In the below code .I get the expected results of x1
import numpy as np
x1 = np.arange(0.5, 10.4, 0.8)
print(x1)
[ 0.5 1.3 2.1 2.9 3.7 4.5 5.3 6.1 6.9 7.7 8.5 9.3 10.1]
But in the code below, when i set dtype=int why the result of x2 is not [ 0 1 2 2 3 4 5 6 6 7 8 9 10] and Instead I am getting the value of x2 as [ 0 1 2 3 4 5 6 7 8 9 10 11 12] where last value 12 overshoots the end value of 10.4.Please clarify my concept regarding this.
import numpy as np
x2 = np.arange(0.5, 10.4, 0.8, dtype=int)
print(x2)
[ 0 1 2 3 4 5 6 7 8 9 10 11 12]
According to the docs: https://docs.scipy.org/doc/numpy1.15.0/reference/generated/numpy.arange.html
stop : number End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.
arange : ndarray Array of evenly spaced values.
For floating point arguments, the length of the result is ceil((stop - start)/step). Because of floating point overflow, this rule may result in the last element of out being greater than stop.
So here the last element will be.
In [33]: np.ceil((10.4-0.5)/0.8)
Out[33]: 13.0
Hence we see the overshoot to 12 in case of np.arange(0.5, 10.4, 0.8, dtype=int), since stop=13 in the above case, and the default value is 0,
hence the output we observe is.
In [35]: np.arange(0.5, 10.4, 0.8, dtype=int)
Out[35]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
Hence the better way of generating integer ranges, is to use integer parameters like so:
In [25]: np.arange(0, 11, 1)
Out[25]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])