CLN: Remove unused code (pandas-dev#57858)

Author: tqa236

pandas/core/array_algos/take.py

@@ -164,64 +164,6 @@ def _take_nd_ndarray(
     return out
 
 
-def take_1d(
-    arr: ArrayLike,
-    indexer: npt.NDArray[np.intp],
-    fill_value=None,
-    allow_fill: bool = True,
-    mask: npt.NDArray[np.bool_] | None = None,
-) -> ArrayLike:
-    """
-    Specialized version for 1D arrays. Differences compared to `take_nd`:
-
-    - Assumes input array has already been converted to numpy array / EA
-    - Assumes indexer is already guaranteed to be intp dtype ndarray
-    - Only works for 1D arrays
-
-    To ensure the lowest possible overhead.
-
-    Note: similarly to `take_nd`, this function assumes that the indexer is
-    a valid(ated) indexer with no out of bound indices.
-
-    Parameters
-    ----------
-    arr : np.ndarray or ExtensionArray
-        Input array.
-    indexer : ndarray
-        1-D array of indices to take (validated indices, intp dtype).
-    fill_value : any, default np.nan
-        Fill value to replace -1 values with
-    allow_fill : bool, default True
-        If False, indexer is assumed to contain no -1 values so no filling
-        will be done.  This short-circuits computation of a mask. Result is
-        undefined if allow_fill == False and -1 is present in indexer.
-    mask : np.ndarray, optional, default None
-        If `allow_fill` is True, and the mask (where indexer == -1) is already
-        known, it can be passed to avoid recomputation.
-    """
-    if not isinstance(arr, np.ndarray):
-        # ExtensionArray -> dispatch to their method
-        return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
-
-    if not allow_fill:
-        return arr.take(indexer)
-
-    dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
-        arr, indexer, fill_value, True, mask
-    )
-
-    # at this point, it's guaranteed that dtype can hold both the arr values
-    # and the fill_value
-    out = np.empty(indexer.shape, dtype=dtype)
-
-    func = _get_take_nd_function(
-        arr.ndim, arr.dtype, out.dtype, axis=0, mask_info=mask_info
-    )
-    func(arr, indexer, out, fill_value)
-
-    return out
-
-
 def take_2d_multi(
     arr: np.ndarray,
     indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]],