fix: handle empty Arrow-backed join keys to prevent merge crashes

datacompy/pandas.py

@@ -363,13 +363,18 @@ def _dataframe_merge(self, ignore_spaces: bool) -> None:
         else:
             params = {"on": self.join_columns}
 
-        for column in self.join_columns:
-            self.df1[column] = pandas_normalize_string_column(
-                self.df1[column], ignore_spaces=ignore_spaces, ignore_case=False
-            )
-            self.df2[column] = pandas_normalize_string_column(
-                self.df2[column], ignore_spaces=ignore_spaces, ignore_case=False
-            )
+        # Skip normalization for empty frames: str.strip() on an empty
+        # Arrow-backed column drops its chunks to zero, which causes pandas
+        # merge to call pa.chunked_array([]) and raise ArrowInvalid when
+        # multiple join columns are present. See issue #514.
+        if len(self.df1) > 0 or len(self.df2) > 0:
+            for column in self.join_columns:
+                self.df1[column] = pandas_normalize_string_column(
+                    self.df1[column], ignore_spaces=ignore_spaces, ignore_case=False
+                )
+                self.df2[column] = pandas_normalize_string_column(
+                    self.df2[column], ignore_spaces=ignore_spaces, ignore_case=False
+                )
 
         outer_join = self.df1.merge(
             self.df2,

tests/test_pandas.py

@@ -2645,6 +2645,40 @@ def test_columns_with_mismatches_empty_dataframes():
     assert result == []
 
 
+def test_compare_empty_arrow_backed_join_keys():
+    """Regression for #514: empty Arrow-backed join keys must not crash merge."""
+    import pyarrow as pa
+
+    for arrow_type in (pa.string(), pa.int64()):
+        dtype = pd.ArrowDtype(arrow_type)
+        df1 = pd.DataFrame(
+            {
+                "key1": pd.Series([], dtype=dtype),
+                "key2": pd.Series([], dtype=dtype),
+                "value": pd.Series([], dtype=dtype),
+            }
+        )
+        df2 = pd.DataFrame(
+            {
+                "key1": pd.Series([], dtype=dtype),
+                "key2": pd.Series([], dtype=dtype),
+                "value": pd.Series([], dtype=dtype),
+            }
+        )
+
+        compare = PandasCompare(
+            df1,
+            df2,
+            join_columns=["key1", "key2"],
+            ignore_spaces=True,
+            ignore_case=False,
+        )
+
+        assert len(compare.intersect_rows) == 0
+        assert len(compare.df1_unq_rows) == 0
+        assert len(compare.df2_unq_rows) == 0
+
+
 def test_columns_with_mismatches_on_index():
     """Test columns_with_mismatches when comparing on index."""
     df1 = pd.DataFrame(