Add Stakeholders section

Author: rgommers

spec/purpose_and_scope.md

@@ -14,7 +14,77 @@
 
 ## Stakeholders
 
-
+Arrays are fundamental to scientific computing, data science, and machine
+learning and deep learning. Hence there are many stakeholders for an array API
+standard. The _direct_ stakeholders of this standard are **authors/maintainers of
+Python array libraries**. There are many more types of _indirect_ stakeholders
+though, including:
+
+- maintainers of libraries and other programs which depend on array libraries
+  (called "array-consuming libraries" in the rest of this document)
+- authors of non-Python array libraries
+- developers of compilers and runtimes with array-specific functionality
+- end users
+
+Libraries that are being actively considered - in terms of current behaviour and
+API surface - during the creation of the first version of this standard
+include:
+
+- [NumPy](https://numpy.org)
+- [TensorFlow](https://www.tensorflow.org/)
+- [PyTorch](https://pytorch.org/)
+- [MXNet](https://numpy.mxnet.io/)
+- [JAX](https://github.com/google/jax)
+- [Dask](https://dask.org/)
+- [CuPy](https://cupy.chainer.org/)
+
+Other Python array libraries that are currently under active development and
+could adopt this API standard include:
+
+- [xarray](https://xarray.pydata.org/)
+- [PyData/Sparse](https://sparse.pydata.org)
+- [Weld](https://github.com/weld-project/weld)
+- [Bohrium](https://bohrium.readthedocs.io/)
+- [Arkouda](https://github.com/mhmerrill/arkouda)
+- [Legate](https://research.nvidia.com/publication/2019-11_Legate-NumPy%3A-Accelerated)
+
+There are a huge amount of array-consuming libraries; some of the most
+prominent ones that are being taken into account - in terms of current array
+API usage or impact of design decisions on them - include (this list is likely
+to grow it over time):
+
+- [Pandas](https://pandas.pydata.org/)
+- [SciPy](https://github.com/scipy/scipy)
+- [scikit-learn](https://scikit-learn.org/)
+- [Matplotlib](https://matplotlib.org/)
+- [scikit-image](https://scikit-image.org/)
+- [NetworkX](https://networkx.github.io/)
+
+Array libraries in other languages, some of which may grow a Python API in the
+future or have taken inspiration from NumPy or other array libraries, include:
+
+- [Xtensor](https://xtensor.readthedocs.io) (C++, cross-language)
+- [XND](https://xnd.io/) (C, cross-language)
+- [stdlib](https://stdlib.io/) (JavaScript)
+- [rust-ndarray](https://github.com/rust-ndarray/ndarray) (Rust)
+- [rray](https://github.com/r-lib/rray) (R)
+- [ND4J](https://github.com/deeplearning4j/nd4j) (JVM)
+- [NumSharp](https://github.com/SciSharp/NumSharp) (C#)
+
+Compilers, runtimes, and dispatching layers for which this API standard may be
+relevant:
+
+- [Cython](https://cython.org/)
+- [Numba](http://numba.pydata.org/)
+- [Pythran](https://pythran.readthedocs.io/en/latest/)
+- [Transonic](https://transonic.readthedocs.io)
+- [ONNX](https://onnx.ai/)
+- [Apache TVM](https://tvm.apache.org/)
+- [MLIR](https://mlir.llvm.org/)
+- [TACO](https://github.com/tensor-compiler/taco)
+- [unumpy](https://github.com/Quansight-Labs/unumpy)
+- [einops](https://github.com/arogozhnikov/einops)
+- [Apache Arrow](https://arrow.apache.org/)
 
 
 ## High-level API overview
@@ -80,4 +150,4 @@ a dimension whose size is one.
 
 The following referenced documents are indispensable for the application of this specification.
 
--   __IEEE 754-2019: IEEE Standard for Floating-Point Arithmetic.__ Institute of Electrical and Electronic Engineers, New York (2019).
+-   __IEEE 754-2019: IEEE Standard for Floating-Point Arithmetic.__ Institute of Electrical and Electronic Engineers, New York (2019).