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# Difference between the methods mod and fmod in NumPy

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Both `mod(~)`

and `fmod(~)`

methods compute the remainder of given two arrays of dividend and divisor.

What differentiates `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.

Let's take a look at a simple example.

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

Here, `mod(-7,3)=2`

, which is equivalent to Python's `%`

implementation `(-7%3=2)`

. Also notice how 8.5, which is a floating number, was parsed correctly.

Now, let's do the same thing using the fmod method:

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

Here, we see that `fmod(-7,3)=-1`

, which is a different answer from above. In fact, Numpy's `fmod(~)`

method follows the main C library's `fmod(~)`

implementation.

# Related

NumPy | mod method

Numpy's mod(~) method computes the remainder element-wise given two arrays. Numpy's remainder(~) method is equivalent to this method.

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NumPy | fmod method

Numpy's fmod(~) method computes the remainder element-wise given two arrays.

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Published by Isshin Inada

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