Well NumPy implements MATLAB's array-creation function, vector, using two functions instead of one--each implicitly specifies a particular axis along which concatenation ought to occur. These functions are:
So for your example, the NumPy equivalent is:
>>> import numpy as NP
>>> v = NP.r_[.2, 1:10, 60.8]
>>> print(v)
[ 0.2 1. 2. 3. 4. 5. 6. 7. 8. 9. 60.8]
The column-wise counterpart is:
>>> NP.c_[.2, 1:10, 60.8]
slice notation works as expected [start:stop:step]:
>>> v = NP.r_[.2, 1:25:7, 60.8]
>>> v
array([ 0.2, 1. , 8. , 15. , 22. , 60.8])
Though if an imaginary number of used as the third argument, the slicing notation behaves like linspace:
>>> v = NP.r_[.2, 1:25:7j, 60.8]
>>> v
array([ 0.2, 1. , 5. , 9. , 13. , 17. , 21. , 25. , 60.8])
Otherwise, it behaves like arange:
>>> v = NP.r_[.2, 1:25:7, 60.8]
>>> v
array([ 0.2, 1. , 8. , 15. , 22. , 60.8])