jsontableschema 0.8.0

JSON Table Schema
=================

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A utility library for working with `JSON Table
Schema <http://dataprotocols.org/json-table-schema/>`__ in Python.

With v0.7 renewed API has been introduced in backward-compatibility
manner. Documentation for deprecated API could be found
`here <https://github.com/frictionlessdata/jsontableschema-py/tree/0.6.5#json-table-schema>`__.
Deprecated API will be removed with v1 release.

Features
--------

- ``Table`` to work with data tables described by JSON Table Schema
- ``Schema`` representing JSON Table Schema
- ``Field`` representing JSON Table Schema field
- ``validate`` to validate JSON Table Schema
- ``infer`` to infer JSON 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
~~~~~~~~~~~~

.. code:: bash

pip install jsontableschema

Example
~~~~~~~

.. code:: python

from jsontableschema 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)

Table
~~~~~

Table represents data described by JSON Table Schema:

.. code:: python

# pip install sqlalchemy jsontableschema-sql
import sqlalchemy as sa
from pprint import pprint
from jsontableschema import Table

# Data source
SOURCE = 'https://raw.githubusercontent.com/okfn/jsontableschema-py/master/data/data_infer.csv'

# Create SQL database
db = sa.create_engine('sqlite://')

# Data processor
def skip_under_30(erows):
for number, headers, row in erows:
krow = dict(zip(headers, row))
if krow['age'] >= 30:
yield (number, headers, row)

# Work with table
table = Table(SOURCE, post_cast=[skip_under_30])
table.schema.save('tmp/persons.json') # Save INFERRED schema
table.save('persons', backend='sql', engine=db) # Save data to SQL
table.save('tmp/persons.csv') # Save data to DRIVE

# Check the result
pprint(Table('persons', backend='sql', engine=db).read(keyed=True))
pprint(Table('tmp/persons.csv').read(keyed=True))
# Will print (twice)
# [{'age': 39, 'id': 1, 'name': 'Paul'},
# {'age': 36, 'id': 3, 'name': 'Jane'}]

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 filepath or url to a JSON file, or a Python dict. The schema is
initially validated (see `validate <#validate>`__ below), and will raise
an exception if not a valid JSON Table Schema.

.. code:: python

from jsontableschema import Schema

# Init schema
schema = Schema('path.json')

# Cast a row
schema.cast_row(['12345', 'a string', 'another field'])

Methods available to ``Schema`` instances:

- ``descriptor`` - return schema descriptor
- ``fields`` - an array of the schema's Field instances
- ``headers`` - an array of the schema headers
- ``primary_key`` - the primary key field for the schema as an array
- ``foreignKey`` - the foreign key property for the schema as an array
- ``get_field(name)`` - return the field object for given name
- ``has_field(name)`` - return a bool if the field exists in the schema
- ``cast_row(row, no_fail_fast=False)`` - return row cast against
schema
- ``save(target)`` - save schema to filesystem

Where the option ``no_fail_fast`` is given, it will collect all errors
it encouters and an exceptions.MultipleInvalid will be raised (if there
are errors).

Field
~~~~~

.. code:: python

from jsontableschemal import Field

# Init field
field = Field({'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 <http://dataprotocols.org/json-table-schema/#field-descriptors>`__.
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.

validate
~~~~~~~~

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

.. code:: python

import io
import json

from jsontableschema import validate

with io.open('schema_to_validate.json') as stream:
descriptor = json.load(stream)

try:
jsontableschema.validate(descriptor)
except jsontableschema.exceptions.SchemaValidationError as exception:
# handle error

It may be useful to report multiple errors when validating a schema.
This can be done with ``no_fail_fast`` flag set to True.

.. code:: python

try:
jsontableschema.validate(descriptor, no_fail_fast=True)
except jsontableschema.exceptions.MultipleInvalid as exception:
for error in exception.errors:
# handle error

infer
~~~~~

Given headers and data, ``infer`` will return a JSON 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

Call ``infer`` with headers and values from the datafile:

.. code:: python

import io
import csv

from jsontableschema import infer

filepath = 'data_to_infer.csv'
with io.open(filepath) as stream:
headers = stream.readline().rstrip('\n').split(',')
values = csv.reader(stream)

schema = infer(headers, values)

``schema`` is now a schema dict:

.. code:: python

{u'fields': [
{
u'description': u'',
u'format': u'default',
u'name': u'id',
u'title': u'',
u'type': u'integer'
},
{
u'description': u'',
u'format': u'default',
u'name': u'age',
u'title': u'',
u'type': u'integer'
},
{
u'description': u'',
u'format': u'default',
u'name': u'name',
u'title': u'',
u'type': u'string'
}]
}

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

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.

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

Example of ``validate`` usage:

::

$ jsontableschema validate path/to-schema.json

Example of ``infer`` usage:

::

$ jsontableschema 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.

Storage
~~~~~~~

The library includes interface declaration to implement tabular
``Storage``:

|Storage|

| An implementor should follow ``jsontableschema.Storage`` interface to
write his
| own storage backend. This backend could be used with ``Table`` class.
See ``plugins``
| system below to know how to integrate custom storage plugin.

plugins
~~~~~~~

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

.. code:: python

from jsontableschema.plugins import <name>

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

A list of officially supported plugins:

- BigQuery Storage -
https://github.com/frictionlessdata/jsontableschema-bigquery-py
- Pandas Storage -
https://github.com/frictionlessdata/jsontableschema-pandas-py
- SQL Storage -
https://github.com/frictionlessdata/jsontableschema-sql-py

API Reference
-------------

Snapshot
~~~~~~~~

::

Table(source, schema=None, post_cast=None, backend=None, **options)
stream -> tabulator.Stream
schema -> Schema
name -> str
iter(keyed/extended=False) -> (generator) (keyed/extended)row[]
read(keyed/extended=False, limit=None) -> (keyed/extended)row[]
save(target, backend=None, **options)
Schema(descriptor)
descriptor -> dict
fields -> Field[]
headers -> str[]
primary_key -> str[]
foreign_keys -> str[]
get_field(name) -> Field
has_field(name) -> bool
cast_row(row, no_fail_fast=False) -> row
save(target)
Field(descriptor)
descriptor -> dict
name -> str
type -> str
format -> str
constraints -> dict
cast_value(value, skip_constraints=False) -> value
test_value(value, skip_constraints=False, constraint=None) -> bool
validate(descriptor, no_fail_fast=False) -> bool
infer(headers, values) -> descriptor
exceptions
~cli
---
Storage(**options)
buckets -> str[]
create(bucket, descriptor, force=False)
delete(bucket=None, ignore=False)
describe(bucket, descriptor=None) -> descriptor
iter(bucket) -> (generator) row[]
read(bucket) -> row[]
write(bucket, rows)
plugins

Detailed
~~~~~~~~

- `Docstrings <https://github.com/frictionlessdata/jsontableschema-py/tree/master/jsontableschema>`__
- `Changelog <https://github.com/frictionlessdata/jsontableschema-py/commits/master>`__

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

Please read the contribution guideline:

`How to Contribute <CONTRIBUTING.md>`__

Thanks!

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