NEP 16 — An abstract base class for identifying “duck arrays”#


Nathaniel J. Smith <>




Standards Track






This NEP has been withdrawn in favor of the protocol based approach described in NEP 22


We propose to add an abstract base class AbstractArray so that third-party classes can declare their ability to “quack like” an ndarray, and an asabstractarray function that performs similarly to asarray except that it passes through AbstractArray instances unchanged.

Detailed description#

Many functions, in NumPy and in third-party packages, start with some code like:

def myfunc(a, b):
    a = np.asarray(a)
    b = np.asarray(b)

This ensures that a and b are np.ndarray objects, so myfunc can carry on assuming that they’ll act like ndarrays both semantically (at the Python level), and also in terms of how they’re stored in memory (at the C level). But many of these functions only work with arrays at the Python level, which means that they don’t actually need ndarray objects per se: they could work just as well with any Python object that “quacks like” an ndarray, such as sparse arrays, dask’s lazy arrays, or xarray’s labeled arrays.

However, currently, there’s no way for these libraries to express that their objects can quack like an ndarray, and there’s no way for functions like myfunc to express that they’d be happy with anything that quacks like an ndarray. The purpose of this NEP is to provide those two features.

Sometimes people suggest using np.asanyarray for this purpose, but unfortunately its semantics are exactly backwards: it guarantees that the object it returns uses the same memory layout as an ndarray, but tells you nothing at all about its semantics, which makes it essentially impossible to use safely in practice. Indeed, the two ndarray subclasses distributed with NumPy – np.matrix and – do have incompatible semantics, and if they were passed to a function like myfunc that doesn’t check for them as a special-case, then it may silently return incorrect results.

Declaring that an object can quack like an array#

There are two basic approaches we could use for checking whether an object quacks like an array. We could check for a special attribute on the class:

def quacks_like_array(obj):
    return bool(getattr(type(obj), "__quacks_like_array__", False))

Or, we could define an abstract base class (ABC):

def quacks_like_array(obj):
    return isinstance(obj, AbstractArray)

If you look at how ABCs work, this is essentially equivalent to keeping a global set of types that have been declared to implement the AbstractArray interface, and then checking it for membership.

Between these, the ABC approach seems to have a number of advantages:

  • It’s Python’s standard, “one obvious way” of doing this.

  • ABCs can be introspected (e.g. help(np.AbstractArray) does something useful).

  • ABCs can provide useful mixin methods.

  • ABCs integrate with other features like mypy type-checking, functools.singledispatch, etc.

One obvious thing to check is whether this choice affects speed. Using the attached benchmark script on a CPython 3.7 prerelease (revision c4d77a661138d, self-compiled, no PGO), on a Thinkpad T450s running Linux, we find:

np.asarray(ndarray_obj)      330 ns
np.asarray([])              1400 ns

Attribute check, success      80 ns
Attribute check, failure      80 ns

ABC, success via subclass    340 ns
ABC, success via register()  700 ns
ABC, failure                 370 ns


  • The first two lines are included to put the other lines in context.

  • This used 3.7 because both getattr and ABCs are receiving substantial optimizations in this release, and it’s more representative of the long-term future of Python. (Failed getattr doesn’t necessarily construct an exception object anymore, and ABCs were reimplemented in C.)

  • The “success” lines refer to cases where quacks_like_array would return True. The “failure” lines are cases where it would return False.

  • The first measurement for ABCs is subclasses defined like:

    class MyArray(AbstractArray):

    The second is for subclasses defined like:

    class MyArray:

    I don’t know why there’s such a large difference between these.

In practice, either way we’d only do the full test after first checking for well-known types like ndarray, list, etc. This is how NumPy currently checks for other double-underscore attributes and the same idea applies here to either approach. So these numbers won’t affect the common case, just the case where we actually have an AbstractArray, or else another third-party object that will end up going through __array__ or __array_interface__ or end up as an object array.

So in summary, using an ABC will be slightly slower than using an attribute, but this doesn’t affect the most common paths, and the magnitude of slowdown is fairly small (~250 ns on an operation that already takes longer than that). Furthermore, we can potentially optimize this further (e.g. by keeping a tiny LRU cache of types that are known to be AbstractArray subclasses, on the assumption that most code will only use one or two of these types at a time), and it’s very unclear that this even matters – if the speed of asarray no-op pass-throughs were a bottleneck that showed up in profiles, then probably we would have made them faster already! (It would be trivial to fast-path this, but we don’t.)

Given the semantic and usability advantages of ABCs, this seems like an acceptable trade-off.

Specification of asabstractarray#

Given AbstractArray, the definition of asabstractarray is simple:

def asabstractarray(a, dtype=None):
    if isinstance(a, AbstractArray):
        if dtype is not None and dtype != a.dtype:
            return a.astype(dtype)
        return a
    return asarray(a, dtype=dtype)

Things to note:

  • asarray also accepts an order= argument, but we don’t include that here because it’s about details of memory representation, and the whole point of this function is that you use it to declare that you don’t care about details of memory representation.

