DEPR: Panel deprecated

doc/source/computation.rst

@@ -505,13 +505,18 @@ two ``Series`` or any combination of ``DataFrame/Series`` or
 - ``DataFrame/DataFrame``: by default compute the statistic for matching column
   names, returning a DataFrame. If the keyword argument ``pairwise=True`` is
   passed then computes the statistic for each pair of columns, returning a
-  ``Panel`` whose ``items`` are the dates in question (see :ref:`the next section
+  ``MultiIndexed DataFrame`` whose ``index`` are the dates in question (see :ref:`the next section
   <stats.moments.corr_pairwise>`).
 
 For example:
 
 .. ipython:: python
 
+   df = pd.DataFrame(np.random.randn(1000, 4),
+                     index=pd.date_range('1/1/2000', periods=1000),
+                     columns=['A', 'B', 'C', 'D'])
+   df = df.cumsum()
+
    df2 = df[:20]
    df2.rolling(window=5).corr(df2['B'])
 
@@ -520,11 +525,16 @@ For example:
 Computing rolling pairwise covariances and correlations
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. warning::
+
+   Prior to version 0.20.0 if ``pairwise=True`` was passed, a ``Panel`` would be returned.
+   This will now return a 2-level MultiIndexed DataFrame, see the whatsnew :ref:`here <whatsnew_0200.api_breaking.rolling_pairwise>`
+
 In financial data analysis and other fields it's common to compute covariance
 and correlation matrices for a collection of time series. Often one is also
 interested in moving-window covariance and correlation matrices. This can be
 done by passing the ``pairwise`` keyword argument, which in the case of
-``DataFrame`` inputs will yield a ``Panel`` whose ``items`` are the dates in
+``DataFrame`` inputs will yield a MultiIndexed ``DataFrame`` whose ``index`` are the dates in
 question. In the case of a single DataFrame argument the ``pairwise`` argument
 can even be omitted:
 
@@ -539,12 +549,12 @@ can even be omitted:
 .. ipython:: python
 
    covs = df[['B','C','D']].rolling(window=50).cov(df[['A','B','C']], pairwise=True)
-   covs[df.index[-50]]
+   covs.loc['2002-09-22':]
 
 .. ipython:: python
 
    correls = df.rolling(window=50).corr()
-   correls[df.index[-50]]
+   correls.loc['2002-09-22':]
 
 You can efficiently retrieve the time series of correlations between two
 columns using ``.loc`` indexing:
@@ -557,7 +567,7 @@ columns using ``.loc`` indexing:
 .. ipython:: python
 
    @savefig rolling_corr_pairwise_ex.png
-   correls.loc[:, 'A', 'C'].plot()
+   correls.loc[:, ('A', 'C')].plot()
 
 .. _stats.aggregate:
 

doc/source/dsintro.rst

@@ -763,6 +763,11 @@ completion mechanism so they can be tab-completed:
 Panel
 -----
 
+.. warning::
+
+    In 0.20.0, ``Panel`` is deprecated and will be removed in
+    a future version. See the section :ref:`Deprecate Panel <dsintro.deprecate_panel>`.
+
 Panel is a somewhat less-used, but still important container for 3-dimensional
 data. The term `panel data <http://en.wikipedia.org/wiki/Panel_data>`__ is
 derived from econometrics and is partially responsible for the name pandas:
@@ -783,6 +788,7 @@ From 3D ndarray with optional axis labels
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. ipython:: python
+   :okwarning:
 
    wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
                  major_axis=pd.date_range('1/1/2000', periods=5),
@@ -794,6 +800,7 @@ From dict of DataFrame objects
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. ipython:: python
+   :okwarning:
 
    data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
            'Item2' : pd.DataFrame(np.random.randn(4, 2))}
@@ -816,6 +823,7 @@ dictionary of DataFrames as above, and the following named parameters:
 For example, compare to the construction above:
 
 .. ipython:: python
+   :okwarning:
 
    pd.Panel.from_dict(data, orient='minor')
 
@@ -824,6 +832,7 @@ DataFrame objects with mixed-type columns, all of the data will get upcasted to
 ``dtype=object`` unless you pass ``orient='minor'``:
 
 .. ipython:: python
+   :okwarning:
 
    df = pd.DataFrame({'a': ['foo', 'bar', 'baz'],
                       'b': np.random.randn(3)})
@@ -851,6 +860,7 @@ This method was introduced in v0.7 to replace ``LongPanel.to_long``, and convert
 a DataFrame with a two-level index to a Panel.
 
