From: Miss Islington (bot) <31488909+miss-islington@users.noreply.github.com> Date: Wed, 6 Oct 2021 13:04:48 +0000 (-0700) Subject: [doc] Update references to NumPy (GH-22458) (GH-28749) X-Git-Tag: v3.9.8~97 X-Git-Url: http://git.ipfire.org/gitweb.cgi?a=commitdiff_plain;h=d747f5e805fa1c33768d9c22605e6324a35b3709;p=thirdparty%2FPython%2Fcpython.git [doc] Update references to NumPy (GH-22458) (GH-28749) Numeric(al) Python to NumPy. It seems the old name hasn't been used for some time. (cherry picked from commit c8bb24166e367d449158015cb9b1093f03c7175d) Co-authored-by: Andre Delfino --- diff --git a/Doc/faq/programming.rst b/Doc/faq/programming.rst index 04d6592aeccd..4e04b10b0dae 100644 --- a/Doc/faq/programming.rst +++ b/Doc/faq/programming.rst @@ -1184,7 +1184,7 @@ difference is that a Python list can contain objects of many different types. The ``array`` module also provides methods for creating arrays of fixed types with compact representations, but they are slower to index than lists. Also -note that the Numeric extensions and others define array-like structures with +note that NumPy and other third party packages define array-like structures with various characteristics as well. To get Lisp-style linked lists, you can emulate cons cells using tuples:: diff --git a/Doc/library/array.rst b/Doc/library/array.rst index f2f7894e1bf0..f892d0983b6b 100644 --- a/Doc/library/array.rst +++ b/Doc/library/array.rst @@ -256,7 +256,6 @@ Examples:: Packing and unpacking of External Data Representation (XDR) data as used in some remote procedure call systems. - `The Numerical Python Documentation `_ - The Numeric Python extension (NumPy) defines another array type; see - http://www.numpy.org/ for further information about Numerical Python. + `NumPy `_ + The NumPy package defines another array type. diff --git a/Doc/library/functions.rst b/Doc/library/functions.rst index 4f967825fcbb..b17ca69760db 100644 --- a/Doc/library/functions.rst +++ b/Doc/library/functions.rst @@ -1513,14 +1513,12 @@ are always available. They are listed here in alphabetical order. .. class:: slice(stop) slice(start, stop[, step]) - .. index:: single: Numerical Python - Return a :term:`slice` object representing the set of indices specified by ``range(start, stop, step)``. The *start* and *step* arguments default to ``None``. Slice objects have read-only data attributes :attr:`~slice.start`, :attr:`~slice.stop` and :attr:`~slice.step` which merely return the argument values (or their default). They have no other explicit functionality; - however they are used by Numerical Python and other third party extensions. + however they are used by NumPy and other third party packages. Slice objects are also generated when extended indexing syntax is used. For example: ``a[start:stop:step]`` or ``a[start:stop, i]``. See :func:`itertools.islice` for an alternate version that returns an iterator. diff --git a/Doc/tutorial/floatingpoint.rst b/Doc/tutorial/floatingpoint.rst index 0c0eb526fa9e..b98de6e56a00 100644 --- a/Doc/tutorial/floatingpoint.rst +++ b/Doc/tutorial/floatingpoint.rst @@ -158,7 +158,7 @@ which implements arithmetic based on rational numbers (so the numbers like 1/3 can be represented exactly). If you are a heavy user of floating point operations you should take a look -at the Numerical Python package and many other packages for mathematical and +at the NumPy package and many other packages for mathematical and statistical operations supplied by the SciPy project. See . Python provides tools that may help on those rare occasions when you really