CLN: remove unnecessary fastpath, transpose kwargs in internals

pandas/core/generic.py

@@ -9175,7 +9175,6 @@ def _where(
                 errors=errors,
                 try_cast=try_cast,
                 axis=block_axis,
-                transpose=self._AXIS_REVERSED,
             )
 
             return self._constructor(new_data).__finalize__(self)

pandas/core/internals/blocks.py

@@ -143,7 +143,7 @@ def _check_ndim(self, values, ndim):
             ndim = values.ndim
 
         if self._validate_ndim and values.ndim != ndim:
-            msg = "Wrong number of dimensions. values.ndim != ndim " "[{} != {}]"
+            msg = "Wrong number of dimensions. values.ndim != ndim [{} != {}]"
             raise ValueError(msg.format(values.ndim, ndim))
 
         return ndim
@@ -259,7 +259,7 @@ def make_block_same_class(self, values, placement=None, ndim=None, dtype=None):
         if dtype is not None:
             # issue 19431 fastparquet is passing this
             warnings.warn(
-                "dtype argument is deprecated, will be removed " "in a future release.",
+                "dtype argument is deprecated, will be removed in a future release.",
                 FutureWarning,
             )
         if placement is None:
@@ -399,7 +399,7 @@ def fillna(self, value, limit=None, inplace=False, downcast=None):
                 raise ValueError("Limit must be greater than 0")
             if self.ndim > 2:
                 raise NotImplementedError(
-                    "number of dimensions for 'fillna' " "is currently limited to 2"
+                    "number of dimensions for 'fillna' is currently limited to 2"
                 )
             mask[mask.cumsum(self.ndim - 1) > limit] = False
 
@@ -533,7 +533,7 @@ def downcast(self, dtypes=None):
 
         if not (dtypes == "infer" or isinstance(dtypes, dict)):
             raise ValueError(
-                "downcast must have a dictionary or 'infer' as " "its argument"
+                "downcast must have a dictionary or 'infer' as its argument"
             )
 
         # operate column-by-column
@@ -1025,7 +1025,7 @@ def putmask(self, mask, new, align=True, inplace=False, axis=0, transpose=False)
                     or mask[mask].shape[-1] == len(new)
                     or len(new) == 1
                 ):
-                    raise ValueError("cannot assign mismatch " "length to masked array")
+                    raise ValueError("cannot assign mismatch length to masked array")
 
             np.putmask(new_values, mask, new)
 
@@ -1381,16 +1381,7 @@ 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,
-        transpose=False,
-    ):
+    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
         """
         evaluate the block; return result block(s) from the result
 
@@ -1402,10 +1393,7 @@ def where(
         errors : str, {'raise', 'ignore'}, default 'raise'
             - ``raise`` : allow exceptions to be raised
             - ``ignore`` : suppress exceptions. On error return original object
-
         axis : int
-        transpose : boolean
-            Set to True if self is stored with axes reversed
 
         Returns
         -------
@@ -1414,6 +1402,7 @@ def where(
         import pandas.core.computation.expressions as expressions
 
         assert errors in ["raise", "ignore"]
+        transpose = self.ndim == 2
 
         values = self.values
         orig_other = other
@@ -1432,7 +1421,7 @@ def where(
                 cond = cond.T
 
         if not hasattr(cond, "shape"):
-            raise ValueError("where must have a condition that is ndarray " "like")
+            raise ValueError("where must have a condition that is ndarray like")
 
         # our where function
         def func(cond, values, other):
@@ -1473,7 +1462,6 @@ def func(cond, values, other):
                     errors=errors,
                     try_cast=try_cast,
                     axis=axis,
-                    transpose=transpose,
                 )
                 return self._maybe_downcast(blocks, "infer")
 
@@ -1917,7 +1905,7 @@ def _slice(self, slicer):
 
         if isinstance(slicer, tuple) and len(slicer) == 2:
             if not com.is_null_slice(slicer[0]):
-                raise AssertionError("invalid slicing for a 1-ndim " "categorical")
+                raise AssertionError("invalid slicing for a 1-ndim categorical")
             slicer = slicer[1]
 
         return self.values[slicer]
@@ -2004,16 +1992,7 @@ def shift(
             )
         ]
 
