**Numpy divide() Function:**

**Numpy divide by scalar: **The divide function in Numpy calculates the division of the two arrays. It calculates the element-by-element split between the two arrays, say l1 and l2. The numpy. divide() function is a universal function, which means it has numerous options that can be used to optimize its performance based on the algorithm’s characteristics.

**Syntax:**

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

**Parameters:**

**l1 and l2: (Required)**

**Numpy divide: **Required. The arrays that will be divided must be specified. They must be broadcastable to a common shape if l1.shape!= l2.shape

**out:**

**Numpy element wise division: **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: **

**Python element wise division: **The divide function returns the result of the division of l1 and l2. The nd-array scalar divide() function can be used. It is determined by the l1 and l2 variables. Numpy is used if l1 and l2 are both scalar. A scalar value will be returned by divide(). Otherwise, an nd-array will be returned.

## NumPy divide() Function in Python

**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 divide() function.
- Pass the first array and some random number to divide() function of NumPy module and print the result.
- Here it divides the second argument for each element of the array.
- Print the result array.
- Pass the first array and second array to divide() function of NumPy module and print the result.
- Here it divides the second array elements for the first array element.
- Print the result array.
- 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([[30,60],[90,120],[150,180]]) # Create some sample arrays of different shapes to test the divide() 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 divide() function of NumPy module and print the result. # Here it divides the second argument for each element of the array. print('Dividing random number say 10 to the first array : ') print('Dividing 10 to first array gives :\n',np.divide(arry1, 10)) # Print the result array. # Pass the first array and second array to divide() function of NumPy module and print the result. # Here it divides the second array elements for the first array element. # Print the result array. print('Dividing first array and second array : ') print(np.divide(arry1, arry2)) # Similarly, test it with other arrays of different shapes. print('Dividing first array and third array : ') print(np.divide(arry1, arry3)) print('Dividing first array and fourth array : ') print(np.divide(arry1, arry4))

**Output:**

Dividing random number say 10 to the first array : Dividing 10 to first array gives : [[ 3. 6.] [ 9. 12.] [15. 18.]] Dividing first array and second array : [[ 3. 3.] [ 9. 6.] [15. 9.]] Dividing first array and third array : [[0.3 0.6 ] [0.45 0.6 ] [0.5 0.6 ]] Dividing first array and fourth array : [[0.54545455 0.92307692] [1.2 1.41176471] [1.57894737 1.71428571]]