The __getitem__ method(array access using ) of numpy arrays is written for python and while using it in cython it has the corrssponding overhead. We can get around it using following suggestion:-
If the array is intermediate in programme, it can be declared as a C or cython array. But it may not work best for returning value to python code, etc. So instead of it we can use a NumPy array. NumPy arrays are already implemented in C and cython has direct interface with it. Hence they can be used.
Accessing elements of NumPy arrays has roughly the same speeed as that of accessing elements from C array. In NumPy arrays modes can also be specified. Like ‘c’ or ‘fortran’ types. These work best when according to our mode of iteration on arrays(row or column wise). ‘c’ mode works best when the iteration is row wise and ‘fortran’ for column wise. By spcifying the mode we don’t get any extra speed if array is arranged in same way but if not it raises and exception. Using it we can use the different array operations, but each element access time is also improved. But for that we have to access the array element wise.
There are some cons of using NumPy arrays also. Passing NumPy array slices between functions can have a significant speed loss, since these slices are Python Objects. For this we have memoryview. It supports the same fast indexing as that of NumPy arrays and on the other hand their slices continue to support the optimized buffer access. They also work well while passing throgh diffrent functions in module. They can be used with inline functions also.
However passing a memoryview object to a NumPy function becomes slower as the memoryview object has to be first converted to NumPy Array. If you need to use NumPy functions and pass memoryviews between functions you can define a ‘memoryview’ object that views the same data as your array. If for some reason a memoryview is to be converted in NumPy array np.asarray() function can be used. The memoryview can also be declared as C contiguous or Fortran contiguous depending upon conditions using syntax ::1. For eg. A two dimensional, int type, fortran contiguous array can be defined as ‘cdef int [::1 , :]’. Cython also supports pointers. Many of the operations can be done using pointers with almost the same speed as that of optimized array lookup, but code readability is compromized heavily. * is not supported in cython for derefrencing pointers.  should be used for that purpose.
I will try to upload an example each of abovesaid statements in the upcoming blogs, which will make them more clear.
Until then </keep coding>