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# NumPy | mod method

NumPy
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schedule Jul 1, 2022
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Numpy's `mod(~)` method computes the remainder element-wise given two arrays. Numpy's `remainder(~)` method is equivalent to this method.

WARNING

Opt to use the `%` operator instead

If you don't need the 3rd and 4th parameters of this method, simply multiply two arrays using the `%` operator - you'll enjoy a performance boost.

# Parameters

1. `x1` | `array_like`

The values that will be dividend.

2. `x2` | `array_like`

The values to divide by.

3. `out` | `Numpy array` | `optional`

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

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

Values that are flagged as False will be ignored, that is, their original value will be uninitialized. If you specified the `out` parameter, the behaviour is slightly different - the original value will be kept intact.

# Return value

A scalar is returned if `x1` and `x2` are scalars, otherwise a Numpy array is returned.

# Examples

## A common divisor

``` x = [3, 8, -7]np.mod(x, 3) array([0, 2, 2]) ```

Here, notice how `-7%3=2`, which is the way Python defines its modulo behaviour.

## Element-wise division

``` x = [5, 8]np.mod(x, [2,3]) array([1, 2]) ```

Here, we're simply performing `5%2=1` and `8%3=2`.

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