 .. ipython:: python
+   :okwarning:
 
    midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]])
    df = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
@@ -880,6 +890,7 @@ A Panel can be rearranged using its ``transpose`` method (which does not make a
 copy by default unless the data are heterogeneous):
 
 .. ipython:: python
+   :okwarning:
 
    wp.transpose(2, 0, 1)
 
@@ -909,6 +920,7 @@ Squeezing
 Another way to change the dimensionality of an object is to ``squeeze`` a 1-len object, similar to ``wp['Item1']``
 
 .. ipython:: python
+   :okwarning:
 
    wp.reindex(items=['Item1']).squeeze()
    wp.reindex(items=['Item1'], minor=['B']).squeeze()
@@ -923,12 +935,55 @@ for more on this. To convert a Panel to a DataFrame, use the ``to_frame``
 method:
 
 .. ipython:: python
+   :okwarning:
 
    panel = pd.Panel(np.random.randn(3, 5, 4), items=['one', 'two', 'three'],
                     major_axis=pd.date_range('1/1/2000', periods=5),
                     minor_axis=['a', 'b', 'c', 'd'])
    panel.to_frame()
 
+
+.. _dsintro.deprecate_panel:
+
+Deprecate Panel
+---------------
+
+Over the last few years, pandas has increased in both breadth and depth, with new features,
+datatype support, and manipulation routines. As a result, supporting efficient indexing and functional
+routines for ``Series``, ``DataFrame`` and ``Panel`` has contributed to an increasingly fragmented and
+difficult-to-understand codebase.
+
+The 3-d structure of a ``Panel`` is much less common for many types of data analysis,
+than the 1-d of the ``Series`` or the 2-D of the ``DataFrame``. Going forward it makes sense for
+pandas to focus on these areas exclusively.
+
+Oftentimes, one can simply use a MultiIndex ``DataFrame`` for easily working with higher dimensional data.
+
+In additon, the ``xarray`` package was built from the ground up, specifically in order to
+support the multi-dimensional analysis that is one of ``Panel`` s main usecases.
+`Here is a link to the xarray panel-transition documentation <http://xarray.pydata.org/en/stable/pandas.html#panel-transition>`__.
+
+.. ipython:: python
+   :okwarning:
+
+   p = tm.makePanel()
+   p
+
+Convert to a MultiIndex DataFrame
+
+.. ipython:: python
+   :okwarning:
+
+   p.to_frame()
+
+Alternatively, one can convert to an xarray ``DataArray``.
+
+.. ipython:: python
+
+   p.to_xarray()
+
+You can see the full-documentation for the `xarray package <http://xarray.pydata.org/en/stable/>`__.
+
 .. _dsintro.panelnd:
 .. _dsintro.panel4d:
 

doc/source/whatsnew/v0.20.0.txt

@@ -10,13 +10,16 @@ users upgrade to this version.
 Highlights include:
 
 - The ``.ix`` indexer has been deprecated, see :ref:`here <whatsnew_0200.api_breaking.deprecate_ix>`
+- ``Panel`` has been deprecated, see :ref:`here <whatsnew_0200.api_breaking.deprecate_panel>`
 - Improved user API when accessing levels in ``.groupby()``, see :ref:`here <whatsnew_0200.enhancements.groupby_access>`
 - Improved support for UInt64 dtypes, see :ref:`here <whatsnew_0200.enhancements.uint64_support>`
 - A new orient for JSON serialization, ``orient='table'``, that uses the Table Schema spec, see :ref:`here <whatsnew_0200.enhancements.table_schema>`
+- Window Binary Corr/Cov operations return a MultiIndexed ``DataFrame`` rather than a ``Panel``, as ``Panel`` is now deprecated, see :ref:`here <whatsnew_0200.api_breaking.rolling_pairwise>`
 - Support for S3 handling now uses ``s3fs``, see :ref:`here <whatsnew_0200.api_breaking.s3>`
 - Google BigQuery support now uses the ``pandas-gbq`` library, see :ref:`here <whatsnew_0200.api_breaking.gbq>`
 - Switched the test framework to use `pytest <http://doc.pytest.org/en/latest>`__ (:issue:`13097`)
 
+
 Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating.
 
