~

.. code:: python

from tableschema import Field

# Init field
field = Field({'name': 'name', type': 'number'})

# Cast a value
field.cast_value('12345') # -> 12345

Data values can be cast to native Python objects with a Field instance.
Type instances can be initialized with `field
descriptors <https://specs.frictionlessdata.io/table-schema/>`__. This
allows formats and constraints to be defined.

Casting a value will check the value is of the expected type, is in the
correct format, and complies with any constraints imposed by a schema.
E.g. a date value (in ISO 8601 format) can be cast with a DateType
instance. Values that can't be cast will raise an ``InvalidCastError``
exception.

Casting a value that doesn't meet the constraints will raise a
``ConstraintError`` exception.

Here is an API reference for the ``Field`` class:

``new Field(descriptor, missingValues=[''])``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Constructor to instantiate ``Field`` class.

- ``descriptor (dict)`` - schema field descriptor
- ``missingValues (str[])`` - an array with string representing missing
values
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(Field)`` - returns field class instance

``field.name``
^^^^^^^^^^^^^^

- ``(str)`` - returns field name

``field.type``
^^^^^^^^^^^^^^

- ``(str)`` - returns field type

``field.format``
^^^^^^^^^^^^^^^^

- ``(str)`` - returns field format

``field.required``
^^^^^^^^^^^^^^^^^^

- ``(bool)`` - returns true if field is required

``field.constraints``
^^^^^^^^^^^^^^^^^^^^^

- ``(dict)`` - returns an object with field constraints

``field.descriptor``
^^^^^^^^^^^^^^^^^^^^

- ``(dict)`` - returns field descriptor

``field.castValue(value, constraints=true)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Cast given value according to the field type and format.

- ``value (any)`` - value to cast against field
- ``constraints (boll/str[])`` - gets constraints configuration
- it could be set to true to disable constraint checks
- it could be an Array of constraints to check e.g. ['minimum',
'maximum']
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(any)`` - returns cast value

``field.testValue(value, constraints=true)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Test if value is compliant to the field.

- ``value (any)`` - value to cast against field
- ``constraints (bool/str[])`` - constraints configuration
- ``(bool)`` - returns if value is compliant to the field

validate
~~~~~~~~

Given a schema as JSON file, url to JSON file, or a Python dict,
``validate`` returns ``True`` for a valid Table Schema, or raises an
exception, ``exceptions.ValidationError``. It validates only **schema**,
not data against schema!

.. code:: python

from tableschema import validate, exceptions

try:
valid = validate(descriptor)
except exceptions.ValidationError as exception:
for error in exception.errors:
# handle individual error

``validate(descriptor)``
^^^^^^^^^^^^^^^^^^^^^^^^

Validate a Table Schema descriptor.

- ``descriptor (str/dict)`` - schema descriptor (one of):
- local path
- remote url
- object
- (exceptions.ValidationError) - raises on invalid
- ``(bool)`` - returns true on valid

infer
~~~~~

Given headers and data, ``infer`` will return a Table Schema as a Python
dict based on the data values. Given the data file,
``data_to_infer.csv``:

::

id,age,name
1,39,Paul
2,23,Jimmy
3,36,Jane
4,28,Judy

Let's call ``infer`` for this file:

.. code:: python

from tableschema import infer

descriptor = infer('data_to_infer.csv')
#{'fields': [
# {
# 'format': 'default',
# 'name': 'id',
# 'type': 'integer'
# },
# {
# 'format': 'default',
# 'name': 'age',
# 'type': 'integer'
# },
# {
# 'format': 'default',
# 'name': 'name',
# 'type': 'string'
# }]
#}

The number of rows used by ``infer`` can be limited with the ``limit``
argument.

``infer(source, headers=1, limit=100, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Infer source schema.

