pandas.plotting now supports table keywords

Author: sinhrks

doc/source/release.rst

@@ -161,6 +161,17 @@ API Changes
 
 - Added ``nunique`` and ``value_counts`` functions to ``Index`` for counting unique elements. (:issue:`6734`)
 
+- ``DataFrame.plot`` and ``Series.plot`` now support a ``table`` keyword for plotting ``matplotlib.Table``. The ``table`` kewyword can receive the following values. 
+  
+  - ``False``: Do nothing (default).
+
+  - ``True``: Draw a table using the ``DataFrame`` or ``Series`` called ``plot`` method. Data will be transposed to meet matplotlib's default layout.
+  
+  - ``DataFrame`` or ``Series``: Draw matplotlib.table using the passed data. The data will be drawn as displayed in print method (not transposed automatically).
+
+  Also, helper function ``pandas.tools.plotting.table`` is added to create a table from ``DataFrame`` and ``Series``, and add it to an ``matplotlib.Axes``.
+
+
 Deprecations
 ~~~~~~~~~~~~
 

doc/source/v0.14.0.txt

@@ -201,6 +201,17 @@ API changes
 
 - Added ``nunique`` and ``value_counts`` functions to ``Index`` for counting unique elements. (:issue:`6734`)
 
+- ``DataFrame.plot`` and ``Series.plot`` now support a ``table`` keyword for plotting ``matplotlib.Table``. The ``table`` kewyword can receive the following values. 
+  
+  - ``False``: Do nothing (default).
+
+  - ``True``: Draw a table using the ``DataFrame`` or ``Series`` called ``plot`` method. Data will be transposed to meet matplotlib's default layout.
+  
+  - ``DataFrame`` or ``Series``: Draw matplotlib.table using the passed data. The data will be drawn as displayed in print method (not transposed automatically).
+
+  Also, helper function ``pandas.tools.plotting.table`` is added to create a table from ``DataFrame`` and ``Series``, and add it to an ``matplotlib.Axes``.
+
+
 MultiIndexing Using Slicers
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

doc/source/visualization.rst

@@ -415,6 +415,48 @@ Here is an example of one way to easily plot group means with standard deviation
    @savefig errorbar_example.png
    means.plot(yerr=errors, ax=ax, kind='bar')
 
+Plotting With Table
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.14
+
+Plotting with matplotlib table is now supported in the ``DataFrame.plot`` and ``Series.plot`` by a ``table`` keyword. The ``table`` keyword can accept ``bool``, ``DataFrame`` or ``Series``. The simple way to draw a table is to specify ``table=True``. Data will be transposed to meet matplotlib's default layout.
+
+.. ipython:: python
+
+   fig, ax = plt.subplots(1, 1)
+   df = DataFrame(rand(5, 3), columns=['a', 'b', 'c'])
+   ax.get_xaxis().set_visible(False)   # Hide Ticks
+
+   @savefig line_plot_table_true.png
+   df.plot(table=True, ax=ax)
+
+Also, you can pass different ``DataFrame`` or ``Series`` for ``table`` keyword. The data will be drawn as displayed in print method (not transposed automatically). If required, it should be transposed manually as below example.
+
+.. ipython:: python
+
+   fig, ax = plt.subplots(1, 1)
+   ax.get_xaxis().set_visible(False)   # Hide Ticks
+   @savefig line_plot_table_data.png
+   df.plot(table=np.round(df.T, 2), ax=ax)
+
+
+Finally, there is a helper function ``pandas.tools.plotting.table`` to create a table from ``DataFrame`` and ``Series``, and add it to an ``matplotlib.Axes``. This function can accept keywords which matplotlib table has.
+
+.. ipython:: python
+
+   from pandas.tools.plotting import table
+   fig, ax = plt.subplots(1, 1)
+
+   table(ax, np.round(df.describe(), 2), 
+         loc='upper right', colWidths=[0.2, 0.2, 0.2])
+
+   @savefig line_plot_table_describe.png
+   df.plot(ax=ax, ylim=(0, 2), legend=None)
+
+**Note**: You can get table instances on the axes using ``axes.tables`` property for further decorations. See the `matplotlib table documenation <http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table>`__ for more.
+
+
 .. _visualization.scatter_matrix:
 
 Scatter plot matrix

pandas/tests/test_graphics.py

