Backport PR #54694 on branch 2.1.x (MAINT: Remove np.in1d function calls) (#54697)

Backport PR #54694: MAINT: Remove `np.in1d` function calls

Co-authored-by: Mateusz Sokół <8431159+mtsokol@users.noreply.github.com>

Author: meeseeksmachine

pandas/core/algorithms.py

@@ -518,9 +518,9 @@ def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
         return isin(np.asarray(comps_array), np.asarray(values))
 
     # GH16012
-    # Ensure np.in1d doesn't get object types or it *may* throw an exception
+    # Ensure np.isin doesn't get object types or it *may* throw an exception
     # Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
-    # in1d is faster for small sizes
+    # isin is faster for small sizes
     if (
         len(comps_array) > _MINIMUM_COMP_ARR_LEN
         and len(values) <= 26
@@ -531,10 +531,10 @@ def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
         if isna(values).any():
 
             def f(c, v):
-                return np.logical_or(np.in1d(c, v), np.isnan(c))
+                return np.logical_or(np.isin(c, v).ravel(), np.isnan(c))
 
         else:
-            f = np.in1d
+            f = lambda a, b: np.isin(a, b).ravel()
 
     else:
         common = np_find_common_type(values.dtype, comps_array.dtype)

pandas/core/arrays/interval.py

@@ -1844,14 +1844,14 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
                 #  complex128 ndarray is much more performant.
                 left = self._combined.view("complex128")
                 right = values._combined.view("complex128")
-                # error: Argument 1 to "in1d" has incompatible type
+                # error: Argument 1 to "isin" has incompatible type
                 # "Union[ExtensionArray, ndarray[Any, Any],
                 # ndarray[Any, dtype[Any]]]"; expected
                 # "Union[_SupportsArray[dtype[Any]],
                 # _NestedSequence[_SupportsArray[dtype[Any]]], bool,
                 # int, float, complex, str, bytes, _NestedSequence[
                 # Union[bool, int, float, complex, str, bytes]]]"
-                return np.in1d(left, right)  # type: ignore[arg-type]
+                return np.isin(left, right).ravel()  # type: ignore[arg-type]
 
             elif needs_i8_conversion(self.left.dtype) ^ needs_i8_conversion(
                 values.left.dtype

pandas/tseries/holiday.py

@@ -283,11 +283,11 @@ def dates(
         holiday_dates = self._apply_rule(dates)
         if self.days_of_week is not None:
             holiday_dates = holiday_dates[
-                np.in1d(
+                np.isin(
                     # error: "DatetimeIndex" has no attribute "dayofweek"
                     holiday_dates.dayofweek,  # type: ignore[attr-defined]
                     self.days_of_week,
-                )
+                ).ravel()
             ]
 
         if self.start_date is not None: