If you're coming from a MATLAB background, you might find this helpful [0].
When I switched from MATLAB to Python/numpy/scipy, I had to deal with these syntactic annoyances. But now I feel comfortable after using it for many years.
Don't forget the biggest edge Python has over MATLAB for numeric simulation, i.e., you can prototype your work as quickly, and for the same program, convert the slow portions into C or Fortran without the need to rewrite the whole thing. Not to mention, a proper programming language underlying the numeric system.
Don't forget the biggest edge Python has over MATLAB for numeric simulation, i.e., you can prototype your work as quickly, and for the same program, convert the slow portions into C or Fortran without the need to rewrite the whole thing.
That statement applies equally to Matlab/Octave. You can link C/C++/Fortran routines into Matlab code using its "mex" interface. Octave also supports this interface.
Python is more flexible, but less simple to start out with because it has a more complicated ecosystem. For example, there are many different ways to link in fast code (ctypes/cffi/cython/numba/weave/f2py/...). Moreover, the data in a numpy array can be stored in memory in different ways (C or Fortran order, not necessarily contiguous, ...), which requires some care when passing it to external routines.
When I switched from MATLAB to Python/numpy/scipy, I had to deal with these syntactic annoyances. But now I feel comfortable after using it for many years.
Don't forget the biggest edge Python has over MATLAB for numeric simulation, i.e., you can prototype your work as quickly, and for the same program, convert the slow portions into C or Fortran without the need to rewrite the whole thing. Not to mention, a proper programming language underlying the numeric system.
[0] https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-u...