CLN: assorted cleanups

pandas/_libs/parsers.pyx

@@ -55,9 +55,7 @@ from pandas.core.dtypes.common import (
     is_bool_dtype, is_object_dtype,
     is_datetime64_dtype,
     pandas_dtype, is_extension_array_dtype)
-from pandas.core.arrays import Categorical
 from pandas.core.dtypes.concat import union_categoricals
-import pandas.io.common as icom
 
 from pandas.compat import _import_lzma, _get_lzma_file
 from pandas.errors import (ParserError, DtypeWarning,
@@ -1149,7 +1147,8 @@ cdef class TextReader:
 
             # Method accepts list of strings, not encoded ones.
             true_values = [x.decode() for x in self.true_values]
-            cat = Categorical._from_inferred_categories(
+            array_type = dtype.construct_array_type()
+            cat = array_type._from_inferred_categories(
                 cats, codes, dtype, true_values=true_values)
             return cat, na_count
 

pandas/core/algorithms.py

@@ -29,7 +29,6 @@
     is_bool_dtype,
     is_categorical_dtype,
     is_complex_dtype,
-    is_datetime64_any_dtype,
     is_datetime64_dtype,
     is_datetime64_ns_dtype,
     is_extension_array_dtype,
@@ -122,12 +121,7 @@ def _ensure_data(values, dtype=None):
         return ensure_object(values), "object"
 
     # datetimelike
-    if (
-        needs_i8_conversion(values)
-        or is_period_dtype(dtype)
-        or is_datetime64_any_dtype(dtype)
-        or is_timedelta64_dtype(dtype)
-    ):
+    if needs_i8_conversion(values) or needs_i8_conversion(dtype):
         if is_period_dtype(values) or is_period_dtype(dtype):
             from pandas import PeriodIndex
 

pandas/core/arrays/datetimelike.py

@@ -638,8 +638,6 @@ def astype(self, dtype, copy=True):
         #   1. PeriodArray.astype handles period -> period
         #   2. DatetimeArray.astype handles conversion between tz.
         #   3. DatetimeArray.astype handles datetime -> period
-        from pandas import Categorical
-
         dtype = pandas_dtype(dtype)
 
         if is_object_dtype(dtype):
@@ -667,7 +665,8 @@ def astype(self, dtype, copy=True):
             msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
             raise TypeError(msg)
         elif is_categorical_dtype(dtype):
-            return Categorical(self, dtype=dtype)
+            arr_cls = dtype.construct_array_type()
+            return arr_cls(self, dtype=dtype)
         else:
             return np.asarray(self, dtype=dtype)
 

pandas/core/arrays/datetimes.py

@@ -22,8 +22,8 @@
 from pandas.errors import PerformanceWarning
 
 from pandas.core.dtypes.common import (
-    _INT64_DTYPE,
     DT64NS_DTYPE,
+    INT64_DTYPE,
     is_bool_dtype,
     is_categorical_dtype,
     is_datetime64_any_dtype,
@@ -404,7 +404,7 @@ def _generate_range(
                     start = start.tz_localize(None)
                 if end is not None:
                     end = end.tz_localize(None)
-            # TODO: consider re-implementing _cached_range; GH#17914
+
             values, _tz = generate_regular_range(start, end, periods, freq)
             index = cls._simple_new(values, freq=freq, dtype=tz_to_dtype(_tz))
 
@@ -1963,7 +1963,7 @@ def sequence_to_dt64ns(
         if tz:
             tz = timezones.maybe_get_tz(tz)
 
-        if data.dtype != _INT64_DTYPE:
+        if data.dtype != INT64_DTYPE:
             data = data.astype(np.int64, copy=False)
         result = data.view(DT64NS_DTYPE)
 

pandas/core/base.py

@@ -1143,8 +1143,7 @@ def _map_values(self, mapper, na_action=None):
                 raise NotImplementedError
             map_f = lambda values, f: values.map(f)
         else:
-            values = self.astype(object)
-            values = getattr(values, "values", values)
+            values = self.astype(object)._values
             if na_action == "ignore":
 
                 def map_f(values, f):

pandas/core/dtypes/cast.py

@@ -21,9 +21,9 @@
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.common import (
-    _INT64_DTYPE,
     _POSSIBLY_CAST_DTYPES,
     DT64NS_DTYPE,
+    INT64_DTYPE,
     TD64NS_DTYPE,
     ensure_int8,
     ensure_int16,
@@ -954,7 +954,7 @@ def astype_nansafe(arr, dtype, copy: bool = True, skipna: bool = False):
                 raise ValueError("Cannot convert NaT values to integer")
             return arr.view(dtype)
 
-        if dtype not in [_INT64_DTYPE, TD64NS_DTYPE]:
+        if dtype not in [INT64_DTYPE, TD64NS_DTYPE]:
 
             # allow frequency conversions
             # we return a float here!

pandas/core/dtypes/common.py

@@ -60,17 +60,14 @@
 
 DT64NS_DTYPE = conversion.DT64NS_DTYPE
 TD64NS_DTYPE = conversion.TD64NS_DTYPE
-_INT64_DTYPE = np.dtype(np.int64)
+INT64_DTYPE = np.dtype(np.int64)
 
