Working with Python arraysΒΆ
Note
This page uses two different syntax variants:
Cython specific
cdef
syntax, which was designed to make type declarations concise and easily readable from a C/C++ perspective.Pure Python syntax which allows static Cython type declarations in pure Python code, following PEP-484 type hints and PEP 526 variable annotations.
To make use of C data types in Python syntax, you need to import the special
cython
module in the Python module that you want to compile, e.g.import cython
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
multiprocessing
.
Compared to the manual approach with malloc()
and 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 array
module is built into both Python and Cython.
Safe usage with memory viewsΒΆ
from cython.cimports.cpython import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
ca = cython.declare(cython.int[:], a)
print(ca[0])
from cpython cimport array
import array
cdef array.array a = array.array('i', [1, 2, 3])
cdef int[:] ca = a
print(ca[0])
NB: the import brings the regular Python array object into the namespace while the cimport adds functions accessible from Cython.
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 cython.cimports.cpython import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
ca = cython.declare(cython.int[:], a)
@cython.cfunc
def overhead(a: cython.object) -> cython.int:
ca: cython.int[:] = a
return ca[0]
@cython.cfunc
def no_overhead(ca: cython.int[:]) -> cython.int:
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
from cpython cimport array
import array
cdef array.array a = array.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
Zero-overhead, unsafe access to raw C pointerΒΆ
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 cython.cimports.cpython import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
# access underlying pointer:
print(a.data.as_ints[0])
from cython.cimports.libc.string import memset
memset(a.data.as_voidptr, 0, len(a) * cython.sizeof(cython.int))
from cpython cimport array
import array
cdef array.array a = array.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))
Note that any length-changing operation on the array object may invalidate the pointer.
Cloning, extending arraysΒΆ
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 cython.cimports.cpython import array
import array
int_array_template = cython.declare(array.array, array.array('i', []))
cython.declare(newarray=array.array)
# create an array with 3 elements with same type as template
newarray = array.clone(int_array_template, 3, zero=False)
from cpython cimport array
import array
cdef array.array int_array_template = array.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 cython.cimports.cpython import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
b = cython.declare(array.array, array.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))
from cpython cimport array
import array
cdef array.array a = array.array('i', [1, 2, 3])
cdef array.array b = array.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))
API referenceΒΆ
Data fieldsΒΆ
data.as_voidptr
data.as_chars
data.as_schars
data.as_uchars
data.as_shorts
data.as_ushorts
data.as_ints
data.as_uints
data.as_longs
data.as_ulongs
data.as_longlongs # requires Python >=3
data.as_ulonglongs # requires Python >=3
data.as_floats
data.as_doubles
data.as_pyunicodes
Direct access to the underlying contiguous C array, with given type;
e.g., myarray.data.as_ints
.
FunctionsΒΆ
The following functions are available to Cython from the array module
@cython.cfunc
@cython.exceptval(-1)
def resize(self: array.array, n: cython.Py_ssize_t) -> cython.int
cdef int resize(array.array self, Py_ssize_t n) except -1
Fast resize / realloc. Not suitable for repeated, small increments; resizes underlying array to exactly the requested amount.
@cython.cfunc
@cython.exceptval(-1)
def resize_smart(self: array.array, n: cython.Py_ssize_t) -> cython.int
cdef int resize_smart(array.array self, Py_ssize_t n) except -1
Efficient for small increments; uses growth pattern that delivers amortized linear-time appends.
@cython.cfunc
@cython.inline
def clone(template: array.array, length: cython.Py_ssize_t, zero: cython.bint) -> array.array
cdef inline array.array clone(array.array template, Py_ssize_t length, bint zero)
Fast creation of a new array, given a template array. Type will be same as
template
. If zero is True
, new array will be initialized with zeroes.
@cython.cfunc
@cython.inline
def copy(self: array.array) -> array.array
cdef inline array.array copy(array.array self)
Make a copy of an array.
@cython.cfunc
@cython.inline
@cython.exceptval(-1)
def extend_buffer(self: array.array, stuff: cython.p_char, n: cython.Py_ssize_t) -> cython.int
cdef inline int extend_buffer(array.array self, char* stuff, Py_ssize_t n) except -1
Efficient appending of new data of same type (e.g. of same array type)
n
: number of elements (not number of bytes!)
@cython.cfunc
@cython.inline
@cython.exceptval(-1)
def extend(self: array.array, other: array.array) -> cython.int
cdef inline int extend(array.array self, array.array other) except -1
Extend array with data from another array; types must match.
@cython.cfunc
@cython.inline
def zero(self: array.array) -> cython.void
cdef inline void zero(array.array self)
Set all elements of array to zero.