NumPy signbit()
The numpy.signbit() function returns a boolean array indicating whether the sign bit of each element in the input array is set (i.e., whether the value is negative).
Syntax
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numpy.signbit(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)Parameters
| Parameter | Type | Description |
|---|---|---|
x | array_like | Input values for which the sign bit will be checked. |
out | ndarray, None, or tuple of ndarray and None, optional | Optional output array to store results. 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 bit. |
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 a boolean array where True indicates that the corresponding input element has a negative sign bit. If the input is a scalar, a scalar boolean is returned.
Examples
1. Checking the Sign Bit for Single Values
Here, we check if a single number has its sign bit set.
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import numpy as np
# Check the sign bit for a positive and a negative number
pos_value = 5.0
neg_value = -3.2
# Compute signbit for both values
result_pos = np.signbit(pos_value)
result_neg = np.signbit(neg_value)
# Print results
print("Sign bit for 5.0:", result_pos)
print("Sign bit for -3.2:", result_neg)Output:
Sign bit for 5.0: False
Sign bit for -3.2: True
2. Checking the Sign Bit for an Array
We check whether each element in an array has its sign bit set.
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import numpy as np
# Define an array of numbers
arr = np.array([3.5, -2.1, 0, -0.0, -10, 8])
# Compute signbit for each element in the array
sign_bits = np.signbit(arr)
# Print results
print("Input array:", arr)
print("Sign bit array:", sign_bits)Output:
Input array: [ 3.5 -2.1 0. -0. -10. 8. ]
Sign bit array: [False True False True True False]
3. Using the out Parameter
Using an output array to store results instead of creating a new array.
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import numpy as np
# Define an array of numbers
arr = np.array([-5, 7, -8, 0, -2.5])
# Create an output array with the same shape
output_array = np.empty_like(arr, dtype=bool)
# Compute signbit and store the result in output_array
np.signbit(arr, out=output_array)
# Print the results
print("Computed sign bit array:", output_array)Output:
Computed sign bit array: [ True False True False True]
4. Using the where Parameter
Using a condition to compute the sign bit only for selected elements.
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import numpy as np
# Define an array of numbers
arr = np.array([3, -2, 5, -7, 0, -1])
# Define a mask (compute signbit only where mask is True)
mask = np.array([False, True, False, True, False, True])
# Compute signbit values where mask is True
result = np.signbit(arr, where=mask)
# Print the results
print("Computed sign bit array with mask:", result)Output:
Computed sign bit array with mask: [False True False True False True]
The sign bit values are computed only for elements where mask=True. The other values remain unchanged.