matplotlib
diff --git a/‎lib/matplotlib/cbook/__init__.py
Copy file name to clipboardExpand all lines: lib/matplotlib/cbook/__init__.py
+12-1Lines changed: 12 additions & 1 deletion b/‎lib/matplotlib/cbook/__init__.py
Copy file name to clipboardExpand all lines: lib/matplotlib/cbook/__init__.py
+12-1Lines changed: 12 additions & 1 deletion
diff --git a/‎lib/matplotlib/tests/test_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_axes.py
+8Lines changed: 8 additions & 0 deletions b/‎lib/matplotlib/tests/test_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_axes.py
+8Lines changed: 8 additions & 0 deletions
@@ -1367,11 +1367,22 @@ def _check_1d(x):
         return np.atleast_1d(x)
     else:
         try:
-            ndim = x[:, None].ndim
             # work around https://github.com/pandas-dev/pandas/issues/27775
             # which mean the shape is not as expected. That this ever worked
             # was an unintentional quirk of pandas the above line will raise
             # an exception in the future.
+            # This warns in pandas >= 1.0 via
+            # https://github.com/pandas-dev/pandas/pull/30588
+            with warnings.catch_warnings(record=True) as w:
+                warnings.filterwarnings("always",
+                                        category=DeprecationWarning,
+                                        module='pandas[.*]')
+
+                ndim = x[:, None].ndim
+                # we have definitely hit a pandas index or series object
+                # cast to a numpy array.
+                if len(w) != 0:
+                    return np.asanyarray(x)
             if ndim < 2:
                 return np.atleast_1d(x)
             return x
 
@@ -1695,6 +1695,14 @@ def test_bar_pandas_indexed(pd):
     ax.bar(df.x, 1., width=df.width)
 
 
+def test_pandas_smoke(pd):
+    # This should not raise any warnings
+    x = pd.Series([], dtype="float64")
+    plt.plot(x, x)
+    plt.plot(x.index, x)
+    plt.plot(x)
+
+
 @image_comparison(['hist_log'], remove_text=True)
 def test_hist_log():
     data0 = np.linspace(0, 1, 200)**3