COMPAT: numpy-dev compat on bool(dtype) change

This is >= 1.13

xref numpy/numpy#8279

closes #15199

Author: jreback

pandas/compat/numpy/__init__.py

@@ -14,6 +14,7 @@
 _np_version_under1p10 = _nlv < '1.10'
 _np_version_under1p11 = _nlv < '1.11'
 _np_version_under1p12 = _nlv < '1.12'
+_np_version_under1p13 = _nlv < '1.13'
 
 if _nlv < '1.7.0':
     raise ImportError('this version of pandas is incompatible with '

pandas/core/ops.py

@@ -444,13 +444,15 @@ def _convert_to_array(self, values, name=None, other=None):
         supplied_dtype = None
         if not is_list_like(values):
             values = np.array([values])
+
         # if this is a Series that contains relevant dtype info, then use this
         # instead of the inferred type; this avoids coercing Series([NaT],
         # dtype='datetime64[ns]') to Series([NaT], dtype='timedelta64[ns]')
         elif (isinstance(values, pd.Series) and
               (is_timedelta64_dtype(values) or is_datetime64_dtype(values))):
             supplied_dtype = values.dtype
-        inferred_type = supplied_dtype or lib.infer_dtype(values)
+
+        inferred_type = lib.infer_dtype(values)
         if (inferred_type in ('datetime64', 'datetime', 'date', 'time') or
                 is_datetimetz(inferred_type)):
             # if we have a other of timedelta, but use pd.NaT here we

pandas/tseries/index.py

@@ -557,7 +557,7 @@ def _simple_new(cls, values, name=None, freq=None, tz=None,
         if we are passed a non-dtype compat, then coerce using the constructor
         """
 
-        if not getattr(values, 'dtype', None):
+        if getattr(values, 'dtype', None) is None:
             # empty, but with dtype compat
             if values is None:
                 values = np.empty(0, dtype=_NS_DTYPE)

pandas/tseries/tdi.py

@@ -272,8 +272,7 @@ def _box_func(self):
 
     @classmethod
     def _simple_new(cls, values, name=None, freq=None, **kwargs):
-        if not getattr(values, 'dtype', None):
-            values = np.array(values, copy=False)
+        values = np.array(values, copy=False)
         if values.dtype == np.object_:
             values = tslib.array_to_timedelta64(values)
         if values.dtype != _TD_DTYPE: