diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
index acf6b8fe54ae..d6fcf855f487 100644
--- a/lib/matplotlib/cbook/__init__.py
+++ b/lib/matplotlib/cbook/__init__.py
@@ -1326,11 +1326,33 @@ 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.
+ # work around
+ # https://github.com/pandas-dev/pandas/issues/27775 which
+ # means the shape of multi-dimensional slicing is not as
+ # expected. That this ever worked was an unintentional
+ # quirk of pandas and will raise an exception in the
+ # future. This slicing warns in pandas >= 1.0rc0 via
+ # https://github.com/pandas-dev/pandas/pull/30588
+ #
+ # < 1.0rc0 : x[:, None].ndim == 1, no warning, custom type
+ # >= 1.0rc1 : x[:, None].ndim == 2, warns, numpy array
+ # future : x[:, None] -> raises
+ #
+ # This code should correctly identify and coerce to a
+ # numpy array all pandas versions.
+ 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)
+ # We have likely hit a pandas object, or at least
+ # something where 2D slicing does not result in a 2D
+ # object.
if ndim < 2:
return np.atleast_1d(x)
return x
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 290dfa64664b..4bf3e8724745 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -1669,6 +1669,15 @@ def test_bar_pandas_indexed(pd):
ax.bar(df.x, 1., width=df.width)
+def test_pandas_minimal_plot(pd):
+ # smoke test that series and index objcets do not warn
+ x = pd.Series([1, 2], dtype="float64")
+ plt.plot(x, x)
+ plt.plot(x.index, x)
+ plt.plot(x)
+ plt.plot(x.index)
+
+
@image_comparison(['hist_log'], remove_text=True)
def test_hist_log():
data0 = np.linspace(0, 1, 200)**3
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