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[doc] Update references to NumPy (GH-22458)
Numeric(al) Python to NumPy. It seems the old name hasn't been used for some time.
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@ -1191,7 +1191,7 @@ difference is that a Python list can contain objects of many different types.
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The ``array`` module also provides methods for creating arrays of fixed types
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with compact representations, but they are slower to index than lists. Also
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note that the Numeric extensions and others define array-like structures with
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note that NumPy and other third party packages define array-like structures with
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various characteristics as well.
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To get Lisp-style linked lists, you can emulate cons cells using tuples::
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@ -257,7 +257,6 @@ Examples::
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Packing and unpacking of External Data Representation (XDR) data as used in some
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remote procedure call systems.
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`The Numerical Python Documentation <https://docs.scipy.org/doc/>`_
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The Numeric Python extension (NumPy) defines another array type; see
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http://www.numpy.org/ for further information about Numerical Python.
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`NumPy <https://numpy.org/>`_
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The NumPy package defines another array type.
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@ -1512,14 +1512,12 @@ are always available. They are listed here in alphabetical order.
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.. class:: slice(stop)
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slice(start, stop[, step])
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.. index:: single: Numerical Python
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Return a :term:`slice` object representing the set of indices specified by
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``range(start, stop, step)``. The *start* and *step* arguments default to
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``None``. Slice objects have read-only data attributes :attr:`~slice.start`,
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:attr:`~slice.stop` and :attr:`~slice.step` which merely return the argument
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values (or their default). They have no other explicit functionality;
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however they are used by Numerical Python and other third party extensions.
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however they are used by NumPy and other third party packages.
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Slice objects are also generated when extended indexing syntax is used. For
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example: ``a[start:stop:step]`` or ``a[start:stop, i]``. See
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:func:`itertools.islice` for an alternate version that returns an iterator.
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@ -158,7 +158,7 @@ which implements arithmetic based on rational numbers (so the numbers like
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1/3 can be represented exactly).
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If you are a heavy user of floating point operations you should take a look
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at the Numerical Python package and many other packages for mathematical and
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at the NumPy package and many other packages for mathematical and
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statistical operations supplied by the SciPy project. See <https://scipy.org>.
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Python provides tools that may help on those rare occasions when you really
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