REF (string): avoid copy in StringArray factorize (#59551)

* REF: avoid copy in StringArray factorize

* mypy fixup

* un-xfail

pandas/_libs/arrays.pyx

@@ -67,6 +67,10 @@ cdef class NDArrayBacked:
         """
         Construct a new ExtensionArray `new_array` with `arr` as its _ndarray.
 
+        The returned array has the same dtype as self.
+
+        Caller is responsible for ensuring `values.dtype == self._ndarray.dtype`.
+
         This should round-trip:
             self == self._from_backing_data(self._ndarray)
         """

pandas/_libs/hashtable.pyx

@@ -30,7 +30,10 @@ from pandas._libs.khash cimport (
     kh_python_hash_func,
     khiter_t,
 )
-from pandas._libs.missing cimport checknull
+from pandas._libs.missing cimport (
+    checknull,
+    is_matching_na,
+)
 
 
 def get_hashtable_trace_domain():

pandas/_libs/hashtable_class_helper.pxi.in

@@ -1171,11 +1171,13 @@ cdef class StringHashTable(HashTable):
             const char **vecs
             khiter_t k
             bint use_na_value
+            bint non_null_na_value
 
         if return_inverse:
             labels = np.zeros(n, dtype=np.intp)
         uindexer = np.empty(n, dtype=np.int64)
         use_na_value = na_value is not None
+        non_null_na_value = not checknull(na_value)
 
         # assign pointers and pre-filter out missing (if ignore_na)
         vecs = <const char **>malloc(n * sizeof(char *))
@@ -1186,7 +1188,12 @@ cdef class StringHashTable(HashTable):
 
             if (ignore_na
                 and (not isinstance(val, str)
-                     or (use_na_value and val == na_value))):
+                     or (use_na_value and (
+                        (non_null_na_value and val == na_value) or
+                        (not non_null_na_value and is_matching_na(val, na_value)))
+                        )
+                    )
+                ):
                 # if missing values do not count as unique values (i.e. if
                 # ignore_na is True), we can skip the actual value, and
                 # replace the label with na_sentinel directly
@@ -1452,18 +1459,23 @@ cdef class PyObjectHashTable(HashTable):
             object val
             khiter_t k
             bint use_na_value
-
+            bint non_null_na_value
         if return_inverse:
             labels = np.empty(n, dtype=np.intp)
         use_na_value = na_value is not None
+        non_null_na_value = not checknull(na_value)
 
         for i in range(n):
             val = values[i]
             hash(val)
 
             if ignore_na and (
                 checknull(val)
-                or (use_na_value and val == na_value)
+                or (use_na_value and (
+                    (non_null_na_value and val == na_value) or
+                    (not non_null_na_value and is_matching_na(val, na_value))
+                    )
+                )
             ):
                 # if missing values do not count as unique values (i.e. if
                 # ignore_na is True), skip the hashtable entry for them, and

pandas/core/arrays/_mixins.py

@@ -496,17 +496,14 @@ def _quantile(
         fill_value = self._internal_fill_value
 
         res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
-
-        res_values = self._cast_quantile_result(res_values)
-        return self._from_backing_data(res_values)
-
-    # TODO: see if we can share this with other dispatch-wrapping methods
-    def _cast_quantile_result(self, res_values: np.ndarray) -> np.ndarray:
-        """
-        Cast the result of quantile_with_mask to an appropriate dtype
-        to pass to _from_backing_data in _quantile.
-        """
-        return res_values
+        if res_values.dtype == self._ndarray.dtype:
+            return self._from_backing_data(res_values)
+        else:
+            # e.g. test_quantile_empty we are empty integer dtype and res_values
+            #  has floating dtype
+            # TODO: technically __init__ isn't defined here.
+            #  Should we raise NotImplementedError and handle this on NumpyEA?
+            return type(self)(res_values)  # type: ignore[call-arg]
 
     # ------------------------------------------------------------------------
     # numpy-like methods

pandas/core/arrays/categorical.py

@@ -2495,11 +2495,6 @@ def unique(self) -> Self:
         """
         return super().unique()
 
-    def _cast_quantile_result(self, res_values: np.ndarray) -> np.ndarray:
-        # make sure we have correct itemsize for resulting codes
-        assert res_values.dtype == self._ndarray.dtype
-        return res_values
-
     def equals(self, other: object) -> bool:
         """
         Returns True if categorical arrays are equal.

pandas/core/arrays/numpy_.py

@@ -137,9 +137,6 @@ def _from_sequence(
             result = result.copy()
         return cls(result)
 
-    def _from_backing_data(self, arr: np.ndarray) -> NumpyExtensionArray:
-        return type(self)(arr)
-
     # ------------------------------------------------------------------------
     # Data
 

pandas/core/arrays/string_.py

@@ -659,11 +659,10 @@ def __arrow_array__(self, type=None):
         values[self.isna()] = None
         return pa.array(values, type=type, from_pandas=True)
 
-    def _values_for_factorize(self) -> tuple[np.ndarray, None]:
+    def _values_for_factorize(self) -> tuple[np.ndarray, libmissing.NAType | float]:  # type: ignore[override]
         arr = self._ndarray.copy()
-        mask = self.isna()
-        arr[mask] = None
-        return arr, None
+
+        return arr, self.dtype.na_value
 
     def __setitem__(self, key, value) -> None:
         value = extract_array(value, extract_numpy=True)
@@ -873,8 +872,3 @@ def _from_sequence(
         if dtype is None:
             dtype = StringDtype(storage="python", na_value=np.nan)
         return super()._from_sequence(scalars, dtype=dtype, copy=copy)
-
-    def _from_backing_data(self, arr: np.ndarray) -> StringArrayNumpySemantics:
-        # need to override NumpyExtensionArray._from_backing_data to ensure
-        # we always preserve the dtype
-        return NDArrayBacked._from_backing_data(self, arr)

pandas/tests/groupby/test_groupby_dropna.py

@@ -387,9 +387,6 @@ def test_groupby_dropna_with_multiindex_input(input_index, keys, series):
     tm.assert_equal(result, expected)
 
 
-@pytest.mark.xfail(
-    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
-)
 def test_groupby_nan_included():
     # GH 35646
     data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]}

pandas/tests/window/test_rolling.py

@@ -6,10 +6,7 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas.compat import (
-    HAS_PYARROW,
     IS64,
     is_platform_arm,
     is_platform_power,
@@ -1329,9 +1326,6 @@ def test_rolling_corr_timedelta_index(index, window):
     tm.assert_almost_equal(result, expected)
 
 
-@pytest.mark.xfail(
-    using_string_dtype() and not HAS_PYARROW, reason="TODO(infer_string)"
-)
 def test_groupby_rolling_nan_included():
     # GH 35542
     data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]}