Always store MicroDataFrame weights as a Series

changelog.d/weights-always-series.yaml

@@ -0,0 +1,6 @@
+- bump: patch
+  changes:
+    fixed:
+    - MicroDataFrame.nullify_weights and set_weight_col now store weights as an
+      index-aligned Series instead of a bare ndarray, so equals() no longer
+      raises.

microdf/microdataframe.py

@@ -362,18 +362,20 @@ def set_weight_col(self, column: str, preserve_old: Optional[bool] = False) -> N
         if preserve_old and self.weights_col is not None:
             self["old_" + self.weights_col] = self.weights
 
-        self.weights = np.array(self[column])
-        self.weights_col = column
-        self._link_all_weights()
+        # Delegate to set_weights: it validates length and builds an
+        # index-aligned float Series rather than a bare ndarray.
+        self.set_weights(column)
 
     def nullify_weights(self) -> None:
         """Set all weights to 1, effectively making the DataFrame unweighted.
 
         This is useful for comparing weighted and unweighted statistics or when
         you want to temporarily ignore weights.
         """
-        self.weights = np.ones(len(self))
-        self._link_all_weights()
+        # Route through set_weights so self.weights stays an index-aligned
+        # float Series. Assigning a bare ndarray here broke every caller
+        # that treats it as a Series (equals(), reindex() in __getitem__).
+        self.set_weights(np.ones(len(self)))
 
     def __getitem__(
         self, key: Union[str, List]

microdf/tests/test_microseries_dataframe.py

@@ -815,3 +815,25 @@ def test_rank_ties_share_bucket() -> None:
     # existing ``test_rank`` expectations hold.
     s = mdf.MicroSeries([1, 2, 3], weights=[4, 5, 6])
     np.testing.assert_array_equal(s.rank().values, [4, 9, 15])
+
+
+def test_weights_stay_a_series_after_nullify():
+    """nullify_weights must leave weights as an index-aligned Series."""
+    df = mdf.MicroDataFrame(pd.DataFrame({"x": [1, 2, 3]}), weights=[4, 5, 6])
+    df.nullify_weights()
+    assert isinstance(df.weights, pd.Series)
+    assert list(df.weights.index) == list(df.index)
+    assert df.equals(df)
+    assert df.sum()["x"] == 6
+
+
+def test_weights_stay_a_series_after_set_weight_col():
+    """The deprecated set_weight_col must also produce a Series."""
+    df = mdf.MicroDataFrame(pd.DataFrame({"x": [1, 2, 3], "w": [1.0, 2.0, 3.0]}))
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", DeprecationWarning)
+        df.set_weight_col("w")
+    assert isinstance(df.weights, pd.Series)
+    assert df.weights_col == "w"
+    assert df.equals(df)
+    assert df.sum()["x"] == 14