DOC: Fixes for 18 broken links (#16472)

* DOC: Fixes for 18 broken links

This, with PR #16465, should fix nearly all the remaining broken links
on the site. 4 or 5 others should be easy to fix and just
need attention from someone more knowledgeable -- will
open an issue. For release notes with dead links,
I could usually find links on archive.org for roughly contemporary
versions.

* DOC: Update to "Fixes for 18 broken links #16472"

* Obsolete links, previously commented out, now deleted:
https://github.com/numpy/numpy/pull/16472#discussion_r433928958

* Semantic markup for reference to Python class:
https://github.com/numpy/numpy/pull/16472#discussion_r433553928

* Missing :ref: in internal link:
https://github.com/numpy/numpy/pull/16472#discussion_r433554484

Not included: Resolution on using external/internal doc link in .py:
https://github.com/numpy/numpy/pull/16472#discussion_r433554824

* DOC: Add internal link for 'Fixes for 18 broken links' PR #16472

Making reference [1] an internal link in function_base.py => numpy.vectorize.html

* DOC: Redirect 2 link fixes in PR #16472

* governance.rst link reverted
* ununcs.rst `overridden` link goes where it was meant to


per https://github.com/numpy/numpy/pull/16472#pullrequestreview-424666070

2 files changed, 9 insertions, 7 deletions

diff --git a/doc/source/user/absolute_beginners.rst b/doc/source/user/absolute_beginners.rst
index bd44b70da..5873eb108 100644
--- a/doc/source/user/absolute_beginners.rst
+++ b/doc/source/user/absolute_beginners.rst
@@ -1531,19 +1531,19 @@ Importing and exporting a CSV
 -----------------------------
 
 .. save a csv
-   
+
    >>> with open('music.csv', 'w') as fid:
    ...     n = fid.write('Artist,Genre,Listeners,Plays\n')
    ...     n = fid.write('Billie Holiday,Jazz,1300000,27000000\n')
    ...     n = fid.write('Jimmie Hendrix,Rock,2700000,70000000\n')
    ...     n = fid.write('Miles Davis,Jazz,1500000,48000000\n')
    ...     n = fid.write('SIA,Pop,2000000,74000000\n')
-   
+
 
 
 It's simple to read in a CSV that contains existing information. The best and
 easiest way to do this is to use
-`Pandas <https://pandas.pydata.org/getpandas.html>`_. ::
+`Pandas <https://pandas.pydata.org>`_. ::
 
   >>> import pandas as pd
 
diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst
index 29a4d80ca..b1af81886 100644
--- a/doc/source/user/quickstart.rst
+++ b/doc/source/user/quickstart.rst
@@ -22,7 +22,7 @@ https://scipy.org/install.html for instructions.
 
 **Learner profile**
 
-This tutorial is intended as a quick overview of 
+This tutorial is intended as a quick overview of
 algebra and arrays in NumPy and want to understand how n-dimensional
 (:math:`n>=2`) arrays are represented and can be manipulated. In particular, if
 you don't know how to apply common functions to n-dimensional arrays (without
@@ -1036,6 +1036,8 @@ Basic Linear Algebra
 Less Basic
 ==========
 
+.. _broadcasting-rules:
+
 Broadcasting rules
 ------------------
 
@@ -1054,7 +1056,7 @@ element is assumed to be the same along that dimension for the
 "broadcast" array.
 
 After application of the broadcasting rules, the sizes of all arrays
-must match. More details can be found in :doc:`basics.broadcasting`.
+must match. More details can be found in :ref:`basics.broadcasting`.
 
 Advanced indexing and index tricks
 ==================================
@@ -1390,8 +1392,8 @@ and then use it as::
             [14, 13, 15, 17, 12]]])
 
 The advantage of this version of reduce compared to the normal
-ufunc.reduce is that it makes use of the `Broadcasting
-Rules <Tentative_NumPy_Tutorial.html#head-c43f3f81719d84f09ae2b33a22eaf50b26333db8>`__
+ufunc.reduce is that it makes use of the
+:ref:`broadcasting rules <broadcasting-rules>`
 in order to avoid creating an argument array the size of the output
 times the number of vectors.