matplotlib · WeatherGod · Dec 30, 2015 · Nov 24, 2015 · Nov 24, 2015 · Dec 13, 2015
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -2331,11 +2331,11 @@ def broken_barh(self, xranges, yrange, **kwargs):
         """
         # process the unit information
         if len(xranges):
-            xdata = six.next(iter(xranges))
+            xdata = cbook.safe_first_element(xranges)
         else:
             xdata = None
         if len(yrange):
-            ydata = six.next(iter(yrange))
+            ydata = cbook.safe_first_element(yrange)
         else:
             ydata = None
         self._process_unit_info(xdata=xdata,
@@ -3016,7 +3016,7 @@ def xywhere(xs, ys, mask):
 
         if ecolor is None:
             if l0 is None and 'color' in self._get_lines._prop_keys:
-                ecolor = six.next(self._get_lines.prop_cycler)['color']
+                ecolor = next(self._get_lines.prop_cycler)['color']
             else:
                 ecolor = l0.get_color()
 
@@ -5875,6 +5875,41 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
         .. plot:: mpl_examples/statistics/histogram_demo_features.py
 
         """
+        def _normalize_input(inp, ename='input'):
+            """Normalize 1 or 2d input into list of np.ndarray or
+            a single 2D np.ndarray.
+
+            Parameters
+            ----------
+            inp : iterable
+            ename : str, optional
+                Name to use in ValueError if `inp` can not be normalized
+
+            """
+            if (isinstance(x, np.ndarray) or
+                    not iterable(cbook.safe_first_element(inp))):
+                # TODO: support masked arrays;
+                inp = np.asarray(inp)
+                if inp.ndim == 2:
+                    # 2-D input with columns as datasets; switch to rows
+                    inp = inp.T
+                elif inp.ndim == 1:
+                    # new view, single row
+                    inp = inp.reshape(1, inp.shape[0])
+                else:
+                    raise ValueError(
+                        "{ename} must be 1D or 2D".format(ename=ename))
+                if inp.shape[1] < inp.shape[0]:
+                    warnings.warn(
+                        '2D hist input should be nsamples x nvariables;\n '
+                        'this looks transposed '
+                        '(shape is %d x %d)' % inp.shape[::-1])
+            else:
+                # multiple hist with data of different length
+                inp = [np.asarray(xi) for xi in inp]
+
+            return inp
+
         if not self._hold:
             self.cla()
 
@@ -5918,58 +5953,34 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
         input_empty = len(flat) == 0
 
         # Massage 'x' for processing.
-        # NOTE: Be sure any changes here is also done below to 'weights'
         if input_empty:
             x = np.array([[]])
-        elif isinstance(x, np.ndarray) or not iterable(x[0]):
-            # TODO: support masked arrays;
-            x = np.asarray(x)
-            if x.ndim == 2:
-                x = x.T  # 2-D input with columns as datasets; switch to rows
-            elif x.ndim == 1:
-                x = x.reshape(1, x.shape[0])  # new view, single row
-            else:
-                raise ValueError("x must be 1D or 2D")
-            if x.shape[1] < x.shape[0]:
-                warnings.warn(
-                    '2D hist input should be nsamples x nvariables;\n '
-                    'this looks transposed (shape is %d x %d)' % x.shape[::-1])
         else:
-            # multiple hist with data of different length
-            x = [np.asarray(xi) for xi in x]
-
+            x = _normalize_input(x, 'x')
         nx = len(x)  # number of datasets
 
+        # We need to do to 'weights' what was done to 'x'
+        if weights is not None:
+            w = _normalize_input(weights, 'weights')
+        else:
+            w = [None]*nx
+
+        if len(w) != nx:
+            raise ValueError('weights should have the same shape as x')
+
+        for xi, wi in zip(x, w):
+            if wi is not None and len(wi) != len(xi):
+                raise ValueError(
+                    'weights should have the same shape as x')
+
         if color is None and 'color' in self._get_lines._prop_keys:
-            color = [six.next(self._get_lines.prop_cycler)['color']
+            color = [next(self._get_lines.prop_cycler)['color']
                      for i in xrange(nx)]
         else:
             color = mcolors.colorConverter.to_rgba_array(color)
             if len(color) != nx:
                 raise ValueError("color kwarg must have one color per dataset")
 
