DOC: cleanup build warnings (#14172)

* DOC: remove examples on Panel4D (caused warnings) and refer to older docs

* DOC: fix build warnings

* resolve comments

Author: jorisvandenbossche

doc/source/dsintro.rst

@@ -935,134 +935,20 @@ method:
                     minor_axis=['a', 'b', 'c', 'd'])
    panel.to_frame()
 
-
-.. _dsintro.panel4d:
-
-Panel4D (Experimental)
-----------------------
-
-.. warning::
-
-   In 0.19.0 ``Panel4D`` is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas provides a :meth:`~Panel4D.to_xarray` method to automate this conversion.
-
-``Panel4D`` is a 4-Dimensional named container very much like a ``Panel``, but
-having 4 named dimensions. It is intended as a test bed for more N-Dimensional named
-containers.
-
-  - **labels**: axis 0, each item corresponds to a Panel contained inside
-  - **items**: axis 1, each item corresponds to a DataFrame contained inside
-  - **major_axis**: axis 2, it is the **index** (rows) of each of the
-    DataFrames
-  - **minor_axis**: axis 3, it is the **columns** of each of the DataFrames
-
-``Panel4D`` is a sub-class of ``Panel``, so most methods that work on Panels are
-applicable to Panel4D. The following methods are disabled:
-
-  - ``join , to_frame , to_excel , to_sparse , groupby``
-
-Construction of Panel4D works in a very similar manner to a ``Panel``
-
-From 4D ndarray with optional axis labels
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. ipython:: python
-
-   p4d = pd.Panel4D(np.random.randn(2, 2, 5, 4),
-                    labels=['Label1','Label2'],
-                    items=['Item1', 'Item2'],
-                    major_axis=pd.date_range('1/1/2000', periods=5),
-                    minor_axis=['A', 'B', 'C', 'D'])
-   p4d
-
-
-From dict of Panel objects
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. ipython:: python
-
-   data = { 'Label1' : pd.Panel({ 'Item1' : pd.DataFrame(np.random.randn(4, 3)) }),
-            'Label2' : pd.Panel({ 'Item2' : pd.DataFrame(np.random.randn(4, 2)) }) }
-   pd.Panel4D(data)
-
-Note that the values in the dict need only be **convertible to Panels**.
-Thus, they can be any of the other valid inputs to Panel as per above.
-
-Slicing
-~~~~~~~
-
-Slicing works in a similar manner to a Panel. ``[]`` slices the first dimension.
-``.ix`` allows you to slice arbitrarily and get back lower dimensional objects
-
-.. ipython:: python
-
-   p4d['Label1']
-
-4D -> Panel
-
-.. ipython:: python
-
-   p4d.ix[:,:,:,'A']
-
-4D -> DataFrame
-
-.. ipython:: python
-
-   p4d.ix[:,:,0,'A']
-
-4D -> Series
-
-.. ipython:: python
-
-   p4d.ix[:,0,0,'A']
-
-Transposing
-~~~~~~~~~~~
-
-A Panel4D can be rearranged using its ``transpose`` method (which does not make a
-copy by default unless the data are heterogeneous):
-
-.. ipython:: python
-
-   p4d.transpose(3, 2, 1, 0)
-
 .. _dsintro.panelnd:
+.. _dsintro.panel4d:
 
-PanelND (Experimental)
-----------------------
+Panel4D and PanelND (Deprecated)
+--------------------------------
 
 .. warning::
 
-   In 0.19.0 ``PanelND`` is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the `xarray package <http://xarray.pydata.org/en/stable/>`__.
+    In 0.19.0 ``Panel4D`` and ``PanelND`` are deprecated and will be removed in
+    a future version. The recommended way to represent these types of
+    n-dimensional data are with the
+    `xarray package <http://xarray.pydata.org/en/stable/>`__.
+    Pandas provides a :meth:`~Panel4D.to_xarray` method to automate
+    this conversion.
 
-PanelND is a module with a set of factory functions to enable a user to construct N-dimensional named
-containers like Panel4D, with a custom set of axis labels. Thus a domain-specific container can easily be
-created.
-
-The following creates a Panel5D. A new panel type object must be sliceable into a lower dimensional object.
-Here we slice to a Panel4D.
-
-.. ipython:: python
-    :okwarning:
-
-    from pandas.core import panelnd
-    Panel5D = panelnd.create_nd_panel_factory(
-        klass_name   = 'Panel5D',
-        orders  = [ 'cool', 'labels','items','major_axis','minor_axis'],
-        slices  = { 'labels' : 'labels', 'items' : 'items',
-	                'major_axis' : 'major_axis', 'minor_axis' : 'minor_axis' },
-        slicer  = pd.Panel4D,
-        aliases = { 'major' : 'major_axis', 'minor' : 'minor_axis' },
-        stat_axis    = 2)
-
-    p5d = Panel5D(dict(C1 = p4d))
-    p5d
-
-    # print a slice of our 5D
-    p5d.ix['C1',:,:,0:3,:]
-
-    # transpose it
-    p5d.transpose(1,2,3,4,0)
-
-    # look at the shape & dim
-    p5d.shape
-    p5d.ndim
+See the `docs of a previous version <http://pandas.pydata.org/pandas-docs/version/0.18.1/dsintro.html#panel4d-experimental>`__
+for documentation on these objects.

