ENH: Arrow backed string array - implement factorize() method without casting to objects

asv_bench/benchmarks/algorithms.py

@@ -5,6 +5,7 @@
 from pandas._libs import lib
 
 import pandas as pd
+from pandas.core.arrays.string_arrow import ArrowStringDtype
 
 from .pandas_vb_common import tm
 
@@ -43,23 +44,34 @@ class Factorize:
             "datetime64[ns, tz]",
             "Int64",
             "boolean",
+            "string_arrow",
         ],
     ]
     param_names = ["unique", "sort", "dtype"]
 
     def setup(self, unique, sort, dtype):
         N = 10 ** 5
+        string_index = tm.makeStringIndex(N)
+        try:
+            string_arrow = pd.array(string_index, dtype=ArrowStringDtype())
+        except ImportError:
+            string_arrow = None
+
+        if dtype == "string_arrow" and not string_arrow:
+            raise NotImplementedError
+
         data = {
             "int": pd.Int64Index(np.arange(N)),
             "uint": pd.UInt64Index(np.arange(N)),
             "float": pd.Float64Index(np.random.randn(N)),
-            "string": tm.makeStringIndex(N),
+            "string": string_index,
             "datetime64[ns]": pd.date_range("2011-01-01", freq="H", periods=N),
             "datetime64[ns, tz]": pd.date_range(
                 "2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
             ),
             "Int64": pd.array(np.arange(N), dtype="Int64"),
             "boolean": pd.array(np.random.randint(0, 2, N), dtype="boolean"),
+            "string_arrow": string_arrow,
         }[dtype]
         if not unique:
             data = data.repeat(5)

pandas/core/arrays/string_arrow.py

@@ -6,6 +6,7 @@
     Any,
     Optional,
     Sequence,
+    Tuple,
     Type,
     Union,
 )
@@ -20,6 +21,7 @@
     Dtype,
     NpDtype,
 )
+from pandas.util._decorators import doc
 from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.base import ExtensionDtype
@@ -273,9 +275,22 @@ def __len__(self) -> int:
         """
         return len(self._data)
 
-    @classmethod
-    def _from_factorized(cls, values, original):
-        return cls._from_sequence(values)
+    @doc(ExtensionArray.factorize)
+    def factorize(self, na_sentinel: int = -1) -> Tuple[np.ndarray, ExtensionArray]:
+        encoded = self._data.dictionary_encode()
+        indices = pa.chunked_array(
+            [c.indices for c in encoded.chunks], type=encoded.type.index_type
+        ).to_pandas()
+        if indices.dtype.kind == "f":
+            indices[np.isnan(indices)] = na_sentinel
+        indices = indices.astype(np.int64, copy=False)
+
+        if encoded.num_chunks:
+            uniques = type(self)(encoded.chunk(0).dictionary)
+        else:
+            uniques = type(self)(pa.array([], type=encoded.type.value_type))
+
+        return indices.values, uniques
 
     @classmethod
     def _concat_same_type(cls, to_concat) -> ArrowStringArray:

pandas/tests/extension/test_string.py

@@ -26,6 +26,29 @@
 from pandas.tests.extension import base
 
 
+def split_array(arr):
+    if not isinstance(arr.dtype, ArrowStringDtype):
+        pytest.skip("chunked array n/a")
+
+    def _split_array(arr):
+        import pyarrow as pa
+
+        arrow_array = arr._data
+        split = len(arrow_array) // 2
+        arrow_array = pa.chunked_array(
+            [*arrow_array[:split].chunks, *arrow_array[split:].chunks]
+        )
+        assert arrow_array.num_chunks == 2
+        return type(arr)(arrow_array)
+
+    return _split_array(arr)
+
+
+@pytest.fixture(params=[True, False])
+def chunked(request):
+    return request.param
+
+
 @pytest.fixture(
     params=[
         StringDtype,
@@ -39,28 +62,32 @@ def dtype(request):
 
 
 @pytest.fixture
-def data(dtype):
+def data(dtype, chunked):
     strings = np.random.choice(list(string.ascii_letters), size=100)
     while strings[0] == strings[1]:
         strings = np.random.choice(list(string.ascii_letters), size=100)
 
-    return dtype.construct_array_type()._from_sequence(strings)
+    arr = dtype.construct_array_type()._from_sequence(strings)
+    return split_array(arr) if chunked else arr
 
 
 @pytest.fixture
-def data_missing(dtype):
+def data_missing(dtype, chunked):
     """Length 2 array with [NA, Valid]"""
-    return dtype.construct_array_type()._from_sequence([pd.NA, "A"])
+    arr = dtype.construct_array_type()._from_sequence([pd.NA, "A"])
+    return split_array(arr) if chunked else arr
 
 
 @pytest.fixture
-def data_for_sorting(dtype):
-    return dtype.construct_array_type()._from_sequence(["B", "C", "A"])
+def data_for_sorting(dtype, chunked):
+    arr = dtype.construct_array_type()._from_sequence(["B", "C", "A"])
+    return split_array(arr) if chunked else arr
 
 
 @pytest.fixture
-def data_missing_for_sorting(dtype):
-    return dtype.construct_array_type()._from_sequence(["B", pd.NA, "A"])
+def data_missing_for_sorting(dtype, chunked):
+    arr = dtype.construct_array_type()._from_sequence(["B", pd.NA, "A"])
+    return split_array(arr) if chunked else arr
 
 
 @pytest.fixture
@@ -69,10 +96,11 @@ def na_value():
 
 
 @pytest.fixture
-def data_for_grouping(dtype):
-    return dtype.construct_array_type()._from_sequence(
+def data_for_grouping(dtype, chunked):
+    arr = dtype.construct_array_type()._from_sequence(
         ["B", "B", pd.NA, pd.NA, "A", "A", "B", "C"]
     )
+    return split_array(arr) if chunked else arr
 
 
 class TestDtype(base.BaseDtypeTests):