NumPy 2.0.0

What's new in this version:

New features:
- A new variable-length string dtype, numpy.dtypes.StringDType and a new numpy.strings namespace with performant ufuncs for string operations, Support for float32 and longdouble in all numpy.fft functions, Support for the array API standard in the main numpy namespace.

Performance improvements:
- Sorting functions sort, argsort, partition, argpartition have been accelerated through the use of the Intel x86-simd-sort and Google Highway libraries, and may see large (hardware-specific) speedups, macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller, numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes, A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
- numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.

Python API improvements:
- A clear split between public and private API, with a new module structure and each public function now available in a single place.
- Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in numpy.lib by ~80%.
- Canonical dtype names and a new numpy.isdtype` introspection function,

C API improvements:
- A new public C API for creating custom dtypes, Many outdated functions and macros removed, and private internals hidden to ease future extensibility
- New, easier to use, initialization functions: PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI.

Improved behavior:
- Improvements to type promotion behavior was changed by adopting NEP 50.
- This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes.
- Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
- The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms
- The maximum number of array dimensions is changed from 32 to 64

Documentation:
- The reference guide navigation was significantly improved, and there is now documentation on NumPy's module structure
- The building from source documentation was completely rewritten