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NumPy | prod method

schedule Aug 12, 2023
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Numpy's `prod(~)` computes the product of the values in the input array.

Parameters

1. `a` | `array-like`

The input array for which to compute the product of values.

2. `axis` | `None` or `int` | `optional`

The axis along which to compute the product. For 2D arrays, the allowed values are as follows:

Axis

Meaning

0

Compute the product column-wise

1

Compute the product row-wise

None

Compute the product of all values

By default, `axis=None`.

3. `dtype` | `string` or `type` | `optional`

The desired data type of the returned array. `dtype` will also be the type used during the computation of the product. By default, the `dtype` of `a` is used.

4. `out` | `Numpy array` | `optional`

Instead of creating a new array, you can place the computed product into the array specified by `out`.

5. `initial` | `scalar` | `optional`

The initial value used for the computation of the product. By default, `initial=1`.

6. `where` | `array` of `boolean` | `optional`

A boolean mask, where values that are flagged as False will be ignored, while those flagged as True will be used in the computation.

Examples

Basic usage

``` np.prod([1,2,3,4]) 24 ```

Computing the product of a 2D array

Consider the following 2D array:

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

All values

``` np.prod(a) 24 ```

Column-wise

``` np.prod(a, axis=0) array([3, 8]) ```

Row-wise

``` np.prod(a, axis=1) array([ 2, 12]) ```

Specifying an output array

``` a = np.zeros(2)np.prod([[1,2],[3,4]], axis=1, out=a) # row-wise producta array([ 2., 12.]) ```

Here, we've outputted the results to the array `a`.

Specifying an initial value

``` np.prod([1,2,3], initial=10) 60 ```

Here, since we set an initial value of 10, we have `10*1*2*3 = 60`.

``` np.prod([4,5,6,7], where=[False, True, True, False]) 30 ```

Here, only the second and third values were included in the computation of the product.

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