iterables#

Operations on iterables.

Functions

adjacent_n_tuples(objects, n)[source]#

Returns the Sequence objects cyclically split into n length tuples.

See also

adjacent_pairs: alias with n=2

Examples

Normal usage:

list(adjacent_n_tuples([1, 2, 3, 4], 2))
# returns [(1, 2), (2, 3), (3, 4), (4, 1)]

list(adjacent_n_tuples([1, 2, 3, 4], 3))
# returns [(1, 2, 3), (2, 3, 4), (3, 4, 1), (4, 1, 2)]

Parameters

objects (Sequence) –
n (int) –

Return type

zip

adjacent_pairs(objects)[source]#

Alias for adjacent_n_tuples(objects, 2).

See also

adjacent_n_tuples

Examples

Normal usage:

list(adjacent_pairs([1, 2, 3, 4]))
# returns [(1, 2), (2, 3), (3, 4), (4, 1)]

Parameters: objects (Sequence) –
Return type: zip

all_elements_are_instances(iterable, Class)[source]#

Returns True if all elements of iterable are instances of Class. False otherwise.

Parameters: iterable (Iterable) –
Return type: bool

batch_by_property(items, property_func)[source]#

Takes in a Sequence, and returns a list of tuples, (batch, prop) such that all items in a batch have the same output when put into the Callable property_func, and such that chaining all these batches together would give the original Sequence (i.e. order is preserved).

Examples

Normal usage:

batch_by_property([(1, 2), (3, 4), (5, 6, 7), (8, 9)], len)
# returns [([(1, 2), (3, 4)], 2), ([(5, 6, 7)], 3), ([(8, 9)], 2)]

Parameters

items (Sequence) –
property_func (Callable) –

Return type

list[tuple[list, Any]]

concatenate_lists(*list_of_lists)[source]#

Combines the Iterables provided as arguments into one list.

Examples

Normal usage:

concatenate_lists([1, 2], [3, 4], [5])
# returns [1, 2, 3, 4, 5]

Parameters: list_of_lists (Iterable) –
Return type: list

hash_obj(obj)[source]#

Determines a hash, even of potentially mutable objects.

Parameters: obj (object) –
Return type: int

list_difference_update(l1, l2)[source]#

Returns a list containing all the elements of l1 not in l2.

Examples

Normal usage:

list_difference_update([1, 2, 3, 4], [2, 4])
# returns [1, 3]

Parameters

l1 (Iterable) –
l2 (Iterable) –

Return type

list

list_update(l1, l2)[source]#

Used instead of set.update() to maintain order,: making sure duplicates are removed from l1, not l2. Removes overlap of l1 and l2 and then concatenates l2 unchanged.

Examples

Normal usage:

list_update([1, 2, 3], [2, 4, 4])
# returns [1, 3, 2, 4, 4]

Parameters

l1 (Iterable) –
l2 (Iterable) –

Return type

list

listify(obj)[source]#

Converts obj to a list intelligently.

Examples

Normal usage:

listify('str')   # ['str']
listify((1, 2))  # [1, 2]
listify(len)     # [<built-in function len>]

Return type: list

make_even(iterable_1, iterable_2)[source]#

Extends the shorter of the two iterables with duplicate values until its: length is equal to the longer iterable (favours earlier elements).

See also

make_even_by_cycling: cycles elements instead of favouring earlier ones

Examples

Normal usage:

make_even([1, 2], [3, 4, 5, 6])
([1, 1, 2, 2], [3, 4, 5, 6])

make_even([1, 2], [3, 4, 5, 6, 7])
# ([1, 1, 1, 2, 2], [3, 4, 5, 6, 7])

Parameters

iterable_1 (Iterable) –
iterable_2 (Iterable) –

Return type

tuple[list, list]

make_even_by_cycling(iterable_1, iterable_2)[source]#

Extends the shorter of the two iterables with duplicate values until its: length is equal to the longer iterable (cycles over shorter iterable).

See also

make_even: favours earlier elements instead of cycling them

Examples

Normal usage:

make_even_by_cycling([1, 2], [3, 4, 5, 6])
([1, 2, 1, 2], [3, 4, 5, 6])

make_even_by_cycling([1, 2], [3, 4, 5, 6, 7])
# ([1, 2, 1, 2, 1], [3, 4, 5, 6, 7])

Parameters

iterable_1 (Collection) –
iterable_2 (Collection) –

Return type

tuple[list, list]

remove_list_redundancies(lst)[source]#

Used instead of list(set(l)) to maintain order. Keeps the last occurrence of each element.

