Add pandas 3.0 compatibility tests

.github/workflows/pr.yaml

@@ -2,7 +2,7 @@ name: Pull request
 
 on:
   pull_request:
-    branches: [ master ]
+    branches: [ main ]
 
 jobs:
   lint:

changelog_entry.yaml

@@ -0,0 +1,8 @@
+- bump: minor
+  changes:
+    fixed:
+      - MicroDataFrame.loc[] and .iloc[] now preserve MicroDataFrame type and weights when filtering rows (fixes issue #265)
+      - MicroDataFrame.groupby(col)["y"].sum() and groupby(col)[["y"]].sum() now use weighted aggregation (fixes issue #193)
+      - Documentation build updated to use Jupyter Book 2.0 / MyST
+    added:
+      - Added pandas 3.0 compatibility test suite

microdf/tests/test_pandas3_compatibility.py

@@ -0,0 +1,232 @@
+"""
+Tests for pandas 3.0.0 compatibility in microdf.
+
+These tests verify that microdf works correctly with pandas 3.0.0,
+which introduces:
+1. PyArrow-backed strings as default (StringDtype)
+2. Copy-on-Write by default
+3. Changes to how Series subclasses are handled
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from microdf.microseries import MicroSeries
+from microdf.microdataframe import MicroDataFrame
+
+
+class TestMicroSeriesSubclassPreservation:
+    """Test that MicroSeries subclass is preserved across operations."""
+
+    def test_microseries_set_weights_after_creation(self):
+        """
+        Ensure set_weights works on MicroSeries.
+        This is the error reported in pandas 3:
+        AttributeError: 'Series' object has no attribute 'set_weights'
+        """
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 1.0, 1.0]))
+        assert hasattr(ms, "set_weights")
+        assert hasattr(ms, "weights")
+
+        # Should be able to call set_weights
+        ms.set_weights(np.array([2.0, 2.0, 2.0]))
+        assert np.allclose(ms.weights, [2.0, 2.0, 2.0])
+
+    def test_microseries_preserved_after_arithmetic(self):
+        """
+        Arithmetic operations should return MicroSeries, not plain Series.
+        """
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 2.0, 3.0]))
+
+        # Addition
+        result = ms + 1
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+        assert hasattr(result, "weights")
+        assert hasattr(result, "set_weights")
+
+        # Multiplication
+        result = ms * 2
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+
+        # Division
+        result = ms / 2
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+
+    def test_microseries_preserved_after_comparison(self):
+        """
+        Comparison operations should return MicroSeries, not plain Series.
+        """
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 2.0, 3.0]))
+
+        # Greater than
+        result = ms > 1
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+        assert hasattr(result, "weights")
+
+        # Less than
+        result = ms < 3
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+
+    def test_microseries_preserved_after_indexing(self):
+        """
+        Indexing operations should return MicroSeries, not plain Series.
+        """
+        ms = MicroSeries([1, 2, 3, 4, 5], weights=np.array([1.0, 2.0, 3.0, 4.0, 5.0]))
+
+        # Boolean indexing
+        result = ms[ms > 2]
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+        assert hasattr(result, "weights")
+
+        # Slice indexing
+        result = ms[1:3]
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+
+
+class TestMicroDataFrameSubclassPreservation:
+    """Test that MicroDataFrame column access returns MicroSeries."""
+
+    def test_microdataframe_column_returns_microseries(self):
+        """
+        Accessing a column from MicroDataFrame should return MicroSeries.
+        """
+        mdf = MicroDataFrame(
+            {"a": [1, 2, 3], "b": [4, 5, 6]},
+            weights=np.array([1.0, 2.0, 3.0])
+        )
+
+        # Column access
+        col = mdf["a"]
+        assert isinstance(col, MicroSeries), f"Got {type(col)} instead of MicroSeries"
+        assert hasattr(col, "weights")
+        assert hasattr(col, "set_weights")
+
+    def test_microdataframe_operations_preserve_type(self):
+        """
+        Operations on MicroDataFrame columns should preserve MicroSeries type.
