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DOC: Remove makePanel from docs (#25609) #25612

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DOC: Remove makePanel from docs (#25609) #25612

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cde4cee
DOC: Remove makePanel from docs (#25609)
ArtificialQualia Mar 8, 2019
9060c9a
Merge branch 'master' of https://github.com/pandas-dev/pandas into re…
ArtificialQualia Mar 8, 2019
63627ff
removing unused import
ArtificialQualia Mar 9, 2019
1a7f6ce
convert old whatsnew using makePanel to code-blocks
ArtificialQualia Mar 9, 2019
14dd01a
fixes for passing linting
ArtificialQualia Mar 9, 2019
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44 changes: 0 additions & 44 deletions doc/source/getting_started/dsintro.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1030,47 +1030,3 @@ method:
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 code base.

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 addition, 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 use cases.
`Here is a link to the xarray panel-transition documentation <https://xarray.pydata.org/en/stable/pandas.html#panel-transition>`__.

.. ipython:: python
:okwarning:
import pandas.util.testing as tm
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
:okwarning:
p.to_xarray()
You can see the full-documentation for the `xarray package <https://xarray.pydata.org/en/stable/>`__.
149 changes: 122 additions & 27 deletions doc/source/whatsnew/v0.13.1.rst
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Expand Up @@ -222,60 +222,155 @@ Enhancements
- Panel :meth:`~pandas.Panel.apply` will work on non-ufuncs. See :ref:`the docs<basics.apply>`.

.. ipython:: python
.. code-block:: ipython
In [28]: import pandas.util.testing as tm
In [29]: panel = tm.makePanel(5)
import pandas.util.testing as tm
jreback marked this conversation as resolved.
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panel = tm.makePanel(5)
panel
panel['ItemA']
In [30]: panel
Out[30]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [31]: panel['ItemA']
Out[31]:
A B C D
2000-01-03 -0.673690 0.577046 -1.344312 -1.469388
2000-01-04 0.113648 -1.715002 0.844885 0.357021
2000-01-05 -1.478427 -1.039268 1.075770 -0.674600
2000-01-06 0.524988 -0.370647 -0.109050 -1.776904
2000-01-07 0.404705 -1.157892 1.643563 -0.968914
[5 rows x 4 columns]
Specifying an ``apply`` that operates on a Series (to return a single element)

.. ipython:: python
.. code-block:: ipython
In [32]: panel.apply(lambda x: x.dtype, axis='items')
Out[32]:
A B C D
2000-01-03 float64 float64 float64 float64
2000-01-04 float64 float64 float64 float64
2000-01-05 float64 float64 float64 float64
2000-01-06 float64 float64 float64 float64
2000-01-07 float64 float64 float64 float64
panel.apply(lambda x: x.dtype, axis='items')
[5 rows x 4 columns]
A similar reduction type operation

.. ipython:: python
.. code-block:: ipython
In [33]: panel.apply(lambda x: x.sum(), axis='major_axis')
Out[33]:
ItemA ItemB ItemC
A -1.108775 -1.090118 -2.984435
B -3.705764 0.409204 1.866240
C 2.110856 2.960500 -0.974967
D -4.532785 0.303202 -3.685193
panel.apply(lambda x: x.sum(), axis='major_axis')
[4 rows x 3 columns]
This is equivalent to

.. ipython:: python
.. code-block:: ipython
In [34]: panel.sum('major_axis')
Out[34]:
ItemA ItemB ItemC
A -1.108775 -1.090118 -2.984435
B -3.705764 0.409204 1.866240
C 2.110856 2.960500 -0.974967
D -4.532785 0.303202 -3.685193
panel.sum('major_axis')
[4 rows x 3 columns]
A transformation operation that returns a Panel, but is computing
the z-score across the major_axis