 # oh the troubles to reduce import time
 _is_scipy_sparse = None
 
 ensure_float64 = algos.ensure_float64
 ensure_float32 = algos.ensure_float32
 
-_ensure_datetime64ns = conversion.ensure_datetime64ns
-_ensure_timedelta64ns = conversion.ensure_timedelta64ns
-
 
 def ensure_float(arr):
     """

pandas/core/generic.py

@@ -3548,8 +3548,6 @@ class   animal   locomotion
         result._set_is_copy(self, copy=not result._is_view)
         return result
 
-    _xs: Callable = xs
-
     def __getitem__(self, item):
         raise AbstractMethodError(self)
 

pandas/core/indexing.py

@@ -1046,7 +1046,7 @@ def _getitem_tuple(self, tup: Tuple):
 
     def _get_label(self, label, axis: int):
         # GH#5667 this will fail if the label is not present in the axis.
-        return self.obj._xs(label, axis=axis)
+        return self.obj.xs(label, axis=axis)
 
     def _handle_lowerdim_multi_index_axis0(self, tup: Tuple):
         # we have an axis0 multi-index, handle or raise

pandas/core/internals/blocks.py

@@ -585,8 +585,7 @@ def astype(self, dtype, copy: bool = False, errors: str = "raise"):
                 newb = self.copy() if copy else self
                 return newb
 
-        # TODO(extension)
-        # should we make this attribute?
+        # TODO(EA2D): special case not needed with 2D EAs
         if isinstance(values, np.ndarray):
             values = values.reshape(self.shape)
 
@@ -1554,13 +1553,15 @@ def __init__(self, values, placement, ndim=None):
 
     @property
     def shape(self):
+        # TODO(EA2D): override unnecessary with 2D EAs
         if self.ndim == 1:
             return ((len(self.values)),)
         return (len(self.mgr_locs), len(self.values))
 
     def iget(self, col):
 
         if self.ndim == 2 and isinstance(col, tuple):
+            # TODO(EA2D): unnecessary with 2D EAs
             col, loc = col
             if not com.is_null_slice(col) and col != 0:
                 raise IndexError(f"{self} only contains one item")
@@ -1669,6 +1670,7 @@ def setitem(self, indexer, value):
         be a compatible shape.
         """
         if isinstance(indexer, tuple):
+            # TODO(EA2D): not needed with 2D EAs
             # we are always 1-D
             indexer = indexer[0]
 
@@ -1678,6 +1680,7 @@ def setitem(self, indexer, value):
 
     def get_values(self, dtype=None):
         # ExtensionArrays must be iterable, so this works.
+        # TODO(EA2D): reshape not needed with 2D EAs
         return np.asarray(self.values).reshape(self.shape)
 
     def array_values(self) -> ExtensionArray:
@@ -1691,6 +1694,7 @@ def to_native_types(self, na_rep="nan", quoting=None, **kwargs):
         values = np.asarray(values.astype(object))
         values[mask] = na_rep
 
+        # TODO(EA2D): reshape not needed with 2D EAs
         # we are expected to return a 2-d ndarray
         return values.reshape(1, len(values))
 
@@ -1703,6 +1707,7 @@ def take_nd(
         if fill_value is lib.no_default:
             fill_value = None
 
+        # TODO(EA2D): special case not needed with 2D EAs
         # axis doesn't matter; we are really a single-dim object
         # but are passed the axis depending on the calling routing
         # if its REALLY axis 0, then this will be a reindex and not a take
@@ -2229,6 +2234,7 @@ def diff(self, n: int, axis: int = 0) -> List["Block"]:
         by apply.
         """
         if axis == 0:
+            # TODO(EA2D): special case not needed with 2D EAs
             # Cannot currently calculate diff across multiple blocks since this
             # function is invoked via apply
             raise NotImplementedError
@@ -2280,7 +2286,7 @@ def quantile(self, qs, interpolation="linear", axis=0):
         blk = self.make_block(naive)
         res_blk = blk.quantile(qs, interpolation=interpolation, axis=axis)
 
-        # ravel is kludge for 2D block with 1D values, assumes column-like
+        # TODO(EA2D): ravel is kludge for 2D block with 1D values, assumes column-like
         aware = self._holder(res_blk.values.ravel(), dtype=self.dtype)
         return self.make_block_same_class(aware, ndim=res_blk.ndim)
 
@@ -2693,6 +2699,7 @@ def make_block(values, placement, klass=None, ndim=None, dtype=None):
     if isinstance(values, ABCPandasArray):
         values = values.to_numpy()
         if ndim and ndim > 1:
+            # TODO(EA2D): special case not needed with 2D EAs
             values = np.atleast_2d(values)
 
     if isinstance(dtype, PandasDtype):
@@ -2759,6 +2766,7 @@ def _safe_reshape(arr, new_shape):
     if isinstance(arr, ABCSeries):
         arr = arr._values
     if not isinstance(arr, ABCExtensionArray):
+        # TODO(EA2D): special case not needed with 2D EAs
         arr = arr.reshape(new_shape)
     return arr
 

pandas/core/internals/concat.py

@@ -251,6 +251,7 @@ def get_reindexed_values(self, empty_dtype, upcasted_na):
                 ):
                     if self.block is None:
                         array = empty_dtype.construct_array_type()
+                        # TODO(EA2D): special case unneeded with 2D EAs
                         return array(
                             np.full(self.shape[1], fill_value.value), dtype=empty_dtype
                         )