CLN: collected cleanups from other branches

pandas/_libs/index.pyx

@@ -47,10 +47,6 @@ cpdef get_value_at(ndarray arr, object loc, object tz=None):
     return util.get_value_at(arr, loc)
 
 
-def get_value_box(arr: ndarray, loc: object) -> object:
-    return get_value_at(arr, loc, tz=None)
-
-
 # Don't populate hash tables in monotonic indexes larger than this
 _SIZE_CUTOFF = 1000000
 

pandas/core/arrays/numpy_.py

@@ -125,7 +125,11 @@ def __init__(self, values, copy=False):
         if isinstance(values, type(self)):
             values = values._ndarray
         if not isinstance(values, np.ndarray):
-            raise ValueError("'values' must be a NumPy array.")
+            raise ValueError(
+                "'values' must be a NumPy array, not {typ}".format(
+                    typ=type(values).__name__
+                )
+            )
 
         if values.ndim != 1:
             raise ValueError("PandasArray must be 1-dimensional.")

pandas/core/arrays/timedeltas.py

@@ -173,8 +173,8 @@ class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
         "ceil",
     ]
 
-    # Needed so that NaT.__richcmp__(DateTimeArray) operates pointwise
-    ndim = 1
+    # Note: ndim must be defined to ensure NaT.__richcmp(TimedeltaArray)
+    #  operates pointwise.
 
     @property
     def _box_func(self):

pandas/core/generic.py

@@ -3556,7 +3556,7 @@ def _iget_item_cache(self, item):
     def _box_item_values(self, key, values):
         raise AbstractMethodError(self)
 
-    def _slice(self, slobj, axis=0, kind=None):
+    def _slice(self, slobj: slice, axis=0, kind=None):
         """
         Construct a slice of this container.
 
@@ -6183,8 +6183,6 @@ def fillna(
             axis = 0
         axis = self._get_axis_number(axis)
 
-        from pandas import DataFrame
-
         if value is None:
 
             if self._is_mixed_type and axis == 1:
@@ -6247,7 +6245,7 @@ def fillna(
                 new_data = self._data.fillna(
                     value=value, limit=limit, inplace=inplace, downcast=downcast
                 )
-            elif isinstance(value, DataFrame) and self.ndim == 2:
+            elif isinstance(value, ABCDataFrame) and self.ndim == 2:
                 new_data = self.where(self.notna(), value)
             else:
                 raise ValueError("invalid fill value with a %s" % type(value))

pandas/core/indexes/base.py

@@ -4701,7 +4701,7 @@ def get_value(self, series, key):
                 raise
 
             try:
-                return libindex.get_value_box(s, key)
+                return libindex.get_value_at(s, key)
             except IndexError:
                 raise
             except TypeError:

pandas/core/internals/blocks.py

@@ -437,7 +437,7 @@ def f(m, v, i):
 
         return self.split_and_operate(mask, f, inplace)
 
-    def split_and_operate(self, mask, f, inplace):
+    def split_and_operate(self, mask, f, inplace: bool):
         """
         split the block per-column, and apply the callable f
         per-column, return a new block for each. Handle
@@ -496,17 +496,15 @@ def make_a_block(nv, ref_loc):
 
         return new_blocks
 
-    def _maybe_downcast(self, blocks, downcast=None):
+    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
 
         # no need to downcast our float
         # unless indicated
-        if downcast is None and self.is_float:
-            return blocks
-        elif downcast is None and (self.is_timedelta or self.is_datetime):
+        if downcast is None and (
+            self.is_float or self.is_timedelta or self.is_datetime
+        ):
             return blocks
 
-        if not isinstance(blocks, list):
-            blocks = [blocks]
         return _extend_blocks([b.downcast(downcast) for b in blocks])
 
     def downcast(self, dtypes=None):
@@ -1351,7 +1349,15 @@ def shift(self, periods, axis=0, fill_value=None):
 
         return [self.make_block(new_values)]
 
-    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
+    def where(
+        self,
+        other,
+        cond,
+        align=True,
+        errors="raise",
+        try_cast: bool = False,
+        axis: int = 0,
+    ) -> List["Block"]:
         """
         evaluate the block; return result block(s) from the result
 
@@ -1450,7 +1456,7 @@ def func(cond, values, other):
             if try_cast:
                 result = self._try_cast_result(result)
 
-            return self.make_block(result)
+            return [self.make_block(result)]
 
         # might need to separate out blocks
         axis = cond.ndim - 1
@@ -1482,9 +1488,9 @@ def _unstack(self, unstacker_func, new_columns, n_rows, fill_value):
         new_columns : Index
             All columns of the unstacked BlockManager.
         n_rows : int
-            Only used in ExtensionBlock.unstack
+            Only used in ExtensionBlock._unstack
         fill_value : int
-            Only used in ExtensionBlock.unstack
+            Only used in ExtensionBlock._unstack
 
