datopy.modeling.apply_recursive#

apply_recursive( func: Callable[[...], Any], obj, ) → dict[str | int, Any] | Any[source]#

Apply func to each terminal value in a nested data structure.

Valid nested data structures include those with explicit or implied key/value pairs.

Parameters:

func (Callable[…, Any]) – _description_.
obj – _description_.

Returns:

A tree-like dictionary representation of the transformed obj.

Return type:

dict

Examples

>>> from datopy.modeling import apply_recursive
>>> import pprint

Define the data

>>> nested_data =  {
...     'type': 'album', 'url': 'link.com', 'audio_features': [
...         {'loudness': -11.4, 'duration_ms': 251},
...         {'loudness': -15.5, 'duration_ms': 284}
...     ]
... }
>>> pprint.pp(nested_data)
{'type': 'album',
 'url': 'link.com',
 'audio_features': [{'loudness': -11.4, 'duration_ms': 251},
                    {'loudness': -15.5, 'duration_ms': 284}]}

Convert to json-friendly representation

>>> serialized = apply_recursive(str, nested_data)
>>> pprint.pp(serialized)
{'type': 'album',
 'url': 'link.com',
 'audio_features': {1: {'loudness': '-11.4', 'duration_ms': '251'},
                    2: {'loudness': '-15.5', 'duration_ms': '284'}}}

Convert to field/type pairs

>>> schema = apply_recursive(lambda x: type(x).__name__, nested_data)
>>> pprint.pp(schema)
{'type': 'str',
 'url': 'str',
 'audio_features': {1: {'loudness': 'float', 'duration_ms': 'int'},
                    2: {'loudness': 'float', 'duration_ms': 'int'}}}