NumPy divide() – Element-wise Division of Two Arrays

NumPy divide()

The numpy.divide() function performs element-wise division of two arrays. It supports broadcasting, allowing division between arrays of different shapes.

Syntax

numpy.divide(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x1	array_like	The dividend array.
x2	array_like	The divisor array. If x1 and x2 have different shapes, they must be broadcastable.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where the result is stored. If None, a new array is created.
where	array_like, optional	Boolean mask specifying which elements to compute. Elements where where=False retain their original value.
casting	str, optional	Defines the casting behavior for the operation.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Defines the data type of the output array.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns an array containing the element-wise division results of x1 / x2. If both x1 and x2 are scalars, a scalar is returned.

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Examples

1. Element-wise Division of Two Arrays

Performing element-wise division of two equally shaped arrays.

import numpy as np

# Define two arrays
x1 = np.array([10, 20, 30, 40])
x2 = np.array([2, 4, 5, 8])

# Compute element-wise division
result = np.divide(x1, x2)

# Print the results
print("Element-wise division result:", result)

Output:

Element-wise division result: [ 5.  5.  6.  5. ]

2. Broadcasting in NumPy divide()

Using broadcasting when one of the arrays is a scalar or a lower-dimensional array.

import numpy as np

# Define an array and a scalar divisor
x1 = np.array([[10, 20, 30], [40, 50, 60]])
x2 = 10  # Scalar divisor

# Compute division using broadcasting
result = np.divide(x1, x2)

# Print the results
print("Broadcasted division result:\n", result)

Output:

Broadcasted division result:
 [[1. 2. 3.]
 [4. 5. 6.]]

3. Using the out Parameter

Storing the output of the division in a pre-allocated array.

import numpy as np

# Define two arrays
x1 = np.array([12, 24, 36])
x2 = np.array([3, 6, 9])

# Create an output array
out_array = np.ndarray(x1.shape)

# Compute division and store result in out_array
np.divide(x1, x2, out=out_array)

# Print the results
print("Stored output array:", out_array)

Output:

Stored output array: [4. 4. 4.]

4. Using the where Parameter

Computing division only for selected elements using a boolean mask.

import numpy as np

# Define two arrays
x1 = np.array([10, 20, 30, 40])
x2 = np.array([2, 4, 5, 8])

# Define a mask (only divide for selected elements)
mask = np.array([True, False, True, False])

# Compute division where mask is True
result = np.divide(x1, x2, where=mask)

# Print the results
print("Division result with mask:", result)

Output:

Division result with mask: [5. 0. 6. 0.]

Only the elements where mask=True are computed. The others retain their original values.