CLN: Replace _coerce_to_dtype() with pandas_dtype()

Changes made to series.py, generic.py in pandas/core and cast.py in pandas/core/dtypes

Author: analyticalmonk

pandas/core/dtypes/cast.py

@@ -22,7 +22,7 @@
                      is_numeric_dtype, is_decimal,
                      is_number,
                      _string_dtypes,
-                     _coerce_to_dtype,
+                     pandas_dtype,
                      _ensure_int8, _ensure_int16,
                      _ensure_int32, _ensure_int64,
                      _NS_DTYPE, _TD_DTYPE, _INT64_DTYPE,
@@ -579,7 +579,7 @@ def astype_nansafe(arr, dtype, copy=True):
     """ return a view if copy is False, but
         need to be very careful as the result shape could change! """
     if not isinstance(dtype, np.dtype):
-        dtype = _coerce_to_dtype(dtype)
+        dtype = pandas_dtype(dtype)
 
     if issubclass(dtype.type, text_type):
         # in Py3 that's str, in Py2 that's unicode

pandas/core/generic.py

@@ -11,7 +11,6 @@
 
 from pandas._libs import tslib, lib
 from pandas.core.dtypes.common import (
-    _coerce_to_dtype,
     _ensure_int64,
     needs_i8_conversion,
     is_scalar,
@@ -165,7 +164,6 @@ def _validate_dtype(self, dtype):
         """ validate the passed dtype """
 
         if dtype is not None:
-            dtype = _coerce_to_dtype(dtype)
             # This would raise an error if an invalid dtype was passed
             dtype = pandas_dtype(dtype)
 

pandas/core/series.py

@@ -14,7 +14,7 @@
 import numpy.ma as ma
 
 from pandas.core.dtypes.common import (
-    _coerce_to_dtype, is_categorical_dtype,
+    is_categorical_dtype,
     is_bool,
     is_integer, is_integer_dtype,
     is_float_dtype,
@@ -28,7 +28,8 @@
     is_dict_like,
     is_scalar,
     _is_unorderable_exception,
-    _ensure_platform_int)
+    _ensure_platform_int,
+    pandas_dtype)
 from pandas.core.dtypes.generic import ABCSparseArray, ABCDataFrame
 from pandas.core.dtypes.cast import (
     maybe_upcast, infer_dtype_from_scalar,
@@ -2869,7 +2870,7 @@ def _sanitize_array(data, index, dtype=None, copy=False,
     """
 
     if dtype is not None:
-        dtype = _coerce_to_dtype(dtype)
+        dtype = pandas_dtype(dtype)
 
     if isinstance(data, ma.MaskedArray):
         mask = ma.getmaskarray(data)