DOC: add doc on ExtensionArray and extending pandas

doc/source/developer.rst

@@ -140,46 +140,3 @@ As an example of fully-formed metadata:
          'metadata': None}
     ],
     'pandas_version': '0.20.0'}
-
-.. _developer.register-accessors:
-
-Registering Custom Accessors
-----------------------------
-
-Libraries can use the decorators
-:func:`pandas.api.extensions.register_dataframe_accessor`,
-:func:`pandas.api.extensions.register_series_accessor`, and
-:func:`pandas.api.extensions.register_index_accessor`, to add additional "namespaces" to
-pandas objects. All of these follow a similar convention: you decorate a class, providing the name of attribute to add. The
-class's `__init__` method gets the object being decorated. For example:
-
-.. code-block:: python
-
-   @pd.api.extensions.register_dataframe_accessor("geo")
-   class GeoAccessor(object):
-       def __init__(self, pandas_obj):
-           self._obj = pandas_obj
-
-       @property
-       def center(self):
-           # return the geographic center point of this DataFarme
-           lon = self._obj.latitude
-           lat = self._obj.longitude
-           return (float(lon.mean()), float(lat.mean()))
-
-       def plot(self):
-           # plot this array's data on a map, e.g., using Cartopy
-           pass
-
-Now users can access your methods using the `geo` namespace:
-
-      >>> ds = pd.DataFrame({'longitude': np.linspace(0, 10),
-      ...                    'latitude': np.linspace(0, 20)})
-      >>> ds.geo.center
-      (5.0, 10.0)
-      >>> ds.geo.plot()
-      # plots data on a map
-
-This can be a convenient way to extend pandas objects without subclassing them.
-If you write a custom accessor, make a pull request adding it to our
-:ref:`ecosystem` page.

doc/source/ecosystem.rst

@@ -262,3 +262,38 @@ Data validation
 
 Engarde is a lightweight library used to explicitly state your assumptions abour your datasets
 and check that they're *actually* true.
+
+.. _ecosystem.extensions:
+
+Extension Data Types
+--------------------
+
+Pandas provides an interface for defining
+:ref:`extension types <extending.extension-types>` to extend NumPy's type
+system. The following libraries implement that interface to provide types not
+found in NumPy or pandas, which work well with pandas' data containers.
+
+`cyberpandas`_
+~~~~~~~~~~~~~~
+
+Cyberpandas provides an extension type for storing arrays of IP Addresses. These
+arrays can be stored inside pandas' Series and DataFrame.
+
+.. _ecosystem.accessors:
+
+Accessors
+---------
+
+A directory of projects providing
+:ref:`extension accessors <extending.register-accessors>`. This is for users to
+discover new accessors and for libraries authors to coordinate on the namespace.
+
+============== ========== =================
+Library        Accessor   Classes
+============== ========== =================
+`cyberpandas`_ ``ip``     Series
+`pdvega`_      ``vgplot`` Series, DataFrame
+============== ========== =================
+
+.. _cyberpandas: https://cyberpandas.readthedocs.io/en/latest
+.. _pdvega: https://jakevdp.github.io/pdvega/