-        # We need to do to 'weights' what was done to 'x'
-        if weights is not None:
-            if isinstance(weights, np.ndarray) or not iterable(weights[0]):
-                w = np.array(weights)
-                if w.ndim == 2:
-                    w = w.T
-                elif w.ndim == 1:
-                    w.shape = (1, w.shape[0])
-                else:
-                    raise ValueError("weights must be 1D or 2D")
-            else:
-                w = [np.asarray(wi) for wi in weights]
-
-            if len(w) != nx:
-                raise ValueError('weights should have the same shape as x')
-            for i in xrange(nx):
-                if len(w[i]) != len(x[i]):
-                    raise ValueError(
-                        'weights should have the same shape as x')
-        else:
-            w = [None]*nx
-
         # Save the datalimits for the same reason:
         _saved_bounds = self.dataLim.bounds
 
@@ -5985,7 +5996,7 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
                     xmax = max(xmax, xi.max())
             bin_range = (xmin, xmax)
 
-        #hist_kwargs = dict(range=range, normed=bool(normed))
+        # hist_kwargs = dict(range=range, normed=bool(normed))
         # We will handle the normed kwarg within mpl until we
         # get to the point of requiring numpy >= 1.5.
         hist_kwargs = dict(range=bin_range)

diff --git a/lib/matplotlib/axes/_base.py b/lib/matplotlib/axes/_base.py
@@ -32,7 +32,7 @@
 import matplotlib.image as mimage
 from matplotlib.offsetbox import OffsetBox
 from matplotlib.artist import allow_rasterization
-from matplotlib.cbook import iterable, index_of
+
 from matplotlib.rcsetup import cycler
 
 rcParams = matplotlib.rcParams

diff --git a/lib/matplotlib/backends/backend_pdf.py b/lib/matplotlib/backends/backend_pdf.py
@@ -1846,7 +1846,7 @@ def draw_tex(self, gc, x, y, s, prop, angle, ismath='TeX!', mtext=None):
         fontsize = prop.get_size_in_points()
         dvifile = texmanager.make_dvi(s, fontsize)
         dvi = dviread.Dvi(dvifile, 72)
-        page = six.next(iter(dvi))
+        page = next(iter(dvi))
         dvi.close()
 
         # Gather font information and do some setup for combining

diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
@@ -12,6 +12,7 @@
 from matplotlib.externals import six
 from matplotlib.externals.six.moves import xrange, zip
 from itertools import repeat
+import collections
 
 import datetime
 import errno
@@ -2536,6 +2537,13 @@ def index_of(y):
         return np.arange(y.shape[0], dtype=float), y
 
 
+def safe_first_element(obj):
+    if isinstance(obj, collections.Iterator):
+        raise RuntimeError("matplotlib does not support generators "
+                           "as input")
+    return next(iter(obj))
+
+
 def get_label(y, default_name):
     try:
         return y.name

diff --git a/lib/matplotlib/dates.py b/lib/matplotlib/dates.py
@@ -1561,8 +1561,8 @@ def default_units(x, axis):
             x = x.ravel()
 
         try:
-            x = x[0]
-        except (TypeError, IndexError):
+            x = cbook.safe_first_element(x)
+        except (TypeError, StopIteration):
             pass
 
         try:

diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -7,6 +7,7 @@
 import io
 
 from nose.tools import assert_equal, assert_raises, assert_false, assert_true
+from nose.plugins.skip import SkipTest
 
 import datetime
 
@@ -4183,6 +4184,36 @@ def test_broken_barh_empty():
     ax.broken_barh([], (.1, .5))
 
 
+@cleanup
+def test_pandas_indexing_dates():
+    try:
+        import pandas as pd
+    except ImportError:
+        raise SkipTest("Pandas not installed")
+
+    dates = np.arange('2005-02', '2005-03', dtype='datetime64[D]')
+    values = np.sin(np.array(range(len(dates))))
+    df = pd.DataFrame({'dates': dates, 'values': values})
+
+    ax = plt.gca()
+
+    without_zero_index = df[np.array(df.index) % 2 == 1].copy()
+    ax.plot('dates', 'values', data=without_zero_index)
+
+
+@cleanup
+def test_pandas_indexing_hist():
+    try:
+        import pandas as pd
+    except ImportError:
+        raise SkipTest("Pandas not installed")
+
+    ser_1 = pd.Series(data=[1, 2, 2, 3, 3, 4, 4, 4, 4, 5])
+    ser_2 = ser_1.iloc[1:]
+    fig, axes = plt.subplots()
+    axes.hist(ser_2)
+
+
 if __name__ == '__main__':
     import nose
     import sys