numpy.mod()
— this is another function for performing math operations in numpy. It returns the elementwise remainder of the division between the two arrays arr1 and arr2, that is, arr1% arr2
. Returns 0 when arr2 is 0 and arr1 and arr2 are equal (arrays) integers.
Syntax: numpy.mod (arr1, arr2, /, out = None, *, where = True, casting = `same_kind`, order = `K`, dtype = None, subok = True [, signature, extobj], ufunc `remainder`)
Parameters :
arr1: [array_like] Dividend array.
arr2: [array_like] Divisor array.
dtype: The type of the returned array. By default, the dtype of arr is used.
out: [ndarray, optional] A location into which the result is stored.
 & gt ; If provided, it must have a shape that the inputs broadcast to.
 & gt; If not provided or None, a freshlyallocated array is returned.
where: [array_like, optional] Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
** kwargs: Allows to pass keyword variable length of argument to a function. Used when we want to handle named argument in a function.Return: [ndarray] The elementwise remainder ie arr1% arr2.
Code # 1:

Output:
Dividend: 6 Divisor: 4 Remainder: 2
Code # 2:

Output:
Dividend array: [2 4 7] Divisor array: [2 3 4] Output remainder array: [0 2 3]
Big data is, admittedly, an overhyped buzzword used by software and hardware companies to boost their sales. Behind the hype, however, there is a real and extremely important technology trend with imp...
10/07/2020
Why this Book? Hadoop has been the base for most of the emerging technologies in today’s big data world. It changed the face of distributed processing by using commodity hardware for large data set...
10/07/2020
Mastering regular expressions by Jeffrey Friedl, 3rd edition. Regular expressions are an extremely powerful tool for manipulating text and data. They are standard features today in a variety of pop...
05/09/2021
The field of Artificial Intelligence (AI), which can definitely be considered to be the parent field of deep learning, has a rich history going back to 1950. While we will not cover this history in mu...
23/09/2020