.. ipython:: python
.. code-block:: ipython
result = panel.apply(lambda x: (x - x.mean()) / x.std(),
axis='major_axis')
result
result['ItemA']
In [35]: result = panel.apply(lambda x: (x - x.mean()) / x.std(),
....: axis='major_axis')
....:
In [36]: result
Out[36]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [37]: result['ItemA'] # noqa E999
Out[37]:
A B C D
2000-01-03 -0.535778 1.500802 -1.506416 -0.681456
2000-01-04 0.397628 -1.108752 0.360481 1.529895
2000-01-05 -1.489811 -0.339412 0.557374 0.280845
2000-01-06 0.885279 0.421830 -0.453013 -1.053785
2000-01-07 0.742682 -0.474468 1.041575 -0.075499
[5 rows x 4 columns]
- Panel :meth:`~pandas.Panel.apply` operating on cross-sectional slabs. (:issue:`1148`)

.. ipython:: python
.. code-block:: ipython
def f(x):
return ((x.T - x.mean(1)) / x.std(1)).T
In [38]: def f(x):
....: return ((x.T - x.mean(1)) / x.std(1)).T
....:
result = panel.apply(f, axis=['items', 'major_axis'])
result
result.loc[:, :, 'ItemA']
In [39]: result = panel.apply(f, axis=['items', 'major_axis'])
This is equivalent to the following
In [40]: result
Out[40]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
.. ipython:: python
In [41]: result.loc[:, :, 'ItemA']
Out[41]:
A B C D
2000-01-03 0.012922 -0.030874 -0.629546 -0.757034
2000-01-04 0.392053 -1.071665 0.163228 0.548188
2000-01-05 -1.093650 -0.640898 0.385734 -1.154310
2000-01-06 1.005446 -1.154593 -0.595615 -0.809185
2000-01-07 0.783051 -0.198053 0.919339 -1.052721
[5 rows x 4 columns]
result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
This is equivalent to the following

.. code-block:: ipython
result
result.loc[:, :, 'ItemA']
In [42]: result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
In [43]: result
Out[43]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [44]: result.loc[:, :, 'ItemA']
Out[44]:
A B C D
2000-01-03 0.012922 -0.030874 -0.629546 -0.757034
2000-01-04 0.392053 -1.071665 0.163228 0.548188
2000-01-05 -1.093650 -0.640898 0.385734 -1.154310
2000-01-06 1.005446 -1.154593 -0.595615 -0.809185
2000-01-07 0.783051 -0.198053 0.919339 -1.052721
[5 rows x 4 columns]
Performance
~~~~~~~~~~~
Expand Down
64 changes: 50 additions & 14 deletions doc/source/whatsnew/v0.20.0.rst
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Expand Up @@ -45,11 +45,6 @@ Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations
New features
~~~~~~~~~~~~

.. ipython:: python
:suppress:
import pandas.util.testing as tm
.. _whatsnew_0200.enhancements.agg:

``agg`` API for DataFrame/Series
Expand Down Expand Up @@ -1363,24 +1358,65 @@ Deprecate Panel
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. For more details see :ref:`Deprecate Panel <dsintro.deprecate_panel>` documentation. (:issue:`13563`).

.. ipython:: python
:okwarning:
.. code-block:: ipython
p = tm.makePanel()
p
In [133]: import pandas.util.testing as tm
In [134]: p = tm.makePanel()
In [135]: p
Out[135]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D
Convert to a MultiIndex DataFrame

.. ipython:: python
.. code-block:: ipython
p.to_frame()
In [136]: p.to_frame()
Out[136]:
ItemA ItemB ItemC
major minor
2000-01-03 A 0.628776 -1.409432 0.209395
B 0.988138 -1.347533 -0.896581
C -0.938153 1.272395 -0.161137
D -0.223019 -0.591863 -1.051539
2000-01-04 A 0.186494 1.422986 -0.592886
B -0.072608 0.363565 1.104352
C -1.239072 -1.449567 0.889157
D 2.123692 -0.414505 -0.319561
2000-01-05 A 0.952478 -2.147855 -1.473116
B -0.550603 -0.014752 -0.431550
C 0.139683 -1.195524 0.288377
D 0.122273 -1.425795 -0.619993
[12 rows x 3 columns]
Convert to an xarray DataArray

