fix: loc setitem dtype issue.

bigframes/core/indexers.py

@@ -192,7 +192,15 @@ def __setitem__(
             and isinstance(key[0], bigframes.series.Series)
             and key[0].dtype == "boolean"
         ) and pd.api.types.is_scalar(value):
-            new_column = key[0].map({True: value, False: None})
+            # For integer scalar, if set value to a new column, the dtype would be default to float.
+            # But if set value to an existing Int64 column, the dtype would still be integer.
+            # So we need to use different NaN type to match this behavior.
+            new_column = key[0].map(
+                {
+                    True: value,
+                    False: pd.NA if key[1] in self._dataframe.columns else None,
+                }
+            )
             try:
                 original_column = self._dataframe[key[1]]
             except KeyError:

tests/system/small/test_dataframe.py

@@ -2918,15 +2918,23 @@ def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs):
     )
 
 
-def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs):
+@pytest.mark.parametrize(
+    ("col", "value"),
+    [
+        ("string_col", "hello"),
+        ("int64_col", 3),
+        ("float64_col", 3.5),
+    ],
+)
+def test_loc_setitem_bool_series_scalar_existing_col(scalars_dfs, col, value):
     if pd.__version__.startswith("1."):
         pytest.skip("this loc overload not supported in pandas 1.x.")
 
     scalars_df, scalars_pandas_df = scalars_dfs
     bf_df = scalars_df.copy()
     pd_df = scalars_pandas_df.copy()
-    bf_df.loc[bf_df["int64_too"] == 1, "string_col"] = "hello"
-    pd_df.loc[pd_df["int64_too"] == 1, "string_col"] = "hello"
+    bf_df.loc[bf_df["int64_too"] == 1, col] = value
+    pd_df.loc[pd_df["int64_too"] == 1, col] = value
 
     pd.testing.assert_frame_equal(
         bf_df.to_pandas(),

third_party/bigframes_vendored/pandas/core/generic.py

@@ -662,9 +662,9 @@ def copy(self):
 
             >>> df.loc[df["b"] == 2, "b"] = 22
             >>> df
-               a     b
-            0  1  22.0
-            1  3   4.0
+               a   b
+            0  1  22
+            1  3   4
             <BLANKLINE>
             [2 rows x 2 columns]
             >>> df_copy