diff --git a/python/cudf/cudf/_lib/CMakeLists.txt b/python/cudf/cudf/_lib/CMakeLists.txt
index 2958c286d20..8a521f19350 100644
--- a/python/cudf/cudf/_lib/CMakeLists.txt
+++ b/python/cudf/cudf/_lib/CMakeLists.txt
@@ -44,7 +44,6 @@ set(cython_sources
     text.pyx
     timezone.pyx
     transform.pyx
-    transpose.pyx
     types.pyx
     utils.pyx
 )
diff --git a/python/cudf/cudf/_lib/__init__.py b/python/cudf/cudf/_lib/__init__.py
index 19dc4488560..27bb486f55b 100644
--- a/python/cudf/cudf/_lib/__init__.py
+++ b/python/cudf/cudf/_lib/__init__.py
@@ -30,7 +30,6 @@
     strings_udf,
     text,
     timezone,
-    transpose,
 )
 
 MAX_COLUMN_SIZE = np.iinfo(np.int32).max
diff --git a/python/cudf/cudf/_lib/transpose.pyx b/python/cudf/cudf/_lib/transpose.pyx
deleted file mode 100644
index 995d278cb88..00000000000
--- a/python/cudf/cudf/_lib/transpose.pyx
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
-
-import pylibcudf as plc
-
-from cudf._lib.column cimport Column
-
-
-def transpose(list source_columns):
-    """Transpose m n-row columns into n m-row columns
-    """
-    input_table = plc.table.Table(
-        [col.to_pylibcudf(mode="read") for col in source_columns]
-    )
-    result_table = plc.transpose.transpose(input_table)
-    return [
-        Column.from_pylibcudf(col, data_ptr_exposed=True)
-        for col in result_table.columns()
-    ]
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index bd78d5dd9f1..728cc47a7c9 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -4113,7 +4113,15 @@ def transpose(self):
         if any(c.dtype != source_columns[0].dtype for c in source_columns):
             raise ValueError("Columns must all have the same dtype")
 
-        result_columns = libcudf.transpose.transpose(source_columns)
+        result_table = plc.transpose.transpose(
+            plc.table.Table(
+                [col.to_pylibcudf(mode="read") for col in source_columns]
+            )
+        )
+        result_columns = [
+            libcudf.column.Column.from_pylibcudf(col, data_ptr_exposed=True)
+            for col in result_table.columns()
+        ]
 
         if isinstance(source_dtype, cudf.CategoricalDtype):
             result_columns = [