FIX: non-indexed pd.Series into hist

tacaswell · tacaswell · commit 9c37e3ffffb6 · 2015-12-28T19:02:08.000-05:00
closes #5557
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -5875,6 +5875,40 @@ 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 is ValueError if can not be normalized
+
+            """
+            if isinstance(x, np.ndarray) or not iterable(next(iter(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()
 
@@ -5918,28 +5952,26 @@ 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']
                      for i in xrange(nx)]
@@ -5948,28 +5980,6 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
             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
 
