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NumPy | fmod method

schedule Aug 12, 2023
Last updated
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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.

robocat
Published by Isshin Inada
Edited by 0 others
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