doc/source/extending.rst

@@ -0,0 +1,260 @@
+.. _extending:
+
+****************
+Extending Pandas
+****************
+
+While pandas provides a rich set of methods, containers, and data types, your
+needs may not be fully satisfied. Pandas offers a few options for extending
+pandas.
+
+.. _extending.register-accessors:
+
+Registering Custom Accessors
+----------------------------
+
+Libraries can use the decorators
+:func:`pandas.api.extensions.register_dataframe_accessor`,
+:func:`pandas.api.extensions.register_series_accessor`, and
+:func:`pandas.api.extensions.register_index_accessor`, to add additional
+"namespaces" to pandas objects. All of these follow a similar convention: you
+decorate a class, providing the name of attribute to add. The class's `__init__`
+method gets the object being decorated. For example:
+
+.. code-block:: python
+
+   @pd.api.extensions.register_dataframe_accessor("geo")
+   class GeoAccessor(object):
+       def __init__(self, pandas_obj):
+           self._obj = pandas_obj
+
+       @property
+       def center(self):
+           # return the geographic center point of this DataFarme
+           lon = self._obj.latitude
+           lat = self._obj.longitude
+           return (float(lon.mean()), float(lat.mean()))
+
+       def plot(self):
+           # plot this array's data on a map, e.g., using Cartopy
+           pass
+
+Now users can access your methods using the `geo` namespace:
+
+      >>> ds = pd.DataFrame({'longitude': np.linspace(0, 10),
+      ...                    'latitude': np.linspace(0, 20)})
+      >>> ds.geo.center
+      (5.0, 10.0)
+      >>> ds.geo.plot()
+      # plots data on a map
+
+This can be a convenient way to extend pandas objects without subclassing them.
+If you write a custom accessor, make a pull request adding it to our
+:ref:`ecosystem` page.
+
+.. _extending.extension-types:
+
+Extension Types
+---------------
+
+Pandas defines an interface for implementing data types and arrays that *extend*
+NumPy's type system. Pandas iteself uses the extension system for some types
+that aren't built into NumPy (categorical, period, interval, datetime with
+timezone).
+
+Libraries can define an custom array and data type. When pandas encounters these
+objects, they will be handled properly (i.e. not converted to an ndarray of
+objects). Many methods like :func:`pandas.isna` will dispatch to the extension
+type's implementation.
+
+If you're building a library that implements the interface, please publicize it
+on :ref:`ecosystem.extensions`.
+
+The interface consists of two classes.
+
+``ExtensionDtype``
+""""""""""""""""""
+
+An ``ExtensionDtype`` is similar to a ``numpy.dtype`` object. It describes the
+data type. Implementors are responsible for a few unique items like the name.
+
+One particularly important item is the ``type`` property. This should be the
+class that is the scalar type for your data. For example, if you were writing an
+extension array for IP Address data, this might be ``ipaddress.IPv4Address``.
+
+See ``pandas/core/dtypes/base.py`` for interface definition.
+
+``ExtensionArray``
+""""""""""""""""""
+
+This class provides all the array-like functionality. ExtensionArrays are
+limited to 1 dimension. An ExtensionArray is linked to an ExtensionDtype via the
+``dtype`` attribute.
+
+Pandas makes no restrictions on how an extension array is created via its
+``__new__`` or ``__init__``, and puts no restrictions on how you store your
+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 ararys. For example,
+``pandas.Categorical`` is an extension array backed by two arrays,
+one for codes and one for categories. An array of IPv6 address 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
+by some other storage type, like Python lists.
+
+See ``pandas/core/arrays/base.py`` for the interface definition. The docstrings
+and comments contain guidance for properly implementing the interface.
+
+.. _ref-subclassing-pandas:
+
+Subclassing pandas Data Structures
+----------------------------------
+
+.. warning:: There are some easier alternatives before considering subclassing ``pandas`` data structures.
+
+  1. Extensible method chains with :ref:`pipe <basics.pipe>`
+
+  2. Use *composition*. See `here <http://en.wikipedia.org/wiki/Composition_over_inheritance>`_.
+
+  3. Extending by :ref:`registering an accessor <extending.register-accessors>`
+
+This section describes how to subclass ``pandas`` data structures to meet more specific needs. There are 2 points which need attention:
+
+1. Override constructor properties.
+2. Define original properties
+
+.. note:: You can find a nice example in `geopandas <https://github.com/geopandas/geopandas>`_ project.
+
+Override Constructor Properties
