fixup xfails

Author: jorisvandenbossche

Diff for: pandas/tests/apply/test_frame_apply.py

@@ -6,6 +6,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 from pandas.core.dtypes.dtypes import CategoricalDtype
 
 import pandas as pd
@@ -1245,6 +1247,9 @@ def test_agg_multiple_mixed():
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.xfail(
+    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
+)
 def test_agg_multiple_mixed_raises():
     # GH 20909
     mdf = DataFrame(

Diff for: pandas/tests/apply/test_invalid_arg.py

@@ -14,6 +14,7 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
 from pandas.errors import SpecificationError
 
 from pandas import (
@@ -212,7 +213,9 @@ def transform(row):
 
 
 # we should raise a proper TypeError instead of propagating the pyarrow error
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+@pytest.mark.xfail(
+    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
+)
 @pytest.mark.parametrize(
     "df, func, expected",
     tm.get_cython_table_params(
@@ -234,7 +237,9 @@ def test_agg_cython_table_raises_frame(df, func, expected, axis, using_infer_str
 
 
 # we should raise a proper TypeError instead of propagating the pyarrow error
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+@pytest.mark.xfail(
+    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
+)
 @pytest.mark.parametrize(
     "series, func, expected",
     chain(

Diff for: pandas/tests/arithmetic/test_object.py

@@ -8,6 +8,9 @@
 import numpy as np
 import pytest
 
+from pandas._config import using_string_dtype
+
+from pandas.compat import HAS_PYARROW
 import pandas.util._test_decorators as td
 
 import pandas as pd
@@ -315,6 +318,9 @@ def test_add(self):
         expected = pd.Index(["1a", "1b", "1c"])
         tm.assert_index_equal("1" + index, expected)
 
+    @pytest.mark.xfail(
+        using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
+    )
     def test_sub_fail(self, using_infer_string):
         index = pd.Index([str(i) for i in range(10)])
 

Diff for: pandas/tests/arrays/categorical/test_analytics.py

@@ -6,7 +6,10 @@
 
 from pandas._config import using_string_dtype
 
-from pandas.compat import PYPY
+from pandas.compat import (
+    HAS_PYARROW,
+    PYPY,
+)
 
 from pandas import (
     Categorical,
@@ -296,7 +299,9 @@ def test_nbytes(self):
         exp = 3 + 3 * 8  # 3 int8s for values + 3 int64s for categories
         assert cat.nbytes == exp
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+    @pytest.mark.xfail(
+        using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)"
+    )
     def test_memory_usage(self):
         cat = Categorical([1, 2, 3])
 

Diff for: pandas/tests/arrays/categorical/test_constructors.py

@@ -8,6 +8,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 from pandas.core.dtypes.common import (
     is_float_dtype,
     is_integer_dtype,
@@ -442,7 +444,9 @@ def test_constructor_str_unknown(self):
         with pytest.raises(ValueError, match="Unknown dtype"):
             Categorical([1, 2], dtype="foo")
 
-    @pytest.mark.xfail(using_string_dtype(), reason="Can't be NumPy strings")
+    @pytest.mark.xfail(
+        using_string_dtype() and HAS_PYARROW, reason="Can't be NumPy strings"
+    )
     def test_constructor_np_strs(self):
         # GH#31499 Hashtable.map_locations needs to work on np.str_ objects
         cat = Categorical(["1", "0", "1"], [np.str_("0"), np.str_("1")])

Diff for: pandas/tests/arrays/integer/test_reduction.py

@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
 
+from pandas.compat import HAS_PYARROW
+
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -102,9 +104,10 @@ def test_groupby_reductions(op, expected):
         ["all", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")],
     ],
 )
-def test_mixed_reductions(op, expected, using_infer_string):
-    if op in ["any", "all"] and using_infer_string:
-        expected = expected.astype("bool")
+def test_mixed_reductions(request, op, expected, using_infer_string):
+    if op in ["any", "all"] and using_infer_string and HAS_PYARROW:
+        # TODO(infer_string) inconsistent result type
+        request.applymarker(pytest.mark.xfail(reason="TODO(infer_string)"))
     df = DataFrame(
         {
             "A": ["a", "b", "b"],

