fix docs as per review & column name changes

doc/source/computation.rst

@@ -534,7 +534,7 @@ 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 ``MultiIndexed DataFrame`` whose ``index`` 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:
 
@@ -549,12 +549,12 @@ can even be omitted:
 .. ipython:: python
 
    covs = df[['B','C','D']].rolling(window=50).cov(df[['A','B','C']], pairwise=True)
-   covs.unstack(-1).iloc[-50]
+   covs.loc['2002-09-22':]
 
 .. ipython:: python
 
    correls = df.rolling(window=50).corr()
-   correls.unstack(-1).iloc[-50]
+   correls.loc['2002-09-22':]
 
 You can efficiently retrieve the time series of correlations between two
 columns using ``.loc`` indexing:
@@ -567,7 +567,7 @@ columns using ``.loc`` indexing:
 .. ipython:: python
 
    @savefig rolling_corr_pairwise_ex.png
-   correls.unstack(-1).[('A', 'C')].plot()
+   correls.loc[:, ('A', 'C')].plot()
 
 .. _stats.aggregate:
 

doc/source/dsintro.rst

@@ -788,6 +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),
@@ -799,6 +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))}
@@ -821,6 +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')
 
@@ -829,6 +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)})
@@ -856,6 +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)
@@ -885,6 +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)
 
@@ -914,6 +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()
@@ -928,6 +935,7 @@ 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),
@@ -964,6 +972,7 @@ support the multi-dimensional analysis that is one of ``Panel`` s main usecases.
 Convert to a MultiIndex DataFrame
 
 .. ipython:: python
+   :okwarning:
 
    p.to_frame()
 

doc/source/whatsnew/v0.20.0.txt

@@ -14,7 +14,7 @@ Highlights include:
 - 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 MultiIndex DataFrame rather than a Panel, see :ref:`here <whhatsnew_0200.api_breaking.rolling_pairwise>`
+- 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`)
@@ -872,8 +872,9 @@ 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``. These are equivalent in function,
-but MultiIndexed DataFrames enjoy more support in pandas.
+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
@@ -883,7 +884,7 @@ See the section on :ref:`Windowed Binary Operations <stats.moments.binary>` for
                      columns=pd.Index(['A', 'B'], name='bar'),
                      index=pd.date_range('20160101',
                                          periods=100, freq='D', name='foo'))
-   df
+   df.tail()
 
 Old Behavior:
 
@@ -902,7 +903,7 @@ New Behavior:
 .. ipython:: python
 
    res = df.rolling(12).corr()
-   res
+   res.tail()
 
 Retrieving a correlation matrix for a cross-section
 

pandas/core/window.py

@@ -1730,9 +1730,11 @@ def dataframe_from_int_dict(data, frame_template):
                         columns=arg2.columns,
                         dtype='float64')
 
-                # reset our names to arg1 names
+                # reset our index names to arg1 names
+                # reset our column names to arg2 names
                 # careful not to mutate the original names
-                result.columns = Index(result.columns).set_names(None)
+                result.columns = Index(result.columns).set_names(
+                    arg2.columns.name)
                 result.index = result.index.set_names(
                     [arg1.index.name, arg1.columns.name])
 

pandas/tests/test_window.py

@@ -2751,7 +2751,7 @@ def test_moment_functions_zero_length_pairwise(self):
         df2_expected = DataFrame(
             index=pd.MultiIndex.from_product([df2.index, df2.columns],
                                              names=['bar', 'foo']),
-            columns=Index(['a']),
+            columns=Index(['a'], name='foo'),
             dtype='float64')
 
         functions = [lambda x: (x.expanding(min_periods=5)
@@ -2789,7 +2789,7 @@ def test_expanding_cov_pairwise_diff_length(self):
         result3 = df1a.expanding().cov(df2, pairwise=True).loc[2]
         result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2]
         expected = DataFrame([[-3.0, -6.0], [-5.0, -10.0]],
-                             columns=['A', 'B'],
+                             columns=Index(['A', 'B'], name='foo'),
                              index=Index(['X', 'Y'], name='foo'))
         tm.assert_frame_equal(result1, expected)
         tm.assert_frame_equal(result2, expected)