This section provides some general troubleshooting advice about commonly-seen errors. If you’re having a problem with Cython it may be worth reading this. If you encounter a commonly-seen error that we haven’t covered here, we’d appreciate a PR adding it to this section!

Where the language is “messy”

By necessity, Cython is a slightly odd mix of the resolved-at-run-time, dynamic behaviour of Python, and the statically-defined, resolved at compile-time behaviour of C. These don’t always combine perfectly, and the places that often cause confusion are often the places they meet.

As example, for a cdef class, Cython is able to access cdef attributes directly (as a simple C lookup). However, if the direct attribute lookup “misses” then Cython doesn’t produce an error message - instead it assumes that it will be able to resolve that attribute through the standard Python “string lookup from a dictionary” mechanism at runtime. The two mechanisms are quite different in how they work and what they can return (the Python mechanism can only return Python objects, while the the direct lookup can return largely any C type).

Much the same can occur when a name is imported rather “cimported” - Cython does not know where the name comes from so treats it as a regular Python object.

This silent-fallback to Python behaviour is often a source of confusion. In the best case it gives the same overall behaviour but slightly slower (for example calling a cpdef function through the Python mechanism rather than directly to C). Often it just causes an AttributeError exception at runtime. Very occasionally it might do something quite different - invoke a Python method with the same name as a cdef method, or cause a convert from a C++ container to a Python one.

This kind of dual-layered behaviour probably isn’t how one would design a language from scratch, but is needed for Cython’s goals for being Python compatible and allowing C types to be used fairly seamlessly.


Untyped objects

A common reason to get AttributeErrors is that Cython does not know the type of your object:

cdef class Counter:
    cdef int count_so_far


The attribute count_so_far is only accessible from Cython code, and Cython accesses it through a direct lookup into the C struct that it defines for Counter (i.e. it’s really quick!). Now try run the following Cython code on a pair of Counter objects:

def bigger_count(c1, c2):
    return c1.count_so_far < c2.count_so_far

This will give an AttributeError because Cython does not know the types of c1 and c2. Typing them as Counter c1 and Counter c2 fixes the problem:

def bigger_count(c1, c2):
    return c1.count_so_far < c2.count_so_far

A common variation of the same problem happens for global objects:

def count_something():
    c = Counter()

    # code goes here!!!

    print(c.count_so_far)  # works

global_count = Counter()
print(global_count.count_so_far)  # AttributeError!

Within a function Cython usually manages to infer the type. So it knows that c is a Counter even though you have not told it. However the same doesn’t apply at global/module scope. Here there’s a strong assumption that you want objects to be exposed as Python attributes of the module (and remember that Python attributes could be modified from elsewhere…), so Cython essentially disables all type-inference. Therefore it doesn’t know the type of global_count.

Writing into extension types

AttributeErrors can also happen when writing into a cdef class, commonly in __init__:

cdef class Company:
    def __init__(self, staff):
        self.staff = staff  # AttributeError!

Unlike a regular class, cdef class has a fixed list of attributes that you can write to and you need to declare them explicitly. For example:

cdef class Company:
   cdef list staff
   # ...

(use cdef staff or cdef object staff if you don’t want to specify a type). If you do want the ability to add arbitrary attributes then you can add a __dict__ member:

cdef class Company:
   cdef dict __dict__
   def __init__(self, staff):
       self.staff = staff

This gives extra flexibility, but loses some of the performance benefits of using an extension type. It also adds restrictions to inheritance.

Extension type class attributes vs instance attributes

A common pattern in Python (used a lot within the Cython code-base itself) is to use instance attributes that shadow class attributes:

class Email:
    message = "hello"  # sensible default

    def actually_I_really_dislike_this_person(self):
        self.message = "go away!"

On access to message Python first looks up the instance dictionary to see if it has a value for message and if that fails looks up the class dictionary to get the default value. The advantages are

  • it provides an easy sensible default,

  • it potentially saves a bit of memory by not populating the instance dictionary if not necessary (although modern versions of Python are pretty good at sharing keys for common attributes between instances),

  • it saves a bit of time reference counting (vs if you initialized the defaults in the constructor),

Cython extension types don’t support this pattern. You should just set the defaults in the constructor. If you don’t set defaults for a cdef attribute then they’ll be set to an “empty” value (None for Python object attributes).

Pitfalls of automatic type conversions

Cython automatically generates type conversions between certain C/C++ types and Python types. These are often undesirable.

First we should look at what conversions Cython generates:

  • C struct to/from Python dict - if all elements of a struct are themselves convertible to a Python object, then the struct will be converted to a Python dict if returned from a function that returns a Python object:

    # taken from the Cython documentation
    cdef struct Grail:
        int age
        float volume
    def get_grail():
        cdef Grail g
        g.age = 100
        g.volume = 2.5
        return g
    # prints something similar to:
    # {'age': 100, 'volume': 2.5}
  • C++ standard library containers to/from their Python equivalent. A common pattern is to use a def function with an argument typed as std::vector. This will be auto-converted from a Python list:

    from libcpp vector cimport vector
    def print_list(vector[int] x):
        for xi in x:

Most of these conversions should work both ways.

They have a couple of non-obvious downsides.

The conversion isn’t free

Especially for the C++ container conversions. Consider the print_list function above. The function is appealing because iteration over the vector is faster than iteration over a Python list. However, Cython must iterate over each element of your input list, checking that it is something convertible to a C integer. Therefore, you haven’t actually saved yourself any time - you’ve just hidden the “expensive” loop in a function signature.

These conversions may be worthwhile if you’re doing sufficient work inside your function. You should also consider also having a single place in your Cython code where the conversion happens as your interface to Python, then keeping the type as the C++ type and working on it across multiple Cython functions.

In many cases it might be better to type your function with a 1D typed memoryview (int[:]) and pass in an array.array or a Numpy array instead of using a C++ vector.

Changes do not propagate back

Especially to attributes of cdef classes exposed to Python via properties (including via cdef public attributes).

For example:

from libcpp.vector cimport vector

cdef class VecHolder:
    def __init__(self, max):
         self.value = list(range(max))  # just fill it for demo purposes

    cdef public vector[double] values

then from Python:

vh = VecHolder(5)
# Output: [ 0, 1, 2, 3, 4 ]

vh.values[0] = 100
# Output: [ 0, 1, 2, 3, 4 ]

# However you can re-assign it completely
vh.values = []
# Output: []

Essentially your Python code modifies the list that is returned to it an not the underlying vector used to generate the list. This is sufficiently non-intuitive that I really recommend against exposing convertible types as attributes!