# NumPy arange#

arange in NumPy is very like the Python range callable with two important differences:

• arange returns an array rather than a range instance;

• arange arguments can be floating point values.

import numpy as np

np.arange(4, 11, 2)

array([ 4,  6,  8, 10])

np.arange(4, 11, 0.5)

array([ 4. ,  4.5,  5. ,  5.5,  6. ,  6.5,  7. ,  7.5,  8. ,  8.5,  9. ,
9.5, 10. , 10.5])


Because arange returns arrays, you can use NumPy element-wise operations to multiply by the step size and add a start value. This is one way to create equally spaced vectors (np.linspace is another):

np.arange(10) * 0.5 + 4

array([4. , 4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. , 8.5])