Basic Tutorial¶


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

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

The Basics of Cython¶

The fundamental nature of Cython can be summed up as follows: Cython is Python with C data types.

Cython is Python: Almost any piece of Python code is also valid Cython code. (There are a few Limitations, but this approximation will serve for now.) The Cython compiler will convert it into C code which makes equivalent calls to the Python/C API.

But Cython is much more than that, because parameters and variables can be declared to have C data types. Code which manipulates Python values and C values can be freely intermixed, with conversions occurring automatically wherever possible. Reference count maintenance and error checking of Python operations is also automatic, and the full power of Python’s exception handling facilities, including the try-except and try-finally statements, is available to you – even in the midst of manipulating C data.

Cython Hello World¶

As Cython can accept almost any valid python source file, one of the hardest things in getting started is just figuring out how to compile your extension.

So lets start with the canonical python hello world:

print("Hello World")

Save this code in a file named helloworld.pyx. Now we need to create the, which is like a python Makefile (for more information see Source Files and Compilation). Your should look like:

from setuptools import setup
from Cython.Build import cythonize

    ext_modules = cythonize("helloworld.pyx")

To use this to build your Cython file use the commandline options:

$ python build_ext --inplace

Which will leave a file in your local directory called in unix or helloworld.pyd in Windows. Now to use this file: start the python interpreter and simply import it as if it was a regular python module:

>>> import helloworld
Hello World

Congratulations! You now know how to build a Cython extension. But so far this example doesn’t really give a feeling why one would ever want to use Cython, so lets create a more realistic example.

pyximport: Cython Compilation for Developers¶

If your module doesn’t require any extra C libraries or a special build setup, then you can use the pyximport module, originally developed by Paul Prescod, to load .pyx files directly on import, without having to run your file each time you change your code. It is shipped and installed with Cython and can be used like this:

>>> import pyximport; pyximport.install()
>>> import helloworld
Hello World

The Pyximport module also has experimental compilation support for normal Python modules. This allows you to automatically run Cython on every .pyx and .py module that Python imports, including the standard library and installed packages. Cython will still fail to compile a lot of Python modules, in which case the import mechanism will fall back to loading the Python source modules instead. The .py import mechanism is installed like this:

>>> pyximport.install(pyimport=True)

Note that it is not recommended to let Pyximport build code on end user side as it hooks into their import system. The best way to cater for end users is to provide pre-built binary packages in the wheel packaging format.

Fibonacci Fun¶

From the official Python tutorial a simple fibonacci function is defined as:

from __future__ import print_function

def fib(n):
    """Print the Fibonacci series up to n."""
    a, b = 0, 1
    while b < n:
        print(b, end=' ')
        a, b = b, a + b


Now following the steps for the Hello World example we first rename the file to have a .pyx extension, lets say fib.pyx, then we create the file. Using the file created for the Hello World example, all that you need to change is the name of the Cython filename, and the resulting module name, doing this we have:

from setuptools import setup
from Cython.Build import cythonize


Build the extension with the same command used for the helloworld.pyx:

$ python build_ext --inplace

And use the new extension with:

>>> import fib
>>> fib.fib(2000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597


Here’s a small example showing some of what can be done. It’s a routine for finding prime numbers. You tell it how many primes you want, and it returns them as a Python list.¶
 1def primes(nb_primes:
 2    i:
 3    p:[1000]
 5    if nb_primes > 1000:
 6        nb_primes = 1000
 8    if not cython.compiled:  # Only if regular Python is running
 9        p = [0] * 1000       # Make p work almost like a C array
11    len_p: = 0  # The current number of elements in p.
12    n: = 2
13    while len_p < nb_primes:
14        # Is n prime?
15        for i in p[:len_p]:
16            if n % i == 0:
17                break
19        # If no break occurred in the loop, we have a prime.
20        else:
21            p[len_p] = n
22            len_p += 1
23        n += 1
25    # Let's copy the result into a Python list:
26    result_as_list = [prime for prime in p[:len_p]]
27    return result_as_list

You’ll see that it starts out just like a normal Python function definition, except that the parameter nb_primes is declared to be of type int. This means that the object passed will be converted to a C integer (or a TypeError. will be raised if it can’t be).