@@ -309,6 +309,11 @@ def test_errorbar_plot(self):
         with tm.assertRaises(TypeError):
             s.plot(yerr=s_err)
 
+    def test_table(self):
+        _check_plot_works(self.series.plot, table=True)
+        _check_plot_works(self.series.plot, table=self.series)
+
+
 @tm.mplskip
 class TestDataFramePlots(tm.TestCase):
     def setUp(self):
@@ -1335,6 +1340,18 @@ def test_errorbar_asymmetrical(self):
 
         tm.close()
 
+    def test_table(self):
+        df = DataFrame(np.random.rand(10, 3),
+                       index=list(string.ascii_letters[:10]))
+        _check_plot_works(df.plot, table=True)
+        _check_plot_works(df.plot, table=df)
+
+        ax = df.plot()
+        self.assert_(len(ax.tables) == 0)
+        plotting.table(ax, df.T)
+        self.assert_(len(ax.tables) == 1)
+
+
 @tm.mplskip
 class TestDataFrameGroupByPlots(tm.TestCase):
     def tearDown(self):

pandas/tools/plotting.py

@@ -793,7 +793,8 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=True,
                  ax=None, fig=None, title=None, xlim=None, ylim=None,
                  xticks=None, yticks=None,
                  sort_columns=False, fontsize=None,
-                 secondary_y=False, colormap=None, **kwds):
+                 secondary_y=False, colormap=None,
+                 table=False, **kwds):
 
         self.data = data
         self.by = by
@@ -849,6 +850,8 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=True,
         else:
             self.colormap = colormap
 
+        self.table = table
+
         self.kwds = kwds
 
         self._validate_color_args()
@@ -915,6 +918,7 @@ def generate(self):
         self._compute_plot_data()
         self._setup_subplots()
         self._make_plot()
+        self._add_table()
         self._post_plot_logic()
         self._adorn_subplots()
 
@@ -1005,6 +1009,21 @@ def _compute_plot_data(self):
     def _make_plot(self):
         raise NotImplementedError
 
+    def _add_table(self):
+        if self.table is False:
+            return
+        elif self.table is True:
+            from pandas.core.frame import DataFrame
+            if isinstance(self.data, Series):
+                data = DataFrame(self.data, columns=[self.data.name])
+            elif isinstance(self.data, DataFrame):
+                data = self.data
+            data = data.transpose()
+        else:
+            data = self.table    
+        ax = self._get_ax(0) 
+        table(ax, data)
+
     def _post_plot_logic(self):
         pass
 
@@ -1664,7 +1683,6 @@ def _post_plot_logic(self):
             for ax in self.axes:
                 ax.legend(loc='best')
 
-
 class BarPlot(MPLPlot):
 
     _default_rot = {'bar': 90, 'barh': 0}
@@ -2594,6 +2612,47 @@ def _grouped_plot_by_column(plotf, data, columns=None, by=None,
     return fig, axes
 
 
+def table(ax, data, rowLabels=None, colLabels=None,
+          **kwargs):
+
+    """
+    Helper function to convert DataFrame and Series to matplotlib.table
+
+    Parameters
+    ----------
+    `ax`: Matplotlib axes object
+    `data`: DataFrame or Series
+        data for table contents
+    `kwargs`: keywords, optional
+        keyword arguments which passed to matplotlib.table.table.
+        If `rowLabels` or `colLabels` is not specified, data index or column name will be used.
+
+    Returns
+    -------
+    matplotlib table object
+    """
+    from pandas import DataFrame
+    if isinstance(data, Series):
+        data = DataFrame(data, columns=[data.name])
+    elif isinstance(data, DataFrame):
+        pass
+    else:
+        raise ValueError('Input data must be dataframe or series')
+
+    if rowLabels is None:
+        rowLabels = data.index
+
+    if colLabels is None:
+        colLabels = data.columns
+
+    cellText = data.values
+
+    import matplotlib.table
+    table = matplotlib.table.table(ax, cellText=cellText,
+        rowLabels=rowLabels, colLabels=colLabels, **kwargs)
+    return table
+
+
 def _get_layout(nplots):
     if nplots == 1:
         return (1, 1)