 .. contents:: What's new in v0.20.0
@@ -425,6 +428,33 @@ Using ``.iloc``. Here we will get the location of the 'A' column, then use *posi
   df.iloc[[0, 2], df.columns.get_loc('A')]
 
 
+.. _whatsnew_0200.api_breaking.deprecate_panel:
+
+Deprecate Panel
+^^^^^^^^^^^^^^^
+
+``Panel`` is deprecated and will be removed in a future version. The recommended way to represent 3-D data are
+with a ``MultiIndex``on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas
+provides a :meth:`~Panel.to_xarray` method to automate this conversion. See the documentation :ref:`Deprecate Panel <dsintro.deprecate_panel>`. (:issue:`13563`).
+
+.. ipython:: python
+   :okwarning:
+
+   p = tm.makePanel()
+   p
+
+Convert to a MultiIndex DataFrame
+
+.. ipython:: python
+
+   p.to_frame()
+
+Convert to an xarray DataArray
+
+.. ipython:: python
+
+   p.to_xarray()
+
 .. _whatsnew.api_breaking.io_compat:
 
 Possible incompat for HDF5 formats for pandas < 0.13.0
@@ -836,6 +866,51 @@ New Behavior:
 
    df.groupby('A').agg([np.mean, np.std, np.min, np.max])
 
+.. _whatsnew_0200.api_breaking.rolling_pairwise:
+
+Window Binary Corr/Cov operations return a MultiIndex DataFrame
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+A binary window operation, like ``.corr()`` or ``.cov()``, when operating on a ``.rolling(..)``, ``.expanding(..)``, or ``.ewm(..)`` object,
+will now return a 2-level ``MultiIndexed DataFrame`` rather than a ``Panel``, as ``Panel`` is now deprecated,
+see :ref:`here <_whatsnew_0200.api_breaking.deprecate_panel>`. These are equivalent in function,
+but MultiIndexed ``DataFrame`` s enjoy more support in pandas.
+See the section on :ref:`Windowed Binary Operations <stats.moments.binary>` for more information. (:issue:`15677`)
+
+.. ipython:: python
+
+   np.random.seed(1234)
+   df = pd.DataFrame(np.random.rand(100, 2),
+                     columns=pd.Index(['A', 'B'], name='bar'),
+                     index=pd.date_range('20160101',
+                                         periods=100, freq='D', name='foo'))
+   df.tail()
+
+Old Behavior:
+
+.. code-block:: ipython
+
+   In [2]: df.rolling(12).corr()
+   Out[2]:
+   <class 'pandas.core.panel.Panel'>
+   Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)
+   Items axis: 2016-01-01 00:00:00 to 2016-04-09 00:00:00
+   Major_axis axis: A to B
+   Minor_axis axis: A to B
+
+New Behavior:
+
+.. ipython:: python
+
+   res = df.rolling(12).corr()
+   res.tail()
+
+Retrieving a correlation matrix for a cross-section
+
+.. ipython:: python
+
+   df.rolling(12).corr().loc['2016-04-07']
+
 .. _whatsnew_0200.api_breaking.hdfstore_where:
 
 HDFStore where string comparison

pandas/core/panel.py

@@ -4,10 +4,8 @@
 # pylint: disable=E1103,W0231,W0212,W0621
 from __future__ import division
 
-import warnings
-
 import numpy as np
-
+import warnings
 from pandas.types.cast import (infer_dtype_from_scalar,
                                maybe_cast_item)
 from pandas.types.common import (is_integer, is_list_like,
@@ -132,6 +130,18 @@ def _constructor(self):
 
     def __init__(self, data=None, items=None, major_axis=None, minor_axis=None,
                  copy=False, dtype=None):
+        # deprecation GH13563
+        warnings.warn("\nPanel is deprecated and will be removed in a "
+                      "future version.\nThe recommended way to represent "
+                      "these types of 3-dimensional data are with a "
+                      "MultiIndex on a DataFrame, via the "
+                      "Panel.to_frame() method\n"
+                      "Alternatively, you can use the xarray package "
+                      "http://xarray.pydata.org/en/stable/.\n"
+                      "Pandas provides a `.to_xarray()` method to help "
+                      "automate this conversion.\n",
+                      DeprecationWarning, stacklevel=3)
+
         self._init_data(data=data, items=items, major_axis=major_axis,
                         minor_axis=minor_axis, copy=copy, dtype=dtype)