The subtract()
function performs element-wise subtraction of two arrays.
Example
import numpy as np
# create two arrays
array1 = np.array([4, 5, 6])
array2 = np.array([2, 1, 3])
# perform element-wise subtraction of the two arrays
result = np.subtract(array1, array2)
print(result)
# Output: [2 4 3]
subtract() Syntax
The syntax of subtract()
is:
numpy.subtract(x1, x2, out = None, where = True, dtype = None)
subtract() Arguments
The subtract()
function takes following arguments:
x1
andx2
- two input arrays or scalars to be subtractedout
(optional) - the output array where the result will be storedwhere
(optional) - a boolean array or condition specifying which elements to subtractdtype
(optional) - data type of the output array
subtract() Return Value
The np.subtract()
function returns an array containing the result of element-wise subtraction between two arrays or between an array and a scalar value.
Example 1: Subtract a Scalar Value From a NumPy Array
import numpy as np
# create an array
arr = np.array([10, 20, 30])
# subtract a scalar value from the array
result = np.subtract(arr, 5)
print(result)
Output
[ 5 15 25]
Here, the np.subtract()
function is used to subtract a scalar value of 5 from each element of the arr array.
Example 2: Use of out and where in subtract()
import numpy as np
# create two input arrays
array1 = np.array([10, 20, 30, 50])
array2 = np.array([1, 2, 3, 5])
# create a Boolean array as a condition for subtraction
condition = np.array([True, False, True, True])
# create an empty array to store the subtracted values
result = np.empty_like(array1)
# perform element-wise subtraction between array1 and array2,
# only where the condition is True and store output in result array
np.subtract(array1, array2, where=condition, out=result)
print(result)
Output
[ 9 0 27 45]
The output shows the result of the subtraction operation, where the elements from array1 and array2 are subtracted together only where the corresponding condition is True
.
The second element in result is 0 because the corresponding condition value is False, and therefore, the subtraction does not take place for that element.
Here, out=result
specifies that the output of np.subtract()
should be stored in the result array
Example 3: Use of dtype Argument in subtract()
import numpy as np
# create two arrays
array1 = np.array([14, 25, 46])
array2 = np.array([7, 12, 23])
# subtract array2 from array1 with a floating-point data type
resultFloat = np.subtract(array1, array2, dtype=np.float64)
# subtract array2 from array1 with a integer data type
resultInt = np.subtract(array1, array2, dtype=np.int32)
print("Floating-point result:")
print(resultFloat)
print("\nInteger result:")
print(resultInt)
Output
Floating-point result: [ 7. 13. 23.] Integer result: [ 7 13 23]
Here, by specifying the desired dtype
, we can control the data type of the output array according to our requirements.
Note: To learn more about the dtype
argument, please visit NumPy Data Types.