The type of the # arguments for a "def" function is checked at run-time when entering the # function. It is possible to access the underlying C array of a Python array from within Cython. np.zeros((len(ArgArray), dtype = np.int32) works in Python just as well as in Cython. When the Python for structure only loops over integer values (e.g. numpy.ndarray¶ class numpy.ndarray [source] ¶. At the same time they are ordinary Python objects which can be stored in lists and serialized between processes when using multiprocessing. The code below defines the variables discussed previously, which are maxval, total, k, t1, t2, and t. There is a new variable named arr which holds the array, with data type numpy.ndarray. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.) so input is a numpy.ndarray that means a python object, and cython refuses to take its address. int** for a pointer to a pointer to a C int. Cython supports numpy arrays but since these are Python objects, we can’t manipulate them without the GIL. To get it work, you need the input to be a cython-numpy array (I don't know how to express it better - take a look at the example): No conversion to a Python 'type' is needed. However, it can be initialized with a Python object that supports the buffer protocol (typically “array” types, for example a Numpy array). This was unexpected for most users, unlikely to be desired, and also inconsistent with other structured type declarations like C arrays of fused types, which were considered the same type. The same is the case for the input[0,0] - it is a python object. Also, when additional Cython declarations are made for NumPy arrays, indexing can be as fast as indexing C arrays. A memoryview alone does not actually own any memory. You can also explicitly define the data type using the dtype option as an argument of array function. When taking Cython into the game that is no longer true. I tried to Cythonize part of my code as following to hopefully gain some speed: # cython: boundscheck=False import numpy as np cimport numpy as np import time cpdef object my_function(np.ndarray[np.double_t, ndim = 1] array_a, np.ndarray[np.double_t, ndim = 1] array_b, int n_rows, int n_columns): cdef double minimum_of_neighbours, difference, change cdef int i cdef … Cython doesn’t support variable length arrays … By declaring the type and dimensions of an array before actually creating it, Cython can access the NumPy array more efficiently: import numpy as np # Normal NumPy import cimport numpy as cnp # Import for NumPY C-API def func (): # declarations can be made only in function scope cdef cnp . No luck so far. int[10], and the size must be known at compile time for stack allocated arrays. Pointer types are constructed as in C, by appending a * to the base type they point to, e.g. In my limited testing both of your cdefs work. Its elements may be Python/C types (dtype), but the array as a whole is an object. An array object represents a multidimensional, homogeneous array of fixed-size items. ndarray [ cnp . Maybe it's a matter of cython version? # # The arrays f, g and h is typed as "np.ndarray" instances. Arrays use the normal C array syntax, e.g. Compile time definitions for NumPy The is done because the Cython “numpy” file has the data types for handling NumPy arrays. A useful Cython type for getting quick access to blocks of memory. In the past, the workaround was to use pointers on the data, but that can get ugly very quickly, especially when you need to care about the memory alignment of 2D arrays ( C vs Fortran ). A numpy array is a Python object. It was thus changed in Cython 0.21 to use the same type for all memory views of a fused type. The dtype method determines the datatype of elements stored in NumPy array. 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