-    def where(
-        self,
-        other,
-        cond,
-        align=True,
-        errors="raise",
-        try_cast=False,
-        axis=0,
-        transpose=False,
-    ):
+    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
         if isinstance(other, ABCDataFrame):
             # ExtensionArrays are 1-D, so if we get here then
             # `other` should be a DataFrame with a single column.
@@ -2321,9 +2300,7 @@ def _try_coerce_args(self, other):
         elif isinstance(other, (datetime, np.datetime64, date)):
             other = self._box_func(other)
             if getattr(other, "tz") is not None:
-                raise TypeError(
-                    "cannot coerce a Timestamp with a tz on a " "naive Block"
-                )
+                raise TypeError("cannot coerce a Timestamp with a tz on a naive Block")
             other = other.asm8.view("i8")
         elif hasattr(other, "dtype") and is_datetime64_dtype(other):
             other = other.astype("i8", copy=False).view("i8")
@@ -2997,7 +2974,7 @@ def _replace_single(
         # only one will survive
         if to_rep_re and regex_re:
             raise AssertionError(
-                "only one of to_replace and regex can be " "regex compilable"
+                "only one of to_replace and regex can be regex compilable"
             )
 
         # if regex was passed as something that can be a regex (rather than a
@@ -3181,16 +3158,7 @@ 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,
-        transpose=False,
-    ):
+    def where(self, other, cond, align=True, errors="raise", try_cast=False, axis=0):
         # TODO(CategoricalBlock.where):
         # This can all be deleted in favor of ExtensionBlock.where once
         # we enforce the deprecation.
@@ -3205,19 +3173,11 @@ def where(
         )
         try:
             # Attempt to do preserve categorical dtype.
-            result = super().where(
-                other, cond, align, errors, try_cast, axis, transpose
-            )
+            result = super().where(other, cond, align, errors, try_cast, axis)
         except (TypeError, ValueError):
             warnings.warn(object_msg, FutureWarning, stacklevel=6)
             result = self.astype(object).where(
-                other,
-                cond,
-                align=align,
-                errors=errors,
-                try_cast=try_cast,
-                axis=axis,
-                transpose=transpose,
+                other, cond, align=align, errors=errors, try_cast=try_cast, axis=axis
             )
         return result
 
@@ -3286,7 +3246,7 @@ def make_block(values, placement, klass=None, ndim=None, dtype=None, fastpath=No
     if fastpath is not None:
         # GH#19265 pyarrow is passing this
         warnings.warn(
-            "fastpath argument is deprecated, will be removed " "in a future release.",
+            "fastpath argument is deprecated, will be removed in a future release.",
             FutureWarning,
         )
     if klass is None:

pandas/core/internals/managers.py

@@ -936,7 +936,7 @@ def _consolidate_inplace(self):
             self._known_consolidated = True
             self._rebuild_blknos_and_blklocs()
 
-    def get(self, item, fastpath=True):
+    def get(self, item):
         """
         Return values for selected item (ndarray or BlockManager).
         """
@@ -954,7 +954,7 @@ def get(self, item, fastpath=True):
                     else:
                         raise ValueError("cannot label index with a null key")
 
-            return self.iget(loc, fastpath=fastpath)
+            return self.iget(loc)
         else:
 
             if isna(item):
@@ -965,18 +965,18 @@ def get(self, item, fastpath=True):
                 new_axis=self.items[indexer], indexer=indexer, axis=0, allow_dups=True
             )
 
-    def iget(self, i, fastpath=True):
+    def iget(self, i):
         """
-        Return the data as a SingleBlockManager if fastpath=True and possible
+        Return the data as a SingleBlockManager if possible
 
         Otherwise return as a ndarray
         """
         block = self.blocks[self._blknos[i]]
         values = block.iget(self._blklocs[i])
-        if not fastpath or values.ndim != 1:
+        if values.ndim != 1:
             return values
 
-        # fastpath shortcut for select a single-dim from a 2-dim BM
+        # shortcut for select a single-dim from a 2-dim BM
         return SingleBlockManager(
             [
                 block.make_block_same_class(

pandas/tests/internals/test_internals.py

@@ -418,9 +418,6 @@ def test_get(self):
         block = make_block(values=values.copy(), placement=np.arange(3))
         mgr = BlockManager(blocks=[block], axes=[cols, np.arange(3)])
 
