Python NumPy remainder() Function

Remainder function python – Python NumPy remainder() Function

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NumPy remainder() Function:

Remainder function python: In numpy, there is also a function called numpy.remainder() that can be used to do mathematical operations. It returns the element-wise division remainder between two arrays arr1 and arr2, i.e. arr1 percent arr2. When arr2 is 0 and both arr1 and arr2 are (arrays of) integers, it returns 0.

Syntax:

numpy.remainder(l1, l2, out=None)

Parameters:

l1 and l2: (Required)

These are required arguments. These are the arrays that have to be divided, here l1 as dividend and l2 as the divisor.

They must be broadcastable to a common shape if l1.shape!= l2.shape.

out:

This is optional. It is the location where the result will be saved. It must have a shape that the inputs broadcast to if it is provided. If None or not given, a newly allocated array is returned.

Return Value:

The element-by-element remainder of dividing l1 and l2 is returned(l1%l2).

NumPy remainder() Function in Python

Example

Approach:

  • Import NumPy module using the import keyword.
  • Pass some random list as an argument to the array() function to create an array.
  • Store it in a variable.
  • Create some sample arrays of different shapes to test the remainder() function.
  • Pass the first array and some random number to remainder() function of NumPy module and print the result.
  • Here it divides each element of the array1 with the given random value say 9 and gives the remainder.
  • Pass the first array and second array to remainder() function of NumPy module and print the result.
  • Here it divides second array elements for the first array element and gives the remainder
  • Similarly, test it with other arrays of different shapes.
  • The Exit of the Program.

Below is the implementation:

# Import NumPy module using the import keyword.
import numpy as np
# Pass some random list as an argument to the array() function to create an array.
# Store it in a variable.
arry1 = np.array([[32,64],[95,128],[150,180]])
# Create some sample arrays of different shapes to test the remainder() function.
arry2 = np.array([[10,20]])
arry3 = np.array([[100],[200],[300]])
arry4 = np.array([[55,65],[75,85],[95,105]])
# Pass the first array and some random number to remainder() function of NumPy module and print the result.
# Here it divides each element of the array1 with the given random value say 9 and gives the remainder.
print('Getting remainder value by dividing first array with 9: ')
print(np.remainder(arry1, 9))
# Pass the first array and second array to remainder() function of NumPy module and print the result.
# Here it divides second array elements for the first array element and gives the remainder
print('Getting remainder value by dividing first array with second array: ')
print(np.remainder(arry1, arry2))
# Similarly, test it with other arrays of different shapes.
print('Getting remainder value by dividing first array with third array: ')
print(np.remainder(arry1, arry3))
print('Getting remainder value by dividing first array with fourth array: ')
print(np.remainder(arry1, arry4))

Output:

Getting remainder value by dividing first array with 9: 
[[5 1]
 [5 2]
 [6 0]]
Getting remainder value by dividing first array with second array: 
[[2 4]
 [5 8]
 [0 0]]
Getting remainder value by dividing first array with third array: 
[[ 32 64]
 [ 95 128]
 [150 180]]
Getting remainder value by dividing first array with fourth array: 
[[32 64]
 [20 43]
 [55 75]]