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Merge remote-tracking branch 'upstream/master' into to_timedelta-float
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‎ci/code_checks.sh

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@@ -241,8 +241,8 @@ fi
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### DOCSTRINGS ###
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if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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MSG='Validate docstrings (GL06, GL07, GL09, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT04, RT05, SA05)' ; echo $MSG
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$BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL06,GL07,GL09,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT04,RT05,SA05
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MSG='Validate docstrings (GL03, GL06, GL07, GL09, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT04, RT05, SA05)' ; echo $MSG
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$BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL06,GL07,GL09,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT04,RT05,SA05
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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fi

‎doc/source/getting_started/dsintro.rst

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@@ -1030,47 +1030,3 @@ method:
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major_axis=pd.date_range('1/1/2000', periods=5),
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minor_axis=['a', 'b', 'c', 'd'])
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panel.to_frame()
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.. _dsintro.deprecate_panel:
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Deprecate Panel
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---------------
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Over the last few years, pandas has increased in both breadth and depth, with new features,
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datatype support, and manipulation routines. As a result, supporting efficient indexing and functional
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routines for ``Series``, ``DataFrame`` and ``Panel`` has contributed to an increasingly fragmented and
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difficult-to-understand code base.
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The 3-D structure of a ``Panel`` is much less common for many types of data analysis,
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than the 1-D of the ``Series`` or the 2-D of the ``DataFrame``. Going forward it makes sense for
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pandas to focus on these areas exclusively.
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Oftentimes, one can simply use a MultiIndex ``DataFrame`` for easily working with higher dimensional data.
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In addition, the ``xarray`` package was built from the ground up, specifically in order to
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support the multi-dimensional analysis that is one of ``Panel`` s main use cases.
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`Here is a link to the xarray panel-transition documentation <https://xarray.pydata.org/en/stable/pandas.html#panel-transition>`__.
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.. ipython:: python
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:okwarning:
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import pandas.util.testing as tm
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p = tm.makePanel()
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p
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Convert to a MultiIndex DataFrame.
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.. ipython:: python
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:okwarning:
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p.to_frame()
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Alternatively, one can convert to an xarray ``DataArray``.
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.. ipython:: python
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:okwarning:
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p.to_xarray()
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You can see the full-documentation for the `xarray package <https://xarray.pydata.org/en/stable/>`__.

