NEP 35 — Array Creation Dispatching With __array_function__


Peter Andreas Entschev <>




Standards Track







We propose the introduction of a new keyword argument like= to all array creation functions to permit dispatching of such functions by the __array_function__ protocol, addressing one of the protocol shortcomings, as described by NEP-18 1.

Detailed description

The introduction of the __array_function__ protocol allowed downstream library developers to use NumPy as a dispatching API. However, the protocol did not – and did not intend to – address the creation of arrays by downstream libraries, preventing those libraries from using such important functionality in that context.

Other NEPs have been written to address parts of that limitation, such as the introduction of the __duckarray__ protocol in NEP-30 2, and the introduction of an overriding mechanism called uarray by NEP-31 3.

The purpose of this NEP is to address that shortcoming in a simple and straighforward way: introduce a new like= keyword argument, similar to how the empty_like family of functions work. When array creation functions receive such an argument, they will trigger the __array_function__ protocol, and call the downstream library’s own array creation function implementation. The like= argument, as its own name suggests, shall be used solely for the purpose of identifying where to dispatch. In contrast to the way __array_function__ has been used so far (the first argument identifies where to dispatch), and to avoid breaking NumPy’s API with regards to array creation, the new like= keyword shall be used for the purpose of dispatching.

Usage Guidance

The new like= keyword is solely intended to identify the downstream library where to dispatch and the object is used only as reference, meaning that no modifications, copies or processing will be performed on that object.

We expect that this functionality will be mostly useful to library developers, allowing them to create new arrays for internal usage based on arrays passed by the user, preventing unnecessary creation of NumPy arrays that will ultimately lead to an additional conversion into a downstream array type.

Support for Python 2.7 has been dropped since NumPy 1.17, therefore we should make use of the keyword-only argument standard described in PEP-3102 4 to implement the like=, thus preventing it from being passed by position.


The implementation requires introducing a new like= keyword to all existing array creation functions of NumPy. As examples of functions that would add this new argument (but not limited to) we can cite those taking array-like objects such as array and asarray, functions that create arrays based on numerical ranges such as range and linspace, as well as the empty family of functions, even though that may be redundant, since there exists already specializations for those with the naming format empty_like. As of the writing of this NEP, a complete list of array creation functions can be found in 5.

This newly proposed keyword shall be removed by the __array_function__ mechanism from the keyword dictionary before dispatching. The purpose for this is twofold:

  1. The object will have no use in the downstream library’s implementation; and

  2. Simplifies adoption of array creation by those libraries already opting-in to implement the __array_function__ protocol, thus removing the requirement to explicitly opt-in for all array creation functions.

Downstream libraries thus shall _NOT_ include the like= keyword to their array creation APIs, which is a NumPy-exclusive keyword.

Function Dispatching

There are two different cases to dispatch: Python functions, and C functions. To permit __array_function__ dispatching, one possible implementation is to decorate Python functions with overrides.array_function_dispatch, but C functions have a different requirement, which we shall describe shortly.

The example below shows a suggestion on how the asarray could be decorated with overrides.array_function_dispatch:

def _asarray_decorator(a, dtype=None, order=None, *, like=None):
    return (like,)

def asarray(a, dtype=None, order=None, *, like=None):
    return array(a, dtype, copy=False, order=order)

Note in the example above that the implementation remains unchanged, the only difference is the decoration, which uses the new _asarray_decorator function to instruct the __array_function__ protocol to dispatch if like is not None.

We will now look at a C function example, and since asarray is anyway a specialization of array, we will use the latter as an example now. As array is a C function, currently all NumPy does regarding its Python source is to import the function and adjust its __module__ to numpy. The function will now be decorated with a specialization of overrides.array_function_from_dispatcher, which shall take care of adjusting the module too.

array_function_nodocs_from_c_func_and_dispatcher = functools.partial(
    module='numpy', docs_from_dispatcher=False, verify=False)

def array(a, dtype=None, *, copy=True, order='K', subok=False, ndmin=0,
    return (like,)

There are two downsides to the implementation above for C functions:

  1. It creates another Python function call; and

  2. To follow current implementation standards, documentation should be attached directly to the Python source code.

Alternatively for C functions, the implementation of like= could be moved into the C implementation itself. This is not the primary suggestion here due to its inherent complexity which would be difficult too long to describe in its entirety here, and too tedious for the reader. However, we leave that as an option open for discussion.


The purpose of this NEP is to keep things simple. Similarly, we can exemplify the usage of like= in a simple way. Imagine you have an array of ones created by a downstream library, such as CuPy. What you need now is a new array that can be created using the NumPy API, but that will in fact be created by the downstream library, a simple way to achieve that is shown below.

x = cupy.ones(2)
np.array([1, 3, 5], like=x)     # Returns cupy.ndarray

As a second example, we could also create an array of evenly spaced numbers using a Dask identity matrix as reference:

x = dask.array.eye(3)
np.linspace(0, 2, like=x)       # Returns dask.array


This proposal does not raise any backward compatibility issues within NumPy, given that it only introduces a new keyword argument to existing array creation functions.

Downstream libraries will benefit from the like= argument automatically, that is, without any explicit changes in their codebase. The only requirement is that they already implement the __array_function__ protocol, as described by NEP-18 2.