Fix cross-references in docs (pandas-dev#42949)

* Fix cross-reference

* Fix cross-reference typo

* Add cross-reference

Author: albertvillanova

doc/source/development/extending.rst

@@ -106,7 +106,7 @@ extension array for IP Address data, this might be ``ipaddress.IPv4Address``.
 
 See the `extension dtype source`_ for interface definition.
 
-:class:`pandas.api.extension.ExtensionDtype` can be registered to pandas to allow creation via a string dtype name.
+:class:`pandas.api.extensions.ExtensionDtype` can be registered to pandas to allow creation via a string dtype name.
 This allows one to instantiate ``Series`` and ``.astype()`` with a registered string name, for
 example ``'category'`` is a registered string accessor for the ``CategoricalDtype``.
 
@@ -125,7 +125,7 @@ data. We do require that your array be convertible to a NumPy array, even if
 this is relatively expensive (as it is for ``Categorical``).
 
 They may be backed by none, one, or many NumPy arrays. For example,
-``pandas.Categorical`` is an extension array backed by two arrays,
+:class:`pandas.Categorical` is an extension array backed by two arrays,
 one for codes and one for categories. An array of IPv6 addresses may
 be backed by a NumPy structured array with two fields, one for the
 lower 64 bits and one for the upper 64 bits. Or they may be backed

doc/source/user_guide/sparse.rst

@@ -294,7 +294,7 @@ To convert back to sparse SciPy matrix in COO format, you can use the :meth:`Dat
 
    sdf.sparse.to_coo()
 
-meth:`Series.sparse.to_coo` is implemented for transforming a ``Series`` with sparse values indexed by a :class:`MultiIndex` to a :class:`scipy.sparse.coo_matrix`.
+:meth:`Series.sparse.to_coo` is implemented for transforming a ``Series`` with sparse values indexed by a :class:`MultiIndex` to a :class:`scipy.sparse.coo_matrix`.
 
 The method requires a ``MultiIndex`` with two or more levels.