CLN: Panel reference from documentation (#25649)

Author: mroeschke

doc/source/conf.py

@@ -16,7 +16,6 @@
 import inspect
 import importlib
 import logging
-import warnings
 import jinja2
 from sphinx.ext.autosummary import _import_by_name
 from numpydoc.docscrape import NumpyDocString
@@ -412,12 +411,6 @@
                      'wiki ')}
 
 
-# ignore all deprecation warnings from Panel during doc build
-# (to avoid the need to add :okwarning: in many places)
-warnings.filterwarnings("ignore", message="\nPanel is deprecated",
-                        category=FutureWarning)
-
-
 ipython_warning_is_error = False
 ipython_exec_lines = [
     'import numpy as np',

doc/source/development/extending.rst

@@ -271,7 +271,7 @@ There are 3 constructor properties to be defined:
 
 * ``_constructor``: Used when a manipulation result has the same dimensions 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()``.
+* ``_constructor_expanddim``: Used when a manipulation result has one higher dimension as the original, such as ``Series.to_frame()``.
 
 Following table shows how ``pandas`` data structures define constructor properties by default.
 
@@ -280,7 +280,7 @@ Property Attributes          ``Series``              ``DataFrame``
 ===========================  ======================= =============
 ``_constructor``             ``Series``              ``DataFrame``
 ``_constructor_sliced``      ``NotImplementedError`` ``Series``
-``_constructor_expanddim``   ``DataFrame``           ``Panel``
+``_constructor_expanddim``   ``DataFrame``           ``NotImplementedError``
 ===========================  ======================= =============
 
 Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame`` overriding constructor properties.

doc/source/ecosystem.rst

@@ -217,7 +217,7 @@ Pandas DataFrames with timeseries indexes.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 PyDatastream is a Python interface to the
 `Thomson Dataworks Enterprise (DWE/Datastream) <http://dataworks.thomson.com/Dataworks/Enterprise/1.0/>`__
-SOAP API to return indexed Pandas DataFrames or Panels with financial data.
+SOAP API to return indexed Pandas DataFrames with financial data.
 This package requires valid credentials for this API (non free).
 