Diff for: pandas/tests/base/test_conversion.py

@@ -1,6 +1,10 @@
 import numpy as np
 import pytest
 
+from pandas._config import using_string_dtype
+
+from pandas.compat import HAS_PYARROW
+
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 
 import pandas as pd
@@ -20,6 +24,7 @@
     SparseArray,
     TimedeltaArray,
 )
+from pandas.core.arrays.string_ import StringArrayNumpySemantics
 from pandas.core.arrays.string_arrow import ArrowStringArrayNumpySemantics
 
 
@@ -218,7 +223,9 @@ def test_iter_box_period(self):
 )
 def test_values_consistent(arr, expected_type, dtype, using_infer_string):
     if using_infer_string and dtype == "object":
-        expected_type = ArrowStringArrayNumpySemantics
+        expected_type = (
+            ArrowStringArrayNumpySemantics if HAS_PYARROW else StringArrayNumpySemantics
+        )
     l_values = Series(arr)._values
     r_values = pd.Index(arr)._values
     assert type(l_values) is expected_type
@@ -355,6 +362,9 @@ def test_to_numpy(arr, expected, index_or_series_or_array, request):
     tm.assert_numpy_array_equal(result, expected)
 
 
+@pytest.mark.xfail(
+    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)", strict=False
+)
 @pytest.mark.parametrize("as_series", [True, False])
 @pytest.mark.parametrize(
     "arr", [np.array([1, 2, 3], dtype="int64"), np.array(["a", "b", "c"], dtype=object)]

Diff for: pandas/tests/copy_view/test_astype.py

@@ -5,6 +5,7 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
 from pandas.compat.pyarrow import pa_version_under12p0
 import pandas.util._test_decorators as td
 
@@ -197,7 +198,7 @@ def test_astype_arrow_timestamp():
         assert np.shares_memory(get_array(df, "a"), get_array(result, "a")._pa_array)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_convert_dtypes_infer_objects():
     ser = Series(["a", "b", "c"])
     ser_orig = ser.copy()
@@ -213,7 +214,7 @@ def test_convert_dtypes_infer_objects():
     tm.assert_series_equal(ser, ser_orig)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_convert_dtypes():
     df = DataFrame({"a": ["a", "b"], "b": [1, 2], "c": [1.5, 2.5], "d": [True, False]})
     df_orig = df.copy()

Diff for: pandas/tests/copy_view/test_functions.py

@@ -3,6 +3,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 from pandas import (
     DataFrame,
     Index,
@@ -14,7 +16,7 @@
 from pandas.tests.copy_view.util import get_array
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_concat_frames():
     df = DataFrame({"b": ["a"] * 3})
     df2 = DataFrame({"a": ["a"] * 3})
@@ -33,7 +35,7 @@ def test_concat_frames():
     tm.assert_frame_equal(df, df_orig)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_concat_frames_updating_input():
     df = DataFrame({"b": ["a"] * 3})
     df2 = DataFrame({"a": ["a"] * 3})
@@ -153,7 +155,7 @@ def test_concat_copy_keyword():
     assert np.shares_memory(get_array(df2, "b"), get_array(result, "b"))
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 @pytest.mark.parametrize(
     "func",
     [
@@ -249,7 +251,7 @@ def test_merge_copy_keyword():
     assert np.shares_memory(get_array(df2, "b"), get_array(result, "b"))
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_join_on_key():
     df_index = Index(["a", "b", "c"], name="key")
 
@@ -277,7 +279,7 @@ def test_join_on_key():
     tm.assert_frame_equal(df2, df2_orig)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_join_multiple_dataframes_on_key():
     df_index = Index(["a", "b", "c"], name="key")
 

Diff for: pandas/tests/copy_view/test_interp_fillna.py

@@ -3,6 +3,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 from pandas import (
     NA,
     DataFrame,
@@ -121,7 +123,7 @@ def test_interpolate_cannot_with_object_dtype():
         df.interpolate()
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_interpolate_object_convert_no_op():
     df = DataFrame({"a": ["a", "b", "c"], "b": 1})
     arr_a = get_array(df, "a")

