Add Comparator equality for pathlib, numpy arrays, and pandas objects

param/parameterized.py

@@ -17,6 +17,7 @@
 import numbers
 import operator
 import os
+import pathlib
 import re
 import sys
 import types
@@ -2337,17 +2338,61 @@ class Comparator:
     To use the Comparator simply call the is_equal function.
     """
 
+    # Maximum number of elements for array/frame equality checks.
+    # Above this threshold the comparator gives up and returns False
+    # (triggering watchers) rather than doing an expensive comparison.
+    array_max_size = 1_000_000
+
     equalities: dict[type | tuple[type, ...] | Callable[[t.Any], bool], Callable[[t.Any, t.Any], bool]] = {
         numbers.Number: operator.eq,
         str: operator.eq,
         bytes: operator.eq,
         type(None): operator.eq,
+        pathlib.PurePath: operator.eq,
         lambda o: hasattr(o, '_infinitely_iterable'): operator.eq,  # Time
+        lambda o: type(o).__module__.startswith('numpy') and all(hasattr(o, a) for a in ('shape', 'dtype', 'size')): lambda a, b: Comparator._array_equal(a, b),
+        lambda o: type(o).__module__.startswith('pandas') and all(hasattr(o, a) for a in ('shape', 'size', 'equals')): lambda a, b: Comparator._pandas_equal(a, b),
     }
     gen_equalities = {
         _dt_types: operator.eq
     }
 
+    @staticmethod
+    def _array_equal(obj1, obj2):
+        """Equality check for numpy arrays with a size cutoff."""
+        if obj1 is obj2:
+            return True
+        if type(obj1) is not type(obj2):
+            return False
+        try:
+            if obj1.shape != obj2.shape or obj1.dtype != obj2.dtype:
+                return False
+            if obj1.size > Comparator.array_max_size:
+                return False
+        except AttributeError:
+            return False
+        try:
+            import numpy as np
+            return bool(np.array_equal(obj1, obj2))
+        except (ImportError, ValueError, TypeError):
+            return False
+
+    @staticmethod
+    def _pandas_equal(obj1, obj2):
+        """Equality check for pandas DataFrame/Series with a size cutoff."""
+        if obj1 is obj2:
+            return True
+        if type(obj1) is not type(obj2):
+            return False
+        try:
+            if obj1.shape != obj2.shape:
+                return False
+            if obj1.size > Comparator.array_max_size:
+                return False
+            return bool(obj1.equals(obj2))
+        except (AttributeError, ValueError, TypeError):
+            return False
+
     @classmethod
     def is_equal(cls, obj1, obj2):
         equals = cls.equalities.copy()

tests/testcomparator.py

@@ -1,5 +1,6 @@
 import datetime
 import decimal
+import pathlib
 
 import pytest
 
@@ -30,15 +31,142 @@
     'dict': {'a': 1, 'b': 2},
     'date': _today,
     'datetime': _now,
+    'pathlib.Path': pathlib.Path('/tmp/test'),
+    'pathlib.PurePosixPath': pathlib.PurePosixPath('/tmp/test'),
 }
 
 if np:
     _supported.update({
         'np.datetime64': np.datetime64(_now),
+        'np.array_int': np.array([1, 2, 3]),
+        'np.array_float': np.array([1.0, 2.0]),
+        'np.array_2d': np.zeros((3, 4)),
     })
 if pd:
-    _supported.update({'pd.Timestamp': pd.Timestamp(_now)})
+    _supported.update({
+        'pd.Timestamp': pd.Timestamp(_now),
+        'pd.Series': pd.Series([1, 2, 3]),
+        'pd.DataFrame': pd.DataFrame({'a': [1, 2], 'b': [3, 4]}),
+    })
 
