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A flexible utility for flattening and unflattening dict-like objects in Python.

Project description

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A flexible utility for flattening and unflattening dict-like objects in Python.

Introduction

This Python package provide a function flatten() for flattening dict-like objects. It also provides some key joining methods (reducer), and you can choose the reducer you want or even implement your own reducer. You can also choose to invert the resulting flat dict.

Documentation

Flatten

def flatten(d, reducer='tuple', inverse=False, enumerate_types=(), keep_empty_types=()):
    """Flatten `Mapping` object.

    Parameters
    ----------
    d : dict-like object
        The dict that will be flattened.
    reducer : {'tuple', 'path', 'underscore', Callable}
        The key joining method. If a `Callable` is given, the `Callable` will be
        used to reduce.
        'tuple': The resulting key will be tuple of the original keys.
        'path': Use `os.path.join` to join keys.
        'underscore': Use underscores to join keys.
    inverse : bool
        Whether you want invert the resulting key and value.
    enumerate_types : Sequence[type]
        Flatten these types using `enumerate`.
        For example, if we set `enumerate_types` to ``(list,)``,
        `list` indices become keys: ``{'a': ['b', 'c']}`` -> ``{('a', 0): 'b', ('a', 1): 'c'}``.
    keep_empty_types : Sequence[type]
        By default, ``flatten({1: 2, 3: {}})`` will give you ``{(1,): 2}``, that is, the key ``3``
        will disappear.
        This is also applied for the types in `enumerate_types`, that is,
        ``flatten({1: 2, 3: []}, enumerate_types=(list,))`` will give you ``{(1,): 2}``.
        If you want to keep those empty values, you can specify the types in `keep_empty_types`:

        >>> flatten({1: 2, 3: {}}, keep_empty_types=(dict,))
        {(1,): 2, (3,): {}}

    Returns
    -------
    flat_dict : dict
    """

Examples

In [1]: from flatten_dict import flatten

In [2]: normal_dict = {
   ...:     'a': '0',
   ...:     'b': {
   ...:         'a': '1.0',
   ...:         'b': '1.1',
   ...:     },
   ...:     'c': {
   ...:         'a': '2.0',
   ...:         'b': {
   ...:             'a': '2.1.0',
   ...:             'b': '2.1.1',
   ...:         },
   ...:     },
   ...: }

In [3]: flatten(normal_dict)
Out[3]:
{('a',): '0',
 ('b', 'a'): '1.0',
 ('b', 'b'): '1.1',
 ('c', 'a'): '2.0',
 ('c', 'b', 'a'): '2.1.0',
 ('c', 'b', 'b'): '2.1.1'}

In [4]: flatten(normal_dict, reducer='path')
Out[4]:
{'a': '0',
 'b/a': '1.0',
 'b/b': '1.1',
 'c/a': '2.0',
 'c/b/a': '2.1.0',
 'c/b/b': '2.1.1'}

In [5]: flatten(normal_dict, reducer='path', inverse=True)
Out[5]:
{'0': 'a',
 '1.0': 'b/a',
 '1.1': 'b/b',
 '2.0': 'c/a',
 '2.1.0': 'c/b/a',
 '2.1.1': 'c/b/b'}

In [6]: def underscore_reducer(k1, k2):
   ...:     if k1 is None:
   ...:         return k2
   ...:     else:
   ...:         return k1 + "_" + k2
   ...:

In [7]: flatten(normal_dict, reducer=underscore_reducer)
Out[7]:
{'a': '0',
 'b_a': '1.0',
 'b_b': '1.1',
 'c_a': '2.0',
 'c_b_a': '2.1.0',
 'c_b_b': '2.1.1'}

If we have some iterable (e.g., list) in the dict, we will normally get this:

In [8]: flatten({'a': [1, 2, 3], 'b': 'c'})
Out[8]:
{('a',): [1, 2, 3],
 ('b',): 'c'}

If we want to use its indices as keys, then we can use the parameter enumerate_types:

