NumPy fabs()
The numpy.fabs() function computes the absolute values (positive magnitude) of each element in an input array, element-wise.
Unlike numpy.abs(), fabs() does not support complex numbers.
Syntax
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numpy.fabs(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)Parameters
| Parameter | Type | Description |
|---|---|---|
x | array_like | An array of real numbers for which absolute values are computed. Complex numbers are not supported. |
out | ndarray, None, or tuple of ndarray and None, optional | Optional output array where results are stored. If None, a new array is created. |
where | array_like, optional | Boolean mask specifying where to compute fabs(). Elements where where=False retain their original value. |
casting | str, optional | Defines casting behavior when computing fabs(). |
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 absolute values of the input. If the input is a scalar, a scalar is returned.
Examples
1. Computing the Absolute Value of a Single Number
Here, we compute the absolute value of a single negative number.
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import numpy as np
# Define a negative number
num = -10.5
# Compute its absolute value
result = np.fabs(num)
# Print the result
print("Absolute value of -10.5:", result)Output:
Absolute value of -10.5: 10.5
2. Computing Absolute Values for an Array
We compute the absolute values for an array of positive and negative numbers.
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import numpy as np
# Define an array with both positive and negative numbers
arr = np.array([-3.2, 4.5, -2.0, -8.9, 0])
# Compute the absolute values
abs_values = np.fabs(arr)
# Print the results
print("Original array:", arr)
print("Absolute values:", abs_values)Output:
Original array: [-3.2 4.5 -2. -8.9 0. ]
Absolute values: [3.2 4.5 2. 8.9 0. ]
3. Using the out Parameter
We store the computed absolute values in a pre-allocated output array.
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import numpy as np
# Define an input array
arr = np.array([-1.5, -2.3, 3.8, -7.1])
# Create an output array with the same shape
output_array = np.empty_like(arr)
# Compute absolute values and store them in output_array
np.fabs(arr, out=output_array)
# Print the results
print("Computed absolute values:", output_array)Output:
Computed absolute values: [1.5 2.3 3.8 7.1]
4. Using the where Parameter
Computing absolute values only for selected elements using a boolean mask.
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import numpy as np
# Define an input array
arr = np.array([-5.2, -3.0, 4.2, -6.1])
# Define a mask (compute absolute value only where mask is True)
mask = np.array([True, False, True, False])
# Compute absolute values where mask is True
result = np.fabs(arr, where=mask)
# Print the results
print("Computed absolute values with mask:", result)Output:
Computed absolute values with mask: [5.2 0. 4.2 0. ]
Only the elements where mask=True are computed, while others retain their initial value.