CLN: Remove unused code (#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]],