diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
index b18b935c8ead..9b26842a766c 100644
--- a/lib/matplotlib/cbook.py
+++ b/lib/matplotlib/cbook.py
@@ -1340,9 +1340,9 @@ def _to_unmasked_float_array(x):
     values are converted to nans.
     """
     if hasattr(x, 'mask'):
-        return np.ma.asarray(x, float).filled(np.nan)
+        return np.ma.asanyarray(x, float).filled(np.nan)
     else:
-        return np.asarray(x, float)
+        return np.asanyarray(x, float)
 
 
 def _check_1d(x):
diff --git a/lib/matplotlib/collections.py b/lib/matplotlib/collections.py
index 2e72926cbe5d..a3f245bbc2c8 100644
--- a/lib/matplotlib/collections.py
+++ b/lib/matplotlib/collections.py
@@ -1242,15 +1242,15 @@ def set_verts(self, verts, closed=True):
             return
 
         # Fast path for arrays
-        if isinstance(verts, np.ndarray) and len(verts.shape) == 3:
+        if isinstance(verts, np.ndarray) and len(verts.shape) == 3 and verts.size:
             verts_pad = np.concatenate((verts, verts[:, :1]), axis=1)
-            # Creating the codes once is much faster than having Path do it
-            # separately each time by passing closed=True.
-            codes = np.empty(verts_pad.shape[1], dtype=mpath.Path.code_type)
-            codes[:] = mpath.Path.LINETO
-            codes[0] = mpath.Path.MOVETO
-            codes[-1] = mpath.Path.CLOSEPOLY
-            self._paths = [mpath.Path(xy, codes) for xy in verts_pad]
+            # It's faster to create the codes and internal flags once in a
+            # template path and reuse them.
+            template_path = mpath.Path(verts_pad[0], closed=True)
+            codes = template_path.codes
+            _make_path = mpath.Path._fast_from_codes_and_verts
+            self._paths = [_make_path(xy, codes, internals_from=template_path)
+                           for xy in verts_pad]
             return
 
         self._paths = []
diff --git a/lib/matplotlib/path.py b/lib/matplotlib/path.py
index 5f5a0f3de423..4784410f0f5e 100644
--- a/lib/matplotlib/path.py
+++ b/lib/matplotlib/path.py
@@ -76,7 +76,6 @@ class Path:
         made up front in the constructor that will not change when the
         data changes.
     """
-
     code_type = np.uint8
 
     # Path codes
diff --git a/lib/matplotlib/tests/test_transforms.py b/lib/matplotlib/tests/test_transforms.py
index 96e78b6828f8..9981c54f9025 100644
--- a/lib/matplotlib/tests/test_transforms.py
+++ b/lib/matplotlib/tests/test_transforms.py
@@ -987,12 +987,6 @@ def test_transformed_path():
                     [(0, 0), (r2, r2), (0, 2 * r2), (-r2, r2)],
                     atol=1e-15)
 
-    # Changing the path does not change the result (it's cached).
-    path.points = [(0, 0)] * 4
-    assert_allclose(trans_path.get_fully_transformed_path().vertices,
-                    [(0, 0), (r2, r2), (0, 2 * r2), (-r2, r2)],
-                    atol=1e-15)
-
 
 def test_transformed_patch_path():
     trans = mtransforms.Affine2D()
diff --git a/lib/mpl_toolkits/mplot3d/art3d.py b/lib/mpl_toolkits/mplot3d/art3d.py
index 88c2ae625c00..26c3c5a28e20 100644
--- a/lib/mpl_toolkits/mplot3d/art3d.py
+++ b/lib/mpl_toolkits/mplot3d/art3d.py
@@ -1367,7 +1367,7 @@ def _zalpha(colors, zs):
     #        Should really normalize against the viewing depth.
     if len(colors) == 0 or len(zs) == 0:
         return np.zeros((0, 4))
-    norm = Normalize(min(zs), max(zs))
+    norm = Normalize(np.min(zs), np.max(zs))
     sats = 1 - norm(zs) * 0.7
     rgba = np.broadcast_to(mcolors.to_rgba_array(colors), (len(zs), 4))
     return np.column_stack([rgba[:, :3], rgba[:, 3] * sats])
diff --git a/lib/mpl_toolkits/mplot3d/proj3d.py b/lib/mpl_toolkits/mplot3d/proj3d.py
index 34a03969c961..87c59ae05714 100644
--- a/lib/mpl_toolkits/mplot3d/proj3d.py
+++ b/lib/mpl_toolkits/mplot3d/proj3d.py
@@ -164,8 +164,7 @@ def _proj_transform_vectors(vecs, M):
 
 def _proj_transform_vec_clip(vec, M, focal_length):
     vecw = np.dot(M, vec.data)
-    w = vecw[3]
-    txs, tys, tzs = vecw[0] / w, vecw[1] / w, vecw[2] / w
+    txs, tys, tzs = vecw[0:3] / vecw[3]
     if np.isinf(focal_length):  # don't clip orthographic projection
         tis = np.ones(txs.shape, dtype=bool)
     else: