NumPy subtract() – Subtraction of Two Arrays Element-wise

NumPy subtract()

The numpy.subtract() function performs element-wise subtraction of two input arrays. If the shapes of the input arrays differ, NumPy attempts to broadcast them to a common shape.

Syntax

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

Parameters

Parameter	Type	Description
x1, x2	array_like	Input arrays to be subtracted element-wise. They must be broadcastable to a common shape.
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 when computing the subtraction.
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 difference between x1 and x2. If both inputs are scalars, a scalar is returned.

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Examples

1. Subtracting Two Arrays Element-wise

Here, we subtract two NumPy arrays element by element.

import numpy as np

# Define two arrays
x1 = np.array([10, 20, 30])
x2 = np.array([1, 5, 10])

# Perform element-wise subtraction
result = np.subtract(x1, x2)

# Print the result
print("Subtraction result:", result)

Output:

Subtraction result: [ 9 15 20]

2. Broadcasting in NumPy Subtraction

NumPy automatically broadcasts smaller arrays when their shape differs.

import numpy as np

# Define a 2D array and a 1D array
x1 = np.array([[10, 20, 30], [40, 50, 60]])
x2 = np.array([1, 2, 3])  # Will be broadcasted

# Perform element-wise subtraction with broadcasting
result = np.subtract(x1, x2)

# Print the result
print("Subtraction result:\n", result)

Output:

Subtraction result:
 [[ 9 18 27]
  [39 48 57]]

3. Using the out Parameter

Using an output array to store the results instead of creating a new array.

import numpy as np

# Define two arrays
x1 = np.array([10, 20, 30])
x2 = np.array([2, 4, 6])

# Create an output array
output_array = np.empty_like(x1)

# Perform subtraction and store the result in output_array
np.subtract(x1, x2, out=output_array)

# Print the results
print("Output array after subtraction:", output_array)

Output:

Output array after subtraction: [ 8 16 24]

4. Using the where Parameter

The where parameter allows selective subtraction based on a condition.

import numpy as np

# Define two arrays
x1 = np.array([10, 20, 30])
x2 = np.array([2, 4, 6])

# Define a mask (perform subtraction only where mask is True)
mask = np.array([True, False, True])

# Perform subtraction using the mask
result = np.subtract(x1, x2, where=mask)

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

Output:

Subtraction result with mask: [                  8 4603214750636966148                  24]

The subtraction is performed only for elements where mask=True, while the others remain unchanged.