Now, let’s dig into the core of the function:

11len_p: = 0  # The current number of elements in p.
12n: = 2

Lines 2, 3, 11 and 12 use the variable annotations to define some local C variables. The result is stored in the C array p during processing, and will be copied into a Python list at the end (line 26).


You cannot create very large arrays in this manner, because they are allocated on the C function call stack, which is a rather precious and scarce resource. To request larger arrays, or even arrays with a length only known at runtime, you can learn how to make efficient use of C memory allocation, Python arrays or NumPy arrays with Cython.

5if nb_primes > 1000:
6    nb_primes = 1000

As in C, declaring a static array requires knowing the size at compile time. We make sure the user doesn’t set a value above 1000 (or we would have a segmentation fault, just like in C)

8if not cython.compiled:  # Only if regular Python is running
9    p = [0] * 1000       # Make p work almost like a C array

When we run this code from Python, we have to initialize the items in the array. This is most easily done by filling it with zeros (as seen on line 8-9). When we compile this with Cython, on the other hand, the array will behave as in C. It is allocated on the function call stack with a fixed length of 1000 items that contain arbitrary data from the last time that memory was used. We will then overwrite those items in our calculation.

10len_p: = 0  # The current number of elements in p.
11n: = 2
12while len_p < nb_primes:

Lines 11-13 set up a while loop which will test numbers-candidates to primes until the required number of primes has been found.

14# Is n prime?
15for i in p[:len_p]:
16    if n % i == 0:
17        break

Lines 15-16, which try to divide a candidate by all the primes found so far, are of particular interest. Because no Python objects are referred to, the loop is translated entirely into C code, and thus runs very fast. You will notice the way we iterate over the p C array.

15for i in p[:len_p]:

The loop gets translated into a fast C loop and works just like iterating over a Python list or NumPy array. If you don’t slice the C array with [:len_p], then Cython will loop over the 1000 elements of the array.

19# If no break occurred in the loop, we have a prime.
21    p[len_p] = n
22    len_p += 1
23n += 1

If no breaks occurred, it means that we found a prime, and the block of code after the else line 20 will be executed. We add the prime found to p. If you find having an else after a for-loop strange, just know that it’s a lesser known features of the Python language, and that Cython executes it at C speed for you. If the for-else syntax confuses you, see this excellent blog post.

25# Let's copy the result into a Python list:
26result_as_list = [prime for prime in p[:len_p]]
27return result_as_list

In line 26, before returning the result, we need to copy our C array into a Python list, because Python can’t read C arrays. Cython can automatically convert many C types from and to Python types, as described in the documentation on type conversion, so we can use a simple list comprehension here to copy the C int values into a Python list of Python int objects, which Cython creates automatically along the way. You could also have iterated manually over the C array and used result_as_list.append(prime), the result would have been the same.

You’ll notice we declare a Python list exactly the same way it would be in Python. Because the variable result_as_list hasn’t been explicitly declared with a type, it is assumed to hold a Python object, and from the assignment, Cython also knows that the exact type is a Python list.

Finally, at line 27, a normal Python return statement returns the result list.

Compiling with the Cython compiler produces an extension module which we can try out in the interactive interpreter as follows:

>>> import primes
>>> primes.primes(10)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]

See, it works! And if you’re curious about how much work Cython has saved you, take a look at the C code generated for this module.

Cython has a way to visualise where interaction with Python objects and Python’s C-API is taking place. For this, pass the annotate=True parameter to cythonize(). It produces a HTML file. Let’s see:


If a line is white, it means that the code generated doesn’t interact with Python, so will run as fast as normal C code. The darker the yellow, the more Python interaction there is in that line. Those yellow lines will usually operate on Python objects, raise exceptions, or do other kinds of higher-level operations than what can easily be translated into simple and fast C code. The function declaration and return use the Python interpreter so it makes sense for those lines to be yellow. Same for the list comprehension because it involves the creation of a Python object. But the line if n % i == 0:, why? We can examine the generated C code to understand:


We can see that some checks happen. Because Cython defaults to the Python behavior, the language will perform division checks at runtime, just like Python does. You can deactivate those checks by using the compiler directives.