         Returns
         -------
@@ -1558,7 +1564,7 @@ def quantile(self, qs, interpolation="linear", axis=0):
             result = result[..., 0]
             result = lib.item_from_zerodim(result)
 
-        ndim = getattr(result, "ndim", None) or 0
+        ndim = np.ndim(result)
         return make_block(result, placement=np.arange(len(result)), ndim=ndim)
 
     def _replace_coerce(
@@ -1946,7 +1952,15 @@ def shift(
             )
         ]
 
-    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
+    def where(
+        self,
+        other,
+        cond,
+        align=True,
+        errors="raise",
+        try_cast: bool = False,
+        axis: int = 0,
+    ) -> List["Block"]:
         if isinstance(other, ABCDataFrame):
             # ExtensionArrays are 1-D, so if we get here then
             # `other` should be a DataFrame with a single column.
@@ -1991,7 +2005,7 @@ def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0)
                 np.where(cond, self.values, other), dtype=dtype
             )
 
-        return self.make_block_same_class(result, placement=self.mgr_locs)
+        return [self.make_block_same_class(result, placement=self.mgr_locs)]
 
     @property
     def _ftype(self):
@@ -2747,7 +2761,7 @@ def f(m, v, i):
 
         return blocks
 
-    def _maybe_downcast(self, blocks, downcast=None):
+    def _maybe_downcast(self, blocks: List["Block"], downcast=None) -> List["Block"]:
 
         if downcast is not None:
             return blocks
@@ -3082,7 +3096,15 @@ def concat_same_type(self, to_concat, placement=None):
             values, placement=placement or slice(0, len(values), 1), ndim=self.ndim
         )
 
-    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
+    def where(
+        self,
+        other,
+        cond,
+        align=True,
+        errors="raise",
+        try_cast: bool = False,
+        axis: int = 0,
+    ) -> List["Block"]:
         # TODO(CategoricalBlock.where):
         # This can all be deleted in favor of ExtensionBlock.where once
         # we enforce the deprecation.

pandas/core/internals/managers.py

@@ -1822,7 +1822,7 @@ def _simple_blockify(tuples, dtype):
     """
     values, placement = _stack_arrays(tuples, dtype)
 
-    # CHECK DTYPE?
+    # TODO: CHECK DTYPE?
     if dtype is not None and values.dtype != dtype:  # pragma: no cover
         values = values.astype(dtype)
 

pandas/core/resample.py

@@ -1630,15 +1630,14 @@ def _get_period_bins(self, ax):
 
 
 def _take_new_index(obj, indexer, new_index, axis=0):
-    from pandas.core.api import Series, DataFrame
 
-    if isinstance(obj, Series):
+    if isinstance(obj, ABCSeries):
         new_values = algos.take_1d(obj.values, indexer)
-        return Series(new_values, index=new_index, name=obj.name)
-    elif isinstance(obj, DataFrame):
+        return obj._constructor(new_values, index=new_index, name=obj.name)
+    elif isinstance(obj, ABCDataFrame):
         if axis == 1:
             raise NotImplementedError("axis 1 is not supported")
-        return DataFrame(
+        return obj._constructor(
             obj._data.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
         )
     else:

pandas/core/window.py

@@ -265,6 +265,8 @@ def _wrap_result(self, result, block=None, obj=None):
             # coerce if necessary
             if block is not None:
                 if is_timedelta64_dtype(block.values.dtype):
+                    # TODO: do we know what result.dtype is at this point?
+                    #  i.e. can we just do an astype?
                     from pandas import to_timedelta
 
                     result = to_timedelta(result.ravel(), unit="ns").values.reshape(

pandas/tests/arithmetic/test_datetime64.py

@@ -666,6 +666,7 @@ def test_comparison_tzawareness_compat_scalars(self, op, box_with_array):
     # Raising in __eq__ will fallback to NumPy, which warns, fails,
     # then re-raises the original exception. So we just need to ignore.
     @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
+    @pytest.mark.filterwarnings("ignore:Converting timezone-aware:FutureWarning")
     def test_scalar_comparison_tzawareness(
         self, op, other, tz_aware_fixture, box_with_array
     ):

pandas/tests/groupby/test_categorical.py

@@ -508,7 +508,7 @@ def test_datetime():
     desc_result = grouped.describe()
 
     idx = cats.codes.argsort()
-    ord_labels = cats.take_nd(idx)
+    ord_labels = cats.take(idx)
     ord_data = data.take(idx)
     expected = ord_data.groupby(ord_labels, observed=False).describe()
     assert_frame_equal(desc_result, expected)

pandas/tests/io/test_parquet.py

@@ -33,6 +33,10 @@
 except ImportError:
     _HAVE_FASTPARQUET = False
 
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:RangeIndex.* is deprecated:DeprecationWarning"
+)
+
 
 # setup engines & skips
 @pytest.fixture(