TST: ArrowExtensionArray with string and binary types

doc/source/whatsnew/v2.0.0.rst

@@ -219,7 +219,7 @@ Conversion
 - Bug in :meth:`DataFrame.eval` incorrectly raising an ``AttributeError`` when there are negative values in function call (:issue:`46471`)
 - Bug in :meth:`Series.convert_dtypes` not converting dtype to nullable dtype when :class:`Series` contains ``NA`` and has dtype ``object`` (:issue:`48791`)
 - Bug where any :class:`ExtensionDtype` subclass with ``kind="M"`` would be interpreted as a timezone type (:issue:`34986`)
--
+- Bug in :class:`.arrays.ArrowExtensionArray` that would raise ``NotImplementedError`` when passed a sequence of strings or binary (:issue:`49172`)
 
 Strings
 ^^^^^^^

pandas/_testing/__init__.py

@@ -201,8 +201,11 @@
     SIGNED_INT_PYARROW_DTYPES = [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
     ALL_INT_PYARROW_DTYPES = UNSIGNED_INT_PYARROW_DTYPES + SIGNED_INT_PYARROW_DTYPES
 
+    # pa.float16 doesn't seem supported
+    # https://github.com/apache/arrow/blob/master/python/pyarrow/src/arrow/python/helpers.cc#L86
     FLOAT_PYARROW_DTYPES = [pa.float32(), pa.float64()]
-    STRING_PYARROW_DTYPES = [pa.string(), pa.utf8()]
+    STRING_PYARROW_DTYPES = [pa.string()]
+    BINARY_PYARROW_DTYPES = [pa.binary()]
 
     TIME_PYARROW_DTYPES = [
         pa.time32("s"),
@@ -225,6 +228,8 @@
     ALL_PYARROW_DTYPES = (
         ALL_INT_PYARROW_DTYPES
         + FLOAT_PYARROW_DTYPES
+        + STRING_PYARROW_DTYPES
+        + BINARY_PYARROW_DTYPES
         + TIME_PYARROW_DTYPES
         + DATE_PYARROW_DTYPES
         + DATETIME_PYARROW_DTYPES

pandas/core/arrays/arrow/array.py

@@ -220,8 +220,13 @@ def _from_sequence_of_strings(
         Construct a new ExtensionArray from a sequence of strings.
         """
         pa_type = to_pyarrow_type(dtype)
-        if pa_type is None:
-            # Let pyarrow try to infer or raise
+        if (
+            pa_type is None
+            or pa.types.is_binary(pa_type)
+            or pa.types.is_string(pa_type)
+        ):
+            # pa_type is None: Let pa.array infer
+            # pa_type is string/binary: scalars already correct type
             scalars = strings
         elif pa.types.is_timestamp(pa_type):
             from pandas.core.tools.datetimes import to_datetime

pandas/tests/extension/test_arrow.py

@@ -16,6 +16,10 @@
     time,
     timedelta,
 )
+from io import (
+    BytesIO,
+    StringIO,
+)
 
 import numpy as np
 import pytest
@@ -90,6 +94,10 @@ def data(dtype):
             + [None]
             + [time(0, 5), time(5, 0)]
         )
+    elif pa.types.is_string(pa_dtype):
+        data = ["a", "b"] * 4 + [None] + ["1", "2"] * 44 + [None] + ["!", ">"]
+    elif pa.types.is_binary(pa_dtype):
+        data = [b"a", b"b"] * 4 + [None] + [b"1", b"2"] * 44 + [None] + [b"!", b">"]
     else:
         raise NotImplementedError
     return pd.array(data, dtype=dtype)
@@ -155,6 +163,14 @@ def data_for_grouping(dtype):
         A = time(0, 0)
         B = time(0, 12)
         C = time(12, 12)
+    elif pa.types.is_string(pa_dtype):
+        A = "a"
+        B = "b"
+        C = "c"
+    elif pa.types.is_binary(pa_dtype):
+        A = b"a"
+        B = b"b"
+        C = b"c"
     else:
         raise NotImplementedError
     return pd.array([B, B, None, None, A, A, B, C], dtype=dtype)
@@ -203,17 +219,30 @@ def na_value():
 
 
 class TestBaseCasting(base.BaseCastingTests):
-    pass
+    def test_astype_str(self, data, request):
+        pa_dtype = data.dtype.pyarrow_dtype
+        if pa.types.is_binary(pa_dtype):
+            request.node.add_marker(
+                pytest.mark.xfail(
+                    reason=f"For {pa_dtype} .astype(str) decodes.",
+                )
+            )
+        super().test_astype_str(data)
 
 
 class TestConstructors(base.BaseConstructorsTests):
     def test_from_dtype(self, data, request):
         pa_dtype = data.dtype.pyarrow_dtype
-        if pa.types.is_timestamp(pa_dtype) and pa_dtype.tz:
+        if (pa.types.is_timestamp(pa_dtype) and pa_dtype.tz) or pa.types.is_string(
+            pa_dtype
+        ):
+            if pa.types.is_string(pa_dtype):
+                reason = "ArrowDtype(pa.string()) != StringDtype('pyarrow')"
+            else:
+                reason = f"pyarrow.type_for_alias cannot infer {pa_dtype}"
             request.node.add_marker(
                 pytest.mark.xfail(
-                    raises=NotImplementedError,
-                    reason=f"pyarrow.type_for_alias cannot infer {pa_dtype}",
+                    reason=reason,
                 )
             )
         super().test_from_dtype(data)
@@ -302,7 +331,7 @@ class TestGetitemTests(base.BaseGetitemTests):
         reason=(
             "data.dtype.type return pyarrow.DataType "
             "but this (intentionally) returns "
-            "Python scalars or pd.Na"
+            "Python scalars or pd.NA"
         )
     )
     def test_getitem_scalar(self, data):
@@ -361,7 +390,11 @@ def test_reduce_series(self, data, all_numeric_reductions, skipna, request):
             or pa.types.is_boolean(pa_dtype)
         ) and not (
             all_numeric_reductions in {"min", "max"}
-            and (pa.types.is_temporal(pa_dtype) and not pa.types.is_duration(pa_dtype))
+            and (
+                (pa.types.is_temporal(pa_dtype) and not pa.types.is_duration(pa_dtype))
+                or pa.types.is_string(pa_dtype)
+                or pa.types.is_binary(pa_dtype)
+            )
         ):
             request.node.add_marker(xfail_mark)
         elif pa.types.is_boolean(pa_dtype) and all_numeric_reductions in {
@@ -494,6 +527,16 @@ def test_construct_from_string_own_name(self, dtype, request):
                     reason=f"pyarrow.type_for_alias cannot infer {pa_dtype}",
                 )
             )
+        elif pa.types.is_string(pa_dtype):
+            request.node.add_marker(
+                pytest.mark.xfail(
+                    raises=TypeError,
+                    reason=(
+                        "Still support StringDtype('pyarrow') "
+                        "over ArrowDtype(pa.string())"
+                    ),
+                )
+            )
         super().test_construct_from_string_own_name(dtype)
 
     def test_is_dtype_from_name(self, dtype, request):
@@ -505,6 +548,15 @@ def test_is_dtype_from_name(self, dtype, request):
                     reason=f"pyarrow.type_for_alias cannot infer {pa_dtype}",
                 )
             )
+        elif pa.types.is_string(pa_dtype):
+            request.node.add_marker(
+                pytest.mark.xfail(
+                    reason=(
+                        "Still support StringDtype('pyarrow') "
+                        "over ArrowDtype(pa.string())"
+                    ),
+                )
+            )
         super().test_is_dtype_from_name(dtype)
 
     def test_construct_from_string(self, dtype, request):
@@ -516,6 +568,16 @@ def test_construct_from_string(self, dtype, request):
                     reason=f"pyarrow.type_for_alias cannot infer {pa_dtype}",
                 )
             )
+        elif pa.types.is_string(pa_dtype):
+            request.node.add_marker(
+                pytest.mark.xfail(
+                    raises=TypeError,
+                    reason=(
+                        "Still support StringDtype('pyarrow') "
+                        "over ArrowDtype(pa.string())"
+                    ),
+                )
+            )
         super().test_construct_from_string(dtype)
 
     def test_construct_from_string_another_type_raises(self, dtype):
@@ -533,6 +595,8 @@ def test_get_common_dtype(self, dtype, request):
                 and (pa_dtype.unit != "ns" or pa_dtype.tz is not None)
             )
             or (pa.types.is_duration(pa_dtype) and pa_dtype.unit != "ns")
+            or pa.types.is_string(pa_dtype)
+            or pa.types.is_binary(pa_dtype)
         ):
             request.node.add_marker(
                 pytest.mark.xfail(
@@ -592,7 +656,21 @@ def test_EA_types(self, engine, data, request):
                     reason=f"Parameterized types with tz={pa_dtype.tz} not supported.",
                 )
             )
-        super().test_EA_types(engine, data)
+        elif pa.types.is_binary(pa_dtype):
+            request.node.add_marker(
+                pytest.mark.xfail(reason="CSV parsers don't correctly handle binary")
+            )
+        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
+        csv_output = df.to_csv(index=False, na_rep=np.nan)
+        if pa.types.is_binary(pa_dtype):
+            csv_output = BytesIO(csv_output)
+        else:
+            csv_output = StringIO(csv_output)
+        result = pd.read_csv(
+            csv_output, dtype={"with_dtype": str(data.dtype)}, engine=engine
+        )
+        expected = df
+        self.assert_frame_equal(result, expected)
 
 
 class TestBaseUnaryOps(base.BaseUnaryOpsTests):
@@ -899,7 +977,11 @@ def test_arith_series_with_scalar(
             or all_arithmetic_operators in ("__sub__", "__rsub__")
             and pa.types.is_temporal(pa_dtype)
         )
-        if all_arithmetic_operators in {
+        if all_arithmetic_operators == "__rmod__" and (
+            pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
+        ):
+            pytest.skip("Skip testing Python string formatting")
+        elif all_arithmetic_operators in {
             "__mod__",
             "__rmod__",
         }:
@@ -965,7 +1047,11 @@ def test_arith_frame_with_scalar(
             or all_arithmetic_operators in ("__sub__", "__rsub__")
             and pa.types.is_temporal(pa_dtype)
         )
-        if all_arithmetic_operators in {
+        if all_arithmetic_operators == "__rmod__" and (
+            pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
+        ):
+            pytest.skip("Skip testing Python string formatting")
+        elif all_arithmetic_operators in {
             "__mod__",
             "__rmod__",
         }:
@@ -1224,7 +1310,11 @@ def test_quantile(data, interpolation, quantile, request):
 )
 def test_mode(data_for_grouping, dropna, take_idx, exp_idx, request):
     pa_dtype = data_for_grouping.dtype.pyarrow_dtype
-    if pa.types.is_temporal(pa_dtype):
+    if (
+        pa.types.is_temporal(pa_dtype)
+        or pa.types.is_string(pa_dtype)
+        or pa.types.is_binary(pa_dtype)
+    ):
         request.node.add_marker(
             pytest.mark.xfail(
                 raises=pa.ArrowNotImplementedError,