Now let’s see if we get a speed increase even if there is a division check. Let’s write the same program, but in Python: /¶
def primes(nb_primes):
    p = []
    n = 2
    while len(p) < nb_primes:
        # Is n prime?
        for i in p:
            if n % i == 0:

        # If no break occurred in the loop
        n += 1
    return p

It is possible to take a plain (unannotated) .py file and to compile it with Cython. Let’s create a copy of primes_python and name it primes_python_compiled to be able to compare it to the (non-compiled) Python module. Then we compile that file with Cython, without changing the code. Now the looks like this:

from setuptools import setup
from Cython.Build import cythonize

        ['',                   # Cython code file with primes() function
         ''],  # Python code file with primes() function
        annotate=True),                 # enables generation of the html annotation file

Now we can ensure that those two programs output the same values:

>>> import primes, primes_python, primes_python_compiled
>>> primes_python.primes(1000) == primes.primes(1000)
>>> primes_python_compiled.primes(1000) == primes.primes(1000)

It’s possible to compare the speed now:

python -m timeit -s "from primes_python import primes" "primes(1000)"
10 loops, best of 3: 23 msec per loop

python -m timeit -s "from primes_python_compiled import primes" "primes(1000)"
100 loops, best of 3: 11.9 msec per loop

python -m timeit -s "from primes import primes" "primes(1000)"
1000 loops, best of 3: 1.65 msec per loop

The cythonize version of primes_python is 2 times faster than the Python one, without changing a single line of code. The Cython version is 13 times faster than the Python version! What could explain this?

Multiple things:
  • In this program, very little computation happen at each line. So the overhead of the python interpreter is very important. It would be very different if you were to do a lot computation at each line. Using NumPy for example.

  • Data locality. It’s likely that a lot more can fit in CPU cache when using C than when using Python. Because everything in python is an object, and every object is implemented as a dictionary, this is not very cache friendly.

Usually the speedups are between 2x to 1000x. It depends on how much you call the Python interpreter. As always, remember to profile before adding types everywhere. Adding types makes your code less readable, so use them with moderation.

Primes with C++¶

With Cython, it is also possible to take advantage of the C++ language, notably, part of the C++ standard library is directly importable from Cython code.

Let’s see what our code becomes when using vector from the C++ standard library.


Vector in C++ is a data structure which implements a list or stack based on a resizeable C array. It is similar to the Python array type in the array standard library module. There is a method reserve available which will avoid copies if you know in advance how many elements you are going to put in the vector. For more details see this page from cppreference.

 1# distutils: language=c++
 3import cython
 4from cython.cimports.libcpp.vector import vector
 6def primes(nb_primes: cython.uint):
 7    i:
 8    p: vector[]
 9    p.reserve(nb_primes)  # allocate memory for 'nb_primes' elements.
11    n: = 2
12    while p.size() < nb_primes:  # size() for vectors is similar to len()
13        for i in p:
14            if n % i == 0:
15                break
16        else:
17            p.push_back(n)  # push_back is similar to append()
18        n += 1
20    # If possible, C values and C++ objects are automatically
21    # converted to Python objects at need.
22    return p  # so here, the vector will be copied into a Python list.


The code provided above / on this page uses an external native (non-Python) library through a cimport (cython.cimports). Cython compilation enables this, but there is no support for this from plain Python. Trying to run this code from Python (without compilation) will fail when accessing the external library. This is described in more detail in Calling C functions.

The first line is a compiler directive. It tells Cython to compile your code to C++. This will enable the use of C++ language features and the C++ standard library. Note that it isn’t possible to compile Cython code to C++ with pyximport. You should use a or a notebook to run this example.

You can see that the API of a vector is similar to the API of a Python list, and can sometimes be used as a drop-in replacement in Cython.

For more details about using C++ with Cython, see Using C++ in Cython.

Language Details¶

For more about the Cython language, see Language Basics. To dive right in to using Cython in a numerical computation context, see Typed Memoryviews.