Python has a builtin array module supporting dynamic 1-dimensional arrays of primitive types. It is possible to access the underlying C array of a Python array from within Cython. At the same time they are ordinary Python objects which can be stored in lists and serialized between processes when using :obj:`multiprocessing`.

Compared to the manual approach with :c:func:`malloc` and :c:func:`free`, this gives the safe and automatic memory management of Python, and compared to a Numpy array there is no need to install a dependency, as the :obj:`array` module is built into both Python and Cython.

from cpython cimport array
from array import array
cdef array.array a = array('i', [1, 2, 3])
cdef int[:] ca = a

print ca[0]

A Python array is constructed with a type signature and sequence of initial values. For the possible type signatures, refer to the Python documentation for the array module.

Notice that when a Python array is assigned to a variable typed as memory view, there will be a slight overhead to construct the memory view. However, from that point on the variable can be passed to other functions without overhead, so long as it is typed:

from cpython cimport array
from array import array
cdef array.array a = array('i', [1, 2, 3])
cdef int[:] ca = a

cdef int overhead(object a):
    cdef int[:] ca = a
    return ca[0]

cdef int no_overhead(int[:] ca):
    return ca[0]

print overhead(a)  # new memory view will be constructed, overhead
print no_overhead(ca)  # ca is already a memory view, so no overhead

To avoid any overhead and to be able to pass a C pointer to other functions, it is possible to access the underlying contiguous array as a pointer. There is no type or bounds checking, so be careful to use the right type and signedness.

from cpython cimport array
from array import array

cdef array.array a = array('i', [1, 2, 3])

# access underlying pointer:
print a.data.as_ints[0]

from libc.string cimport memset
memset(a.data.as_voidptr, 0, len(a) * sizeof(int))

To avoid having to use the array constructor from the Python module, it is possible to create a new array with the same type as a template, and preallocate a given number of elements. The array is initialized to zero when requested.

from cpython cimport array
from array import array

cdef array.array int_array_template = array('i', [])
cdef array.array newarray

# create an array with 3 elements with same type as template
newarray = array.clone(int_array_template, 3, zero=False)

An array can also be extended and resized; this avoids repeated memory reallocation which would occur if elements would be appended or removed one by one.

from cpython cimport array
from array import array

cdef array.array a = array('i', [1, 2, 3])
cdef array.array b = array('i', [4, 5, 6])

# extend a with b, resize as needed
array.extend(a, b)
# resize a, leaving just original three elements
array.resize(a, len(a) - len(b))