doc/source/install.rst

@@ -255,6 +255,7 @@ Optional Dependencies
 
 * `matplotlib <http://matplotlib.org/>`__: for plotting
 * For Excel I/O:
+
   * `xlrd/xlwt <http://www.python-excel.org/>`__: Excel reading (xlrd) and writing (xlwt)
   * `openpyxl <http://packages.python.org/openpyxl/>`__: openpyxl version 1.6.1
     or higher (but lower than 2.0.0), or version 2.2 or higher, for writing .xlsx files (xlrd >= 0.9.0)
@@ -296,8 +297,8 @@ Optional Dependencies
        <html-gotchas>`. It explains issues surrounding the installation and
        usage of the above three libraries
      * You may need to install an older version of `BeautifulSoup4`_:
-       - Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and
-         32-bit Ubuntu/Debian
+       Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and 32-bit
+       Ubuntu/Debian
      * Additionally, if you're using `Anaconda`_ you should definitely
        read :ref:`the gotchas about HTML parsing libraries <html-gotchas>`
 

doc/source/sparse.rst

@@ -9,7 +9,7 @@
    import pandas as pd
    import pandas.util.testing as tm
    np.set_printoptions(precision=4, suppress=True)
-   options.display.max_rows = 15
+   pd.options.display.max_rows = 15
 
 **********************
 Sparse data structures
@@ -90,38 +90,10 @@ can be converted back to a regular ndarray by calling ``to_dense``:
 SparseList
 ----------
 
-.. note:: The ``SparseList`` class has been deprecated and will be removed in a future version.
+The ``SparseList`` class has been deprecated and will be removed in a future version.
+See the `docs of a previous version <http://pandas.pydata.org/pandas-docs/version/0.18.1/sparse.html#sparselist>`__
+for documentation on ``SparseList``.
 
-``SparseList`` is a list-like data structure for managing a dynamic collection
-of SparseArrays. To create one, simply call the ``SparseList`` constructor with
-a ``fill_value`` (defaulting to ``NaN``):
-
-.. ipython:: python
-
-   spl = pd.SparseList()
-   spl
-
-The two important methods are ``append`` and ``to_array``. ``append`` can
-accept scalar values or any 1-dimensional sequence:
-
-.. ipython:: python
-   :suppress:
-
-.. ipython:: python
-
-   spl.append(np.array([1., np.nan, np.nan, 2., 3.]))
-   spl.append(5)
-   spl.append(sparr)
-   spl
-
-As you can see, all of the contents are stored internally as a list of
-memory-efficient ``SparseArray`` objects. Once you've accumulated all of the
-data, you can call ``to_array`` to get a single ``SparseArray`` with all the
-data:
-
-.. ipython:: python
-
-   spl.to_array()
 
 SparseIndex objects
 -------------------

doc/source/timeseries.rst

@@ -1219,7 +1219,7 @@ objects.
    ts.shift(1)
 
 The shift method accepts an ``freq`` argument which can accept a
-``DateOffset`` class or other ``timedelta``-like object or also a :ref:`offset alias <timeseries.alias>`:
+``DateOffset`` class or other ``timedelta``-like object or also a :ref:`offset alias <timeseries.offset_aliases>`:
 
 .. ipython:: python
 
@@ -1494,7 +1494,7 @@ level of ``MultiIndex``, its name or location can be passed to the
 
 .. ipython:: python
 
-   df.resample(level='d').sum()
+   df.resample('M', level='d').sum()
 
 
 .. _timeseries.periods:
@@ -1630,8 +1630,6 @@ Period Dtypes
 ``PeriodIndex`` has a custom ``period`` dtype. This is a pandas extension
 dtype similar to the :ref:`timezone aware dtype <timeseries.timezone_series>` (``datetime64[ns, tz]``).
 
