schemadict 0.0.9

Validate Python dictionaries like JSON schema

A schemadict is a regular Python dictionary which specifies the type and format of values for some given key. To check if a test dictionary is conform with the expected schema, schemadict provides the validate() method. If the test dictionary is ill-defined, an error will be thrown, otherwise None is returned.

Examples

Basic usage

>>> from schemadict import schemadict

>>> schema = schemadict({
...     'name': {
...         'type': str,
...         'min_len': 3,
...         'max_len': 12,
...     },
...     'age': {
...         'type': int,
...         '>=': 0,
...         '<': 150,
...     },
... })
>>>
>>> testdict = {'name': 'Neil', 'age': 55}
>>> schema.validate(testdict)
>>>

>>> testdict = {'name': 'Neil', 'age': -12}
>>> schema.validate(testdict)
Traceback (most recent call last):
    ...
ValueError: 'age' too small: expected >= 0, but was -12
>>>

>>> testdict = {'name': 'Neil', 'age': '55'}
>>> schema.validate(testdict)
Traceback (most recent call last):
    ...
TypeError: unexpected type for 'age': expected <class 'int'>, but was <class 'str'>
>>>

Nested schemadict

It is possible to check individual item in a list. For instance, in the following example we check if each item (of type str) looks like a valid IPv4 address. How each item should look like can be specified with the item_schema keyword.

>>> schema = schemadict({
...     'ip_addrs': {
...         'type': list,
...         'item_schema': {
...             'type': str,
...             'regex': r'^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$',
...         },
...     },
... })
>>>
>>>
>>> schema.validate({'ip_addrs': ['127.0.0.1', '192.168.1.1']})  # Valid
>>> schema.validate({'ip_addrs': ['127.0.0.1', '192.168.1.1', '1234.5678']})  # Last item invalid
Traceback (most recent call last):
    ...
ValueError: regex mismatch for 'ip_addrs': expected pattern '^\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}$', got '1234.5678'
>>>

Items in a list (or tuple) may themselves be dictionaries which can be described with schemadicts. In this case, we use the keyword item_schemadict as illustrated in the following example.

>>> schema_city = schemadict({
...     'name': {
...         'type': str
...     },
...     'population': {
...         'type': int,
...         '>=': 0,
...     },
... })
>>>
>>> schema_country = schemadict({
...     'name': {'type': str},
...     'cities': {
...         'type': list,
...         'item_type': dict,
...         'item_schemadict': schema_city,
...     },
... })
>>>
>>> test_country = {
...     'name': 'Neverland',
...     'cities': [
...         {'name': 'Faketown', 'population': 3},
...         {'name': 'Evergreen', 'population': True},
...     ],
... }
>>>
>>> schema_country.validate(test_country)
Traceback (most recent call last):
    ...
TypeError: unexpected type for 'population': expected <class 'int'>, but was <class 'bool'>
>>>

Custom validation functions

Each type (int, bool, str, etc.) defines its own set of validation keywords and corresponding test functions. The dictionary STANDARD_VALIDATORS provided by the schemadict module contains the default validation functions for the Python’s built-in types. However, it is also possible to modify or extend this dictionary with custom validation functions.

>>> from schemadict import schemadict, STANDARD_VALIDATORS

>>> # Add a custom validation function
>>> def is_divisible(key, value, comp_value, _):
...     if value % comp_value != 0:
...             raise ValueError(f"{key!r} is not divisible by {comp_value}")
...
...
...
>>>

>>> # Update the standard validator dictionary
>>> my_validators = STANDARD_VALIDATORS
>>> my_validators[int]['%'] = is_divisible

>>> # Register the updated validator dictionary in the new schemadict instance
>>> s = schemadict({'my_num': {'type': int, '%': 3}}, validators=my_validators)

>>> s.validate({'my_num': 33})
>>> s.validate({'my_num': 4})
Traceback (most recent call last):
    ...
ValueError: 'my_num' is not divisible by 3
>>>

It is also possible to define custom types and custom test functions as shown in the following example.

>>> from schemadict import schemadict, STANDARD_VALIDATORS

>>> class MyOcean:
...     has_dolphins = True
...     has_plastic = False
...
>>>

>>> def has_dolphins(key, value, comp_value, _):
...     if getattr(value, 'has_dolphins') is not comp_value:
...         raise ValueError(f"{key!r} does not have dolphins")
...
>>>

>>> my_validators = STANDARD_VALIDATORS
>>> my_validators.update({MyOcean: {'has_dolphins': has_dolphins}})
>>>

>>> schema_ocean = schemadict(
...     {'ocean': {'type': MyOcean, 'has_dolphins': True}},
...     validators=my_validators,
... )
>>>

>>> ocean1 = MyOcean()
>>> schema_ocean.validate({'ocean': ocean1})
>>>

>>> ocean2 = MyOcean()
>>> ocean2.has_dolphins = False
>>> schema_ocean.validate({'ocean': ocean2})
Traceback (most recent call last):
    ...
ValueError: 'ocean' does not have dolphins

Full documentation: https://schemadict.readthedocs.io/

Features

What schemadict offers:

• Built-in support for Python’s primitive types

• Specify required and optional keys

• Validate nested schemas

• Add custom validation functions to built-in types

• Add custom validation functions to custom types

• Support for Regex checks of strings

Features currently in development

• Metaschema validation

• Lazy validation and summary of all errors

• Allow schema variations: schmea 1 OR schema 2

• Add support for validation of type number.Number

Installation

Schemadict is available on PyPI and may simply be installed with

pip install schemadict

Idea

Schemadict is loosely inspired by JSON schema and jsonschema, a JSON schema validator for Python.

License

License: Apache-2.0