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authorAndre Delfino <adelfino@gmail.com>2020-10-01 23:22:14 (GMT)
committerGitHub <noreply@github.com>2020-10-01 23:22:14 (GMT)
commitc8bb24166e367d449158015cb9b1093f03c7175d (patch)
tree4dfe17e665e4db0fcb163322a92e0f23d475d826
parentbd0a08ea90e4c7a2ebf29697937e9786d4d8e5ee (diff)
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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.
-rw-r--r--Doc/faq/programming.rst2
-rw-r--r--Doc/library/array.rst5
-rw-r--r--Doc/library/functions.rst4
-rw-r--r--Doc/tutorial/floatingpoint.rst2
4 files changed, 5 insertions, 8 deletions
diff --git a/Doc/faq/programming.rst b/Doc/faq/programming.rst
index 76ae4d2..0b486d7 100644
--- a/Doc/faq/programming.rst
+++ b/Doc/faq/programming.rst
@@ -1191,7 +1191,7 @@ 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 7802073..ff3ec6b 100644
--- a/Doc/library/array.rst
+++ b/Doc/library/array.rst
@@ -257,7 +257,6 @@ Examples::
Packing and unpacking of External Data Representation (XDR) data as used in some
remote procedure call systems.
- `The Numerical Python Documentation <https://docs.scipy.org/doc/>`_
- The Numeric Python extension (NumPy) defines another array type; see
- http://www.numpy.org/ for further information about Numerical Python.
+ `NumPy <https://numpy.org/>`_
+ The NumPy package defines another array type.
diff --git a/Doc/library/functions.rst b/Doc/library/functions.rst
index 7543fc4..c49bb0c 100644
--- a/Doc/library/functions.rst
+++ b/Doc/library/functions.rst
@@ -1512,14 +1512,12 @@ 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 0c0eb52..b98de6e 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 <https://scipy.org>.
Python provides tools that may help on those rare occasions when you really