matplotlib
diff --git a/‎lib/matplotlib/axes/_axes.py
+31-10 b/‎lib/matplotlib/axes/_axes.py
+31-10
diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/hlines_blended_transform.png
17.7 KB b/‎lib/matplotlib/tests/baseline_images/test_axes/hlines_blended_transform.png
17.7 KB
diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/vlines_blended_transform.png
17.6 KB b/‎lib/matplotlib/tests/baseline_images/test_axes/vlines_blended_transform.png
17.6 KB
diff --git a/‎lib/matplotlib/tests/test_axes.py
+22 b/‎lib/matplotlib/tests/test_axes.py
+22
@@ -1093,21 +1093,32 @@ def hlines(self, y, xmin, xmax, colors=None, linestyles='solid',
 
         lines = mcoll.LineCollection(masked_verts, colors=colors,
                                      linestyles=linestyles, label=label)
+        lines._internal_update(kwargs)
+        if 'transform' in kwargs:
+            lines.set_transform(kwargs['transform'])
         self.add_collection(lines, autolim=False)
+        self._unstale_viewLim()
         lines._internal_update(kwargs)
 
         if len(y) > 0:
             # Extreme values of xmin/xmax/y.  Using masked_verts here handles
             # the case of y being a masked *object* array (as can be generated
             # e.g. by errorbar()), which would make nanmin/nanmax stumble.
-            minx = np.nanmin(masked_verts[..., 0])
-            maxx = np.nanmax(masked_verts[..., 0])
-            miny = np.nanmin(masked_verts[..., 1])
-            maxy = np.nanmax(masked_verts[..., 1])
+            if self.name == "rectilinear":
+                datalim = lines.get_datalim(self.transData)
+                minx = np.nanmin(datalim.xmin)
+                maxx = np.nanmax(datalim.xmax)
+                miny = np.nanmin(datalim.ymin)
+                maxy = np.nanmax(datalim.ymax)
+            else:
+                minx = np.nanmin(masked_verts[..., 0])
+                maxx = np.nanmax(masked_verts[..., 0])
+                miny = np.nanmin(masked_verts[..., 1])
+                maxy = np.nanmax(masked_verts[..., 1])
+
             corners = (minx, miny), (maxx, maxy)
             self.update_datalim(corners)
             self._request_autoscale_view()
-
         return lines
 
     @_preprocess_data(replace_names=["x", "ymin", "ymax", "colors"],
@@ -1173,21 +1184,31 @@ def vlines(self, x, ymin, ymax, colors=None, linestyles='solid',
 
         lines = mcoll.LineCollection(masked_verts, colors=colors,
                                      linestyles=linestyles, label=label)
+        if 'transform' in kwargs:
+            lines.set_transform(kwargs['transform'])
         self.add_collection(lines, autolim=False)
+        self._unstale_viewLim()
         lines._internal_update(kwargs)
 
         if len(x) > 0:
             # Extreme values of x/ymin/ymax.  Using masked_verts here handles
             # the case of x being a masked *object* array (as can be generated
             # e.g. by errorbar()), which would make nanmin/nanmax stumble.
-            minx = np.nanmin(masked_verts[..., 0])
-            maxx = np.nanmax(masked_verts[..., 0])
-            miny = np.nanmin(masked_verts[..., 1])
-            maxy = np.nanmax(masked_verts[..., 1])
+            if self.name == "rectilinear":
+                datalim = lines.get_datalim(self.transData)
+                minx = np.nanmin(datalim.xmin)
+                maxx = np.nanmax(datalim.xmax)
+                miny = np.nanmin(datalim.ymin)
+                maxy = np.nanmax(datalim.ymax)
+            else:
+                minx = np.nanmin(masked_verts[..., 0])
+                maxx = np.nanmax(masked_verts[..., 0])
+                miny = np.nanmin(masked_verts[..., 1])
+                maxy = np.nanmax(masked_verts[..., 1])
+
             corners = (minx, miny), (maxx, maxy)
             self.update_datalim(corners)
             self._request_autoscale_view()
-
         return lines
 
     @_preprocess_data(replace_names=["positions", "lineoffsets",
 
@@ -4938,6 +4938,28 @@ def test_lines_with_colors(fig_test, fig_ref, data):
                                         colors=expect_color, linewidth=5)
 
 
+@image_comparison(['hlines_blended_transform'],
+                  extensions=['png'], style='mpl20')
+def test_hlines_blended_transform():
+    t = np.arange(5.0, 10.0, 0.1)
+    s = np.exp(-t) + np.sin(2 * np.pi * t) + 10
+    vax = plt.figure(figsize=(12, 6)).gca()
+    vax.plot(t, s, '^')
+    vax.hlines([10, 9], xmin=0, xmax=0.5,
+               transform=vax.get_yaxis_transform(), colors='r')
+
+
+@image_comparison(['vlines_blended_transform'],
+                  extensions=['png'], style='mpl20')
+def test_vlines_blended_transform():
+    t = np.arange(5.0, 10.0, 0.1)
+    s = np.exp(-t) + np.sin(2 * np.pi * t) + 10
+    vax = plt.figure(figsize=(12, 6)).gca()
+    vax.plot(t, s, '^')
+    vax.vlines([6, 7], ymin=0, ymax=0.15, transform=vax.get_xaxis_transform(),
+               colors='r')
+
+
 @image_comparison(['step_linestyle', 'step_linestyle'], remove_text=True)
 def test_step_linestyle():
     x = y = np.arange(10)