-.. _timeseries.timezone_series:
-
 The ``period`` dtype holds the ``freq`` attribute and is represented with
 ``period[freq]`` like ``period[D]`` or ``period[M]``, using :ref:`frequency strings <timeseries.offset_aliases>`.
 

doc/source/whatsnew/v0.14.1.txt

@@ -156,7 +156,7 @@ Experimental
 ~~~~~~~~~~~~
 
 - ``pandas.io.data.Options`` has a new method, ``get_all_data`` method, and now consistently returns a
-  multi-indexed ``DataFrame``, see :ref:`the docs <remote_data.yahoo_options>`. (:issue:`5602`)
+  multi-indexed ``DataFrame`` (:issue:`5602`)
 - ``io.gbq.read_gbq`` and ``io.gbq.to_gbq`` were refactored to remove the
   dependency on the Google ``bq.py`` command line client. This submodule
   now uses ``httplib2`` and the Google ``apiclient`` and ``oauth2client`` API client

doc/source/whatsnew/v0.15.1.txt

@@ -185,8 +185,6 @@ API changes
              2014-11-22 call AAPL141122C00110000  1.02
              2014-11-28 call AAPL141128C00110000  1.32
 
-  See the Options documentation in :ref:`Remote Data  <remote_data.yahoo_options>`
-
 .. _whatsnew_0151.datetime64_plotting:
 
 - pandas now also registers the ``datetime64`` dtype in matplotlib's units registry
@@ -257,7 +255,7 @@ Enhancements
 
 - Added support for 3-character ISO and non-standard country codes in :func:`io.wb.download()` (:issue:`8482`)
 
-- :ref:`World Bank data requests <remote_data.wb>` now will warn/raise based
+- World Bank data requests now will warn/raise based
   on an ``errors`` argument, as well as a list of hard-coded country codes and
   the World Bank's JSON response.  In prior versions, the error messages
   didn't look at the World Bank's JSON response.  Problem-inducing input were

doc/source/whatsnew/v0.8.0.txt

@@ -59,7 +59,7 @@ Time series changes and improvements
   aggregation functions, and control over how the intervals and result labeling
   are defined. A suite of high performance Cython/C-based resampling functions
   (including Open-High-Low-Close) have also been implemented.
-- Revamp of :ref:`frequency aliases <timeseries.alias>` and support for
+- Revamp of :ref:`frequency aliases <timeseries.offset_aliases>` and support for
   **frequency shortcuts** like '15min', or '1h30min'
 - New :ref:`DatetimeIndex class <timeseries.datetimeindex>` supports both fixed
   frequency and irregular time

pandas/core/generic.py

@@ -3998,7 +3998,7 @@ def asfreq(self, freq, method=None, how=None, normalize=False):
         converted : type of caller
 
         To learn more about the frequency strings, please see `this link
-        <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
+<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
         """
         from pandas.tseries.resample import asfreq
         return asfreq(self, freq, method=method, how=how, normalize=normalize)

pandas/io/gbq.py

@@ -630,16 +630,20 @@ def read_gbq(query, project_id=None, index_col=None, col_order=None,
     https://developers.google.com/api-client-library/python/apis/bigquery/v2
 
     Authentication to the Google BigQuery service is via OAuth 2.0.
+
     - If "private_key" is not provided:
-        By default "application default credentials" are used.
 
-        .. versionadded:: 0.19.0
+      By default "application default credentials" are used.
+
+      .. versionadded:: 0.19.0
+
+      If default application credentials are not found or are restrictive,
+      user account credentials are used. In this case, you will be asked to
+      grant permissions for product name 'pandas GBQ'.
 
-        If default application credentials are not found or are restrictive,
-        user account credentials are used. In this case, you will be asked to
-        grant permissions for product name 'pandas GBQ'.
     - If "private_key" is provided:
-        Service account credentials will be used to authenticate.
+
+      Service account credentials will be used to authenticate.
 
     Parameters
     ----------
@@ -747,16 +751,20 @@ def to_gbq(dataframe, destination_table, project_id, chunksize=10000,
     https://developers.google.com/api-client-library/python/apis/bigquery/v2
 
     Authentication to the Google BigQuery service is via OAuth 2.0.
+
     - If "private_key" is not provided:
-        By default "application default credentials" are used.
 
-        .. versionadded:: 0.19.0
+      By default "application default credentials" are used.
+
+      .. versionadded:: 0.19.0
+
+      If default application credentials are not found or are restrictive,
+      user account credentials are used. In this case, you will be asked to
+      grant permissions for product name 'pandas GBQ'.
 
-        If default application credentials are not found or are restrictive,
-        user account credentials are used. In this case, you will be asked to
-        grant permissions for product name 'pandas GBQ'.
     - If "private_key" is provided:
-        Service account credentials will be used to authenticate.
+
+      Service account credentials will be used to authenticate.
 
     Parameters
     ----------