NEP 47 — Adopting the array API standard


Ralf Gommers <>


Stephan Hoyer <>


Aaron Meurer <>




Standards Track





We propose to adopt the Python array API standard, developed by the Consortium for Python Data API Standards. Implementing this as a separate new namespace in NumPy will allow authors of libraries which depend on NumPy as well as end users to write code that is portable between NumPy and all other array/tensor libraries that adopt this standard.


We expect that this NEP will remain in a draft state for quite a while. Given the large scope we don’t expect to propose it for acceptance any time soon; instead, we want to solicit feedback on both the high-level design and implementation, and learn what needs describing better in this NEP or changing in either the implementation or the array API standard itself.

Motivation and Scope

Python users have a wealth of choice for libraries and frameworks for numerical computing, data science, machine learning, and deep learning. New frameworks pushing forward the state of the art in these fields are appearing every year. One unintended consequence of all this activity and creativity has been fragmentation in multidimensional array (a.k.a. tensor) libraries - which are the fundamental data structure for these fields. Choices include NumPy, Tensorflow, PyTorch, Dask, JAX, CuPy, MXNet, and others.

The APIs of each of these libraries are largely similar, but with enough differences that it’s quite difficult to write code that works with multiple (or all) of these libraries. The array API standard aims to address that issue, by specifying an API for the most common ways arrays are constructed and used. The proposed API is quite similar to NumPy’s API, and deviates mainly in places where (a) NumPy made design choices that are inherently not portable to other implementations, and (b) where other libraries consistently deviated from NumPy on purpose because NumPy’s design turned out to have issues or unnecessary complexity.

For a longer discussion on the purpose of the array API standard we refer to the Purpose and Scope section of the array API standard and the two blog posts announcing the formation of the Consortium 1 and the release of the first draft version of the standard for community review 2.

The scope of this NEP includes:

  • Adopting the 2021 version of the array API standard

  • Adding a separate namespace, tentatively named numpy.array_api

  • Changes needed/desired outside of the new namespace, for example new dunder methods on the ndarray object

  • Implementation choices, and differences between functions in the new namespace with those in the main numpy namespace

  • A new array object conforming to the array API standard

  • Maintenance effort and testing strategy

  • Impact on NumPy’s total exposed API surface and on other future and under-discussion design choices

  • Relation to existing and proposed NumPy array protocols (__array_ufunc__, __array_function__, __array_module__).

  • Required improvements to existing NumPy functionality

Out of scope for this NEP are:

  • Changes in the array API standard itself. Those are likely to come up during review of this NEP, but should be upstreamed as needed and this NEP subsequently updated.

Usage and Impact

This section will be fleshed out later, for now we refer to the use cases given in the array API standard Use Cases section

In addition to those use cases, the new namespace contains functionality that is widely used and supported by many array libraries. As such, it is a good set of functions to teach to newcomers to NumPy and recommend as “best practice”. That contrasts with NumPy’s main namespace, which contains many functions and objects that have been superceded or we consider mistakes - but that we can’t remove because of backwards compatibility reasons.

The usage of the numpy.array_api namespace by downstream libraries is intended to enable them to consume multiple kinds of arrays, without having to have a hard dependency on all of those array libraries:


Adoption in downstream libraries

The prototype implementation of the array_api namespace will be used with SciPy, scikit-learn and other libraries of interest that depend on NumPy, in order to get more experience with the design and find out if any important parts are missing.

The pattern to support multiple array libraries is intended to be something like:

def somefunc(x, y):
    # Retrieves standard namespace. Raises if x and y have different
    # namespaces.  See Appendix for possible get_namespace implementation
    xp = get_namespace(x, y)
    out = xp.mean(x, axis=0) + 2*xp.std(y, axis=0)
    return out

The get_namespace call is effectively the library author opting in to using the standard API namespace, and thereby explicitly supporting all conforming array libraries.

The asarray / asanyarray pattern

Many existing libraries use the same asarray (or asanyarray) pattern as NumPy itself does; accepting any object that can be coerced into a np.ndarray. We consider this design pattern problematic - keeping in mind the Zen of Python, “explicit is better than implicit”, as well as the pattern being historically problematic in the SciPy ecosystem for ndarray subclasses and with over-eager object creation. All other array/tensor libraries are more strict, and that works out fine in practice. We would advise authors of new libraries to avoid the asarray pattern. Instead they should either accept just NumPy arrays or, if they want to support multiple kinds of arrays, check if the incoming array object supports the array API standard by checking for __array_namespace__ as shown in the example above.

