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Fix cross-references in docs #42949

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Aug 10, 2021
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Fix cross-references in docs #42949

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9de3ae3
Fix cross-reference
albertvillanova Aug 9, 2021
a0b8f73
Fix cross-reference typo
albertvillanova Aug 9, 2021
b8ea0d4
Add cross-reference
albertvillanova Aug 9, 2021
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4 changes: 2 additions & 2 deletions doc/source/development/extending.rst
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Expand Up @@ -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``.

Expand All @@ -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
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2 changes: 1 addition & 1 deletion doc/source/user_guide/sparse.rst
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Expand Up @@ -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.

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