CLN: Assorted Cleanups

pandas/core/arrays/categorical.py

@@ -22,7 +22,6 @@
     ensure_int64,
     ensure_object,
     ensure_platform_int,
-    is_categorical,
     is_categorical_dtype,
     is_datetime64_dtype,
     is_datetimelike,
@@ -2659,18 +2658,18 @@ def _get_codes_for_values(values, categories):
     return coerce_indexer_dtype(t.lookup(vals), cats)
 
 
-def _recode_for_categories(codes, old_categories, new_categories):
+def _recode_for_categories(codes: np.ndarray, old_categories, new_categories):
     """
     Convert a set of codes for to a new set of categories
 
     Parameters
     ----------
-    codes : array
+    codes : np.ndarray
     old_categories, new_categories : Index
 
     Returns
     -------
-    new_codes : array
+    new_codes : np.ndarray[np.int64]
 
     Examples
     --------
@@ -2725,17 +2724,15 @@ def _factorize_from_iterable(values):
         If `values` has a categorical dtype, then `categories` is
         a CategoricalIndex keeping the categories and order of `values`.
     """
-    from pandas.core.indexes.category import CategoricalIndex
-
     if not is_list_like(values):
         raise TypeError("Input must be list-like")
 
-    if is_categorical(values):
-        values = CategoricalIndex(values)
-        # The CategoricalIndex level we want to build has the same categories
+    if is_categorical_dtype(values):
+        values = extract_array(values)
+        # The Categorical we want to build has the same categories
         # as values but its codes are by def [0, ..., len(n_categories) - 1]
         cat_codes = np.arange(len(values.categories), dtype=values.codes.dtype)
-        categories = values._create_from_codes(cat_codes)
+        categories = Categorical.from_codes(cat_codes, dtype=values.dtype)
         codes = values.codes
     else:
         # The value of ordered is irrelevant since we don't use cat as such,

pandas/core/arrays/datetimelike.py

@@ -161,8 +161,8 @@ def strftime(self, date_format):
 
         Returns
         -------
-        Index
-            Index of formatted strings.
+        ndarray
+            NumPy ndarray of formatted strings.
 
         See Also
         --------
@@ -180,9 +180,7 @@ def strftime(self, date_format):
                'March 10, 2018, 09:00:02 AM'],
               dtype='object')
         """
-        from pandas import Index
-
-        return Index(self._format_native_types(date_format=date_format))
+        return self._format_native_types(date_format=date_format).astype(object)
 
 
 class TimelikeOps:
@@ -1018,9 +1016,9 @@ def _add_delta_tdi(self, other):
 
         if isinstance(other, np.ndarray):
             # ndarray[timedelta64]; wrap in TimedeltaIndex for op
-            from pandas import TimedeltaIndex
+            from pandas.core.arrays import TimedeltaArray
 
-            other = TimedeltaIndex(other)
+            other = TimedeltaArray._from_sequence(other)
 
         self_i8 = self.asi8
         other_i8 = other.asi8

pandas/core/arrays/sparse.py

@@ -1781,11 +1781,11 @@ def sparse_arithmetic_method(self, other):
 
     @classmethod
     def _create_comparison_method(cls, op):
-        def cmp_method(self, other):
-            op_name = op.__name__
+        op_name = op.__name__
+        if op_name in {"and_", "or_"}:
+            op_name = op_name[:-1]
 
-            if op_name in {"and_", "or_"}:
-                op_name = op_name[:-1]
+        def cmp_method(self, other):
 
             if isinstance(other, (ABCSeries, ABCIndexClass)):
                 # Rely on pandas to unbox and dispatch to us.