- ``source (any)`` - source as path, url or inline data
- ``headers (int/str[])`` - headers rows number or headers list
- ``(exceptions.TableSchemaException)`` - raises any error occured in
the process
- ``(dict)`` - returns schema descriptor

Exceptions
~~~~~~~~~~

``exceptions.TableSchemaException``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Base class for all library exceptions. If there are multiple errors it
could be read from an exceptions object:

.. code:: python


try:
# lib action
except exceptions.TableSchemaException as exception:
if exception.multiple:
for error in exception.errors:
# handle error

``exceptions.LoadError``
^^^^^^^^^^^^^^^^^^^^^^^^

All loading errors.

``exceptions.ValidationError``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

All validation errors.

``exceptions.CastError``
^^^^^^^^^^^^^^^^^^^^^^^^

All value cast errors.

``exceptions.RelationError``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

All integrity errors.

``exceptions.StorageError``
^^^^^^^^^^^^^^^^^^^^^^^^^^^

All storage errors.

Storage
~~~~~~~

The library includes interface declaration to implement tabular
``Storage``. This interface allow to use different data storage systems
like SQL with ``tableschema.Table`` class (load/save) as well as on the
data package level:

|Storage|

For instantiation of concrete storage instances ``tableschema.Storage``
provides a unified factory method ``connect`` (under the hood the plugin
system will be used):

.. code:: python

# pip install tableschema_sql
from tableschema import Storage

storage = Storage.connect('sql', **options)
storage.create('bucket', descriptor)
storage.write('bucket', rows)
storage.read('bucket')

``Storage.connect(name, **options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Create tabular ``storage`` based on storage name.

- ``name (str)`` - storage name like ``sql``
- ``options (dict)`` - concrete storage options
- ``(exceptions.StorageError)`` - raises on any error
- ``(Storage)`` - returns ``Storage`` instance

--------------

An implementor should follow ``tableschema.Storage`` interface to write
his own storage backend. Concrete storage backends could include
additional functionality specific to conrete storage system. See
``plugins`` system below to know how to integrate custom storage plugin
into your workflow.

``<<Interface>>Storage(**options)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Create tabular ``storage``. Implementations should fully implement this
interface to be compatible to ``Storage`` API.

- ``options (dict)`` - concrete storage options
- ``(exceptions.StorageError)`` - raises on any error
- ``(Storage)`` - returns ``Storage`` instance

``storage.buckets``
^^^^^^^^^^^^^^^^^^^

Return list of storage bucket names. A ``bucket`` is a special term
which has almost the same meaning as the term ``table``. You should
consider ``bucket`` as a ``table`` stored in the ``storage``.

- ``(exceptions.StorageError)`` - raises on any error
- ``str[]`` - return list of bucket names

``create(bucket, descriptor, force=False)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Create one/multiple buckets.

- ``bucket (str/list)`` - bucket name or list of bucket names
- ``descriptor (dict/dict[])`` - schema descriptor or list of
descriptors
- ``force (bool)`` - delete and re-create already existent buckets
- ``(exceptions.StorageError)`` - raises on any error

``delete(bucket=None, ignore=False)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Delete one/multiple/all buckets.

- ``bucket (str/list/None)`` - bucket name or list of bucket names to
delete. If None all buckets will be deleted
- ``descriptor (dict/dict[])`` - schema descriptor or list of
descriptors
- ``ignore (bool)`` - don't raise an error on non-existent bucket
deletion from storage
- ``(exceptions.StorageError)`` - raises on any error

``describe(bucket, descriptor=None)``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Get/set bucket's Table Schema descriptor.

- ``bucket (str)`` - bucket name
- ``descriptor (dict/None)`` - schema descriptor to set
- ``(exceptions.StorageError)`` - raises on any error
- ``(dict)`` - returns Table Schema descriptor

``iter(bucket)``
^^^^^^^^^^^^^^^^

This method should iter typed values based on the schema of this bucket.

- ``bucket (str)`` - bucket name
- ``(exceptions.StorageError)`` - raises on any error
- ``(list[])`` - yields data rows

``read(bucket)``
^^^^^^^^^^^^^^^^

This method should read typed values based on the schema of this bucket.