.. ipython:: python
:okwarning:
.. code-block:: ipython
p.to_xarray()
In [137]: p.to_xarray()
Out[137]:
<xarray.DataArray (items: 3, major_axis: 3, minor_axis: 4)>
array([[[ 0.628776, 0.988138, -0.938153, -0.223019],
[ 0.186494, -0.072608, -1.239072, 2.123692],
[ 0.952478, -0.550603, 0.139683, 0.122273]],
[[-1.409432, -1.347533, 1.272395, -0.591863],
[ 1.422986, 0.363565, -1.449567, -0.414505],
[-2.147855, -0.014752, -1.195524, -1.425795]],
[[ 0.209395, -0.896581, -0.161137, -1.051539],
[-0.592886, 1.104352, 0.889157, -0.319561],
[-1.473116, -0.43155 , 0.288377, -0.619993]]])
Coordinates:
* items (items) object 'ItemA' 'ItemB' 'ItemC'
* major_axis (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05
* minor_axis (minor_axis) object 'A' 'B' 'C' 'D'
.. _whatsnew_0200.api_breaking.deprecate_group_agg_dict:

Expand Down
60 changes: 48 additions & 12 deletions doc/source/whatsnew/v0.23.0.rst
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Expand Up @@ -646,29 +646,65 @@ Deprecate Panel
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. For more details see :ref:`Deprecate Panel <dsintro.deprecate_panel>` documentation. (:issue:`13563`, :issue:`18324`).

.. ipython:: python
:suppress:
.. code-block:: ipython
import pandas.util.testing as tm
In [75]: import pandas.util.testing as tm
.. ipython:: python
:okwarning:
In [76]: p = tm.makePanel()
p = tm.makePanel()
p
In [77]: p
Out[77]:
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to D
Convert to a MultiIndex DataFrame

.. ipython:: python
.. code-block:: ipython
p.to_frame()
In [78]: p.to_frame()
Out[78]:
ItemA ItemB ItemC
major minor
2000-01-03 A 0.469112 0.721555 0.404705
B -1.135632 0.271860 -1.039268
C 0.119209 0.276232 -1.344312
D -2.104569 0.113648 -0.109050
2000-01-04 A -0.282863 -0.706771 0.577046
B 1.212112 -0.424972 -0.370647
C -1.044236 -1.087401 0.844885
D -0.494929 -1.478427 1.643563
2000-01-05 A -1.509059 -1.039575 -1.715002
B -0.173215 0.567020 -1.157892
C -0.861849 -0.673690 1.075770
D 1.071804 0.524988 -1.469388
[12 rows x 3 columns]
Convert to an xarray DataArray

.. ipython:: python
:okwarning:
.. code-block:: ipython
p.to_xarray()
In [79]: p.to_xarray()
Out[79]:
<xarray.DataArray (items: 3, major_axis: 3, minor_axis: 4)>
array([[[ 0.469112, -1.135632, 0.119209, -2.104569],
[-0.282863, 1.212112, -1.044236, -0.494929],
[-1.509059, -0.173215, -0.861849, 1.071804]],
[[ 0.721555, 0.27186 , 0.276232, 0.113648],
[-0.706771, -0.424972, -1.087401, -1.478427],
[-1.039575, 0.56702 , -0.67369 , 0.524988]],
[[ 0.404705, -1.039268, -1.344312, -0.10905 ],
[ 0.577046, -0.370647, 0.844885, 1.643563],
[-1.715002, -1.157892, 1.07577 , -1.469388]]])
Coordinates:
* items (items) object 'ItemA' 'ItemB' 'ItemC'
* major_axis (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05
* minor_axis (minor_axis) object 'A' 'B' 'C' 'D'
.. _whatsnew_0230.api_breaking.core_common:
Expand Down