STYLE: fix pylint redefined-outer-name warnings (#49656)

Fixed warnings for the following files :-
- pandas/core/arrays/datetimelike.py
- pandas/core/base.py
- pandas/core/computation/pytables.py

Author: grtcoder

pandas/core/arrays/datetimelike.py

@@ -112,15 +112,10 @@
 )
 
 from pandas.core import (
+    algorithms,
     nanops,
     ops,
 )
-from pandas.core.algorithms import (
-    checked_add_with_arr,
-    isin,
-    mode,
-    unique1d,
-)
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays._mixins import (
     NDArrayBackedExtensionArray,
@@ -805,14 +800,14 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
                         pass
 
                     elif "mixed" in inferred:
-                        return isin(self.astype(object), values)
+                        return algos.isin(self.astype(object), values)
                     else:
                         return np.zeros(self.shape, dtype=bool)
 
             try:
                 values = type(self)._from_sequence(values)
             except ValueError:
-                return isin(self.astype(object), values)
+                return algos.isin(self.astype(object), values)
 
         if self.dtype.kind in ["m", "M"]:
             self = cast("DatetimeArray | TimedeltaArray", self)
@@ -824,7 +819,7 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
             # Includes tzawareness mismatch and IncompatibleFrequencyError
             return np.zeros(self.shape, dtype=bool)
 
-        return isin(self.asi8, values.asi8)
+        return algos.isin(self.asi8, values.asi8)
 
     # ------------------------------------------------------------------
     # Null Handling
@@ -993,7 +988,7 @@ def _is_monotonic_decreasing(self) -> bool:
 
     @property
     def _is_unique(self) -> bool:
-        return len(unique1d(self.asi8.ravel("K"))) == self.size
+        return len(algos.unique1d(self.asi8.ravel("K"))) == self.size
 
     # ------------------------------------------------------------------
     # Arithmetic Methods
@@ -1125,7 +1120,7 @@ def _add_datetimelike_scalar(self, other) -> DatetimeArray:
         self = cast("TimedeltaArray", self)
 
         other_i8, o_mask = self._get_i8_values_and_mask(other)
-        result = checked_add_with_arr(
+        result = algos.checked_add_with_arr(
             self.asi8, other_i8, arr_mask=self._isnan, b_mask=o_mask
         )
         res_values = result.view(f"M8[{self.unit}]")
@@ -1188,7 +1183,7 @@ def _sub_datetimelike(self, other: Timestamp | DatetimeArray) -> TimedeltaArray:
             raise type(err)(new_message) from err
 
         other_i8, o_mask = self._get_i8_values_and_mask(other)
-        res_values = checked_add_with_arr(
+        res_values = algos.checked_add_with_arr(
             self.asi8, -other_i8, arr_mask=self._isnan, b_mask=o_mask
         )
         res_m8 = res_values.view(f"timedelta64[{self.unit}]")
@@ -1254,7 +1249,7 @@ def _add_timedeltalike(self, other: Timedelta | TimedeltaArray):
         self = cast("DatetimeArray | TimedeltaArray", self)
 
         other_i8, o_mask = self._get_i8_values_and_mask(other)
-        new_values = checked_add_with_arr(
+        new_values = algos.checked_add_with_arr(
             self.asi8, other_i8, arr_mask=self._isnan, b_mask=o_mask
         )
         res_values = new_values.view(self._ndarray.dtype)
@@ -1309,7 +1304,7 @@ def _sub_periodlike(self, other: Period | PeriodArray) -> npt.NDArray[np.object_
         self._check_compatible_with(other)
 
         other_i8, o_mask = self._get_i8_values_and_mask(other)
-        new_i8_data = checked_add_with_arr(
+        new_i8_data = algos.checked_add_with_arr(
             self.asi8, -other_i8, arr_mask=self._isnan, b_mask=o_mask
         )
         new_data = np.array([self.freq.base * x for x in new_i8_data])
@@ -1690,7 +1685,7 @@ def _mode(self, dropna: bool = True):
         if dropna:
             mask = self.isna()
 
-        i8modes = mode(self.view("i8"), mask=mask)
+        i8modes = algorithms.algos.mode(self.view("i8"), mask=mask)
         npmodes = i8modes.view(self._ndarray.dtype)
         npmodes = cast(np.ndarray, npmodes)
         return self._from_backing_data(npmodes)

pandas/core/base.py

@@ -61,11 +61,13 @@
     ops,
 )
 from pandas.core.accessor import DirNamesMixin
-from pandas.core.algorithms import (
-    duplicated,
-    unique1d,
-    value_counts,
-)
+from pandas.core.algorithms import algos
+
+# from pandas.core.algorithms import (
+#     duplicated as duplicated_func,
+#     unique1d,
+#     value_counts,
+# )
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import ExtensionArray
 from pandas.core.construction import (
@@ -991,7 +993,7 @@ def value_counts(
         NaN    1
         dtype: int64
         """
-        return value_counts(
+        return algos.value_counts(
             self,
             sort=sort,
             ascending=ascending,
@@ -1006,7 +1008,7 @@ def unique(self):
             # i.e. ExtensionArray
             result = values.unique()
         else:
-            result = unique1d(values)
+            result = algos.unique1d(values)
         return result
 
     @final
@@ -1294,7 +1296,7 @@ def drop_duplicates(self, *, keep: DropKeep = "first"):
 
     @final
     def _duplicated(self, keep: DropKeep = "first") -> npt.NDArray[np.bool_]:
-        return duplicated(self._values, keep=keep)
+        return algos.duplicated(self._values, keep=keep)
 
     def _arith_method(self, other, op):
         res_name = ops.get_op_result_name(self, other)

pandas/core/computation/pytables.py

@@ -650,7 +650,7 @@ def maybe_expression(s) -> bool:
     """loose checking if s is a pytables-acceptable expression"""
     if not isinstance(s, str):
         return False
-    ops = PyTablesExprVisitor.binary_ops + PyTablesExprVisitor.unary_ops + ("=",)
+    operations = PyTablesExprVisitor.binary_ops + PyTablesExprVisitor.unary_ops + ("=",)
 
     # make sure we have an op at least
-    return any(op in s for op in ops)
+    return any(op in s for op in operations)