[ArrowStringArray] REF: str.extract accessor method

pandas/core/strings/accessor.py

@@ -15,7 +15,10 @@
 import numpy as np
 
 import pandas._libs.lib as lib
-from pandas._typing import FrameOrSeriesUnion
+from pandas._typing import (
+    ArrayLike,
+    FrameOrSeriesUnion,
+)
 from pandas.util._decorators import Appender
 
 from pandas.core.dtypes.common import (
@@ -3084,9 +3087,9 @@ def _get_group_names(regex: Pattern) -> List[Hashable]:
     return [names.get(1 + i, i) for i in range(regex.groups)]
 
 
-def _str_extract_noexpand(arr, pat, flags=0):
+def _str_extract_noexpand(arr: ArrayLike, pat: str, flags=0):
     """
-    Find groups in each string in the Series/Index using passed regular expression.
+    Find groups in each string in the array using passed regular expression.
 
     This function is called from str_extract(expand=False) when there is a single group
     in the regex.
@@ -3095,65 +3098,69 @@ def _str_extract_noexpand(arr, pat, flags=0):
     -------
     np.ndarray
     """
-    from pandas import array as pd_array
-
     regex = re.compile(pat, flags=flags)
     groups_or_na = _groups_or_na_fun(regex)
-    result_dtype = _result_dtype(arr)
 
     result = np.array([groups_or_na(val)[0] for val in np.asarray(arr)], dtype=object)
-    # not dispatching, so we have to reconstruct here.
-    result = pd_array(result, dtype=result_dtype)
     return result
 
 
-def _str_extract_frame(arr, pat, flags=0):
+def _str_extract_expand(arr: ArrayLike, pat: str, flags: int = 0) -> List[List]:
     """
-    Find groups in each string in the Series/Index using passed regular expression.
+    Find groups in each string in the array using passed regular expression.
 
-    For each subject string in the Series/Index, extract groups from the first match of
+    For each subject string in the array, extract groups from the first match of
     regular expression pat. This function is called from str_extract(expand=True) or
     str_extract(expand=False) when there is more than one group in the regex.
 
     Returns
     -------
-    DataFrame
+    list of lists
 
     """
-    from pandas import DataFrame
-
     regex = re.compile(pat, flags=flags)
     groups_or_na = _groups_or_na_fun(regex)
-    columns = _get_group_names(regex)
-    result_dtype = _result_dtype(arr)
 
-    if arr.size == 0:
-        return DataFrame(columns=columns, dtype=result_dtype)
+    return [groups_or_na(val) for val in np.asarray(arr)]
 
-    result_index: Optional["Index"]
-    if isinstance(arr, ABCSeries):
-        result_index = arr.index
-    else:
-        result_index = None
-    return DataFrame(
-        [groups_or_na(val) for val in np.asarray(arr)],
-        columns=columns,
-        index=result_index,
-        dtype=result_dtype,
-    )
 
+def str_extract(accessor: StringMethods, pat: str, flags: int = 0, expand: bool = True):
+    from pandas import (
+        DataFrame,
+        array as pd_array,
+    )
 
-def str_extract(arr, pat, flags=0, expand=True):
+    obj = accessor._data
+    result_dtype = _result_dtype(obj)
     regex = re.compile(pat, flags=flags)
     returns_df = regex.groups > 1 or expand
 
     if returns_df:
         name = None
-        result = _str_extract_frame(arr._orig, pat, flags=flags)
+        columns = _get_group_names(regex)
+
+        if obj.array.size == 0:
+            result = DataFrame(columns=columns, dtype=result_dtype)
+
+        else:
+            result_list = _str_extract_expand(obj.array, pat, flags=flags)
+
+            result_index: Optional["Index"]
+            if isinstance(obj, ABCSeries):
+                result_index = obj.index
+            else:
+                result_index = None
+
+            result = DataFrame(
+                result_list, columns=columns, index=result_index, dtype=result_dtype
+            )
+
     else:
         name = _get_single_group_name(regex)
-        result = _str_extract_noexpand(arr._orig, pat, flags=flags)
-    return arr._wrap_result(result, name=name)
+        result_arr = _str_extract_noexpand(obj.array, pat, flags=flags)
+        # not dispatching, so we have to reconstruct here.
+        result = pd_array(result_arr, dtype=result_dtype)
+    return accessor._wrap_result(result, name=name)
 
 
 def str_extractall(arr, pat, flags=0):