diff --git a/examples/pylab_examples/demo_tight_layout.py b/examples/pylab_examples/demo_tight_layout.py
index 789dab3944ce..88a5c37dc7b9 100644
--- a/examples/pylab_examples/demo_tight_layout.py
+++ b/examples/pylab_examples/demo_tight_layout.py
@@ -1,6 +1,6 @@
 
 import matplotlib.pyplot as plt
-
+import warnings
 
 import random
 fontsizes = [8, 16, 24, 32]
@@ -83,16 +83,26 @@ def example_plot(ax):
 example_plot(ax2)
 example_plot(ax3)
 
-gs1.tight_layout(fig, rect=[None, None, 0.45, None])
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore", UserWarning)
+    # This raises warnings since tight layout cannot
+    # handle gridspec automatically. We are going to
+    # do that manually so we can filter the warning.
+    gs1.tight_layout(fig, rect=[None, None, 0.45, None])
 
 gs2 = gridspec.GridSpec(2, 1)
 ax4 = fig.add_subplot(gs2[0])
 ax5 = fig.add_subplot(gs2[1])
 
-#example_plot(ax4)
-#example_plot(ax5)
-
-gs2.tight_layout(fig, rect=[0.45, None, None, None])
+example_plot(ax4)
+example_plot(ax5)
+
+with warnings.catch_warnings():
+    # This raises warnings since tight layout cannot
+    # handle gridspec automatically. We are going to
+    # do that manually so we can filter the warning.
+    warnings.simplefilter("ignore", UserWarning)
+    gs2.tight_layout(fig, rect=[0.45, None, None, None])
 
 # now match the top and bottom of two gridspecs.
 top = min(gs1.top, gs2.top)
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 64f3b53f4a0b..81352c360596 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -4065,7 +4065,11 @@ def coarse_bin(x, y, coarse):
             ind = coarse.searchsorted(x).clip(0, len(coarse) - 1)
             mus = np.zeros(len(coarse))
             for i in range(len(coarse)):
-                mu = reduce_C_function(y[ind == i])
+                yi = y[ind == i]
+                if len(yi) > 0:
+                    mu = reduce_C_function(yi)
+                else:
+                    mu = np.nan
                 mus[i] = mu
             return mus
 
diff --git a/lib/matplotlib/projections/polar.py b/lib/matplotlib/projections/polar.py
index 5277d5ef9fa9..5e815a0654a6 100644
--- a/lib/matplotlib/projections/polar.py
+++ b/lib/matplotlib/projections/polar.py
@@ -146,7 +146,13 @@ def transform_non_affine(self, xy):
         x = xy[:, 0:1]
         y = xy[:, 1:]
         r = np.sqrt(x*x + y*y)
-        theta = np.arccos(x / r)
+        with np.errstate(invalid='ignore'):
+            # At x=y=r=0 this will raise an
+            # invalid value warning when doing 0/0
+            # Divide by zero warnings are only raised when
+            # the numerator is different from 0. That
+            # should not happen here.
+            theta = np.arccos(x / r)
         theta = np.where(y < 0, 2 * np.pi - theta, theta)
 
         theta -= theta_offset
diff --git a/lib/mpl_toolkits/axisartist/angle_helper.py b/lib/mpl_toolkits/axisartist/angle_helper.py
index a47aaeab81ed..5d921b0ba553 100644
--- a/lib/mpl_toolkits/axisartist/angle_helper.py
+++ b/lib/mpl_toolkits/axisartist/angle_helper.py
@@ -385,13 +385,18 @@ def __call__(self, transform_xy, x1, y1, x2, y2):
         lon, lat = transform_xy(np.ravel(x), np.ravel(y))
 
         # iron out jumps, but algorithm should be improved.
-        # Tis is just naive way of doing and my fail for some cases.
-        if self.lon_cycle is not None:
-            lon0 = np.nanmin(lon)
-            lon -= 360. * ((lon - lon0) > 180.)
-        if self.lat_cycle is not None:
-            lat0 = np.nanmin(lat)
-            lat -= 360. * ((lat - lat0) > 180.)
+        # This is just naive way of doing and my fail for some cases.
+        # Consider replacing this with numpy.unwrap
+        # We are ignoring invalid warnings. They are triggered when
+        # comparing arrays with NaNs using > We are already handling
+        # that correctly using np.nanmin and np.nanmax
+        with np.errstate(invalid='ignore'):
+            if self.lon_cycle is not None:
+                lon0 = np.nanmin(lon)
+                lon -= 360. * ((lon - lon0) > 180.)
+            if self.lat_cycle is not None:
+                lat0 = np.nanmin(lat)
+                lat -= 360. * ((lat - lat0) > 180.)
 
         lon_min, lon_max = np.nanmin(lon), np.nanmax(lon)
         lat_min, lat_max = np.nanmin(lat), np.nanmax(lat)