What you are trying to do actually corresponds to np.arange.

time = np.arange(dt, T, dt)

To include the T time you will need to change the stop boundary to T + dt.

This is nothing to do with numpy, consider:

>>> temp = np.linspace(1,2,11)
>>> temp
array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. ])
>>> #                                     ^ look, numpy displays it fine
>>> for t in temp:
...     print(t)
... 
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7000000000000002
1.8
1.9
2.0

The "issue" is with how computers represent floats in general. See: https://docs.python.org/3/tutorial/floatingpoint.html.

When you call linspace like np.linspace(start,stop,n_elements), you are telling numpy to create an array of length n_elements that have an equal distance and that include start and stop. Due to including start and stop, the space/distance is equal to `(stop-start)/(n_elements - 1)? which should explain the numbers you got.

If you just want integers, you can use np.arange(start, end, step), however, this would not include end. Or, for you example, you can do np.linspace(0, 100, 11).astype(int)