‎doc/source/whatsnew/v0.13.1.rst

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@@ -222,60 +222,155 @@ Enhancements
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- Panel :meth:`~pandas.Panel.apply` will work on non-ufuncs. See :ref:`the docs<basics.apply>`.
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.. ipython:: python
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.. code-block:: ipython
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In [28]: import pandas.util.testing as tm
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In [29]: panel = tm.makePanel(5)
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import pandas.util.testing as tm
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panel = tm.makePanel(5)
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panel
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panel['ItemA']
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In [30]: panel
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Out[30]:
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<class 'pandas.core.panel.Panel'>
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Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
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Items axis: ItemA to ItemC
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Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
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Minor_axis axis: A to D
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In [31]: panel['ItemA']
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Out[31]:
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A B C D
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2000-01-03 -0.673690 0.577046 -1.344312 -1.469388
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2000-01-04 0.113648 -1.715002 0.844885 0.357021
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2000-01-05 -1.478427 -1.039268 1.075770 -0.674600
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2000-01-06 0.524988 -0.370647 -0.109050 -1.776904
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2000-01-07 0.404705 -1.157892 1.643563 -0.968914
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[5 rows x 4 columns]
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Specifying an ``apply`` that operates on a Series (to return a single element)
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.. ipython:: python
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.. code-block:: ipython
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In [32]: panel.apply(lambda x: x.dtype, axis='items')
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Out[32]:
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A B C D
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2000-01-03 float64 float64 float64 float64
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2000-01-04 float64 float64 float64 float64
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2000-01-05 float64 float64 float64 float64
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2000-01-06 float64 float64 float64 float64
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2000-01-07 float64 float64 float64 float64
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panel.apply(lambda x: x.dtype, axis='items')
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[5 rows x 4 columns]
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A similar reduction type operation
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.. ipython:: python
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.. code-block:: ipython
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In [33]: panel.apply(lambda x: x.sum(), axis='major_axis')
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Out[33]:
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ItemA ItemB ItemC
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A -1.108775 -1.090118 -2.984435
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B -3.705764 0.409204 1.866240
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C 2.110856 2.960500 -0.974967
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D -4.532785 0.303202 -3.685193
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panel.apply(lambda x: x.sum(), axis='major_axis')
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[4 rows x 3 columns]
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This is equivalent to
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.. ipython:: python
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.. code-block:: ipython
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In [34]: panel.sum('major_axis')
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Out[34]:
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ItemA ItemB ItemC
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A -1.108775 -1.090118 -2.984435
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B -3.705764 0.409204 1.866240
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C 2.110856 2.960500 -0.974967
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D -4.532785 0.303202 -3.685193
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panel.sum('major_axis')
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[4 rows x 3 columns]
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A transformation operation that returns a Panel, but is computing
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the z-score across the major_axis
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.. ipython:: python
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.. code-block:: ipython
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result = panel.apply(lambda x: (x - x.mean()) / x.std(),
256-
axis='major_axis')
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result
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result['ItemA']
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In [35]: result = panel.apply(lambda x: (x - x.mean()) / x.std(),
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....: axis='major_axis')
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....:
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In [36]: result
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Out[36]:
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<class 'pandas.core.panel.Panel'>
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Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
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Items axis: ItemA to ItemC
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Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
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Minor_axis axis: A to D
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In [37]: result['ItemA'] # noqa E999
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Out[37]:
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A B C D
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2000-01-03 -0.535778 1.500802 -1.506416 -0.681456
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2000-01-04 0.397628 -1.108752 0.360481 1.529895
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2000-01-05 -1.489811 -0.339412 0.557374 0.280845
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2000-01-06 0.885279 0.421830 -0.453013 -1.053785
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2000-01-07 0.742682 -0.474468 1.041575 -0.075499
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[5 rows x 4 columns]
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- Panel :meth:`~pandas.Panel.apply` operating on cross-sectional slabs. (:issue:`1148`)
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.. ipython:: python
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.. code-block:: ipython
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def f(x):
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return ((x.T - x.mean(1)) / x.std(1)).T
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In [38]: def f(x):
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....: return ((x.T - x.mean(1)) / x.std(1)).T
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....:
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result = panel.apply(f, axis=['items', 'major_axis'])
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result
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result.loc[:, :, 'ItemA']
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In [39]: result = panel.apply(f, axis=['items', 'major_axis'])
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This is equivalent to the following
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In [40]: result
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Out[40]:
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<class 'pandas.core.panel.Panel'>
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Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
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Items axis: A to D
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Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
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Minor_axis axis: ItemA to ItemC
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.. ipython:: python
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In [41]: result.loc[:, :, 'ItemA']
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Out[41]:
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A B C D
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2000-01-03 0.012922 -0.030874 -0.629546 -0.757034
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2000-01-04 0.392053 -1.071665 0.163228 0.548188
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2000-01-05 -1.093650 -0.640898 0.385734 -1.154310
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2000-01-06 1.005446 -1.154593 -0.595615 -0.809185
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2000-01-07 0.783051 -0.198053 0.919339 -1.052721
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[5 rows x 4 columns]
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result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
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This is equivalent to the following
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.. code-block:: ipython
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result
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result.loc[:, :, 'ItemA']
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In [42]: result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
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In [43]: result
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Out[43]:
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<class 'pandas.core.panel.Panel'>
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Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
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Items axis: A to D
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Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
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Minor_axis axis: ItemA to ItemC
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In [44]: result.loc[:, :, 'ItemA']
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Out[44]:
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A B C D
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2000-01-03 0.012922 -0.030874 -0.629546 -0.757034
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2000-01-04 0.392053 -1.071665 0.163228 0.548188
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2000-01-05 -1.093650 -0.640898 0.385734 -1.154310
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2000-01-06 1.005446 -1.154593 -0.595615 -0.809185
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2000-01-07 0.783051 -0.198053 0.919339 -1.052721
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[5 rows x 4 columns]
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Performance
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~~~~~~~~~~~

