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# Return element-wise True where signbit is set (less than zero) and store the result in a new location in Numpy

To return element-wise True where signbit is set (less than zero), use the **numpy.signbit()** method in Python Numpy. The new location where we will store the result is a new array.

Returns the output array, or reference to out if that was supplied. This is a scalar if x is a scalar. The out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

The condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized

## Steps

At first, import the required library −

import numpy as np

Create an array −

arr = np.array([10, 87, -45, -7.9, 6.5, 89])

Display the array −

print("Array...

", arr)

Get the type of the array −

print("

Our Array type...

", arr.dtype)

Get the dimensions of the Array −

print("

Our Array Dimensions...

",arr.ndim)

Get the number of elements in the Array −

print("

Number of elements...

", arr.size)

Create another array with the same shape to store the result −

arrRes = np.array([3.5, 9.7, 5.9, 3.1, 4.6, 6.9])

To return element-wise True where signbit is set (less than zero), use the numpy.signbit() method in Python Numpy. The new location where we will store the result is arrRes −

print("

Return True where signbit is set...

",np.signbit(arr, arrRes))

Check the value of the new array where our result is stored −

print("

Result...

",arrRes)

## Example

import numpy as np # Create an array arr = np.array([10, 87, -45, -7.9, 6.5, 89]) # Display the array print("Array...

", arr) # Get the type of the array print("

Our Array type...

", arr.dtype) # Get the dimensions of the Array print("

Our Array Dimensions...

",arr.ndim) # Get the number of elements in the Array print("

Number of elements...

", arr.size) # Create another array with the same shape to store the result arrRes = np.array([3.5, 9.7, 5.9, 3.1, 4.6, 6.9]) # To return element-wise True where signbit is set (less than zero), use the numpy.signbit() method in Python Numpy # The new location where we will store the result is arrRes print("

Return True where signbit is set...

",np.signbit(arr, arrRes)) # Check the value of the new array where our result is stored print("

Result...

",arrRes)

## Output

Array... [ 10. 87. -45. -7.9 6.5 89. ] Our Array type... float64 Our Array Dimensions... 1 Number of elements... 6 Return True where signbit is set... [0. 0. 1. 1. 0. 0.] Result... [0. 0. 1. 1. 0. 0.]

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