-        assert_almost_equal(mgr.get("a", fastpath=False), values[0])
-        assert_almost_equal(mgr.get("b", fastpath=False), values[1])
-        assert_almost_equal(mgr.get("c", fastpath=False), values[2])
         assert_almost_equal(mgr.get("a").internal_values(), values[0])
         assert_almost_equal(mgr.get("b").internal_values(), values[1])
         assert_almost_equal(mgr.get("c").internal_values(), values[2])
@@ -701,6 +698,7 @@ def test_consolidate_ordering_issues(self, mgr):
         )
 
     def test_reindex_index(self):
+        # TODO: should this be pytest.skip?
         pass
 
     def test_reindex_items(self):
@@ -710,18 +708,6 @@ def test_reindex_items(self):
         reindexed = mgr.reindex_axis(["g", "c", "a", "d"], axis=0)
         assert reindexed.nblocks == 2
         tm.assert_index_equal(reindexed.items, pd.Index(["g", "c", "a", "d"]))
-        assert_almost_equal(
-            mgr.get("g", fastpath=False), reindexed.get("g", fastpath=False)
-        )
-        assert_almost_equal(
-            mgr.get("c", fastpath=False), reindexed.get("c", fastpath=False)
-        )
-        assert_almost_equal(
-            mgr.get("a", fastpath=False), reindexed.get("a", fastpath=False)
-        )
-        assert_almost_equal(
-            mgr.get("d", fastpath=False), reindexed.get("d", fastpath=False)
-        )
         assert_almost_equal(
             mgr.get("g").internal_values(), reindexed.get("g").internal_values()
         )
@@ -747,18 +733,12 @@ def test_get_numeric_data(self):
         tm.assert_index_equal(
             numeric.items, pd.Index(["int", "float", "complex", "bool"])
         )
-        assert_almost_equal(
-            mgr.get("float", fastpath=False), numeric.get("float", fastpath=False)
-        )
         assert_almost_equal(
             mgr.get("float").internal_values(), numeric.get("float").internal_values()
         )
 
         # Check sharing
         numeric.set("float", np.array([100.0, 200.0, 300.0]))
-        assert_almost_equal(
-            mgr.get("float", fastpath=False), np.array([100.0, 200.0, 300.0])
-        )
         assert_almost_equal(
             mgr.get("float").internal_values(), np.array([100.0, 200.0, 300.0])
         )
@@ -768,9 +748,6 @@ def test_get_numeric_data(self):
             numeric.items, pd.Index(["int", "float", "complex", "bool"])
         )
         numeric2.set("float", np.array([1000.0, 2000.0, 3000.0]))
-        assert_almost_equal(
-            mgr.get("float", fastpath=False), np.array([100.0, 200.0, 300.0])
-        )
         assert_almost_equal(
             mgr.get("float").internal_values(), np.array([100.0, 200.0, 300.0])
         )
@@ -785,27 +762,18 @@ def test_get_bool_data(self):
 
         bools = mgr.get_bool_data()
         tm.assert_index_equal(bools.items, pd.Index(["bool"]))
-        assert_almost_equal(
-            mgr.get("bool", fastpath=False), bools.get("bool", fastpath=False)
-        )
         assert_almost_equal(
             mgr.get("bool").internal_values(), bools.get("bool").internal_values()
         )
 
         bools.set("bool", np.array([True, False, True]))
-        tm.assert_numpy_array_equal(
-            mgr.get("bool", fastpath=False), np.array([True, False, True])
-        )
         tm.assert_numpy_array_equal(
             mgr.get("bool").internal_values(), np.array([True, False, True])
         )
 
         # Check sharing
         bools2 = mgr.get_bool_data(copy=True)
         bools2.set("bool", np.array([False, True, False]))
-        tm.assert_numpy_array_equal(
-            mgr.get("bool", fastpath=False), np.array([True, False, True])
-        )
         tm.assert_numpy_array_equal(
             mgr.get("bool").internal_values(), np.array([True, False, True])
         )