REF: implement replace_regex, remove unreachable branch in ObjectBlock.replace

pandas/core/array_algos/replace.py

@@ -3,7 +3,7 @@
 """
 import operator
 import re
-from typing import Pattern, Union
+from typing import Optional, Pattern, Union
 
 import numpy as np
 
@@ -12,8 +12,10 @@
 from pandas.core.dtypes.common import (
     is_datetimelike_v_numeric,
     is_numeric_v_string_like,
+    is_re,
     is_scalar,
 )
+from pandas.core.dtypes.missing import isna
 
 
 def compare_or_regex_search(
@@ -87,3 +89,45 @@ def _check_comparison_types(
 
     _check_comparison_types(result, a, b)
     return result
+
+
+def replace_regex(values: ArrayLike, rx: re.Pattern, value, mask: Optional[np.ndarray]):
+    """
+    Parameters
+    ----------
+    values : ArrayLike
+        Object dtype.
+    rx : re.Pattern
+    value : Any
+    mask : np.ndarray[bool], optional
+
+    Notes
+    -----
+    Alters values in-place.
+    """
+
+    # deal with replacing values with objects (strings) that match but
+    # whose replacement is not a string (numeric, nan, object)
+    if isna(value) or not isinstance(value, str):
+
+        def re_replacer(s):
+            if is_re(rx) and isinstance(s, str):
+                return value if rx.search(s) is not None else s
+            else:
+                return s
+
+    else:
+        # value is guaranteed to be a string here, s can be either a string
+        # or null if it's null it gets returned
+        def re_replacer(s):
+            if is_re(rx) and isinstance(s, str):
+                return rx.sub(value, s)
+            else:
+                return s
+
+    f = np.vectorize(re_replacer, otypes=[values.dtype])
+
+    if mask is None:
+        values[:] = f(values)
+    else:
+        values[mask] = f(values[mask])

pandas/core/internals/blocks.py

@@ -60,7 +60,7 @@
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna, isna_compat
 
 import pandas.core.algorithms as algos
-from pandas.core.array_algos.replace import compare_or_regex_search
+from pandas.core.array_algos.replace import compare_or_regex_search, replace_regex
 from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays import (
     Categorical,
@@ -2563,32 +2563,7 @@ def _replace_single(
             return super().replace(to_replace, value, inplace=inplace, regex=regex)
 
         new_values = self.values if inplace else self.values.copy()
-
-        # deal with replacing values with objects (strings) that match but
-        # whose replacement is not a string (numeric, nan, object)
-        if isna(value) or not isinstance(value, str):
-
-            def re_replacer(s):
-                if is_re(rx) and isinstance(s, str):
-                    return value if rx.search(s) is not None else s
-                else:
-                    return s
-
-        else:
-            # value is guaranteed to be a string here, s can be either a string
-            # or null if it's null it gets returned
-            def re_replacer(s):
-                if is_re(rx) and isinstance(s, str):
-                    return rx.sub(value, s)
-                else:
-                    return s
-
-        f = np.vectorize(re_replacer, otypes=[self.dtype])
-
-        if mask is None:
-            new_values[:] = f(new_values)
-        else:
-            new_values[mask] = f(new_values[mask])
+        replace_regex(new_values, rx, value, mask)
 
         # convert
         block = self.make_block(new_values)