TST: fixed up plotting issues

doc/source/v0.15.0.txt

@@ -27,7 +27,7 @@ users upgrade to this version.
 
 .. warning::
 
-   In 0.15.0 ``Index`` has internaly been refactored to no longer sub-class ``ndarray``
+   In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray``
    but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be
    a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`)
 
@@ -141,7 +141,7 @@ API changes
 Internal Refactoring
 ~~~~~~~~~~~~~~~~~~~~
 
-In 0.15.0 ``Index`` has internaly been refactored to no longer sub-class ``ndarray``
+In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray``
 but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. This should be
 a transparent change with only very limited API implications (:issue:`5080`,:issue:`7439`,:issue:`7796`)
 
@@ -158,6 +158,7 @@ a transparent change with only very limited API implications (:issue:`5080`,:iss
      arr<=idx
      arr>idx
 
+- when plotting with a ``PeriodIndex``. The ``matplotlib`` internal axes will now be arrays of ``Period`` rather than a ``PeriodIndex``. (this is similar to how a ``DatetimeIndex`` passess arrays of ``datetimes`` now)
 - ``:func:`~pandas.core.index.MultiIndex.repeat` now is available
 
 .. _whatsnew_0150.cat:
@@ -284,7 +285,7 @@ Performance
 ~~~~~~~~~~~
 
 - Performance improvements in ``DatetimeIndex.__iter__`` to allow faster iteration (:issue:`7683`)
-
+- Performance improvements in ``Period`` creation (and ``PeriodIndex`` setitem) (:issue:`5155`)
 
 
 

pandas/tests/test_graphics.py

@@ -1000,7 +1000,8 @@ def test_xcompat(self):
         pd.plot_params['x_compat'] = False
         ax = df.plot()
         lines = ax.get_lines()
-        tm.assert_isinstance(lines[0].get_xdata(), PeriodIndex)
+        self.assertNotIsInstance(lines[0].get_xdata(), PeriodIndex)
+        self.assertIsInstance(PeriodIndex(lines[0].get_xdata()), PeriodIndex)
 
         tm.close()
         # useful if you're plotting a bunch together
@@ -1012,7 +1013,8 @@ def test_xcompat(self):
         tm.close()
         ax = df.plot()
         lines = ax.get_lines()
-        tm.assert_isinstance(lines[0].get_xdata(), PeriodIndex)
+        self.assertNotIsInstance(lines[0].get_xdata(), PeriodIndex)
+        self.assertIsInstance(PeriodIndex(lines[0].get_xdata()), PeriodIndex)
 
     def test_unsorted_index(self):
         df = DataFrame({'y': np.arange(100)},

pandas/tseries/converter.py

@@ -117,7 +117,7 @@ def convert(values, units, axis):
         if isinstance(values, Index):
             return values.map(lambda x: get_datevalue(x, axis.freq))
         if isinstance(values, (list, tuple, np.ndarray, Index)):
-            return [get_datevalue(x, axis.freq) for x in values]
+            return PeriodIndex(values, freq=axis.freq).values
         return values
 
 

pandas/tseries/period.py

@@ -63,6 +63,14 @@ class Period(PandasObject):
     __slots__ = ['freq', 'ordinal']
     _comparables = ['name','freqstr']
 
+    @classmethod
+    def from_ordinal(cls, ordinal, freq):
+        """ fast creation from an ordinal and freq that are already validated! """
+        self = object.__new__(cls)
+        self.ordinal = ordinal
+        self.freq = freq
+        return self
+
     def __init__(self, value=None, freq=None, ordinal=None,
                  year=None, month=1, quarter=None, day=1,
                  hour=0, minute=0, second=0):
@@ -705,7 +713,7 @@ def __contains__(self, key):
 
     @property
     def _box_func(self):
-        return lambda x: Period(ordinal=x, freq=self.freq)
+        return lambda x: Period.from_ordinal(ordinal=x, freq=self.freq)
 
     def asof_locs(self, where, mask):
         """
@@ -809,9 +817,7 @@ def map(self, f):
 
     def _get_object_array(self):
         freq = self.freq
-        boxfunc = lambda x: Period(ordinal=x, freq=freq)
-        boxer = np.frompyfunc(boxfunc, 1, 1)
-        return boxer(self.values)
+        return np.array([ Period.from_ordinal(ordinal=x, freq=freq) for x in self.values], copy=False)
 
     def _mpl_repr(self):
         # how to represent ourselves to matplotlib

pandas/tseries/plotting.py

@@ -61,7 +61,7 @@ def tsplot(series, plotf, **kwargs):
     if not hasattr(ax, '_plot_data'):
         ax._plot_data = []
     ax._plot_data.append((series, plotf, kwargs))
-    lines = plotf(ax, series.index, series.values, **kwargs)
+    lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs)
 
     # set date formatter, locators and rescale limits
     format_dateaxis(ax, ax.freq)
@@ -152,7 +152,7 @@ def _replot_ax(ax, freq, kwargs):
             idx = series.index.asfreq(freq, how='S')
             series.index = idx
             ax._plot_data.append(series)
-            lines.append(plotf(ax, series.index, series.values, **kwds)[0])
+            lines.append(plotf(ax, series.index._mpl_repr(), series.values, **kwds)[0])
             labels.append(com.pprint_thing(series.name))
 
     return lines, labels

pandas/tseries/tests/test_plotting.py

@@ -298,7 +298,7 @@ def test_dataframe(self):
         bts = DataFrame({'a': tm.makeTimeSeries()})
         ax = bts.plot()
         idx = ax.get_lines()[0].get_xdata()
-        assert_array_equal(bts.index.to_period(), idx)
+        assert_array_equal(bts.index.to_period(), PeriodIndex(idx))
 
     @slow
     def test_axis_limits(self):
@@ -605,8 +605,8 @@ def test_mixed_freq_regular_first(self):
         ax = s1.plot()
         ax2 = s2.plot(style='g')
         lines = ax2.get_lines()
-        idx1 = lines[0].get_xdata()
-        idx2 = lines[1].get_xdata()
+        idx1 = PeriodIndex(lines[0].get_xdata())
+        idx2 = PeriodIndex(lines[1].get_xdata())
         self.assertTrue(idx1.equals(s1.index.to_period('B')))
         self.assertTrue(idx2.equals(s2.index.to_period('B')))
         left, right = ax2.get_xlim()
@@ -881,9 +881,9 @@ def test_secondary_upsample(self):
         low.plot()
         ax = high.plot(secondary_y=True)
         for l in ax.get_lines():
-            self.assertEqual(l.get_xdata().freq, 'D')
+            self.assertEqual(PeriodIndex(l.get_xdata()).freq, 'D')
         for l in ax.right_ax.get_lines():
-            self.assertEqual(l.get_xdata().freq, 'D')
+            self.assertEqual(PeriodIndex(l.get_xdata()).freq, 'D')
 
     @slow
     def test_secondary_legend(self):