Python NumPy divide() Function

In NumPy, the numpy.divide() function is used to divide the elements of one array by the elements of another array. It performs element-wise division, broadcasting the arrays if necessary. This function provides several parameters that allow the user to specify what value to divide with. Use numpy.divide() function to divide the first array elements (arr) with the second array elements (arr1). Both arr and arr1 must have the same shape and the element in arr2 must not be zero; otherwise, it will raise an error. In this article, I will explain how to use the NumPy divide() function with examples.

1. Quick Examples of NumPy divide() Function

If you are in a hurry, below are some quick examples of how to use Python NumPy divide() function.

# Quick examples of numpy divide() function

# Example 1: Using divide() function
# Get division values 
arr = np.array([8, 27, 15, 18, 22, 34])
arr2 = np.divide(arr,4)

# Example 2: Use numpy.divide() 
# Divide numpy array elementwise
arr = [16, 28, 33, 38, 45]
arr1 = [4, 8, 13, 5, 3]
arr2 = np.divide(arr, arr1)

# Example 3: Divide arrays using / operator
arr = np.array([16, 28, 33, 38, 45])
arr1 = np.array([4, 8, 13, 5, 3])
arr2 = arr/arr1

# Example 4: Get division values
# Between two multi-dimensional arrays
arr = [[15, 23, 32], [9, -17, 25]]
arr1 = [[3, 5, 8], [-2, 4, 7]]
arr2 = np.divide(arr, arr1) 

# Example 5: Divide the array values by zero
arr = [16, 28, 33, 38, 45]
arr1 = [4, 8, 0, 5, 0]
arr2 = np.divide(arr, arr1) 

2. Syntax of NumPy divide()

Following is the syntax of the numpy.divide() function.

#  Syntax of numpy.divide() 
numpy.divide(arr1, arr2, out=None, where=True, casting='same_kind', order='K', dtype=None) 

2.1 Parameters of divide()

Following are the parameters of the divide() function.

• arr1 The first input array or object works as a dividend.
• arr2 – Second input array or object which works as a divisor.
• out – It is ndarray, None, or tuple of ndarray and None, optional. Out will be the location where the result is to be stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly allocated array is returned.
• where – This condition is broadcast over the input. True value means to calculate the unfunc at that position, whereas if the False value means to leave the value in output only.
• casting (optional) – Controls what kind of data casting may occur during the operation. The default is ‘same_kind’.

2.2 Return Value of NumPy divide()

It returns an array that contains the division values of an input array.

3. Usage of NumPy divide() Function

The numpy.divide() function is used to perform element-wise division between two arrays or between an array and a scalar (numeric value). Returns a true division of the inputs, element-wise.

4 Divide NumPy Array by scalar (Single Value)

You can use the numpy.divide() function to divide a NumPy array by a scalar (single value). For instance, each element in the array arr is divided by the scalar value 4. The result is a new NumPy array with the element-wise division.

# Import numpy
import numpy as np
                 
# Create a 1D array
arr = np.array([8, 27, 15, 18, 22, 34])
print("Original 1D array:\n",arr)

# Using divide() function
# Get division values 
arr2 = np.divide(arr,4)
print("After getting division values:\n",arr2)

Yields below output.

5. Divide NumPy Array Elementwise

If you have two NumPy arrays and you want to perform element-wise division, you can use the numpy.divide() function. For example, the numpy.divide() function is used to perform element-wise division between the corresponding elements of arr and arr1. The result is a new NumPy array with the element-wise division values.

# Creating two input array
arr = np.array([16, 28, 33, 38, 45])
print("First array:",arr)
arr1 =np.array([4, 8, 13, 5, 3])
print("Second array:",arr1)

# Use numpy.divide() 
# Divide numpy array elementwise
arr2 = np.divide(arr, arr1)
print("Result after element-wise division:\n",arr2)

Yields below output.

6. Divide NumPy Arrays With the / Operator

You can use the division operator (/) directly for element-wise division of NumPy arrays. For example, the division operator (/) performs element-wise division between the corresponding elements of the two NumPy arrays, producing a new array with the division results.

# Creating two input array
arr = np.array([16, 28, 33, 38, 45])
print("First array:",arr)
arr1 =np.array([4, 8, 13, 5, 3])
print("Second array:",arr1)

# Use numpy.divide() 
# Use the division operator for element-wise division
arr2 = arr / arr1
print("Result after element-wise division using / operator:\n",arr2)

# Output:
# First array: [16 28 33 38 45]
# Second array: [ 4  8 13  5  3]
# Result after element-wise division using / operator:
#  [ 4.          3.5         2.53846154  7.6        15.        ]

7. Divide NumPy Two Multi-Dimensional Arrays

When you have two multi-dimensional NumPy arrays and you want to perform element-wise division, you can use the division operator (/) or the numpy.divide() function.

In the below example, both the division operator (/) and the numpy.divide() function performs element-wise division between the corresponding elements of the two multi-dimensional arrays, producing new arrays with the division results.

# Creating two multi-dimensional array
arr = np.array([[15, 23, 32], [9, -17, 25]])
arr1 = np.array([[3, 5, 8], [-2, 4, 7]])

# Using the division operator 
# For element-wise division
arr2 = arr / arr1
print("Result using / operator:\n",arr2)

# Using numpy.divide() for element-wise division
arr2 = np.divide(arr, arr1)
print("Result using numpy.divide():\n",arr2)

# Output:
# Result using / operator:
#  [[ 5.          4.6         4.        ]
#  [-4.5        -4.25        3.57142857]]

8. Divide NumPy Array by Using 0

Alternatively, element-wise division of the array arr by the array arr1 using numpy.divide(). However, you have some zeros in the divisor (arr1), which can lead to division by zero issues and result in inf or nan values.

As I mentioned above, the divisor array must not be zero otherwise it will raise an error. In this example, I have declared some of the divisor array elements are zero. Let’s see what happens.

# Creating two multi-dimensional array
arr = np.array([16, 28, 33, 38, 45])
arr1 = np.array([4, 8, 0, 5, 0])

# Use numpy division 
arr2 = np.divide(arr, arr1) 
print("After getting division values:\n",arr2)

# Output:
# RuntimeWarning: divide by zero encountered in true_divide
# [4.  3.5 inf 7.6 inf]

As you can see, division by zero results in inf values in the output array. It’s essential to handle such cases in your code to avoid unexpected results

Frequently Asked Questions

Conclusion

In this article, I have explained how to use numpy.divide() function syntax, parameters, and usage of how to calculate true division values of NumPy arrays with examples.

Happy Learning!!

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References

• https://numpy.org/doc/stable/reference/generated/numpy.seterr.html#numpy.seterr