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Each data structure has constructor properties to specifying data constructors. By overriding these properties, you can retain defined-classes through ``pandas`` data manipulations.
+
+There are 3 constructors to be defined:
+
+- ``_constructor``: Used when a manipulation result has the same dimesions as the original.
+- ``_constructor_sliced``: Used when a manipulation result has one lower dimension(s) as the original, such as ``DataFrame`` single columns slicing.
+- ``_constructor_expanddim``: Used when a manipulation result has one higher dimension as the original, such as ``Series.to_frame()`` and ``DataFrame.to_panel()``.
+
+Following table shows how ``pandas`` data structures define constructor properties by default.
+
+===========================  ======================= =================== =======================
+Property Attributes          ``Series``              ``DataFrame``       ``Panel``
+===========================  ======================= =================== =======================
+``_constructor``             ``Series``              ``DataFrame``       ``Panel``
+``_constructor_sliced``      ``NotImplementedError`` ``Series``          ``DataFrame``
+``_constructor_expanddim``   ``DataFrame``           ``Panel``           ``NotImplementedError``
+===========================  ======================= =================== =======================
+
+Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame`` overriding constructor properties.
+
+.. code-block:: python
+
+   class SubclassedSeries(Series):
+
+       @property
+       def _constructor(self):
+           return SubclassedSeries
+
+       @property
+       def _constructor_expanddim(self):
+           return SubclassedDataFrame
+
+   class SubclassedDataFrame(DataFrame):
+
+       @property
+       def _constructor(self):
+           return SubclassedDataFrame
+
+       @property
+       def _constructor_sliced(self):
+           return SubclassedSeries
+
+.. code-block:: python
+
+   >>> s = SubclassedSeries([1, 2, 3])
+   >>> type(s)
+   <class '__main__.SubclassedSeries'>
+
+   >>> to_framed = s.to_frame()
+   >>> type(to_framed)
+   <class '__main__.SubclassedDataFrame'>
+
+   >>> df = SubclassedDataFrame({'A', [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
+   >>> df
+      A  B  C
+   0  1  4  7
+   1  2  5  8
+   2  3  6  9
+
+   >>> type(df)
+   <class '__main__.SubclassedDataFrame'>
+
+   >>> sliced1 = df[['A', 'B']]
+   >>> sliced1
+      A  B
+   0  1  4
+   1  2  5
+   2  3  6
+   >>> type(sliced1)
+   <class '__main__.SubclassedDataFrame'>
+
+   >>> sliced2 = df['A']
+   >>> sliced2
+   0    1
+   1    2
+   2    3
+   Name: A, dtype: int64
+   >>> type(sliced2)
+   <class '__main__.SubclassedSeries'>
+
+Define Original Properties
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To let original data structures have additional properties, you should let ``pandas`` know what properties are added. ``pandas`` maps unknown properties to data names overriding ``__getattribute__``. Defining original properties can be done in one of 2 ways:
+
+1. Define ``_internal_names`` and ``_internal_names_set`` for temporary properties which WILL NOT be passed to manipulation results.
+2. Define ``_metadata`` for normal properties which will be passed to manipulation results.
+
+Below is an example to define 2 original properties, "internal_cache" as a temporary property and "added_property" as a normal property
+
+.. code-block:: python
+
+   class SubclassedDataFrame2(DataFrame):
+
+       # temporary properties
+       _internal_names = pd.DataFrame._internal_names + ['internal_cache']
+       _internal_names_set = set(_internal_names)
+
+       # normal properties
+       _metadata = ['added_property']
+
+       @property
+       def _constructor(self):
+           return SubclassedDataFrame2
+
+.. code-block:: python
+
+   >>> df = SubclassedDataFrame2({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
+   >>> df
+      A  B  C
+   0  1  4  7
+   1  2  5  8
+   2  3  6  9
+
+   >>> df.internal_cache = 'cached'
+   >>> df.added_property = 'property'
+
+   >>> df.internal_cache
+   cached
+   >>> df.added_property
+   property
+
+   # properties defined in _internal_names is reset after manipulation
+   >>> df[['A', 'B']].internal_cache
+   AttributeError: 'SubclassedDataFrame2' object has no attribute 'internal_cache'
+
+   # properties defined in _metadata are retained
+   >>> df[['A', 'B']].added_property
+   property

doc/source/index.rst.template

@@ -152,5 +152,6 @@ See the package overview for more detail about what's in the library.
     {% if not single_doc -%}
     developer
     internals
+    extending
     release
     {% endif -%}