 `pandaSDMX <https://pandasdmx.readthedocs.io>`__

doc/source/getting_started/10min.rst

@@ -449,8 +449,8 @@ Merge
 Concat
 ~~~~~~
 
-pandas provides various facilities for easily combining together Series,
-DataFrame, and Panel objects with various kinds of set logic for the indexes
+pandas provides various facilities for easily combining together Series and
+DataFrame objects with various kinds of set logic for the indexes
 and relational algebra functionality in the case of join / merge-type
 operations.
 

doc/source/getting_started/basics.rst

@@ -16,9 +16,6 @@ the previous section:
    s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
    df = pd.DataFrame(np.random.randn(8, 3), index=index,
                      columns=['A', 'B', 'C'])
-   wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
-                 major_axis=pd.date_range('1/1/2000', periods=5),
-                 minor_axis=['A', 'B', 'C', 'D'])
 
 .. _basics.head_tail:
 
@@ -46,7 +43,6 @@ pandas objects have a number of attributes enabling you to access the metadata
 * Axis labels
     * **Series**: *index* (only axis)
     * **DataFrame**: *index* (rows) and *columns*
-    * **Panel**: *items*, *major_axis*, and *minor_axis*
 
 Note, **these attributes can be safely assigned to**!
 
@@ -118,7 +114,7 @@ columns, :meth:`DataFrame.to_numpy` will return the underlying data:
 
    df.to_numpy()
 
-If a DataFrame or Panel contains homogeneously-typed data, the ndarray can
+If a DataFrame contains homogeneously-typed data, the ndarray can
 actually be modified in-place, and the changes will be reflected in the data
 structure. For heterogeneous data (e.g. some of the DataFrame's columns are not
 all the same dtype), this will not be the case. The values attribute itself,
@@ -239,26 +235,6 @@ Furthermore you can align a level of a MultiIndexed DataFrame with a Series.
                                           names=['first', 'second'])
    dfmi.sub(column, axis=0, level='second')
 
-With Panel, describing the matching behavior is a bit more difficult, so
-the arithmetic methods instead (and perhaps confusingly?) give you the option
-to specify the *broadcast axis*. For example, suppose we wished to demean the
-data over a particular axis. This can be accomplished by taking the mean over
-an axis and broadcasting over the same axis:
-
-.. ipython:: python
-
-   major_mean = wp.mean(axis='major')
-   major_mean
-   wp.sub(major_mean, axis='major')
-
-And similarly for ``axis="items"`` and ``axis="minor"``.
-
-.. note::
-
-   I could be convinced to make the **axis** argument in the DataFrame methods
-   match the broadcasting behavior of Panel. Though it would require a
-   transition period so users can change their code...
-
 Series and Index also support the :func:`divmod` builtin. This function takes
 the floor division and modulo operation at the same time returning a two-tuple
 of the same type as the left hand side. For example:
@@ -407,7 +383,7 @@ This is because NaNs do not compare as equals:
 
    np.nan == np.nan
 
-So, NDFrames (such as Series, DataFrames, and Panels)
+So, NDFrames (such as Series and DataFrames)
 have an :meth:`~DataFrame.equals` method for testing equality, with NaNs in
 corresponding locations treated as equal.
 
@@ -515,7 +491,7 @@ Descriptive statistics
 
 There exists a large number of methods for computing descriptive statistics and
 other related operations on :ref:`Series <api.series.stats>`, :ref:`DataFrame
-<api.dataframe.stats>`, and :ref:`Panel <api.panel.stats>`. Most of these
+<api.dataframe.stats>`. Most of these
 are aggregations (hence producing a lower-dimensional result) like
 :meth:`~DataFrame.sum`, :meth:`~DataFrame.mean`, and :meth:`~DataFrame.quantile`,
 but some of them, like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod`,
@@ -525,8 +501,6 @@ specified by name or integer:
 
 * **Series**: no axis argument needed
 * **DataFrame**: "index" (axis=0, default), "columns" (axis=1)
-* **Panel**: "items" (axis=0), "major" (axis=1, default), "minor"
-  (axis=2)
 
 For example:
 
@@ -1481,15 +1455,14 @@ Iteration
 
 The behavior of basic iteration over pandas objects depends on the type.
 When iterating over a Series, it is regarded as array-like, and basic iteration
-produces the values. Other data structures, like DataFrame and Panel,
+produces the values. Other data structures, like DataFrame,
 follow the dict-like convention of iterating over the "keys" of the
 objects.
 
 In short, basic iteration (``for i in object``) produces:
 
 * **Series**: values
 * **DataFrame**: column labels
-* **Panel**: item labels
 
 Thus, for example, iterating over a DataFrame gives you the column names:
 
@@ -1559,13 +1532,12 @@ through key-value pairs:
 
 * **Series**: (index, scalar value) pairs
 * **DataFrame**: (column, Series) pairs
-* **Panel**: (item, DataFrame) pairs
 
 For example:
 
 .. ipython:: python
 
-   for item, frame in wp.iteritems():
+   for item, frame in df.iteritems():
        print(item)
        print(frame)
 

doc/source/getting_started/dsintro.rst

@@ -847,186 +847,3 @@ completion mechanism so they can be tab-completed:
 
     In [5]: df.fo<TAB>  # noqa: E225, E999
     df.foo1  df.foo2
-
-.. _basics.panel:
-
-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 <https://en.wikipedia.org/wiki/Panel_data>`__ is
-derived from econometrics and is partially responsible for the name pandas:
