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Utilities for working with json-style datastructures.

Project description

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


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


pip install dictoo


import dictoo.Dicty as D

# you can configure dictoo to fit your needs
# dictoo only imports and uses what is enabled
	'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)]))


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

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