The problem is your endpoint: 3 * 0.1
, which is not considered equal to 0.3
(remember that Python and NumPy use floating point arithmetic where some numbers, i.e. 0.1
, cannot be represented exactly).
>>> 3 * 0.1
0.30000000000000004
>>> 0.3
0.3 # or more exactly 0.299999999999999988897769753748...
>>> 3 * 0.1 == 0.3
False
So it's not really surprising that 0.3
is included because the endpoint is (very slightly) bigger.
Note that the numpy.arange
also contains the formula how many elements will be in the result array:
ceil((stop - start)/step
>>> (3 * 0.1 - 0)/0.1
3.0000000000000004
>>> ceil(_)
4
Floating point math is tricky, especially when comparing floats for equality. Why not just create an integer array and create the desired float array by division:
>>> import numpy as np
>>> np.arange(0, 3, 1) / 10
array([0. , 0.1, 0.2])
Or the numpy.linspace
function which offers more options for floating point values:
>>> import numpy as np
>>> np.linspace(0.0, 3 * 0.1, 3, endpoint=False)
array([0. , 0.1, 0.2])
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