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unique_counts#

ivy.unique_counts(x, /)[source]#

Return the unique elements of an input array x and the corresponding counts for each unique element in x.

Data-dependent output shape

The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) may find this function difficult to implement without knowing array values. Accordingly, such libraries may choose to omit this function. See data-dependent-output-shapes section for more details.

Note

Uniqueness should be determined based on value equality (i.e., x_i == x_j). For input arrays having floating-point data types, value-based equality implies the following behavior.

  • As nan values compare as False, nan values should be considered distinct.

  • As -0 and +0 compare as True, signed zeros should not be considered distinct, and the corresponding unique element will be implementation-dependent (e.g., an implementation could choose to return -0 if -0 occurs before +0).

Parameters:

x (Union[Array, NativeArray]) – input array. If x has more than one dimension, the function must flatten x and return the unique elements of the flattened array.

Return type:

Tuple[Union[Array, NativeArray], Union[Array, NativeArray]]

Returns:

  • ret – a namedtuple (values, counts) whose

    • first element must have the field name values and must be an array containing the unique elements of x. The array must have the same data type as x.

    • second element must have the field name counts and must be an array containing the number of times each unique element occurs in x. The returned array must have same shape as values and must have the default array index data type.

  • .. note:: – The order of unique elements is not specified and may vary between implementations.

This function conforms to the Array API Standard. This docstring is an extension of the docstring in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments.

Examples

With ivy.Array input:

>>> x = ivy.array([1,2,1,3,4,1,3])
>>> y = ivy.unique_counts(x)
>>> print(y)
Results(values=ivy.array([1, 2, 3, 4]), counts=ivy.array([3, 1, 2, 1]))

>>> x = ivy.asarray([[1,2,3,4],[2,3,4,5],[3,4,5,6]])
>>> y = ivy.unique_counts(x)
>>> print(y)
Results(values=ivy.array([1, 2, 3, 4, 5, 6]), counts=ivy.array([1, 2, 3, 3, 2, 1]))

>>> x = ivy.array([0.2,0.3,0.4,0.2,1.4,2.3,0.2])
>>> y = ivy.unique_counts(x)
>>> print(y)
Results(values=ivy.array([0.2       , 0.30000001, 0.40000001, 1.39999998,
                          2.29999995]),
        counts=ivy.array([3, 1, 1, 1, 1]))

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0., 1., 3. , 2. , 1. , 0.]),
...                   b=ivy.array([1, 2, 1, 3, 4, 1, 3]))
>>> y = ivy.unique_counts(x)
>>> print(y)
{
    a: (list[2],<classivy.array.array.Array>shape=[4]),
    b: (list[2],<classivy.array.array.Array>shape=[4])
}
Array.unique_counts(self)[source]#

ivy.Array instance method variant of ivy.unique_counts. This method simply wraps the function, and so the docstring for ivy.unique_counts also applies to this method with minimal changes.

Parameters:

self (Array) – input array. If x has more than one dimension, the function must flatten x and return the unique elements of the flattened array.

Return type:

Tuple[Array, Array]

Returns:

ret – a namedtuple (values, counts) whose

  • first element must have the field name values and must be an

array containing the unique elements of x. The array must have the same data type as x. - second element must have the field name counts and must be an array containing the number of times each unique element occurs in x. The returned array must have same shape as values and must have the default array index data type.

Examples

>>> x = ivy.array([0., 1., 2. , 1. , 0.])
>>> y = x.unique_counts()
>>> print(y)
Results(values=ivy.array([0.,1.,2.]),counts=ivy.array([2,2,1]))
Container.unique_counts(self, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.unique_counts. This method simply wraps the function, and so the docstring for ivy.unique_counts also applies to this method with minimal changes.

Parameters:

  • self (Container) – input container. If x has more than one dimension, the function must flatten x and return the unique elements of the flattened array.

  • key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.

  • to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.

  • prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.

  • map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – a namedtuple (values, counts) whose

  • first element must have the field name values and must be an

array containing the unique elements of x. The array must have the same data type as x. - second element must have the field name counts and must be an array containing the number of times each unique element occurs in x. The returned array must have same shape as values and must have the default array index data type.

Examples

With ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([0., 1., 3. , 2. , 1. , 0.]),
...                   b=ivy.array([1,2,1,3,4,1,3]))
>>> y = x.unique_counts()
>>> print(y)
[{
    a: ivy.array([0., 1., 2., 3.]),
    b: ivy.array([1, 2, 3, 4])
}, {
    a: ivy.array([2, 2, 1, 1]),
    b: ivy.array([3, 1, 2, 1])
}]
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