 @pytest.mark.parametrize('obj', _supported.values(), ids=_supported.keys())
 def test_comparator_equal(obj):
     assert Comparator.is_equal(obj, obj)
+
+
+# ---- pathlib tests ----
+
+def test_path_equal():
+    assert Comparator.is_equal(pathlib.Path('/a/b'), pathlib.Path('/a/b'))
+
+def test_path_not_equal():
+    assert not Comparator.is_equal(pathlib.Path('/a/b'), pathlib.Path('/a/c'))
+
+def test_purepath_equal():
+    assert Comparator.is_equal(pathlib.PurePosixPath('/x'), pathlib.PurePosixPath('/x'))
+
+
+# ---- numpy tests ----
+
+@pytest.mark.skipif(np is None, reason='numpy not available')
+class TestComparatorNumpy:
+
+    def test_array_equal(self):
+        a = np.array([1, 2, 3])
+        b = np.array([1, 2, 3])
+        assert Comparator.is_equal(a, b)
+
+    def test_array_not_equal(self):
+        a = np.array([1, 2, 3])
+        b = np.array([1, 2, 4])
+        assert not Comparator.is_equal(a, b)
+
+    def test_array_different_shape(self):
+        a = np.array([1, 2, 3])
+        b = np.array([[1, 2, 3]])
+        assert not Comparator.is_equal(a, b)
+
+    def test_array_different_dtype(self):
+        a = np.array([1, 2], dtype=np.int32)
+        b = np.array([1, 2], dtype=np.float64)
+        assert not Comparator.is_equal(a, b)
+
+    def test_array_identity(self):
+        a = np.array([1, 2, 3])
+        assert Comparator.is_equal(a, a)
+
+    def test_array_large_skips(self):
+        """Arrays larger than array_max_size should return False."""
+        old = Comparator.array_max_size
+        try:
+            Comparator.array_max_size = 5
+            a = np.arange(10)
+            b = np.arange(10)
+            assert not Comparator.is_equal(a, b)
+        finally:
+            Comparator.array_max_size = old
+
+    def test_array_with_nan(self):
+        a = np.array([1.0, float('nan'), 3.0])
+        b = np.array([1.0, float('nan'), 3.0])
+        # np.array_equal treats NaN == NaN as False
+        assert not Comparator.is_equal(a, b)
+
+
+# ---- pandas tests ----
+
+@pytest.mark.skipif(pd is None, reason='pandas not available')
+class TestComparatorPandas:
+
+    def test_series_equal(self):
+        a = pd.Series([1, 2, 3])
+        b = pd.Series([1, 2, 3])
+        assert Comparator.is_equal(a, b)
+
+    def test_series_not_equal(self):
+        a = pd.Series([1, 2, 3])
+        b = pd.Series([1, 2, 4])
+        assert not Comparator.is_equal(a, b)
+
+    def test_dataframe_equal(self):
+        a = pd.DataFrame({'x': [1, 2], 'y': [3, 4]})
+        b = pd.DataFrame({'x': [1, 2], 'y': [3, 4]})
+        assert Comparator.is_equal(a, b)
+
+    def test_dataframe_not_equal(self):
+        a = pd.DataFrame({'x': [1, 2]})
+        b = pd.DataFrame({'x': [1, 3]})
+        assert not Comparator.is_equal(a, b)
+
+    def test_dataframe_different_shape(self):
+        a = pd.DataFrame({'x': [1, 2]})
+        b = pd.DataFrame({'x': [1, 2], 'y': [3, 4]})
+        assert not Comparator.is_equal(a, b)
+
+    def test_dataframe_with_nan(self):
+        a = pd.DataFrame({'x': [1.0, float('nan')]})
+        b = pd.DataFrame({'x': [1.0, float('nan')]})
+        # pd.DataFrame.equals treats NaN as equal
+        assert Comparator.is_equal(a, b)
+
+    def test_series_large_skips(self):
+        """Series larger than array_max_size should return False."""
+        old = Comparator.array_max_size
+        try:
+            Comparator.array_max_size = 5
+            a = pd.Series(range(10))
+            b = pd.Series(range(10))
+            assert not Comparator.is_equal(a, b)
+        finally:
+            Comparator.array_max_size = old
+
+    def test_dataframe_large_skips(self):
+        """DataFrames larger than array_max_size should return False."""
+        old = Comparator.array_max_size
+        try:
+            Comparator.array_max_size = 5
+            a = pd.DataFrame({'x': range(10)})
+            b = pd.DataFrame({'x': range(10)})
+            assert not Comparator.is_equal(a, b)
+        finally:
+            Comparator.array_max_size = old