diff --git a/src/matchbox/common/transform.py b/src/matchbox/common/transform.py
index 1903bfbb..be120ce2 100644
--- a/src/matchbox/common/transform.py
+++ b/src/matchbox/common/transform.py
@@ -2,8 +2,7 @@
 import multiprocessing
 from collections import defaultdict
 from concurrent.futures import ProcessPoolExecutor
-from typing import Callable, Generic, Hashable, Iterable, Literal, TypeVar
-from uuid import uuid4
+from typing import Callable, Generic, Hashable, Iterable, TypeVar
 
 import numpy as np
 import pyarrow as pa
@@ -386,48 +385,3 @@ def to_hierarchical_clusters(
         )
 
     return pa.concat_tables(results)
-
-
-def drop_duplicates(
-    table: pa.Table,
-    on: list[str] | None = None,
-    keep: Literal["first", "last"] = "first",
-) -> pa.Table:
-    """
-    Remove duplicate rows from a PyArrow table based on specified columns.
-
-    This function efficiently removes duplicate rows from a PyArrow table,
-    keeping either the first or last occurrence of each unique combination
-    of values in the specified columns.
-
-    Lifted with love from this gist:
-    https://gist.github.com/nmehran/57f264bd951b2f77af08f760eafea40e
-
-    An alternative:
-    https://github.com/TomScheffers/pyarrow_ops/
-    """
-    if not isinstance(table, pa.Table):
-        raise TypeError("Parameter 'table' must be a PyArrow Table")
-
-    if keep not in ["first", "last"]:
-        raise ValueError("Parameter 'keep' must be either 'first' or 'last'")
-
-    if not on:
-        on = table.column_names
-
-    # Generate a unique column name for row index
-    index_column = f"index_{uuid4().hex}"
-    index_aggregate_column = f"{index_column}_{keep}"
-
-    # Create row numbers
-    num_rows = table.num_rows
-    row_numbers = pa.array(np.arange(num_rows, dtype=np.int64))
-
-    # Append row numbers, group by specified columns, and aggregate
-    unique_indices = (
-        table.append_column(index_column, row_numbers)
-        .group_by(on, use_threads=False)
-        .aggregate([(index_column, keep)])
-    ).column(index_aggregate_column)
-
-    return pc.take(table, unique_indices, boundscheck=False)