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# NumPy | fmod method

schedule Aug 12, 2023
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NumPy's `fmod(~)` method computes the remainder element-wise given two arrays.

NOTE

What differentiates this `mod(~)` from NumPy's `fmod(~)` is confusingly not whether or not one is for floating numbers; they are both capable of parsing floating numbers. The defining difference is how they handle negative numbers - take a look at the examples below for clarification.

# 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 result 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 scalar is returned if `x1` and `x2` are scalars, otherwise a NumPy array is returned.

# Examples

## A common divisor

``` x = [3, 8, -7]np.fmod(x, 3) array([ 0, 2, -1]) ```

Here, notice how `fmod(-7,3)=-1`, which is different from Python's standard % behaviour, which gives you `-7%3=2`. In fact, NumPy's `fmod(~)` method follows the main C library's `fmod(~)` implementation.

## 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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