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NumPy | cumprod method

schedule Aug 11, 2023
Last updated
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NumPy's cumprod(~) method returns an array holding the cumulative products of the input array.

Parameters

1. a | array_like

The input array.

2. axis | None or int | optional

The allowed values are as follows:

Axis

Meaning

0

Cumulative product is computed column-wise

1

Cumulative product is computed row-wise

None

Cumulative product is computed using entire array

By default, axis=None.

3. dtype | string or type | optional

The desired data-type of the returned array. By default, the data-type is the same as that of the input array.

4. out | NumPy array | optional

A NumPy array to place the results in.

Return value

A NumPy array holding the cumulative product of the input elements.

Examples

1D array

To compute the cumulative product of a 1D array:

x = np.array([1,2,3])
np.cumprod(x)
array([1, 2, 6])

Here, the we are performing the following computations:

[0] 1 = 1
[1] 1 * 2 = 2
[2] 1 * 2 * 3 = 6

2D array

Suppose we have the following 2D array:

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

All values

To compute the cumulative products of all values in the array:

x = np.array([[1,2], [3,4]])
np.cumprod(x)
array([ 1, 2, 6, 24])

Column-wise

To compute the cumulative products column-wise, set axis=0:

x = np.array([[1,2], [3,4]])
np.cumprod(x, axis=0)
array([[1, 2],
[3, 8]])

Row-wise

To compute the cumulative products row-wise, set axis=1:

x = np.array([[1,2], [3,4]])
np.cumprod(x, axis=1)
array([[ 1, 2],
[ 3, 12]])

Specifying a datatype

To obtain an array of data-type float:

x = np.array([1,2,3])
np.cumprod(x, dtype=float)
array([1., 2., 6.])

Here, the . means that the numbers are floats.

robocat
Published by Isshin Inada
Edited by 0 others
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