Existing libraries can do such a check as well, and only call asarray if the check fails. This is very similar to the __duckarray__ idea in NEP 30 — Duck Typing for NumPy Arrays - Implementation.

Adoption in application code

The new namespace can be seen by end users as a cleaned up and slimmed down version of NumPy’s main namespace. Encouraging end users to use this namespace like:

import numpy.array_api as xp

x = xp.linspace(0, 2*xp.pi, num=100)
y = xp.cos(x)

seems perfectly reasonable, and potentially beneficial - users get offered only one function for each purpose (the one we consider best-practice), and they then write code that is more easily portable to other libraries.

Backward compatibility

No deprecations or removals of existing NumPy APIs or other backwards incompatible changes are proposed.

High-level design

The array API standard consists of approximately 120 objects, all of which have a direct NumPy equivalent. This figure shows what is included at a high level:


The most important changes compared to what NumPy currently offers are:

  • A new array object which:

    • conforms to the casting rules and indexing behaviour specified by the standard,

    • does not have methods other than dunder methods,

    • does not support the full range of NumPy indexing behaviour. Advanced indexing with integers is not supported. Only boolean indexing with a single (possibly multi-dimensional) boolean array is supported. An indexing expression that selects a single element returns a 0-D array rather than a scalar.

  • Functions in the array_api namespace:

    • do not accept array_like inputs, only NumPy arrays and Python scalars

    • do not support __array_ufunc__ and __array_function__,

    • use positional-only and keyword-only parameters in their signatures,

    • have inline type annotations,

    • may have minor changes to signatures and semantics of individual functions compared to their equivalents already present in NumPy,

    • only support dtype literals, not format strings or other ways of specifying dtypes

  • DLPack support will be added to NumPy,

  • New syntax for “device support” will be added, through a .device attribute on the new array object, and device= keywords in array creation functions in the array_api namespace,

  • Casting rules that differ from those NumPy currently has. Output dtypes can be derived from input dtypes (i.e. no value-based casting), and 0-D arrays are treated like >=1-D arrays.

  • Not all dtypes NumPy has are part of the standard. Only boolean, signed and unsigned integers, and floating-point dtypes up to float64 are supported. Complex dtypes are expected to be added in the next version of the standard. Extended precision, string, void, object and datetime dtypes, as well as structured dtypes, are not included.

Improvements to existing NumPy functionality that are needed include:

  • Add support for stacks of matrices to some functions in numpy.linalg that are currently missing such support.

  • Add the keepdims keyword to np.argmin and np.argmax.

  • Add a “never copy” mode to np.asarray.

Functions in the array_api namespace

Let’s start with an example of a function implementation that shows the most important differences with the equivalent function in the main namespace:

def max(x: array, /, *,
        axis: Optional[Union[int, Tuple[int, ...]]] = None,
        keepdims: bool = False
    ) -> array:
    Array API compatible wrapper for :py:func:`np.max <numpy.max>`.
    return np.max._implementation(x, axis=axis, keepdims=keepdims)

This function does not accept array_like inputs, only ndarray. There are multiple reasons for this. Other array libraries all work like this. Letting the user do coercion of lists, generators, or other foreign objects separately results in a cleaner design with less unexpected behaviour. It’s higher-performance - less overhead from asarray calls. Static typing is easier. Subclasses will work as expected. And the slight increase in verbosity because users have to explicitly coerce to ndarray on rare occasions seems like a small price to pay.

This function does not support __array_ufunc__ nor __array_function__. These protocols serve a similar purpose as the array API standard module itself, but through a different mechanisms. Because only ndarray instances are accepted, dispatching via one of these protocols isn’t useful anymore.

This function uses positional-only parameters in its signature. This makes code more portable - writing max(x=x, ...) is no longer valid, hence if other libraries call the first parameter input rather than x, that is fine. The rationale for keyword-only parameters (not shown in the above example) is two-fold: clarity of end user code, and it being easier to extend the signature in the future with keywords in the desired order.

This function has inline type annotations. Inline annotations are far easier to maintain than separate stub files. And because the types are simple, this will not result in a large amount of clutter with type aliases or unions like in the current stub files NumPy has.

DLPack support for zero-copy data interchange

The ability to convert one kind of array into another kind is valuable, and indeed necessary when downstream libraries want to support multiple kinds of arrays. This requires a well-specified data exchange protocol. NumPy already supports two of these, namely the buffer protocol (i.e., PEP 3118), and the __array_interface__ (Python side) / __array_struct__ (C side) protocol. Both work similarly, letting the “producer” describe how the data is laid out in memory so the “consumer” can construct its own kind of array with a view on that data.

