NumPy sign() – Indication of Sign of Number of Array Element-wise

NumPy sign()

The numpy.sign() function returns an element-wise indication of the sign of a number. It determines whether each element in an array is negative (-1), zero (0), or positive (1). For complex numbers, it returns x / abs(x).

Syntax

numpy.sign(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x	array_like	Input array containing numeric values.
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 sign function.
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 with -1 for negative numbers, 0 for zero values, and 1 for positive numbers. If the input is a complex number, it returns x / abs(x). If the input is a scalar, a scalar is returned.

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Examples

1. Computing Sign for a Single Number

Here, we compute the sign of a single numeric value.

import numpy as np

# Define a number
num = -7

# Compute the sign of the number
result = np.sign(num)

# Print the result
print("Sign of -7:", result)

Output:

Sign of -7: -1

2. Computing Sign for an Array of Numbers

We compute the sign values for multiple numbers provided in an array.

import numpy as np

# Define an array of numbers
numbers = np.array([-10, -3, 0, 4, 12])

# Compute the sign of each number
sign_values = np.sign(numbers)

# Print the results
print("Numbers:", numbers)
print("Sign values:", sign_values)

Output:

Numbers: [-10  -3   0   4  12]
Sign values: [-1 -1  0  1  1]

3. Computing Sign for Complex Numbers

For complex numbers, the sign function returns x / abs(x).

import numpy as np

# Define an array of complex numbers
complex_numbers = np.array([3 + 4j, -2 - 2j, 0 + 0j])

# Compute the sign of each complex number
sign_complex = np.sign(complex_numbers)

# Print the results
print("Complex numbers:", complex_numbers)
print("Sign values:", sign_complex)

Output:

Complex numbers: [ 3.+4.j -2.-2.j  0.+0.j]
Sign values: [ 0.6+0.8j -0.70710678-0.70710678j  0. +0.j ]

4. Using the where Parameter

Using a condition to compute sign only for selected elements.

import numpy as np

# Define an array of numbers
numbers = np.array([-5, 0, 3, -8, 7])

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

# Compute sign values where mask is True
result = np.sign(numbers, where=mask)

# Print the results
print("Computed sign values with mask:", result)

Output:

Computed sign values with mask: [                 -1 4602678819172646912                   1
 4609434218613702656                   1]

The sign values are computed only for elements where mask=True.