-pan(el)-da(ta)-s. The names for the 3 axes are intended to give some semantic
-meaning to describing operations involving panel data and, in particular,
-econometric analysis of panel data. However, for the strict purposes of slicing
-and dicing a collection of DataFrame objects, you may find the axis names
-slightly arbitrary:
-
-* **items**: axis 0, each item corresponds to a DataFrame contained inside
-* **major_axis**: axis 1, it is the **index** (rows) of each of the
-  DataFrames
-* **minor_axis**: axis 2, it is the **columns** of each of the DataFrames
-
-Construction of Panels works about like you would expect:
-
-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),
-                 minor_axis=['A', 'B', 'C', 'D'])
-   wp
-
-
-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))}
-   pd.Panel(data)
-
-Note that the values in the dict need only be **convertible to
-DataFrame**. Thus, they can be any of the other valid inputs to DataFrame as
-per above.
-
-One helpful factory method is ``Panel.from_dict``, which takes a
-dictionary of DataFrames as above, and the following named parameters:
-
-.. csv-table::
-   :header: "Parameter", "Default", "Description"
-   :widths: 10, 10, 40
-
-   intersect, ``False``, drops elements whose indices do not align
-   orient, ``items``, use ``minor`` to use DataFrames' columns as panel items
-
-For example, compare to the construction above:
-
-.. ipython:: python
-   :okwarning:
-
-   pd.Panel.from_dict(data, orient='minor')
-
-Orient is especially useful for mixed-type DataFrames. If you pass a dict of
-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)})
-   df
-   data = {'item1': df, 'item2': df}
-   panel = pd.Panel.from_dict(data, orient='minor')
-   panel['a']
-   panel['b']
-   panel['b'].dtypes
-
-.. note::
-
-   Panel, being less commonly used than Series and DataFrame,
-   has been slightly neglected feature-wise. A number of methods and options
-   available in DataFrame are not available in Panel.
-
-.. _dsintro.to_panel:
-
-From DataFrame using ``to_panel`` method
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-``to_panel`` converts a DataFrame with a two-level index to a Panel.
-
-.. ipython:: python
-   :okwarning:
-
-   midx = pd.MultiIndex(levels=[['one', 'two'], ['x', 'y']],
-                        codes=[[1, 1, 0, 0], [1, 0, 1, 0]])
-   df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
-   df.to_panel()
-
-.. _dsintro.panel_item_selection:
-
-Item selection / addition / deletion
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Similar to DataFrame functioning as a dict of Series, Panel is like a dict
-of DataFrames:
-
-.. ipython:: python
-
-   wp['Item1']
-   wp['Item3'] = wp['Item1'] / wp['Item2']
-
-The API for insertion and deletion is the same as for DataFrame. And as with
-DataFrame, if the item is a valid Python identifier, you can access it as an
-attribute and tab-complete it in IPython.
-
-Transposing
-~~~~~~~~~~~
-
-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)
-
-Indexing / Selection
-~~~~~~~~~~~~~~~~~~~~
-
-.. csv-table::
-    :header: "Operation", "Syntax", "Result"
-    :widths: 30, 20, 10
-
-    Select item, ``wp[item]``, DataFrame
-    Get slice at major_axis label, ``wp.major_xs(val)``, DataFrame
-    Get slice at minor_axis label, ``wp.minor_xs(val)``, DataFrame
-
-For example, using the earlier example data, we could do:
-
-.. ipython:: python
-
-    wp['Item1']
-    wp.major_xs(wp.major_axis[2])
-    wp.minor_axis
-    wp.minor_xs('C')
-
-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()
-
-
-Conversion to DataFrame
-~~~~~~~~~~~~~~~~~~~~~~~
-
-A Panel can be represented in 2D form as a hierarchically indexed
-DataFrame. See the section :ref:`hierarchical indexing <advanced.hierarchical>`
-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()

doc/source/install.rst

@@ -255,7 +255,7 @@ Optional Dependencies
 * `Cython <http://www.cython.org>`__: Only necessary to build development
   version. Version 0.28.2 or higher.
 * `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.18.1 or higher
-* `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
+* `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims. Version 0.7.0 or higher is recommended.
 * `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage, Version 3.4.2 or higher
 * `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.9.0): necessary for feather-based storage.
 * `Apache Parquet <https://parquet.apache.org/>`__, either `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.7.0) or `fastparquet <https://fastparquet.readthedocs.io/en/latest>`__ (>= 0.2.1) for parquet-based storage. The `snappy <https://pypi.org/project/python-snappy>`__ and `brotli <https://pypi.org/project/brotlipy>`__ are available for compression support.

doc/source/reference/index.rst

@@ -57,15 +57,6 @@ public functions related to data types in pandas.
         api/pandas.Index.is_type_compatible
         api/pandas.Index.nlevels
         api/pandas.Index.sort
-        api/pandas.Panel.agg
-        api/pandas.Panel.aggregate
-        api/pandas.Panel.blocks
-        api/pandas.Panel.empty
-        api/pandas.Panel.is_copy
-        api/pandas.Panel.items
-        api/pandas.Panel.ix
-        api/pandas.Panel.major_axis
-        api/pandas.Panel.minor_axis
         api/pandas.Series.asobject
         api/pandas.Series.blocks
         api/pandas.Series.from_array