DLPack works in a very similar way. The main reasons to prefer DLPack over the options already present in NumPy are:

  1. DLPack is the only protocol with device support (e.g., GPUs using CUDA or ROCm drivers, or OpenCL devices). NumPy is CPU-only, but other array libraries are not. Having one protocol per device isn’t tenable, hence device support is a must.

  2. Widespread support. DLPack has the widest adoption of all protocols, only NumPy is missing support. And the experiences of other libraries with it are positive. This contrasts with the protocols NumPy does support, which are used very little - when other libraries want to interoperate with NumPy, they typically use the (more limited, and NumPy-specific) __array__ protocol.

Adding support for DLPack to NumPy entails:

  • Adding a ndarray.__dlpack__ method

  • Adding a from_dlpack function, which takes as input an object supporting __dlpack__, and returns an ndarray.

DLPack is currently a ~200 LoC header, and is meant to be included directly, so no external dependency is needed. Implementation should be straightforward.

Syntax for device support

NumPy itself is CPU-only, so it clearly doesn’t have a need for device support. However, other libraries (e.g. TensorFlow, PyTorch, JAX, MXNet) support multiple types of devices: CPU, GPU, TPU, and more exotic hardware. To write portable code on systems with multiple devices, it’s often necessary to create new arrays on the same device as some other array, or check that two arrays live on the same device. Hence syntax for that is needed.

The array object will have a .device attribute which enables comparing devices of different arrays (they only should compare equal if both arrays are from the same library and it’s the same hardware device). Furthermore, device= keywords in array creation functions are needed. For example:

def empty(shape: Union[int, Tuple[int, ...]], /, *,
          dtype: Optional[dtype] = None,
          device: Optional[device] = None) -> array:
    Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`.
    return np.empty(shape, dtype=dtype, device=device)

The implementation for NumPy may be as simple as setting the device attribute to the string 'cpu' and raising an exception if array creation functions encounter any other value.

Dtypes and casting rules

The supported dtypes in this namespace are boolean, 8/16/32/64-bit signed and unsigned integer, and 32/64-bit floating-point dtypes. These will be added to the namespace as dtype literals with the expected names (e.g., bool, uint16, float64).

The most obvious omissions are the complex dtypes. The rationale for the lack of complex support in the first version of the array API standard is that several libraries (PyTorch, MXNet) are still in the process of adding support for complex dtypes. The next version of the standard is expected to include complex64 and complex128 (see this issue for more details).

Specifying dtypes to functions, e.g. via the dtype= keyword, is expected to only use the dtype literals. Format strings, Python builtin dtypes, or string representations of the dtype literals are not accepted - this will improve readability and portability of code at little cost.

Casting rules are only defined between different dtypes of the same kind. The rationale for this is that mixed-kind (e.g., integer to floating-point) casting behavior differs between libraries. NumPy’s mixed-kind casting behavior doesn’t need to be changed or restricted, it only needs to be documented that if users use mixed-kind casting, their code may not be portable.


Type promotion diagram. Promotion between any two types is given by their join on this lattice. Only the types of participating arrays matter, not their values. Dashed lines indicate that behaviour for Python scalars is undefined on overflow. Boolean, integer and floating-point dtypes are not connected, indicating mixed-kind promotion is undefined.

The most important difference between the casting rules in NumPy and in the array API standard is how scalars and 0-dimensional arrays are handled. In the standard, array scalars do not exist and 0-dimensional arrays follow the same casting rules as higher-dimensional arrays.

See the Type Promotion Rules section of the array API standard for more details.


It is not clear what the best way is to support the different casting rules for 0-dimensional arrays and no value-based casting. One option may be to implement this second set of casting rules, keep them private, mark the array API functions with a private attribute that says they adhere to these different rules, and let the casting machinery check whether for that attribute.

This needs discussion.


An indexing expression that would return a scalar with ndarray, e.g. arr_2d[0, 0], will return a 0-D array with the new array object. There are several reasons for that: array scalars are largely considered a design mistake which no other array library copied; it works better for non-CPU libraries (typically arrays can live on the device, scalars live on the host); and it’s simply a consistent design. To get a Python scalar out of a 0-D array, one can simply use the builtin for the type, e.g. float(arr_0d).

