**Numpy subtract() Function:**

**Numpy subtract: **The difference between the two NumPy arrays is calculated using the NumPy subtract function. It calculates the element-by-element difference between two arrays, say l1 and l2. Numpy. subtract() 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.subtract(l1, l2, out=None)

**Parameters:**

**l1 and l2: (Required)**

**np.subtract: **Required. The arrays that will be subtracted must be specified. 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: **

**NP subtract:** The difference between the two NumPy arrays is calculated using the NumPy subtract function. It calculates the element-by-element difference between two arrays, say x1 and x2. Numpy. subtract() is a universal function, which means it has numerous options that can be used to optimize its performance based on the algorithm’s characteristics.

## NumPy subtract() 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 subtract() function.
- Pass the first array and some random number to subtract() function of NumPy module and print the result.
- Here it subtracts the second argument for each element of the array.
- Print the result array.
- Pass the first array and second array to subtract() function of the NumPy module and print the result.
- Here it subtracts 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 subtract() 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 subtract() function of NumPy module and print the result. # Here it subtracts the second argument for each element of the array. print('Subtracting random number say 10 to the first array : ') print('Subtracting 10 to first array gives :\n', np.subtract(arry1, 10)) # Print the result array. # Pass the first array and second array to subtract() function of NumPy module and print the result. # Here it subtracts the second array elements for the first array element. # Print the result array. print('Subtracting first array and second array : ') print(np.subtract(arry1, arry2)) # Similarly, test it with other arrays of different shapes. print('Subtracting first array and third array : ') print(np.subtract(arry1, arry3)) print('Subtracting first array and fourth array : ') print(np.subtract(arry1, arry4))

**Output:**

Subtracting random number say 3 to the first array : Subtracting 10 to first array gives : [[ 20 50] [ 80 110] [140 170]] Subtracting first array and second array : [[ 20 40] [ 80 100] [140 160]] Subtracting first array and third array : [[ -70 -40] [-110 -80] [-150 -120]] Subtracting first array and fourth array : [[-25 -5] [ 15 35] [ 55 75]]