# Return an element-wise indication of the sign of complex types in Numpy

To return an element-wise indication of the sign of complex types, use the numpy.sign() method in Python Numpy.

The sign function returns -1 if x < 0, 0 if x==0, 1 if x > 0. nan is returned for nan inputs. For complex inputs, the sign function returns sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j.

The complex(nan, 0) is returned for complex nan inputs. There is more than one definition of sign in common use for complex numbers. The definition used here is equivalent to x/x*x which is different from a common alternative, x/|x|.

## Steps

At first, import the required library −

import numpy as np

Create an array with complex type using the array() method −

arr = np.array([56.+0.j, 27.+0.j, 68.-2.j, 49.+0.j, 120.-5.j,3 + 4.j])


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 Dimension...",arr.ndim)

Get the shape of the Array −

print("Our Array Shape...",arr.shape)


To return an element-wise indication of the sign of complex types, use the numpy.sign() method −

print("Result...",np.sign(arr))

## Example

import numpy as np

# Create an array with complex type using the array() method
arr = np.array([56.+0.j, 27.+0.j, 68.-2.j, 49.+0.j, 120.-5.j,3 + 4.j])

# 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 Dimension...",arr.ndim)

# Get the shape of the Array
print("Our Array Shape...",arr.shape)

# To return an element-wise indication of the sign of complex types, use the numpy.sign() method in Python Numpy
print("Result...",np.sign(arr))

## Output

Array...
[ 56.+0.j 27.+0.j 68.-2.j 49.+0.j 120.-5.j 3.+4.j]

Our Array type...
complex128

Our Array Dimension...
1

Our Array Shape...
(6,)

Result...
[1.+0.j 1.+0.j 1.+0.j 1.+0.j 1.+0.j 1.+0.j]