In [9]: flatten({'a': [1, 2, 3], 'b': 'c'}, enumerate_types=(list,))
Out[9]:
{('a', 0): 1,
 ('a', 1): 2,
 ('a', 2): 3,
 ('b',): 'c'}

We can even flatten a list directly:

In [10]: flatten([1, 2, 3], enumerate_types=(list,))
Out[10]:
{(0,): 1,
 (1,): 2,
 (2,): 3}

If there is an empty dict in the values, by default, it will disappear after flattened:

In [4]: flatten({1: 2, 3: {}})
Out[4]: {(1,): 2}

We can keep the empty dict in the result using keep_empty_types=(dict,):

In [5]: flatten({1: 2, 3: {}}, keep_empty_types=(dict,))
Out[5]: {(1,): 2, (3,): {}}

Unflatten

def unflatten(d, splitter='tuple', inverse=False):
    """Unflatten dict-like object.

    Parameters
    ----------
    d : dict-like object
        The dict that will be unflattened.
    splitter : {'tuple', 'path', 'underscore', Callable}
        The key splitting method. If a Callable is given, the Callable will be
        used to split.
        'tuple': Use each element in the tuple key as the key of the unflattened dict.
        'path': Use `pathlib.Path.parts` to split keys.
        'underscore': Use underscores to split keys.
    inverse : bool
        Whether you want to invert the key and value before flattening.

    Returns
    -------
    unflattened_dict : dict
    """

Examples

In [1]: from flatten_dict import unflatten

In [2]: flat_dict = {
   ...:     ('a',): '0',
   ...:     ('b', 'a'): '1.0',
   ...:     ('b', 'b'): '1.1',
   ...:     ('c', 'a'): '2.0',
   ...:     ('c', 'b', 'a'): '2.1.0',
   ...:     ('c', 'b', 'b'): '2.1.1',
   ...: }

In [3]: unflatten(flat_dict)
Out[3]:
{'a': '0',
 'b': {'a': '1.0', 'b': '1.1'},
 'c': {'a': '2.0', 'b': {'a': '2.1.0', 'b': '2.1.1'}}}

In [4]: flat_dict = {
   ...:     'a': '0',
   ...:     'b/a': '1.0',
   ...:     'b/b': '1.1',
   ...:     'c/a': '2.0',
   ...:     'c/b/a': '2.1.0',
   ...:     'c/b/b': '2.1.1',
   ...: }

In [5]: unflatten(flat_dict, splitter='path')
Out[5]:
{'a': '0',
 'b': {'a': '1.0', 'b': '1.1'},
 'c': {'a': '2.0', 'b': {'a': '2.1.0', 'b': '2.1.1'}}}

In [6]: flat_dict = {
   ...:     '0': 'a',
   ...:     '1.0': 'b/a',
   ...:     '1.1': 'b/b',
   ...:     '2.0': 'c/a',
   ...:     '2.1.0': 'c/b/a',
   ...:     '2.1.1': 'c/b/b',
   ...: }

In [7]: unflatten(flat_dict, splitter='path', inverse=True)
Out[7]:
{'a': '0',
 'b': {'a': '1.0', 'b': '1.1'},
 'c': {'a': '2.0', 'b': {'a': '2.1.0', 'b': '2.1.1'}}}

In [8]: def underscore_splitter(flat_key):
   ...:     return flat_key.split("_")
   ...:

In [9]: flat_dict = {
   ...:     'a': '0',
   ...:     'b_a': '1.0',
   ...:     'b_b': '1.1',
   ...:     'c_a': '2.0',
   ...:     'c_b_a': '2.1.0',
   ...:     'c_b_b': '2.1.1',
   ...: }

In [10]: unflatten(flat_dict, splitter=underscore_splitter)
Out[10]:
{'a': '0',
 'b': {'a': '1.0', 'b': '1.1'},
 'c': {'a': '2.0', 'b': {'a': '2.1.0', 'b': '2.1.1'}}}

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