datacast 0.3.5

Basic Usage

Install with pip:

pip install datacast

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

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.

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.

from datacast import EnvironConfig

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

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

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.

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

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.