- ``bucket (str)`` - bucket name
- ``(exceptions.StorageError)`` - raises on any error
- ``(list[])`` - returns data rows

``write(bucket, rows)``
^^^^^^^^^^^^^^^^^^^^^^^

This method writes data rows into the ``storage``. It should store
values of unsupported types as strings internally (like csv does).

- ``bucket (str)`` - bucket name
- ``rows (list[])`` - data rows to write
- ``(exceptions.StorageError)`` - raises on any error

Plugins
~~~~~~~

Table Schema has a plugin system. Any package with the name like
``tableschema_<name>`` could be imported as:

.. code:: python

from tableschema.plugins import <name>

If a plugin is not installed ``ImportError`` will be raised with a
message describing how to install the plugin.

Official plugins
^^^^^^^^^^^^^^^^

- `BigQuery
Storage <https://github.com/frictionlessdata/tableschema-bigquery-py>`__
- `Elasticsearch
Storage <https://github.com/frictionlessdata/tableschema-elasticsearch-py>`__
- `Pandas
Storage <https://github.com/frictionlessdata/tableschema-pandas-py>`__
- `SQL
Storage <https://github.com/frictionlessdata/tableschema-sql-py>`__
- `SPSS
Storage <https://github.com/frictionlessdata/tableschema-spss-py>`__

CLI
~~~

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

Table Schema features a CLI called ``tableschema``. This CLI exposes the
``infer`` and ``validate`` functions for command line use.

Example of ``validate`` usage:

::

$ tableschema validate path/to-schema.json

Example of ``infer`` usage:

::

$ tableschema infer path/to/data.csv

The response is a schema as JSON. The optional argument ``--encoding``
allows a character encoding to be specified for the data file. The
default is utf-8.

Contributing
------------

The project follows the `Open Knowledge International coding
standards <https://github.com/okfn/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:

.. code:: bash

$ make test

| For linting ``pylama`` configured in ``pylama.ini`` is used. On this
stage it's already
| installed into your environment and could be used separately with more
fine-grained control
| as described in documentation -
https://pylama.readthedocs.io/en/latest/.

For example to sort results by error type:

.. code:: bash

$ pylama --sort <path>

| For testing ``tox`` configured in ``tox.ini`` is used.
| It's already installed into your environment and could be used
separately with more fine-grained control as described in documentation
- https://testrun.org/tox/latest/.

| For example to check subset of tests against Python 2 environment with
increased verbosity.
| All positional arguments and options after ``--`` will be passed to
``py.test``:

.. code:: bash

tox -e py27 -- -v tests/<path>

| Under the hood ``tox`` uses ``pytest`` configured in ``pytest.ini``,
``coverage``
| and ``mock`` packages. This packages are available only in tox
envionments.

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 <https://github.com/frictionlessdata/tableschema-py/commits/master>`__.

v1.0
~~~~

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

v0.10
~~~~~

Last pre-v1 stable version of the library.

.. |Travis| image:: https://travis-ci.org/frictionlessdata/tableschema-py.svg?branch=master
:target: https://travis-ci.org/frictionlessdata/tableschema-py
.. |Coveralls| image:: http://img.shields.io/coveralls/frictionlessdata/tableschema-py.svg?branch=master
:target: https://coveralls.io/r/frictionlessdata/tableschema-py?branch=master
.. |PyPi| image:: https://img.shields.io/pypi/v/tableschema.svg
:target: https://pypi.python.org/pypi/tableschema
.. |SemVer| image:: https://img.shields.io/badge/versions-SemVer-brightgreen.svg
:target: http://semver.org/
.. |Gitter| image:: https://img.shields.io/gitter/room/frictionlessdata/chat.svg
:target: https://gitter.im/frictionlessdata/chat
.. |Storage| image:: https://raw.githubusercontent.com/frictionlessdata/tableschema-py/master/data/storage.png