pandas/core/indexes/datetimes.py

@@ -69,7 +69,7 @@ class DatetimeDelegateMixin(DatetimelikeDelegateMixin):
     # Some are "raw" methods, the result is not not re-boxed in an Index
     # We also have a few "extra" attrs, which may or may not be raw,
     # which we we dont' want to expose in the .dt accessor.
-    _extra_methods = ["to_period", "to_perioddelta", "to_julian_date"]
+    _extra_methods = ["to_period", "to_perioddelta", "to_julian_date", "strftime"]
     _extra_raw_methods = ["to_pydatetime", "_local_timestamps", "_has_same_tz"]
     _extra_raw_properties = ["_box_func", "tz", "tzinfo"]
     _delegated_properties = DatetimeArray._datetimelike_ops + _extra_raw_properties
@@ -1184,7 +1184,6 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
     is_normalized = cache_readonly(DatetimeArray.is_normalized.fget)  # type: ignore
     _resolution = cache_readonly(DatetimeArray._resolution.fget)  # type: ignore
 
-    strftime = ea_passthrough(DatetimeArray.strftime)
     _has_same_tz = ea_passthrough(DatetimeArray._has_same_tz)
 
     @property

pandas/core/indexes/period.py

@@ -63,7 +63,10 @@ class PeriodDelegateMixin(DatetimelikeDelegateMixin):
 
     _delegate_class = PeriodArray
     _delegated_properties = PeriodArray._datetimelike_ops
-    _delegated_methods = set(PeriodArray._datetimelike_methods) | {"_addsub_int_array"}
+    _delegated_methods = set(PeriodArray._datetimelike_methods) | {
+        "_addsub_int_array",
+        "strftime",
+    }
     _raw_properties = {"is_leap_year"}
 
 

pandas/io/pytables.py

@@ -3202,7 +3202,9 @@ def read(self, start=None, stop=None, **kwargs):
             values = self.read_array(
                 "block{idx}_values".format(idx=i), start=_start, stop=_stop
             )
-            blk = make_block(values, placement=items.get_indexer(blk_items))
+            blk = make_block(
+                values, placement=items.get_indexer(blk_items), ndim=len(axes)
+            )
             blocks.append(blk)
 
         return self.obj_type(BlockManager(blocks, axes))
@@ -4462,7 +4464,7 @@ def read(self, where=None, columns=None, **kwargs):
             if values.ndim == 1 and isinstance(values, np.ndarray):
                 values = values.reshape((1, values.shape[0]))
 
-            block = make_block(values, placement=np.arange(len(cols_)))
+            block = make_block(values, placement=np.arange(len(cols_)), ndim=2)
             mgr = BlockManager([block], [cols_, index_])
             frames.append(DataFrame(mgr))
 

pandas/tests/arrays/test_datetimelike.py

@@ -462,6 +462,13 @@ def test_concat_same_type_different_freq(self):
 
         tm.assert_datetime_array_equal(result, expected)
 
+    def test_strftime(self, datetime_index):
+        arr = DatetimeArray(datetime_index)
+
+        result = arr.strftime("%Y %b")
+        expected = np.array(datetime_index.strftime("%Y %b"))
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestTimedeltaArray(SharedTests):
     index_cls = pd.TimedeltaIndex
@@ -652,6 +659,13 @@ def test_array_interface(self, period_index):
         expected = np.asarray(arr).astype("S20")
         tm.assert_numpy_array_equal(result, expected)
 
+    def test_strftime(self, period_index):
+        arr = PeriodArray(period_index)
+
+        result = arr.strftime("%Y")
+        expected = np.array(period_index.strftime("%Y"))
+        tm.assert_numpy_array_equal(result, expected)
+
 
 @pytest.mark.parametrize(
     "array,casting_nats",

pandas/tests/arrays/test_integer.py

@@ -379,8 +379,6 @@ def test_compare_array(self, data, all_compare_operators):
 
 
 class TestCasting:
-    pass
-
     @pytest.mark.parametrize("dropna", [True, False])
     def test_construct_index(self, all_data, dropna):
         # ensure that we do not coerce to Float64Index, rather

pandas/tests/series/test_period.py

@@ -71,10 +71,9 @@ def test_NaT_scalar(self):
         series[2] = val
         assert pd.isna(series[2])
 
-    @pytest.mark.xfail(reason="PeriodDtype Series not supported yet")
     def test_NaT_cast(self):
         result = Series([np.nan]).astype("period[D]")
-        expected = Series([pd.NaT])
+        expected = Series([pd.NaT], dtype="period[D]")
         tm.assert_series_equal(result, expected)
 
     def test_set_none(self):