@@ -1367,12 +1367,20 @@ def _check_1d(x):
13671367 return np .atleast_1d (x )
13681368 else :
13691369 try :
1370- # work around https://github.com/pandas-dev/pandas/issues/27775
1371- # which mean the shape is not as expected. That this ever worked
1372- # was an unintentional quirk of pandas the above line will raise
1373- # an exception in the future.
1374- # This warns in pandas >= 1.0 via
1370+ # work around
1371+ # https://github.com/pandas-dev/pandas/issues/27775 which
1372+ # means the shape of multi-dimensional slicing is not as
1373+ # expected. That this ever worked was an unintentional
1374+ # quirk of pandas and will raise an exception in the
1375+ # future. This slicing warns in pandas >= 1.0rc0 via
13751376 # https://github.com/pandas-dev/pandas/pull/30588
1377+ #
1378+ # < 1.0rc0 : x[:, None].ndim == 1, no warning, custom type
1379+ # >= 1.0rc1 : x[:, None].ndim == 2, warns, numpy array
1380+ # future : x[:, None] -> raises
1381+ #
1382+ # This code should correctly identify and coerce to a
1383+ # numpy array all pandas versions.
13761384 with warnings .catch_warnings (record = True ) as w :
13771385 warnings .filterwarnings ("always" ,
13781386 category = DeprecationWarning ,
@@ -1381,8 +1389,11 @@ def _check_1d(x):
13811389 ndim = x [:, None ].ndim
13821390 # we have definitely hit a pandas index or series object
13831391 # cast to a numpy array.
1384- if len (w ) != 0 :
1392+ if len (w ) > 0 :
13851393 return np .asanyarray (x )
1394+ # We have likely hit a pandas object, or at least
1395+ # something where 2D slicing does not result in a 2D
1396+ # object.
13861397 if ndim < 2 :
13871398 return np .atleast_1d (x )
13881399 return x
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