‎doc/source/whatsnew/v0.20.0.rst

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New features
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~~~~~~~~~~~~
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.. ipython:: python
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:suppress:
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import pandas.util.testing as tm
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.. _whatsnew_0200.enhancements.agg:
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``agg`` API for DataFrame/Series
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13631358
with a ``MultiIndex`` on a ``DataFrame`` via the :meth:`~Panel.to_frame` or with the `xarray package <http://xarray.pydata.org/en/stable/>`__. Pandas
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provides a :meth:`~Panel.to_xarray` method to automate this conversion. For more details see :ref:`Deprecate Panel <dsintro.deprecate_panel>` documentation. (:issue:`13563`).
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1366-
.. ipython:: python
1367-
:okwarning:
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.. code-block:: ipython
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p = tm.makePanel()
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p
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In [133]: import pandas.util.testing as tm
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In [134]: p = tm.makePanel()
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In [135]: p
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Out[135]:
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<class 'pandas.core.panel.Panel'>
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Dimensions: 3 (items) x 3 (major_axis) x 4 (minor_axis)
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Items axis: ItemA to ItemC
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Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
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Minor_axis axis: A to D
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Convert to a MultiIndex DataFrame
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.. ipython:: python
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.. code-block:: ipython
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p.to_frame()
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In [136]: p.to_frame()
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Out[136]:
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ItemA ItemB ItemC
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major minor
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2000-01-03 A 0.628776 -1.409432 0.209395
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B 0.988138 -1.347533 -0.896581
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C -0.938153 1.272395 -0.161137
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D -0.223019 -0.591863 -1.051539
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2000-01-04 A 0.186494 1.422986 -0.592886
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B -0.072608 0.363565 1.104352
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C -1.239072 -1.449567 0.889157
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D 2.123692 -0.414505 -0.319561
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2000-01-05 A 0.952478 -2.147855 -1.473116
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B -0.550603 -0.014752 -0.431550
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C 0.139683 -1.195524 0.288377
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D 0.122273 -1.425795 -0.619993
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[12 rows x 3 columns]
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Convert to an xarray DataArray
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1380-
.. ipython:: python
1381-
:okwarning:
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.. code-block:: ipython
13821401
1383-
p.to_xarray()
1402+
In [137]: p.to_xarray()
1403+
Out[137]:
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<xarray.DataArray (items: 3, major_axis: 3, minor_axis: 4)>
1405+
array([[[ 0.628776, 0.988138, -0.938153, -0.223019],
1406+
[ 0.186494, -0.072608, -1.239072, 2.123692],
1407+
[ 0.952478, -0.550603, 0.139683, 0.122273]],
1408+
1409+
[[-1.409432, -1.347533, 1.272395, -0.591863],
1410+
[ 1.422986, 0.363565, -1.449567, -0.414505],
1411+
[-2.147855, -0.014752, -1.195524, -1.425795]],
1412+
1413+
[[ 0.209395, -0.896581, -0.161137, -1.051539],
1414+
[-0.592886, 1.104352, 0.889157, -0.319561],
1415+
[-1.473116, -0.43155 , 0.288377, -0.619993]]])
1416+
Coordinates:
1417+
* items (items) object 'ItemA' 'ItemB' 'ItemC'
1418+
* major_axis (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05
1419+
* minor_axis (minor_axis) object 'A' 'B' 'C' 'D'
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.. _whatsnew_0200.api_breaking.deprecate_group_agg_dict:
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