# cumprod#

ivy.cumprod(x, /, *, axis=0, exclusive=False, reverse=False, dtype=None, out=None)[source]#

Return the cumulative product of the elements along a given axis.

Parameters:
• x (`Union`[`Array`, `NativeArray`]) – Input array.

• axis (`int`, default: `0`) – int , axis along which the cumulative product is computed. By default 0.

• exclusive (`bool`, default: `False`) – optional bool, Whether to perform the cumprod exclusively. Defaults is False.

• reverse (`bool`, default: `False`) – Whether to perform the cumprod from last to first element in the selected axis. Default is `False` (from first to last element)

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

ret – Input array with cumulatively multiplied elements along axis.

Examples

With `ivy.Array` input:

```>>> x = ivy.array([2, 3, 4])
>>> y = ivy.cumprod(x)
>>> print(y)
ivy.array([2, 6, 24])
```
```>>> x = ivy.array([2, 3, 4])
>>> y = ivy.cumprod(x, exclusive=True)
>>> print(y)
ivy.array([1, 2, 6])
```
```>>> x = ivy.array([[2, 3],
[5, 7],
[11, 13]])
>>> y = ivy.zeros((3, 2))
>>> ivy.cumprod(x, axis=1, exclusive=True, out=y)
>>> print(y)
ivy.array([[ 1.,  2.],
[ 1.,  5.],
[ 1., 11.]])
```
```>>> x = ivy.array([[2, 3],[5, 7],[11, 13]])
>>> ivy.cumprod(x, axis=0, exclusive=True, out=x)
>>> print(x)
ivy.array([[1,  1],
[2,  3],
[10, 21]])
```
```>>> x = ivy.array([[2, 3],[5, 7],[11, 13]])
>>> y = ivy.zeros((3, 2))
>>> x.cumprod(axis=0, exclusive=True, out=y)
>>> print(x)
ivy.array([[1.,  1.],
[2.,  3.],
[10., 21.]])
```

With `ivy.Container` input:

```>>> x = ivy.Container(a=ivy.array([2, 3, 4]), b=ivy.array([3, 4, 5]))
>>> y = ivy.cumprod(x)
>>> print(y)
{
a: ivy.array([2, 6, 24]),
b: ivy.array([3, 12, 60])
}
```
```>>> x = ivy.Container(a=ivy.array([2, 3, 4]), b=ivy.array([3, 4, 5]))
>>> y = ivy.cumprod(x, exclusive=True)
>>> print(y)
{
a: ivy.array([1, 2, 6]),
b: ivy.array([1, 3, 12])
}
```
```>>> x = ivy.Container(a=ivy.array([[2, 3],
[5, 7],
[11, 13]]),
b=ivy.array([[3, 4],
[4, 5],
[5, 6]]))
>>> y = ivy.Container(a = ivy.zeros((3, 2)), b = ivy.zeros((3, 2)))
>>> ivy.cumprod(x, axis=1, exclusive=True, out=y)
>>> print(y)
{
a: ivy.array([[1, 2],
[1, 5],
[1, 11]]),
b: ivy.array([[1, 3],
[1, 4],
[1, 5]])
}
```
```>>> x = ivy.Container(a=ivy.array([[2, 3],
[5, 7],
[11, 13]]),
b=ivy.array([[3, 4],
[4, 5],
[5, 6]]))
>>> x.cumprod(axis=0, exclusive=True, out=x)
>>> print(x)
{
a: ivy.array([[1, 1],
[2, 3],
[10, 21]]),
b: ivy.array([[1, 1],
[3, 4],
[15, 42]])
}
```
Array.cumprod(self, /, *, axis=0, exclusive=False, reverse=False, dtype=None, out=None)[source]#

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

Parameters:
• self (`Array`) – input array

• axis (`int`, default: `0`) – int, axis along which to take the cumulative product. Default is `0`.

• exclusive (`bool`, default: `False`) – optional bool, whether to exclude the first value of the input array. Default is `False`.

• reverse (`bool`, default: `False`) – Whether to perform the cumprod from last to first element in the selected axis. Default is `False` (from first to last element)

• dtype (`Optional`[`Union`[`Dtype`, `NativeDtype`]], default: `None`) – data type of the returned array. If None, if the default data type corresponding to the data type “kind” (integer or floating-point) of x has a smaller range of values than the data type of x (e.g., x has data type int64 and the default data type is int32, or x has data type uint64 and the default data type is int64), the returned array must have the same data type as x. if x has a floating-point data type, the returned array must have the default floating-point data type. if x has a signed integer data type (e.g., int16), the returned array must have the default integer data type. if x has an unsigned integer data type (e.g., uint16), the returned array must have an unsigned integer data type having the same number of bits as the default integer data type (e.g., if the default integer data type is int32, the returned array must have a uint32 data type). If the data type (either specified or resolved) differs from the data type of x, the input array should be cast to the specified data type before computing the product. Default: `None`.

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to.

Return type:

`Array`

Returns:

ret – Input array with cumulatively multiplied elements along the specified axis.

Examples

```>>> x = ivy.array([1, 2, 3, 4, 5])
>>> y = x.cumprod()
>>> print(y)
ivy.array([1, 2, 6, 24, 120])
```
```>>> x = ivy.array([[2, 3], [5, 7], [11, 13]])
>>> y = ivy.zeros((3, 2), dtype="int32")
>>> x.cumprod(axis=1, exclusive=True, out=y)
>>> print(y)
ivy.array([[0, 0],
[0, 0],
[0, 0]])
```
Container.cumprod(self, /, *, axis=0, exclusive=False, reverse=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, dtype=None, out=None)[source]#

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

Parameters:
• self (`Container`) – Input container to cumprod at leaves.

• axis (`Union`[`int`, `Container`], default: `0`) – Axis along which the cumulative product is computed. Default is `0`.

• exclusive (`Union`[`bool`, `Container`], default: `False`) – Whether to exclude the first element of the input array. Default is `False`.

• 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`.

• dtype (`Optional`[`Union`[`Dtype`, `NativeDtype`, `Container`]], default: `None`) – Data type of the returned array. Default is `None`.

• out (`Optional`[`Container`], default: `None`) – Optional output container. Default is `None`.

Return type:

`Container`

Returns:

ret – Containers with arrays cumprod at leaves along specified axis.

Examples

With one `ivy.Container` instances:

```>>> x = ivy.Container(a=ivy.array([1, 2, 3]), b=ivy.array([4, 5, 6]))
>>> y = x.cumprod(axis=0)
>>> print(y)
{
a: ivy.array([1, 2, 6]),
b: ivy.array([4, 20, 120])
}
```
```>>> x = ivy.Container(a=ivy.array([[2, 3], [5, 7], [11, 13]]),
b=ivy.array([[3, 4], [4, 5], [5, 6]]))
>>> y = ivy.Container(a = ivy.zeros((3, 2)), b = ivy.zeros((3, 2)))
>>> x.cumprod(axis=1, exclusive=True, out=y)
{
a: ivy.array([[1, 2],
[1, 5],
[1, 11]]),
b: ivy.array([[1, 3],
[1, 4],
[1, 5]])
}
```