datacast 0.3.1

Simple way to cast your data.

*Datacast* is a Python package that validates and converts your data.

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|pypi| |python_version| |coverage| |license|

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Basic Usage
-----------

Install with pip:

.. code:: bash

pip install datacast


Define schema (can be any class with annotations) and use ``cast`` function.

.. code:: python

from datacast import cast

class SimpleSchema:
one: int
two: str
three: (lambda x: x ** 2)
zero: (int, bool)
four: float = 0.4
five: None = 'five'

cast({'one': 1, 'two': 2, 'three': 3, 'zero': '0', 'five': 5}, SimpleSchema)
# {'one': 1, 'two': '2', 'three': 9, 'zero': False, 'four': 0.4, 'five': 5}

Rules are simple:

- Params without annotations will be ignored.
- Annotation is a *caster*, which will be called with the provided value,
eg. ``bool(0)``.
- *Caster* is **any** callable. Functions, lambdas, classes etc.
- It also can be list or tuple (or another iterable).
Then it acts like a chain of *casters*, eg. ``int('0') -> bool(0) -> False``.
- If there is no default value - param is required and
will raise ``RequiredFieldError`` if not provided.
- ``None`` in annotation means no casting.


Config
--------
You can use ``Config`` class which acts like a schema AND stores result data.

.. code:: python

from datacast import Config

class SimpleConfig(Config):
spam: bool
ham: None
rabbit: float = None

config = SimpleConfig({'spam': 0, 'ham': 1})
assert config.spam == False
assert config.ham == 1
assert config.rabbit == None
assert config._asdict() == {'spam': False, 'ham': 1, 'rabbit': None}

Also there is ``EnvironConfig`` which loads input data from environment,
casts strings to appropriate types and ignores extra vars.

.. code:: python

from datacast import EnvironConfig

class SimpleEnvironConfig(EnvironConfig):
SPAM: bool
HAM: int
RABBIT: str
AUTOCAST: None
NONE_VAL: None

os.environ['SPAM'] = '0'
os.environ['HAM'] = '1'
os.environ['RABBIT'] = '2'
os.environ['AUTOCAST'] = '3.5'
os.environ['NONE_VAL'] = 'null'
config = SimpleEnvironConfig()
assert config.SPAM == False
assert config.HAM == 1
assert config.RABBIT == '2'
assert config.AUTOCAST == 3.5
assert config.NONE_VAL == None

Note that you can actually not specify a *caster* and use ``None`` instead,
it will still (most likely) make a cast.

:Valid ``None`` strings: ``'none', 'null', 'nil'``
:Valid ``True`` strings: ``'true', 't', 'yes', 'y', 'on', '1'``
:Valid ``False`` strings: ``'false', 'f', 'no', 'n', 'off', '0', ''``
Case doesn't matter.


Settings
---------

You can specify various settings and apply them in a bunch of different ways.

.. code:: python

from datacast import apply_settings, Settings

@apply_settings(
on_missing='store',
missing_value=False
)
class SimpleSchema:
...

# OR

class SimpleSettings(Settings):
on_missing = 'store'
missing_value = False

@apply_settings(SimpleSettings)
class SimpleSchema:
...

# OR pass it to the cast function or Config creation

cast(input_data, SimpleSchema, settings=SimpleSettings)
cast(input_data, SimpleSchema, on_missing='store', missing_value=False)
Config(input_data, settings=SimpleSettings)
Config(input_data, on_missing='store', missing_value=False)

# OR use class attribute

class SimpleSchema:
__settings__ = SimpleSettings
# OR
__settings__ = {'on_missing': 'store', 'missing_value': False}
...


**List of settings**

=============== ============ ===============================================
Name Default Description
=============== ============ ===============================================
on_extra ``'ignore'`` What to do with values that absent from schema.
on_invalid ``'raise'`` What to do when casting has failed.
on_missing ``'raise'`` What to do when value is missing but required.
missing_value ``None`` What to store when value is missing.
store_callables ``False`` If ``False`` - execute callable value on store.
result_class ``dict`` Class which stores result data.
precasters ``()`` Prepend additional casters.
postcasters ``()`` Append additional casters.
=============== ============ ===============================================

**Options for 'on_extra', 'on_invalid' and 'on_missing'**

:ignore: Value will be ignored and not be stored in the result.
:store: Value will be stored in the result as is. In case of ``on_missing`` it
will store ``missing_value``.
:raise: Corresponding exception will be raised.
:cast: Value will be casted with precasters, postcasters and then stored.
Works only with ``on_extra``!

With ``precasters`` and ``postcasters`` you will transform every caster in
schema into a chain, which starts and/or ends with those casters.


.. |pypi| image:: https://img.shields.io/pypi/v/datacast.svg?style=flat-square&label=version
:target: https://pypi.org/project/datacast
:alt: Latest version released on PyPI

.. |python_version| image:: https://img.shields.io/badge/python-%3E%3D3.3-blue.svg?style=flat-square
:alt: Minimal Python version

.. |coverage| image:: https://img.shields.io/badge/coverage-86%25-yellowgreen.svg?style=flat-square
:alt: Test coverage

.. |license| image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat-square
:target: https://raw.githubusercontent.com/fatemonk/datacast/master/LICENSE
:alt: Package license