CLN: remove simple _DATELIKE_DTYPES test and replace with is_datetimelike accessor

pandas/core/groupby.py

@@ -14,10 +14,10 @@
 from pandas.compat.numpy import function as nv
 from pandas.compat.numpy import _np_version_under1p8
 
-from pandas.types.common import (_DATELIKE_DTYPES,
-                                 is_numeric_dtype,
+from pandas.types.common import (is_numeric_dtype,
                                  is_timedelta64_dtype, is_datetime64_dtype,
                                  is_categorical_dtype,
+                                 is_datetimelike,
                                  is_datetime_or_timedelta_dtype,
                                  is_bool, is_integer_dtype,
                                  is_complex_dtype,
@@ -3453,10 +3453,10 @@ def first_non_None_value(values):
                 # if we have date/time like in the original, then coerce dates
                 # as we are stacking can easily have object dtypes here
                 so = self._selected_obj
-                if (so.ndim == 2 and so.dtypes.isin(_DATELIKE_DTYPES).any()):
+                if (so.ndim == 2 and so.dtypes.apply(is_datetimelike).any()):
                     result = result._convert(numeric=True)
                     date_cols = self._selected_obj.select_dtypes(
-                        include=list(_DATELIKE_DTYPES)).columns
+                        include=['datetime', 'timedelta']).columns
                     date_cols = date_cols.intersection(result.columns)
                     result[date_cols] = (result[date_cols]
                                          ._convert(datetime=True,

pandas/types/cast.py

@@ -7,6 +7,7 @@
 from pandas.compat import string_types, text_type, PY3
 from .common import (_ensure_object, is_bool, is_integer, is_float,
                      is_complex, is_datetimetz, is_categorical_dtype,
+                     is_datetimelike,
                      is_extension_type, is_object_dtype,
                      is_datetime64tz_dtype, is_datetime64_dtype,
                      is_timedelta64_dtype, is_dtype_equal,
@@ -18,7 +19,7 @@
                      _ensure_int8, _ensure_int16,
                      _ensure_int32, _ensure_int64,
                      _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE,
-                     _DATELIKE_DTYPES, _POSSIBLY_CAST_DTYPES)
+                     _POSSIBLY_CAST_DTYPES)
 from .dtypes import ExtensionDtype
 from .generic import ABCDatetimeIndex, ABCPeriodIndex, ABCSeries
 from .missing import isnull, notnull
@@ -164,7 +165,7 @@ def _maybe_upcast_putmask(result, mask, other):
         # in np.place:
         #   NaN -> NaT
         #   integer or integer array -> date-like array
-        if result.dtype in _DATELIKE_DTYPES:
+        if is_datetimelike(result.dtype):
             if is_scalar(other):
                 if isnull(other):
                     other = result.dtype.type('nat')
@@ -666,7 +667,7 @@ def _possibly_castable(arr):
     # otherwise try to coerce
     kind = arr.dtype.kind
     if kind == 'M' or kind == 'm':
-        return arr.dtype in _DATELIKE_DTYPES
+        return is_datetime64_dtype(arr.dtype)
 
     return arr.dtype.name not in _POSSIBLY_CAST_DTYPES
 

pandas/types/common.py

@@ -23,10 +23,6 @@
 _TD_DTYPE = np.dtype('m8[ns]')
 _INT64_DTYPE = np.dtype(np.int64)
 
-_DATELIKE_DTYPES = set([np.dtype(t)
-                        for t in ['M8[ns]', '<M8[ns]', '>M8[ns]',
-                                  'm8[ns]', '<m8[ns]', '>m8[ns]']])
-
 _ensure_float64 = algos.ensure_float64
 _ensure_float32 = algos.ensure_float32
 
@@ -127,7 +123,8 @@ def is_datetime_arraylike(arr):
 
 
 def is_datetimelike(arr):
-    return (arr.dtype in _DATELIKE_DTYPES or
+    return (is_datetime64_dtype(arr) or is_datetime64tz_dtype(arr) or
+            is_timedelta64_dtype(arr) or
             isinstance(arr, ABCPeriodIndex) or
             is_datetimetz(arr))
 

pandas/types/missing.py

@@ -19,8 +19,7 @@
                      is_object_dtype,
                      is_integer,
                      _TD_DTYPE,
-                     _NS_DTYPE,
-                     _DATELIKE_DTYPES)
+                     _NS_DTYPE)
 from .inference import is_list_like
 
 
@@ -169,7 +168,7 @@ def _isnull_ndarraylike_old(obj):
             vec = lib.isnullobj_old(values.ravel())
             result[:] = vec.reshape(shape)
 
-    elif dtype in _DATELIKE_DTYPES:
+    elif is_datetime64_dtype(dtype):
         # this is the NaT pattern
         result = values.view('i8') == iNaT
     else: