BUG: Fix StringArray use_inf_as_na bug

doc/source/whatsnew/v1.1.0.rst

@@ -732,9 +732,9 @@ ExtensionArray
 ^^^^^^^^^^^^^^
 
 - Fixed bug where :meth:`Series.value_counts` would raise on empty input of ``Int64`` dtype (:issue:`33317`)
+- Fixed bug where :meth:`StringArray.isna` would return ``False`` for NA values when ``pandas.options.mode.use_inf_as_na`` was set to ``True`` (:issue:`33655`)
 -
 
-
 Other
 ^^^^^
 - Appending a dictionary to a :class:`DataFrame` without passing ``ignore_index=True`` will raise ``TypeError: Can only append a dict if ignore_index=True``

pandas/_libs/missing.pyx

@@ -90,11 +90,6 @@ cpdef bint checknull_old(object val):
     return False
 
 
-cdef inline bint _check_none_nan_inf_neginf(object val):
-    return val is None or (isinstance(val, float) and
-                           (val != val or val == INF or val == NEGINF))
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
 cpdef ndarray[uint8_t] isnaobj(ndarray arr):
@@ -141,6 +136,7 @@ def isnaobj_old(arr: ndarray) -> ndarray:
      - INF
      - NEGINF
      - NaT
+     - NA
 
     Parameters
     ----------
@@ -161,7 +157,7 @@ def isnaobj_old(arr: ndarray) -> ndarray:
     result = np.zeros(n, dtype=np.uint8)
     for i in range(n):
         val = arr[i]
-        result[i] = val is NaT or _check_none_nan_inf_neginf(val)
+        result[i] = checknull(val) or val == INF or val == NEGINF
     return result.view(np.bool_)
 
 

pandas/core/dtypes/missing.py

@@ -210,7 +210,7 @@ def _isna_ndarraylike(obj, inf_as_na: bool = False):
 
     if is_extension_array_dtype(dtype):
         if inf_as_na:
-            result = values.isna() | (values == -np.inf) | (values == np.inf)
+            result = libmissing.isnaobj_old(values.to_numpy())
         else:
             result = values.isna()
     elif is_string_dtype(dtype):

pandas/tests/arrays/string_/test_string.py

@@ -303,3 +303,25 @@ def test_value_counts_na():
     result = arr.value_counts(dropna=True)
     expected = pd.Series([2, 1], index=["a", "b"], dtype="Int64")
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values, expected",
+    [
+        (pd.array(["a", "b", "c"]), np.array([False, False, False])),
+        (pd.array(["a", "b", None]), np.array([False, False, True])),
+    ],
+)
+def test_use_inf_as_na(values, expected):
+    # https://github.com/pandas-dev/pandas/issues/33655
+    with pd.option_context("mode.use_inf_as_na", True):
+        result = values.isna()
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = pd.Series(values).isna()
+        expected = pd.Series(expected)
+        tm.assert_series_equal(result, expected)
+
+        result = pd.DataFrame(values).isna()
+        expected = pd.DataFrame(expected)
+        tm.assert_frame_equal(result, expected)

pandas/tests/extension/base/missing.py

@@ -127,3 +127,10 @@ def test_fillna_fill_other(self, data):
         expected = pd.DataFrame({"A": data, "B": [0.0] * len(result)})
 
         self.assert_frame_equal(result, expected)
+
+    def test_use_inf_as_na_no_effect(self, data_missing):
+        ser = pd.Series(data_missing)
+        expected = ser.isna()
+        with pd.option_context("mode.use_inf_as_na", True):
+            result = ser.isna()
+        self.assert_series_equal(result, expected)

pandas/tests/series/test_missing.py

@@ -509,12 +509,12 @@ def test_fillna_nat(self):
         tm.assert_frame_equal(filled2, expected)
 
     def test_isna_for_inf(self):
-        s = Series(["a", np.inf, np.nan, 1.0])
+        s = Series(["a", np.inf, np.nan, pd.NA, 1.0])
         with pd.option_context("mode.use_inf_as_na", True):
             r = s.isna()
             dr = s.dropna()
-        e = Series([False, True, True, False])
-        de = Series(["a", 1.0], index=[0, 3])
+        e = Series([False, True, True, True, False])
+        de = Series(["a", 1.0], index=[0, 4])
         tm.assert_series_equal(r, e)
         tm.assert_series_equal(dr, de)