feat(python): Raise DuplicateError if given a pyarrow Table object …

…with duplicate column names (#20624)

py-polars/polars/_utils/construction/dataframe.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import contextlib
+from collections import Counter
 from collections.abc import Generator, Mapping, Sequence
 from datetime import date, datetime, time, timedelta
 from functools import singledispatch
@@ -52,7 +53,7 @@
 from polars.dependencies import numpy as np
 from polars.dependencies import pandas as pd
 from polars.dependencies import pyarrow as pa
-from polars.exceptions import DataOrientationWarning, ShapeError
+from polars.exceptions import DataOrientationWarning, DuplicateError, ShapeError
 from polars.meta import thread_pool_size
 
 with contextlib.suppress(ImportError):  # Module not available when building docs
@@ -209,7 +210,7 @@ def _parse_schema_overrides(
 
     schema_overrides = _parse_schema_overrides(schema_overrides)
 
-    # Fast path for empty schema
+    # fast path for empty schema
     if not schema:
         columns = (
             [f"column_{i}" for i in range(n_expected)] if n_expected is not None else []
@@ -1163,7 +1164,6 @@ def arrow_to_pydf(
     column_names, schema_overrides = _unpack_schema(
         (schema or data.schema.names), schema_overrides=schema_overrides
     )
-
     try:
         if column_names != data.schema.names:
             data = data.rename_columns(column_names)
@@ -1180,6 +1180,12 @@ def arrow_to_pydf(
     # supply the arrow schema so the metadata is intact
     pydf = PyDataFrame.from_arrow_record_batches(batches, data.schema)
 
+    # arrow tables allow duplicate names; we don't
+    if len(data.columns) != pydf.width():
+        col_name, _ = Counter(column_names).most_common(1)[0]
+        msg = f"column {col_name!r} appears more than once; names must be unique"
+        raise DuplicateError(msg)
+
     if rechunk:
         pydf = pydf.rechunk()
 

py-polars/tests/unit/constructors/test_constructors.py

@@ -18,7 +18,7 @@
 from polars._utils.construction.utils import try_get_type_hints
 from polars.datatypes import numpy_char_code_to_dtype
 from polars.dependencies import dataclasses, pydantic
-from polars.exceptions import ShapeError
+from polars.exceptions import DuplicateError, ShapeError
 from polars.testing import assert_frame_equal, assert_series_equal
 
 if TYPE_CHECKING:
@@ -723,6 +723,21 @@ def test_init_arrow() -> None:
         pl.DataFrame(pa.table({"a": [1, 2, 3], "b": [4, 5, 6]}), schema=["c", "d", "e"])
 
 
+def test_init_arrow_dupes() -> None:
+    tbl = pa.Table.from_arrays(
+        arrays=[
+            pa.array([1, 2, 3], type=pa.int32()),
+            pa.array([4, 5, 6], type=pa.int32()),
+        ],
+        schema=pa.schema([("col", pa.int32()), ("col", pa.int32())]),
+    )
+    with pytest.raises(
+        DuplicateError,
+        match="column 'col' appears more than once; names must be unique",
+    ):
+        pl.DataFrame(tbl)
+
+
 def test_init_from_frame() -> None:
     df1 = pl.DataFrame({"id": [0, 1], "misc": ["a", "b"], "val": [-10, 10]})
     assert_frame_equal(df1, pl.DataFrame(df1))