levelflatten 0.10

performs customizable level-wise flattening of nested iterables.

performs customizable level-wise flattening of nested iterables.

pip install levelflatten

Tested against Windows 10 / Python 3.10 / Anaconda

The level_flatten function provides the following advantages:

Customizable flattening:

It allows you to control the flattening behavior for different data types. You can specify how dictionaries should be treated (by items, keys, or values) and which non-iterable data types should be considered for individual element flattening.

Level-wise flattening:

It performs flattening in a level-wise manner, allowing you to control the depth of flattening. You can specify the maximum number of levels to flatten, which provides flexibility in handling complex nested structures.

Support for various data types:

It handles various data types such as lists, tuples, sets, dictionaries, strings, bytes, frozensets, bytearrays, NumPy arrays, and more. This makes it suitable for working with diverse data structures.

Maintains order:

The function preserves the order of elements while flattening, ensuring that the relative order of elements is maintained in the flattened list.

Args:
 iterable (iterable): The input iterable to be flattened.
 n (int, optional): The maximum number of levels to flatten. Defaults to sys.maxsize.
 dict_treatment (str, optional): Treatment for dictionaries. Can be one of "items",
	 "keys", or "values". Defaults to "items".
 consider_non_iter (tuple or list, optional): Tuple or list of data types to be
	 considered non-iterable and flattened as individual elements. Defaults to
	 (str, bytes).

Returns:
 list: A flattened list containing the elements from the input iterable.
 
 
 from levelflatten import level_flatten
 import numpy as np
 from collections import defaultdict

 d = defaultdict(list)
 d[1].append((1, 2, 3, [44, 5, 5]))
 iti = [
	 {(1, 2), (3, 4)},
	 ["a", 111, 3],
	 (1, 2),
	 d,
	 "stristristri",
	 range(100, 110),
	 455j,
	 b"xxxxaaa",
	 frozenset({1, 2, 3, 4}),
	 [
		 11,
		 [
			 222,
			 33,
			 4,
			 (333, 4, {1, 2, 3, 4}, {3333: 333}, [33312, 33323], bytearray(b"xxxxx")),
		 ],
	 ],
	 [
		 {2: 3},
		 (
			 2,
			 3,
			 4,
		 ),
		 ["babab", "dd", {10: 12}, (("bbb", 12), [333, 4, {4: 32}])],
	 ],
	 (1.2, 11, 1232),
	 bytearray(b"xxxxx"),
	 np.array([1, 2, 3, 4, 5]),
	 [None, np.nan],
	 [[True, False, True]],
 ]

 t1 = level_flatten(
	 iti,
	 n=1,
	 dict_treatment="items",
	 consider_non_iter=(str, bytes, frozenset, bytearray),
 )
 print(t1)
 # [(1, 2), (3, 4), 'a', 111, 3, 1, 2, (1, [(1, 2, 3, [44, 5, 5])]), 'stristristri', 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 455j, b'xxxxaaa', frozenset({1, 2, 3, 4}), 11, [222, 33, 4, (333, 4, {1, 2, 3, 4}, {3333: 333}, [33312, 33323], bytearray(b'xxxxx'))], {2: 3}, (2, 3, 4), ['babab', 'dd', {10: 12}, (('bbb', 12), [333, 4, {4: 32}])], 1.2, 11, 1232, bytearray(b'xxxxx'), 1, 2, 3, 4, 5, None, nan, [True, False, True]]

 t1 = level_flatten(
	 iti, n=2, dict_treatment="values", consider_non_iter=(str, bytes, dict)
 )
 print(t1)
 # [1, 2, 3, 4, 'a', 111, 3, 1, 2, (1, 2, 3, [44, 5, 5]), 'stristristri', 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 455j, b'xxxxaaa', 1, 2, 3, 4, 11, 222, 33, 4, (333, 4, {1, 2, 3, 4}, {3333: 333}, [33312, 33323], bytearray(b'xxxxx')), {2: 3}, 2, 3, 4, 'babab', 'dd', {10: 12}, (('bbb', 12), [333, 4, {4: 32}]), 1.2, 11, 1232, 120, 120, 120, 120, 120, 1, 2, 3, 4, 5, None, nan, True, False, True]


 t1 = level_flatten(
	 iti, n=3, dict_treatment="keys", consider_non_iter=(str, bytes, set, tuple)
 )
 print(t1)
 # [{(1, 2), (3, 4)}, 'a', 111, 3, (1, 2), 1, 'stristristri', 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 455j, b'xxxxaaa', 1, 2, 3, 4, 11, 222, 33, 4, (333, 4, {1, 2, 3, 4}, {3333: 333}, [33312, 33323], bytearray(b'xxxxx')), 2, (2, 3, 4), 'babab', 'dd', 10, (('bbb', 12), [333, 4, {4: 32}]), (1.2, 11, 1232), 120, 120, 120, 120, 120, 1, 2, 3, 4, 5, None, nan, True, False, True]


 t1 = level_flatten(
	 iti,
	 n=4,
	 dict_treatment="items",
	 consider_non_iter=(str, bytes, set, tuple, dict, bytearray),
 )
 print(t1)
 # [{(1, 2), (3, 4)}, 'a', 111, 3, (1, 2), (1, [(1, 2, 3, [44, 5, 5])]), 'stristristri', 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 455j, b'xxxxaaa', 1, 2, 3, 4, 11, 222, 33, 4, (333, 4, {1, 2, 3, 4}, {3333: 333}, [33312, 33323], bytearray(b'xxxxx')), {2: 3}, (2, 3, 4), 'babab', 'dd', {10: 12}, (('bbb', 12), [333, 4, {4: 32}]), (1.2, 11, 1232), bytearray(b'xxxxx'), 1, 2, 3, 4, 5, None, nan, True, False, True]


 t1 = level_flatten(
	 iti, n=sys.maxsize, dict_treatment="items", consider_non_iter=(np.array, np.ndarray)
 )
 print(t1)
 # [1, 2, 3, 4, 'a', 111, 3, 1, 2, 1, 1, 2, 3, 44, 5, 5, 's', 't', 'r', 'i', 's', 't', 'r', 'i', 's', 't', 'r', 'i', 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 455j, 120, 120, 120, 120, 97, 97, 97, 1, 2, 3, 4, 11, 222, 33, 4, 333, 4, 1, 2, 3, 4, 3333, 333, 33312, 33323, 120, 120, 120, 120, 120, 2, 3, 2, 3, 4, 'b', 'a', 'b', 'a', 'b', 'd', 'd', 10, 12, 'b', 'b', 'b', 12, 333, 4, 4, 32, 1.2, 11, 1232, 120, 120, 120, 120, 120, array([1, 2, 3, 4, 5]), None, nan, True, False, True]