The other indexing modes in the standard do work largely the same as they do for numpy.ndarray. One noteworthy difference is that clipping in slice indexing (e.g., a[:n] where n is larger than the size of the first axis) is unspecified behaviour, because that kind of check can be expensive on accelerators.

The lack of advanced indexing, and boolean indexing being limited to a single n-D boolean array, is due to those indexing modes not being suitable for all types of arrays or JIT compilation. Their absence does not seem to be problematic; if a user or library author wants to use them, they can do so through zero-copy conversion to numpy.ndarray. This will signal correctly to whomever reads the code that it is then NumPy-specific rather than portable to all conforming array types.

The array object

The array object in the standard does not have methods other than dunder methods. The rationale for that is that not all array libraries have methods on their array object (e.g., TensorFlow does not). It also provides only a single way of doing something, rather than have functions and methods that are effectively duplicate.

Mixing operations that may produce views (e.g., indexing, nonzero) in combination with mutation (e.g., item or slice assignment) is explicitly documented in the standard to not be supported. This cannot easily be prohibited in the array object itself; instead this will be guidance to the user via documentation.

The standard current does not prescribe a name for the array object itself. We propose to simply name it ndarray. This is the most obvious name, and because of the separate namespace should not clash with numpy.ndarray.



This section needs a lot more detail, which will gradually be added when the implementation progresses.

A prototype of the array_api namespace can be found in The docstring in its has notes on completeness of the implementation. The code for the wrapper functions also contains # Note: comments everywhere there is a difference with the NumPy API. Two important parts that are not implemented yet are the new array object and DLPack support. Functions may need changes to ensure the changed casting rules are respected.

The array object

Regarding the array object implementation, we plan to start with a regular Python class that wraps a numpy.ndarray instance. Attributes and methods can forward to that wrapped instance, applying input validation and implementing changed behaviour as needed.

The casting rules are probably the most challenging part. The in-progress dtype system refactor (NEPs 40-43) should make implementing the correct casting behaviour easier - it is already moving away from value-based casting for example.

The dtype objects

We must be able to compare dtypes for equality, and expressions like these must be possible:

np.array_api.some_func(..., dtype=x.dtype)

The above implies it would be nice to have np.array_api.float32 == np.array_api.ndarray(...).dtype.

Dtypes should not be assumed to have a class hierarchy by users, however we are free to implement it with a class hierarchy if that’s convenient. We considered the following options to implement dtype objects:

  1. Alias dtypes to those in the main namespace. E.g., np.array_api.float32 = np.float32.

  2. Make the dtypes instances of np.dtype. E.g., np.array_api.float32 = np.dtype(np.float32).

  3. Create new singleton classes with only the required methods/attributes (currently just __eq__).

It seems like (2) would be easiest from the perspective of interacting with functions outside the main namespace. And (3) would adhere best to the standard.

TBD: the standard does not yet have a good way to inspect properties of a dtype, to ask questions like “is this an integer dtype?”. Perhaps this is easy enough to do for users, like so:

def _get_dtype(dt_or_arr):
    return dt_or_arr.dtype if hasattr(dt_or_arr, 'dtype') else dt_or_arr

def is_floating(dtype_or_array):
    dtype = _get_dtype(dtype_or_array)
    return dtype in (float32, float64)

def is_integer(dtype_or_array):
    dtype = _get_dtype(dtype_or_array)
    return dtype in (uint8, uint16, uint32, uint64, int8, int16, int32, int64)

However it could make sense to add to the standard. Note that NumPy itself currently does not have a great for asking such questions, see gh-17325.

Feedback from downstream library authors

TODO - this can only be done after trying out some use cases

Leo Fang (CuPy): “My impression is for CuPy we could simply take this new array object and s/numpy/cupy”


Appendix - a possible get_namespace implementation

The get_namespace function mentioned in the Adoption in application code section can be implemented like:

def get_namespace(*xs):
    # `xs` contains one or more arrays, or possibly Python scalars (accepting
    # those is a matter of taste, but doesn't seem unreasonable).
    namespaces = {
        x.__array_namespace__() if hasattr(x, '__array_namespace__') else None for x in xs if not isinstance(x, (bool, int, float, complex))

    if not namespaces:
        # one could special-case np.ndarray above or use np.asarray here if
        # older numpy versions need to be supported.
        raise ValueError("Unrecognized array input")

    if len(namespaces) != 1:
        raise ValueError(f"Multiple namespaces for array inputs: {namespaces}")

    xp, = namespaces
    if xp is None:
        raise ValueError("The input is not a supported array type")

    return xp