matplotlib
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diff --git a/‎lib/matplotlib/axes/_axes.py
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diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/hist2d.pdf
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diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/hist2d.svg
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diff --git a/‎lib/matplotlib/tests/test_axes.py
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diff --git a/‎lib/matplotlib/tests/test_image.py
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@@ -0,0 +1,7 @@
+`.Axes.hist2d` now uses `~.Axes.pcolormesh` instead of `~.Axes.pcolorfast`
+--------------------------------------------------------------------------
+
+`.Axes.hist2d` now uses `~.Axes.pcolormesh` instead of `~.Axes.pcolorfast`,
+which will improve the handling of log-axes.  Note that the
+returned *image* now is of type `~.matplotlib.collections.QuadMesh`
+instead of `~.matplotlib.image.AxesImage`.
@@ -6505,7 +6505,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
             The bin edges along the x axis.
         yedges : 1D array
             The bin edges along the y axis.
-        image : AxesImage
+        image : `~.matplotlib.collections.QuadMesh`
 
         Other Parameters
         ----------------
@@ -6545,7 +6545,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
         if cmax is not None:
             h[h > cmax] = None
 
-        pc = self.pcolorfast(xedges, yedges, h.T, **kwargs)
+        pc = self.pcolormesh(xedges, yedges, h.T, **kwargs)
         self.set_xlim(xedges[0], xedges[-1])
         self.set_ylim(yedges[0], yedges[-1])
 
 
@@ -1646,24 +1646,26 @@ def test_contour_colorbar():
     cbar.add_lines(cs2, erase=False)
 
 
-@image_comparison(baseline_images=['hist2d', 'hist2d'])
+@image_comparison(baseline_images=['hist2d', 'hist2d'],
+        remove_text=True, style='mpl20')
 def test_hist2d():
     np.random.seed(0)
     # make it not symmetric in case we switch x and y axis
     x = np.random.randn(100)*2+5
     y = np.random.randn(100)-2
     fig = plt.figure()
     ax = fig.add_subplot(111)
-    ax.hist2d(x, y, bins=10)
+    ax.hist2d(x, y, bins=10, rasterized=True)
 
     # Reuse testcase from above for a labeled data test
     data = {"x": x, "y": y}
     fig = plt.figure()
     ax = fig.add_subplot(111)
-    ax.hist2d("x", "y", bins=10, data=data)
+    ax.hist2d("x", "y", bins=10, data=data, rasterized=True)
 
 
-@image_comparison(baseline_images=['hist2d_transpose'])
+@image_comparison(baseline_images=['hist2d_transpose'],
+        remove_text=True, style='mpl20')
 def test_hist2d_transpose():
     np.random.seed(0)
     # make sure the output from np.histogram is transposed before
@@ -1672,7 +1674,7 @@ def test_hist2d_transpose():
     y = np.random.randn(100)-2
     fig = plt.figure()
     ax = fig.add_subplot(111)
-    ax.hist2d(x, y, bins=10)
+    ax.hist2d(x, y, bins=10, rasterized=True)
 
 
 @image_comparison(baseline_images=['scatter', 'scatter'])
 
@@ -586,12 +586,6 @@ def test_pcolorimage_setdata():
     assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
 
 
-def test_pcolorimage_extent():
-    im = plt.hist2d([1, 2, 3], [3, 5, 6],
-                    bins=[[0, 3, 7], [1, 2, 3]])[-1]
-    assert im.get_extent() == (0, 7, 1, 3)
-
-
 def test_minimized_rasterized():
     # This ensures that the rasterized content in the colorbars is
     # only as thick as the colorbar, and doesn't extend to other parts