Working with Python arrays

Note

This page uses two different syntax variants:

  • Cython specificcdef syntax, which was designed to make type declarationsconcise and easily readable from a C/C++ perspective.

  • Pure Python syntax which allows static Cython type declarations inpure Python code,followingPEP-484 type hintsandPEP 526 variable annotations.

    To make use of C data types in Python syntax, you need to import the specialcython module in the Python module that you want to compile, e.g.

    importcython

    If you use the pure Python syntax we strongly recommend you use a recentCython 3 release, since significant improvements have been made herecompared to the 0.29.x releases.

Python has a builtin array module supporting dynamic 1-dimensional arrays ofprimitive types. It is possible to access the underlying C array of a Pythonarray from within Cython. At the same time they are ordinary Python objectswhich can be stored in lists and serialized between processes when usingmultiprocessing.

Compared to the manual approach withmalloc() andfree(), thisgives the safe and automatic memory management of Python, and compared to aNumpy array there is no need to install a dependency, as thearraymodule is built into both Python and Cython.

Safe usage with memory views

fromcython.cimports.cpythonimportarrayimportarraya=cython.declare(array.array,array.array('i',[1,2,3]))ca=cython.declare(cython.int[:],a)print(ca[0])

NB: the import brings the regular Python array object into the namespacewhile the cimport adds functions accessible from Cython.

A Python array is constructed with a type signature and sequence ofinitial values. For the possible type signatures, refer to the Pythondocumentation for thearray module.

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

fromcython.cimports.cpythonimportarrayimportarraya=cython.declare(array.array,array.array('i',[1,2,3]))ca=cython.declare(cython.int[:],a)@cython.cfuncdefoverhead(a:cython.object)->cython.int:ca:cython.int[:]=areturnca[0]@cython.cfuncdefno_overhead(ca:cython.int[:])->cython.int:returnca[0]print(overhead(a))# new memory view will be constructed, overheadprint(no_overhead(ca))# ca is already a memory view, so no overhead

Zero-overhead, unsafe access to raw C pointer

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

fromcython.cimports.cpythonimportarrayimportarraya=cython.declare(array.array,array.array('i',[1,2,3]))# access underlying pointer:print(a.data.as_ints[0])fromcython.cimports.libc.stringimportmemsetmemset(a.data.as_voidptr,0,len(a)*cython.sizeof(cython.int))

Note that any length-changing operation on the array object may invalidate thepointer.

Cloning, extending arrays

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 tozero when requested.

fromcython.cimports.cpythonimportarrayimportarrayint_array_template=cython.declare(array.array,array.array('i',[]))cython.declare(newarray=array.array)# create an array with 3 elements with same type as templatenewarray=array.clone(int_array_template,3,zero=False)

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

fromcython.cimports.cpythonimportarrayimportarraya=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 neededarray.extend(a,b)# resize a, leaving just original three elementsarray.resize(a,len(a)-len(b))

API reference

Data fields

data.as_voidptrdata.as_charsdata.as_scharsdata.as_ucharsdata.as_shortsdata.as_ushortsdata.as_intsdata.as_uintsdata.as_longsdata.as_ulongsdata.as_longlongs# requires Python >=3data.as_ulonglongs# requires Python >=3data.as_floatsdata.as_doublesdata.as_pyunicodes

Direct access to the underlying contiguous C array, with given type;e.g.,myarray.data.as_ints.

Functions

The following functions are available to Cython from the array module

@cython.cfunc@cython.exceptval(-1)defresize(self:array.array,n:cython.Py_ssize_t)->cython.int

Fast resize / realloc. Not suitable for repeated, small increments; resizesunderlying array to exactly the requested amount.

@cython.cfunc@cython.exceptval(-1)defresize_smart(self:array.array,n:cython.Py_ssize_t)->cython.int

Efficient for small increments; uses growth pattern that deliversamortized linear-time appends.

@cython.cfunc@cython.inlinedefclone(template:array.array,length:cython.Py_ssize_t,zero:cython.bint)->array.array

Fast creation of a new array, given a template array. Type will be same astemplate. If zero isTrue, new array will be initialized with zeroes.

@cython.cfunc@cython.inlinedefcopy(self:array.array)->array.array

Make a copy of an array.

@cython.cfunc@cython.inline@cython.exceptval(-1)defextend_buffer(self:array.array,stuff:cython.p_char,n:cython.Py_ssize_t)->cython.int

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)defextend(self:array.array,other:array.array)->cython.int

Extend array with data from another array; types must match.

@cython.cfunc@cython.inlinedefzero(self:array.array)->cython.void

Set all elements of array to zero.