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CLN: Removed trellis rplot #13855

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248 changes: 5 additions & 243 deletions doc/source/visualization.rst
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Expand Up @@ -1615,246 +1615,8 @@ Trellis plotting interface

.. warning::

The ``rplot`` trellis plotting interface is **deprecated and will be removed
in a future version**. We refer to external packages like
`seaborn <https://github.com/mwaskom/seaborn>`_ for similar but more
refined functionality.

The docs below include some example on how to convert your existing code to
``seaborn``.

.. ipython:: python
:suppress:

tips_data = pd.read_csv('data/tips.csv')
iris_data = pd.read_csv('data/iris.data')
plt.close('all')


.. note::

The tips data set can be downloaded `here
<https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv>`__. Once you download it execute

.. code-block:: python

tips_data = pd.read_csv('tips.csv')

from the directory where you downloaded the file.

We import the rplot API:

.. ipython:: python
:okwarning:

import pandas.tools.rplot as rplot

Examples
~~~~~~~~

RPlot was an API for producing Trellis plots. These plots allow you to
arrange data in a rectangular grid by values of certain attributes.
In the example below, data from the tips data set is arranged by the attributes
'sex' and 'smoker'. Since both of those attributes can take on one of two
values, the resulting grid has two columns and two rows. A histogram is
displayed for each cell of the grid.

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='total_bill', y='tip')
plot.add(rplot.TrellisGrid(['sex', 'smoker']))
plot.add(rplot.GeomHistogram())

@savefig rplot1_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

A similar plot can be made with ``seaborn`` using the ``FacetGrid`` object,
resulting in the following image:

.. code-block:: python

import seaborn as sns
g = sns.FacetGrid(tips_data, row="sex", col="smoker")
g.map(plt.hist, "total_bill")

.. image:: _static/rplot-seaborn-example1.png


Example below is the same as previous except the plot is set to kernel density
estimation. A ``seaborn`` example is included beneath.

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='total_bill', y='tip')
plot.add(rplot.TrellisGrid(['sex', 'smoker']))
plot.add(rplot.GeomDensity())

@savefig rplot2_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

.. code-block:: python

g = sns.FacetGrid(tips_data, row="sex", col="smoker")
g.map(sns.kdeplot, "total_bill")

.. image:: _static/rplot-seaborn-example2.png

The plot below shows that it is possible to have two or more plots for the same
data displayed on the same Trellis grid cell.

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='total_bill', y='tip')
plot.add(rplot.TrellisGrid(['sex', 'smoker']))
plot.add(rplot.GeomScatter())
plot.add(rplot.GeomPolyFit(degree=2))

@savefig rplot3_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

A seaborn equivalent for a simple scatter plot:

.. code-block:: python

g = sns.FacetGrid(tips_data, row="sex", col="smoker")
g.map(plt.scatter, "total_bill", "tip")

.. image:: _static/rplot-seaborn-example3.png

and with a regression line, using the dedicated ``seaborn`` ``regplot`` function:

.. code-block:: python

g = sns.FacetGrid(tips_data, row="sex", col="smoker", margin_titles=True)
g.map(sns.regplot, "total_bill", "tip", order=2)

.. image:: _static/rplot-seaborn-example3b.png


Below is a similar plot but with 2D kernel density estimation plot superimposed,
followed by a ``seaborn`` equivalent:

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='total_bill', y='tip')
plot.add(rplot.TrellisGrid(['sex', 'smoker']))
plot.add(rplot.GeomScatter())
plot.add(rplot.GeomDensity2D())

@savefig rplot4_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

.. code-block:: python

g = sns.FacetGrid(tips_data, row="sex", col="smoker")
g.map(plt.scatter, "total_bill", "tip")
g.map(sns.kdeplot, "total_bill", "tip")

.. image:: _static/rplot-seaborn-example4.png

It is possible to only use one attribute for grouping data. The example above
only uses 'sex' attribute. If the second grouping attribute is not specified,
the plots will be arranged in a column.

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='total_bill', y='tip')
plot.add(rplot.TrellisGrid(['sex', '.']))
plot.add(rplot.GeomHistogram())

@savefig rplot5_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

If the first grouping attribute is not specified the plots will be arranged in a row.

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='total_bill', y='tip')
plot.add(rplot.TrellisGrid(['.', 'smoker']))
plot.add(rplot.GeomHistogram())

@savefig rplot6_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

In ``seaborn``, this can also be done by only specifying one of the ``row``
and ``col`` arguments.

In the example below the colour and shape of the scatter plot graphical
objects is mapped to 'day' and 'size' attributes respectively. You use
scale objects to specify these mappings. The list of scale classes is
given below with initialization arguments for quick reference.

.. ipython:: python
:okwarning:

plt.figure()

plot = rplot.RPlot(tips_data, x='tip', y='total_bill')
plot.add(rplot.TrellisGrid(['sex', 'smoker']))
plot.add(rplot.GeomPoint(size=80.0, colour=rplot.ScaleRandomColour('day'), shape=rplot.ScaleShape('size'), alpha=1.0))

@savefig rplot7_tips.png
plot.render(plt.gcf())

.. ipython:: python
:suppress:

plt.close('all')

This can also be done in ``seaborn``, at least for 3 variables:

.. code-block:: python

g = sns.FacetGrid(tips_data, row="sex", col="smoker", hue="day")
g.map(plt.scatter, "tip", "total_bill")
g.add_legend()

.. image:: _static/rplot-seaborn-example6.png
The ``rplot`` trellis plotting interface has been **removed**. Please use
external packages like `seaborn <https://github.com/mwaskom/seaborn>`_ for
similar but more refined functionality and refer to our 0.18.1 documentation
`here <http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html>`__
for how to convert to using it.
2 changes: 2 additions & 0 deletions doc/source/whatsnew/v0.19.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -693,6 +693,8 @@ Removal of prior version deprecations/changes
- The ``pd.sandbox`` module has been removed in favor of the external library ``pandas-qt`` (:issue:`13670`)
- The ``pandas.io.data`` and ``pandas.io.wb`` modules are removed in favor of
the `pandas-datareader package <https://github.com/pydata/pandas-datareader>`__ (:issue:`13724`).
- The ``pandas.tools.rplot`` module has been removed in favor of
the `seaborn package <https://github.com/mwaskom/seaborn>`__ (:issue:`13855`)
- ``DataFrame.to_csv()`` has dropped the ``engine`` parameter, as was deprecated in 0.17.1 (:issue:`11274`, :issue:`13419`)
- ``DataFrame.to_dict()`` has dropped the ``outtype`` parameter in favor of ``orient`` (:issue:`13627`, :issue:`8486`)
- ``pd.Categorical`` has dropped setting of the ``ordered`` attribute directly in favor of the ``set_ordered`` method (:issue:`13671`)
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