Getting Started

The bread and butter of datatyping is the validate function.

datatyping.validate(structure, data, *, strict=True)

Verify that values in a dataset are of correct types.

Example

>>> validate([str], ['a', 'b', 'c'])
>>> validate(
...     {'id': int, 'lucky_numbers': [int]},
...     {'id': 700, 'lucky_numbers': [1, 3, 7, 13]}
... )
>>> validate([int], [1, 2, 3, 4.5])
TypeError: 4.5 is of type float, expected type int.

Parameters:

• structure (type or collection of types) – The data structure that data should follow.
• data (anything) – The data you want type checked.
• strict (bool) – Dicts in data must have the exact keys specified in structure. No more.

Raises:

• TypeError – If an elements in data has wrong type.
• ValueError – If the length of structure doesn’t make sense for validating data.
• KeyError – If a dict in data misses a key or strict is True and a dict has keys not in structure.

Examples

The following short examples are meant to clarify. If these are not sufficient, let me know (or maybe check out the unit tests :).

>>> validate([int, str], [1, 'a'])
>>> validate([[int], [str]], [[1, 2, 3], ['a', 'b', 'c'])

>>> validate([dict], [{'can have': 1}, {'any keys': 2}])
>>> validate({'a': int, 'b': str}, {'a': 4, 'b': 'c'})
>>> validate({'a': int}, {'a': 2, 'b': 'oops'})
KeyError: {'b'}
>>> validate({'a': int}, {'a': 2, 'b': 'yay'}, strict=False)