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# NumPy | divmod method

schedule Aug 11, 2023
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Numpy's `divmod(~)` method returns the tuple `(x//y, x%y)` for each dividend `x` and divisor `y` in the input arrays. Here, `//` denotes integer division, while `%` denotes the standard Python modulo.

If you need both of these quantities, then use the `divmod(~)` method as it prevents redundant computations.

# Parameters

1. `x1` | `array_like`

The dividends.

2. `x2` | `array_like`

The divisors.

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 behavior is slightly different - the original value will be kept intact.

# Return value

A tuple, `(x//y, x%y)`, for each dividend `x` and divisor `y` in the input arrays

# Examples

## Common divisor

``` x = [3, 8.5, -7]np.divmod(x, 3) (array([ 1., 2., -3.]), array([0. , 2.5, 2. ])) ```

To clarify how the output was computed:

``` 3 // 3 = 1 8.5 // 3 = 2-7 // 3 = -33 % 3 = 08.5 % 3 = 2.5-7 % 3 = 2 ```

## Multiple divisors

``` x = [3, 8.5, -7]np.divmod(x, [1,2,3]) (array([ 3., 4., -3.]), array([0. , 0.5, 2. ])) ```
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