Parameters: lst (Reversible) –
Return type: list

remove_nones(sequence)[source]#

Removes elements where bool(x) evaluates to False.

Examples

Normal usage:

remove_nones(['m', '', 'l', 0, 42, False, True])
# ['m', 'l', 42, True]

Parameters: sequence (Iterable) –
Return type: list

resize_array(nparray, length)[source]#

Extends/truncates nparray so that len(result) == length.: The elements of nparray are cycled to achieve the desired length.

See also

resize_preserving_order: favours earlier elements instead of cycling them
make_even_by_cycling: similar cycling behaviour for balancing 2 iterables

Examples

Normal usage:

>>> points = np.array([[1, 2], [3, 4]])
>>> resize_array(points, 1)
array([[1, 2]])
>>> resize_array(points, 3)
array([[1, 2],
       [3, 4],
       [1, 2]])
>>> resize_array(points, 2)
array([[1, 2],
       [3, 4]])

Parameters

nparray (ndarray) –
length (int) –

Return type

ndarray

resize_preserving_order(nparray, length)[source]#

Extends/truncates nparray so that len(result) == length.

The elements of nparray are duplicated to achieve the desired length (favours earlier elements).

Constructs a zeroes array of length if nparray is empty.

See also

resize_array: cycles elements instead of favouring earlier ones
make_even: similar earlier-favouring behaviour for balancing 2 iterables

Examples

Normal usage:

resize_preserving_order(np.array([]), 5)
# np.array([0., 0., 0., 0., 0.])

nparray = np.array([[1, 2],
                    [3, 4]])

resize_preserving_order(nparray, 1)
# np.array([[1, 2]])

resize_preserving_order(nparray, 3)
# np.array([[1, 2],
#           [1, 2],
#           [3, 4]])

Parameters

nparray (ndarray) –
length (int) –

Return type

ndarray

resize_with_interpolation(nparray, length)[source]#

Extends/truncates nparray so that len(result) == length.

New elements are interpolated to achieve the desired length.

Note that if nparray’s length changes, its dtype may too (e.g. int -> float: see Examples)

See also

resize_array: cycles elements instead of interpolating
resize_preserving_order: favours earlier elements instead of interpolating

Examples

Normal usage:

nparray = np.array([[1, 2],
                    [3, 4]])

resize_with_interpolation(nparray, 1)
# np.array([[1., 2.]])

resize_with_interpolation(nparray, 4)
# np.array([[1.        , 2.        ],
#           [1.66666667, 2.66666667],
#           [2.33333333, 3.33333333],
#           [3.        , 4.        ]])

nparray = np.array([[[1, 2],[3, 4]]])
resize_with_interpolation(nparray, 3)
# np.array([[[1., 2.], [3., 4.]],
#           [[1., 2.], [3., 4.]],
#           [[1., 2.], [3., 4.]]])

nparray = np.array([[1, 2], [3, 4], [5, 6]])
resize_with_interpolation(nparray, 4)
# np.array([[1.        , 2.        ],
#           [2.33333333, 3.33333333],
#           [3.66666667, 4.66666667],
#           [5.        , 6.        ]])

nparray = np.array([[1, 2], [3, 4], [1, 2]])
resize_with_interpolation(nparray, 4)
# np.array([[1.        , 2.        ],
#           [2.33333333, 3.33333333],
#           [2.33333333, 3.33333333],
#           [1.        , 2.        ]])

Parameters

nparray (ndarray) –
length (int) –

Return type

ndarray

stretch_array_to_length(nparray, length)[source]#

Parameters

nparray (ndarray) –
length (int) –

Return type

ndarray

tuplify(obj)[source]#

Converts obj to a tuple intelligently.

Examples

Normal usage:

tuplify('str')   # ('str',)
tuplify([1, 2])  # (1, 2)
tuplify(len)     # (<built-in function len>,)

Return type: tuple

uniq_chain(*args)[source]#

Returns a generator that yields all unique elements of the Iterables: provided via args in the order provided.

Examples

Normal usage:

uniq_chain([1, 2], [2, 3], [1, 4, 4])
# yields 1, 2, 3, 4

Parameters: args (Iterable) –
Return type: Generator