BUG: handle NaNs in FloatingArray.equals (#44390)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -719,6 +719,7 @@ Other
 - Bug in :meth:`RangeIndex.difference` with ``sort=None`` and ``step<0`` failing to sort (:issue:`44085`)
 - Bug in :meth:`Series.to_frame` and :meth:`Index.to_frame` ignoring the ``name`` argument when ``name=None`` is explicitly passed (:issue:`44212`)
 - Bug in :meth:`Series.replace` and :meth:`DataFrame.replace` with ``value=None`` and ExtensionDtypes (:issue:`44270`)
+- Bug in :meth:`FloatingArray.equals` failing to consider two arrays equal if they contain ``np.nan`` values (:issue:`44382`)
 -
 
 .. ***DO NOT USE THIS SECTION***

pandas/core/arrays/masked.py

@@ -627,6 +627,21 @@ def value_counts(self, dropna: bool = True) -> Series:
 
         return Series(counts, index=index)
 
+    @doc(ExtensionArray.equals)
+    def equals(self, other) -> bool:
+        if type(self) != type(other):
+            return False
+        if other.dtype != self.dtype:
+            return False
+
+        # GH#44382 if e.g. self[1] is np.nan and other[1] is pd.NA, we are NOT
+        #  equal.
+        return np.array_equal(self._mask, other._mask) and np.array_equal(
+            self._data[~self._mask],
+            other._data[~other._mask],
+            equal_nan=True,
+        )
+
     def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
         if name in {"any", "all"}:
             return getattr(self, name)(skipna=skipna, **kwargs)

pandas/tests/arrays/floating/test_comparison.py

@@ -1,7 +1,9 @@
+import numpy as np
 import pytest
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
 from pandas.tests.arrays.masked_shared import (
     ComparisonOps,
     NumericOps,
@@ -34,3 +36,30 @@ def test_equals():
     a1 = pd.array([1, 2, None], dtype="Float64")
     a2 = pd.array([1, 2, None], dtype="Float32")
     assert a1.equals(a2) is False
+
+
+def test_equals_nan_vs_na():
+    # GH#44382
+
+    mask = np.zeros(3, dtype=bool)
+    data = np.array([1.0, np.nan, 3.0], dtype=np.float64)
+
+    left = FloatingArray(data, mask)
+    assert left.equals(left)
+    tm.assert_extension_array_equal(left, left)
+
+    assert left.equals(left.copy())
+    assert left.equals(FloatingArray(data.copy(), mask.copy()))
+
+    mask2 = np.array([False, True, False], dtype=bool)
+    data2 = np.array([1.0, 2.0, 3.0], dtype=np.float64)
+    right = FloatingArray(data2, mask2)
+    assert right.equals(right)
+    tm.assert_extension_array_equal(right, right)
+
+    assert not left.equals(right)
+
+    # with mask[1] = True, the only difference is data[1], which should
+    #  not matter for equals
+    mask[1] = True
+    assert left.equals(right)