Diff for: pandas/tests/copy_view/test_methods.py

@@ -3,6 +3,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -714,7 +716,7 @@ def test_head_tail(method):
     tm.assert_frame_equal(df, df_orig)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_infer_objects():
     df = DataFrame({"a": [1, 2], "b": "c", "c": 1, "d": "x"})
     df_orig = df.copy()
@@ -730,6 +732,9 @@ def test_infer_objects():
     tm.assert_frame_equal(df, df_orig)
 
 
+@pytest.mark.xfail(
+    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
+)
 def test_infer_objects_no_reference():
     df = DataFrame(
         {
@@ -899,7 +904,7 @@ def test_sort_values_inplace(obj, kwargs):
     tm.assert_equal(view, obj_orig)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 @pytest.mark.parametrize("decimals", [-1, 0, 1])
 def test_round(decimals):
     df = DataFrame({"a": [1, 2], "b": "c"})

Diff for: pandas/tests/copy_view/test_replace.py

@@ -3,6 +3,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 from pandas import (
     Categorical,
     DataFrame,
@@ -59,7 +61,7 @@ def test_replace_regex_inplace_refs():
     tm.assert_frame_equal(view, df_orig)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 def test_replace_regex_inplace():
     df = DataFrame({"a": ["aaa", "bbb"]})
     arr = get_array(df, "a")
@@ -257,7 +259,7 @@ def test_replace_empty_list():
     assert not df2._mgr._has_no_reference(0)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+@pytest.mark.xfail(using_string_dtype() and HAS_PYARROW, reason="TODO(infer_string)")
 @pytest.mark.parametrize("value", ["d", None])
 def test_replace_object_list_inplace(value):
     df = DataFrame({"a": ["a", "b", "c"]})

Diff for: pandas/tests/extension/base/ops.py

@@ -7,6 +7,8 @@
 
 from pandas._config import using_string_dtype
 
+from pandas.compat import HAS_PYARROW
+
 from pandas.core.dtypes.common import is_string_dtype
 
 import pandas as pd
@@ -140,7 +142,12 @@ class BaseArithmeticOpsTests(BaseOpsUtil):
     series_array_exc: type[Exception] | None = TypeError
     divmod_exc: type[Exception] | None = TypeError
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+    # TODO(infer_string) need to remove import of pyarrow
+    @pytest.mark.xfail(
+        using_string_dtype() and not HAS_PYARROW,
+        reason="TODO(infer_string)",
+        strict=False,
+    )
     def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
         # series & scalar
         if all_arithmetic_operators == "__rmod__" and is_string_dtype(data.dtype):
@@ -150,7 +157,11 @@ def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
         ser = pd.Series(data)
         self.check_opname(ser, op_name, ser.iloc[0])
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+    @pytest.mark.xfail(
+        using_string_dtype() and not HAS_PYARROW,
+        reason="TODO(infer_string)",
+        strict=False,
+    )
     def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
         # frame & scalar
         if all_arithmetic_operators == "__rmod__" and is_string_dtype(data.dtype):
@@ -160,14 +171,22 @@ def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
         df = pd.DataFrame({"A": data})
         self.check_opname(df, op_name, data[0])
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+    @pytest.mark.xfail(
+        using_string_dtype() and not HAS_PYARROW,
+        reason="TODO(infer_string)",
+        strict=False,
+    )
     def test_arith_series_with_array(self, data, all_arithmetic_operators):
         # ndarray & other series
         op_name = all_arithmetic_operators
         ser = pd.Series(data)
         self.check_opname(ser, op_name, pd.Series([ser.iloc[0]] * len(ser)))
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
+    @pytest.mark.xfail(
+        using_string_dtype() and not HAS_PYARROW,
+        reason="TODO(infer_string)",
+        strict=False,
+    )
     def test_divmod(self, data):
         ser = pd.Series(data)
         self._check_divmod_op(ser, divmod, 1)