TST/CLN: Remove seldomly use items in _testing/__init__.py

pandas/_testing/__init__.py

@@ -3,7 +3,6 @@
 from decimal import Decimal
 import operator
 import os
-import re
 from sys import byteorder
 from typing import (
     TYPE_CHECKING,
@@ -27,7 +26,6 @@
 import pandas as pd
 from pandas import (
     ArrowDtype,
-    Categorical,
     DataFrame,
     Index,
     MultiIndex,
@@ -243,9 +241,6 @@
     ALL_PYARROW_DTYPES = []
 
 
-EMPTY_STRING_PATTERN = re.compile("^$")
-
-
 arithmetic_dunder_methods = [
     "__add__",
     "__radd__",
@@ -353,42 +348,6 @@ def _constructor_sliced(self):
         return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
 
 
-class SubclassedCategorical(Categorical):
-    pass
-
-
-def _make_skipna_wrapper(alternative, skipna_alternative=None):
-    """
-    Create a function for calling on an array.
-
-    Parameters
-    ----------
-    alternative : function
-        The function to be called on the array with no NaNs.
-        Only used when 'skipna_alternative' is None.
-    skipna_alternative : function
-        The function to be called on the original array
-
-    Returns
-    -------
-    function
-    """
-    if skipna_alternative:
-
-        def skipna_wrapper(x):
-            return skipna_alternative(x.values)
-
-    else:
-
-        def skipna_wrapper(x):
-            nona = x.dropna()
-            if len(nona) == 0:
-                return np.nan
-            return alternative(nona)
-
-    return skipna_wrapper
-
-
 def convert_rows_list_to_csv_str(rows_list: list[str]) -> str:
     """
     Convert list of CSV rows to single CSV-formatted string for current OS.
@@ -621,7 +580,6 @@ def shares_memory(left, right) -> bool:
     "convert_rows_list_to_csv_str",
     "DATETIME64_DTYPES",
     "decompress_file",
-    "EMPTY_STRING_PATTERN",
     "ENDIAN",
     "ensure_clean",
     "external_error_raised",
@@ -654,7 +612,6 @@ def shares_memory(left, right) -> bool:
     "SIGNED_INT_EA_DTYPES",
     "SIGNED_INT_NUMPY_DTYPES",
     "STRING_DTYPES",
-    "SubclassedCategorical",
     "SubclassedDataFrame",
     "SubclassedSeries",
     "TIMEDELTA64_DTYPES",

pandas/tests/arrays/categorical/test_subclass.py

@@ -2,21 +2,25 @@
 import pandas._testing as tm
 
 
+class SubclassedCategorical(Categorical):
+    pass
+
+
 class TestCategoricalSubclassing:
     def test_constructor(self):
-        sc = tm.SubclassedCategorical(["a", "b", "c"])
-        assert isinstance(sc, tm.SubclassedCategorical)
+        sc = SubclassedCategorical(["a", "b", "c"])
+        assert isinstance(sc, SubclassedCategorical)
         tm.assert_categorical_equal(sc, Categorical(["a", "b", "c"]))
 
     def test_from_codes(self):
-        sc = tm.SubclassedCategorical.from_codes([1, 0, 2], ["a", "b", "c"])
-        assert isinstance(sc, tm.SubclassedCategorical)
+        sc = SubclassedCategorical.from_codes([1, 0, 2], ["a", "b", "c"])
+        assert isinstance(sc, SubclassedCategorical)
         exp = Categorical.from_codes([1, 0, 2], ["a", "b", "c"])
         tm.assert_categorical_equal(sc, exp)
 
     def test_map(self):
-        sc = tm.SubclassedCategorical(["a", "b", "c"])
+        sc = SubclassedCategorical(["a", "b", "c"])
         res = sc.map(lambda x: x.upper(), na_action=None)
-        assert isinstance(res, tm.SubclassedCategorical)
+        assert isinstance(res, SubclassedCategorical)
         exp = Categorical(["A", "B", "C"])
         tm.assert_categorical_equal(res, exp)

pandas/tests/frame/test_reductions.py

@@ -40,6 +40,38 @@
 is_windows_or_is32 = is_platform_windows() or not IS64
 
 
+def make_skipna_wrapper(alternative, skipna_alternative=None):
+    """
+    Create a function for calling on an array.
+
+    Parameters
+    ----------
+    alternative : function
+        The function to be called on the array with no NaNs.
+        Only used when 'skipna_alternative' is None.
+    skipna_alternative : function
+        The function to be called on the original array
+
+    Returns
+    -------
+    function
+    """
+    if skipna_alternative:
+
+        def skipna_wrapper(x):
+            return skipna_alternative(x.values)
+
+    else:
+
+        def skipna_wrapper(x):
+            nona = x.dropna()
+            if len(nona) == 0:
+                return np.nan
+            return alternative(nona)
+
+    return skipna_wrapper
+
+
 def assert_stat_op_calc(
     opname,
     alternative,
@@ -96,7 +128,7 @@ def assert_stat_op_calc(
         def wrapper(x):
             return alternative(x.values)
 
-        skipna_wrapper = tm._make_skipna_wrapper(alternative, skipna_alternative)
+        skipna_wrapper = make_skipna_wrapper(alternative, skipna_alternative)
         result0 = f(axis=0, skipna=False)
         result1 = f(axis=1, skipna=False)
         tm.assert_series_equal(

pandas/tests/io/json/test_normalize.py

@@ -200,7 +200,7 @@ def test_empty_array(self):
     )
     def test_accepted_input(self, data, record_path, exception_type):
         if exception_type is not None:
-            with pytest.raises(exception_type, match=tm.EMPTY_STRING_PATTERN):
+            with pytest.raises(exception_type, match=""):
                 json_normalize(data, record_path=record_path)
         else:
             result = json_normalize(data, record_path=record_path)

pandas/tests/series/test_ufunc.py

@@ -413,7 +413,7 @@ def test_outer():
     ser = pd.Series([1, 2, 3])
     obj = np.array([1, 2, 3])
 
-    with pytest.raises(NotImplementedError, match=tm.EMPTY_STRING_PATTERN):
+    with pytest.raises(NotImplementedError, match=""):
         np.subtract.outer(ser, obj)