CLN: typos

asv_bench/benchmarks/groupby.py

@@ -454,6 +454,16 @@ def setup(self, dtype, method, application, ncols):
             # DataFrameGroupBy doesn't have these methods
             raise NotImplementedError
 
+        if application == "transformation" and method in [
+            "head",
+            "tail",
+            "unique",
+            "value_counts",
+            "size",
+        ]:
+            # DataFrameGroupBy doesn't have these methods
+            raise NotImplementedError
+
         ngroups = 1000
         size = ngroups * 2
         rng = np.arange(ngroups).reshape(-1, 1)
@@ -480,7 +490,7 @@ def setup(self, dtype, method, application, ncols):
         if len(cols) == 1:
             cols = cols[0]
 
-        if application == "transform":
+        if application == "transformation":
             if method == "describe":
                 raise NotImplementedError
 

pandas/_libs/internals.pyi

@@ -51,7 +51,7 @@ class BlockPlacement:
     def __len__(self) -> int: ...
     def delete(self, loc) -> BlockPlacement: ...
     def append(self, others: list[BlockPlacement]) -> BlockPlacement: ...
-    def tile_for_unstack(self, factor: int) -> np.ndarray: ...
+    def tile_for_unstack(self, factor: int) -> npt.NDArray[np.intp]: ...
 
 class SharedBlock:
     _mgr_locs: BlockPlacement

pandas/_libs/reduction.pyx

@@ -1,24 +1,11 @@
-
-from libc.stdlib cimport (
-    free,
-    malloc,
-)
-
 import numpy as np
 
 cimport numpy as cnp
-from numpy cimport (
-    int64_t,
-    intp_t,
-    ndarray,
-)
 
 cnp.import_array()
 
 from pandas._libs.util cimport is_array
 
-from pandas._libs.lib import is_scalar
-
 
 cdef cnp.dtype _dtype_obj = np.dtype("object")
 

pandas/_libs/tslibs/strptime.pyi

@@ -9,4 +9,4 @@ def array_strptime(
     errors: str = "raise",
 ) -> tuple[np.ndarray, np.ndarray]: ...
 
-# first  ndarray is M8[ns], second is object ndarray of tzinfo | None
+# first ndarray is M8[ns], second is object ndarray of tzinfo | None

pandas/core/groupby/grouper.py

@@ -617,7 +617,7 @@ def codes(self) -> np.ndarray:
     def group_arraylike(self) -> ArrayLike:
         """
         Analogous to result_index, but holding an ArrayLike to ensure
-        we can can retain ExtensionDtypes.
+        we can retain ExtensionDtypes.
         """
         if self._group_index is not None:
             # _group_index is set in __init__ for MultiIndex cases

pandas/core/indexes/base.py

@@ -3060,7 +3060,7 @@ def _union(self, other: Index, sort):
             try:
                 return self._outer_indexer(other)[0]
             except (TypeError, IncompatibleFrequency):
-                # incomparable objects
+                # incomparable objects; should only be for object dtype
                 value_list = list(lvals)
 
                 # worth making this faster? a very unusual case
@@ -3074,7 +3074,7 @@ def _union(self, other: Index, sort):
             result = algos.union_with_duplicates(lvals, rvals)
             return _maybe_try_sort(result, sort)
 
-        # Self may have duplicates
+        # Self may have duplicates; other already checked as unique
         # find indexes of things in "other" that are not in "self"
         if self._index_as_unique:
             indexer = self.get_indexer(other)
@@ -3089,6 +3089,7 @@ def _union(self, other: Index, sort):
             result = lvals
 
         if not self.is_monotonic or not other.is_monotonic:
+            # if both are monotonic then result should already be sorted
             result = _maybe_try_sort(result, sort)
 
         return result
@@ -3194,6 +3195,7 @@ def _intersection(self, other: Index, sort=False):
             try:
                 result = self._inner_indexer(other)[0]
             except TypeError:
+                # non-comparable; should only be for object dtype
                 pass
             else:
                 # TODO: algos.unique1d should preserve DTA/TDA
@@ -4485,7 +4487,7 @@ def _get_leaf_sorter(labels: list[np.ndarray]) -> npt.NDArray[np.intp]:
     def _join_monotonic(
         self, other: Index, how: str_t = "left"
     ) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
-        # We only get here with matching dtypes
+        # We only get here with matching dtypes and both monotonic increasing
         assert other.dtype == self.dtype
 
         if self.equals(other):

pandas/core/internals/array_manager.py

@@ -1035,7 +1035,7 @@ def quantile(
 
     def unstack(self, unstacker, fill_value) -> ArrayManager:
         """
-        Return a BlockManager with all blocks unstacked..
+        Return a BlockManager with all blocks unstacked.
 
         Parameters
         ----------

pandas/core/internals/blocks.py

@@ -1291,6 +1291,7 @@ def _unstack(self, unstacker, fill_value, new_placement, allow_fill: bool):
         unstacker : reshape._Unstacker
         fill_value : int
             Only used in ExtensionBlock._unstack
+        new_placement : np.ndarray[np.intp]
         allow_fill : bool
 
         Returns

pandas/core/internals/managers.py

@@ -1376,7 +1376,7 @@ def quantile(
 
     def unstack(self, unstacker, fill_value) -> BlockManager:
         """
-        Return a BlockManager with all blocks unstacked..
+        Return a BlockManager with all blocks unstacked.
 
         Parameters
         ----------