REF: remove useless code (dtype has info about ordering, numpy old versions)

Author: toobaz

pandas/core/categorical.py

@@ -293,29 +293,13 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
 
         # sanitize input
         if is_categorical_dtype(values):
-
-            # we are either a Series or a CategoricalIndex
-            if isinstance(values, (ABCSeries, ABCCategoricalIndex)):
-                values = values._values
-
-            if ordered is None:
-                ordered = values.ordered
-            if categories is None:
-                categories = values.categories
             values = values.get_values()
 
         elif isinstance(values, (ABCIndexClass, ABCSeries)):
             # we'll do inference later
             pass
 
         else:
-
-            # on numpy < 1.6 datetimelike get inferred to all i8 by
-            # _sanitize_array which is fine, but since factorize does this
-            # correctly no need here this is an issue because _sanitize_array
-            # also coerces np.nan to a string under certain versions of numpy
-            # as well
-            values = maybe_infer_to_datetimelike(values, convert_dates=True)
             if not isinstance(values, np.ndarray):
                 values = _convert_to_list_like(values)
                 from pandas.core.series import _sanitize_array
@@ -334,7 +318,7 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
                 codes, categories = factorize(values, sort=True)
             except TypeError:
                 codes, categories = factorize(values, sort=False)
-                if ordered:
+                if dtype.ordered:
                     # raise, as we don't have a sortable data structure and so
                     # the user should give us one by specifying categories
                     raise TypeError("'values' is not ordered, please "
@@ -348,7 +332,7 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
 
             if dtype.categories is None:
                 # we're inferring from values
-                dtype = CategoricalDtype(categories, ordered)
+                dtype = CategoricalDtype(categories, dtype.ordered)
 
         else:
             codes = _get_codes_for_values(values, dtype.categories)