# DICTionary Operations
Is a library that makes applying functions to and performing operations on nested dicts easy.
This repository attempts to unify numerous utilities in one package.
Python >=3.7 is required as retained dict order is wanted.
Python library that inspired this project:
# TODO: links
https://github.com/glowdigitalmedia/dict-utils/tree/master/dict_utils
- https://github.com/katsudonik/dict_util
- https://github.com/gmr/flatdict/blob/master/flatdict.py
- https://github.com/bunbun/nested-dict
- https://github.com/jacobflanagan/nesteddictionary
- https://github.com/ducdetronquito/scalpl
- https://github.com/mewwts/addict
- https://github.com/zphang/sndict
- https://github.com/nickstanisha/nesteddict
- https://github.com/bunbun/nested-dict
# Features
Scalpl seems to be the most polished library. Dicty differs with some slight changes in the api
and additional set of functionality for operating on json like data structure:
- apply an operation to multiple dictoos with same shape
- setting the type for the dictoos values
# Setup
pip install dictoo
# Usage
import dictoo.Dicty as D
# you can configure dictoo to fit your needs
# dictoo only imports and uses what is enabled
dictoo.CONFIG.update({
'delimiter': '.'
})
# you can create dictoo dicts from many things
d = D(dict(a=1, b=[3, dict(c=4)]))
d = D.from_file('dict.yaml')
d = D.from_file('dict.json')
d = D("{a:1, b[0]:3, b[1].c=4}") //not sure whether this is a good idea
assert d.b[1].c == d['b.[1].c']
d.update(dict(b=[5, dict(c=9)]))
d.flat()
dictoo.apply(lambda x, y: x + y, d1, d2, kwargs)
dictoo.reduce(fn, [d1, d2, d3], kwargs)
dictoo.remap({'old_key_name': 'new_key_name'})
d = D([{'a': 1, 'b': 2}, {'a':4, 'b':5})
isinstance(d, list) == True
isinstance(d, dict) == False
assert d != [D({'a': 1, 'b': 2}), D({'a':4, 'b':5})] # try not to leave lists of Ds as you would also not do with numpy arrays
assert d == D([D({'a': 1, 'b': 2}), D({'a':4, 'b':5})])
d[:, 'a'] = 8 # D([{'a': 8, 'b': 2}, {'a':8, 'b':5}]) # this is hard to get used to, maybe be more strict here such that the code is more readable. Should be configurable
d[:1, 'a'] = 8 # only modifies a slice
d.batch()['a'][:] = 8
d: Dict[str, Union[np.ndarray, dict]] = D(dict(...), dtype=np.ndarray)
d.batch() # D({'a': [1, 4], 'b': [2, 5]}), batch goes from lowest level to top level and unbatch inreverse
# more features:
# cli for json and yaml files
# regex keys