+        """
+        mdf = MicroDataFrame(
+            {"a": [1, 2, 3], "b": [4, 5, 6]},
+            weights=np.array([1.0, 2.0, 3.0])
+        )
+
+        # Column operations
+        result = mdf["a"] + mdf["b"]
+        assert isinstance(result, MicroSeries), f"Got {type(result)} instead of MicroSeries"
+        assert hasattr(result, "weights")
+
+
+class TestStringDtypeHandling:
+    """Test that MicroSeries/MicroDataFrame handle pandas 3 string dtypes."""
+
+    def test_microseries_with_string_data(self):
+        """
+        MicroSeries should work with string data in pandas 3.
+        """
+        # Create with string data
+        ms = MicroSeries(["a", "b", "c"], weights=np.array([1.0, 2.0, 3.0]))
+        assert len(ms) == 3
+        assert hasattr(ms, "weights")
+
+    def test_microdataframe_with_string_columns(self):
+        """
+        MicroDataFrame should work with string columns in pandas 3.
+        """
+        mdf = MicroDataFrame(
+            {"names": ["alice", "bob", "charlie"], "values": [1, 2, 3]},
+            weights=np.array([1.0, 2.0, 3.0])
+        )
+        assert len(mdf) == 3
+
+        # String column access should still work
+        names = mdf["names"]
+        assert len(names) == 3
+
+
+class TestWeightedOperationsWithPandas3:
+    """Test that weighted operations work correctly with pandas 3."""
+
+    def test_weighted_sum(self):
+        """Weighted sum should work correctly."""
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 2.0, 3.0]))
+        # Weighted sum: 1*1 + 2*2 + 3*3 = 1 + 4 + 9 = 14
+        assert ms.sum() == 14
+
+    def test_weighted_mean(self):
+        """Weighted mean should work correctly."""
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 2.0, 3.0]))
+        # Weighted mean: (1*1 + 2*2 + 3*3) / (1 + 2 + 3) = 14 / 6 ≈ 2.333
+        assert np.isclose(ms.mean(), 14 / 6)
+
+    def test_weighted_count(self):
+        """Weighted count should return sum of weights."""
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 2.0, 3.0]))
+        assert ms.count() == 6.0
+
+
+class TestCopyOnWriteCompatibility:
+    """Test compatibility with pandas 3 Copy-on-Write."""
+
+    def test_microseries_copy_independent(self):
+        """
+        Copying a MicroSeries should create an independent copy.
+        """
+        ms = MicroSeries([1, 2, 3], weights=np.array([1.0, 2.0, 3.0]))
+        ms_copy = ms.copy()
+
+        # Modify original
+        ms.set_weights(np.array([4.0, 5.0, 6.0]))
+
+        # Copy should be unchanged
+        assert np.allclose(ms_copy.weights, [1.0, 2.0, 3.0])
+
+    def test_microdataframe_copy_independent(self):
+        """
+        Copying a MicroDataFrame should create an independent copy.
+        """
+        mdf = MicroDataFrame(
+            {"a": [1, 2, 3]},
+            weights=np.array([1.0, 2.0, 3.0])
+        )
+        mdf_copy = mdf.copy()
+
+        # Modify original
+        mdf.set_weights(np.array([4.0, 5.0, 6.0]))
+
+        # Copy should be unchanged
+        assert np.allclose(mdf_copy.weights, [1.0, 2.0, 3.0])
+
+
+class TestGroupByWithPandas3:
+    """Test groupby operations with pandas 3."""
+
+    def test_microseries_groupby_preserves_weights(self):
+        """
+        GroupBy operations should preserve weights.
+        """
+        ms = MicroSeries([1, 2, 3, 4], weights=np.array([1.0, 2.0, 3.0, 4.0]))
+        groups = pd.Series(["a", "a", "b", "b"])
+
+        gb = ms.groupby(groups)
+        # Should be able to call weighted operations
+        result = gb.sum()
+        # Group a: 1*1 + 2*2 = 5
+        # Group b: 3*3 + 4*4 = 25
+        assert result["a"] == 5
+        assert result["b"] == 25
+
+    def test_microdataframe_groupby_preserves_weights(self):
+        """
+        MicroDataFrame groupby should preserve weights on columns.
+        """
+        mdf = MicroDataFrame(
+            {"group": ["a", "a", "b", "b"], "value": [1, 2, 3, 4]},
+            weights=np.array([1.0, 2.0, 3.0, 4.0])
+        )
+
+        gb = mdf.groupby("group")
+        result = gb.sum()
+
+        # Check that weighted sum was computed
+        assert "value" in result.columns