  • Using the astype method allows the a object to decide how to implement casting for its particular type.

  • For strict compatibility with asarray, we skip calling astype when the dtype is already correct. Compare:

    >>> a = np.arange(10)
    # astype() always returns a view:
    >>> a.astype(a.dtype) is a
    # asarray() returns the original object if possible:
    >>> np.asarray(a, dtype=a.dtype) is a

What exactly are you promising if you inherit from AbstractArray?#

This will presumably be refined over time. The ideal of course is that your class should be indistinguishable from a real ndarray, but nothing enforces that except the expectations of users. In practice, declaring that your class implements the AbstractArray interface simply means that it will start passing through asabstractarray, and so by subclassing it you’re saying that if some code works for ndarrays but breaks for your class, then you’re willing to accept bug reports on that.

To start with, we should declare __array_ufunc__ to be an abstract method, and add the NDArrayOperatorsMixin methods as mixin methods.

Declaring astype as an @abstractmethod probably makes sense as well, since it’s used by asabstractarray. We might also want to go ahead and add some basic attributes like ndim, shape, dtype.

Adding new abstract methods will be a bit tricky, because ABCs enforce these at subclass time; therefore, simply adding a new @abstractmethod will be a backwards compatibility break. If this becomes a problem then we can use some hacks to implement an @upcoming_abstractmethod decorator that only issues a warning if the method is missing, and treat it like a regular deprecation cycle. (In this case, the thing we’d be deprecating is “support for abstract arrays that are missing feature X”.)


The name of the ABC doesn’t matter too much, because it will only be referenced rarely and in relatively specialized situations. The name of the function matters a lot, because most existing instances of asarray should be replaced by this, and in the future it’s what everyone should be reaching for by default unless they have a specific reason to use asarray instead. This suggests that its name really should be shorter and more memorable than asarray… which is difficult. I’ve used asabstractarray in this draft, but I’m not really happy with it, because it’s too long and people are unlikely to start using it by habit without endless exhortations.

One option would be to actually change asarray's semantics so that it passes through AbstractArray objects unchanged. But I’m worried that there may be a lot of code out there that calls asarray and then passes the result into some C function that doesn’t do any further type checking (because it knows that its caller has already used asarray). If we allow asarray to return AbstractArray objects, and then someone calls one of these C wrappers and passes it an AbstractArray object like a sparse array, then they’ll get a segfault. Right now, in the same situation, asarray will instead invoke the object’s __array__ method, or use the buffer interface to make a view, or pass through an array with object dtype, or raise an error, or similar. Probably none of these outcomes are actually desirable in most cases, so maybe making it a segfault instead would be OK? But it’s dangerous given that we don’t know how common such code is. OTOH, if we were starting from scratch then this would probably be the ideal solution.

We can’t use asanyarray or array, since those are already taken.

Any other ideas? np.cast, np.coerce?


  1. Rename NDArrayOperatorsMixin to AbstractArray (leaving behind an alias for backwards compatibility) and make it an ABC.

  2. Add asabstractarray (or whatever we end up calling it), and probably a C API equivalent.

  3. Begin migrating NumPy internal functions to using asabstractarray where appropriate.

Backward compatibility#

This is purely a new feature, so there are no compatibility issues. (Unless we decide to change the semantics of asarray itself.)

Rejected alternatives#

One suggestion that has come up is to define multiple abstract classes for different subsets of the array interface. Nothing in this proposal stops either NumPy or third-parties from doing this in the future, but it’s very difficult to guess ahead of time which subsets would be useful. Also, “the full ndarray interface” is something that existing libraries are written to expect (because they work with actual ndarrays) and test (because they test with actual ndarrays), so it’s by far the easiest place to start.

Appendix: benchmark script#

import perf
import abc
import numpy as np

class NotArray:

class AttrArray:
    __array_implementer__ = True

class ArrayBase(abc.ABC):

class ABCArray1(ArrayBase):

class ABCArray2:


not_array = NotArray()
attr_array = AttrArray()
abc_array_1 = ABCArray1()
abc_array_2 = ABCArray2()

# Make sure ABC cache is primed
isinstance(not_array, ArrayBase)
isinstance(abc_array_1, ArrayBase)
isinstance(abc_array_2, ArrayBase)

runner = perf.Runner()
def t(name, statement):
    runner.timeit(name, statement, globals=globals())

t("np.asarray([])", "np.asarray([])")
arrobj = np.array([])
t("np.asarray(arrobj)", "np.asarray(arrobj)")

t("attr, False",
  "getattr(not_array, '__array_implementer__', False)")
t("attr, True",
  "getattr(attr_array, '__array_implementer__', False)")

t("ABC, False", "isinstance(not_array, ArrayBase)")
t("ABC, True, via inheritance", "isinstance(abc_array_1, ArrayBase)")
t("ABC, True, via register", "isinstance(abc_array_2, ArrayBase)")