TYP: misc typing cleanup for core/computation/expressions.py

pandas/core/computation/expressions.py

@@ -6,6 +6,7 @@
 
 """
 import operator
+from typing import List, Set
 import warnings
 
 import numpy as np
@@ -21,7 +22,7 @@
     import numexpr as ne
 
 _TEST_MODE = None
-_TEST_RESULT = None
+_TEST_RESULT: List[bool] = list()
 _USE_NUMEXPR = _NUMEXPR_INSTALLED
 _evaluate = None
 _where = None
@@ -75,7 +76,7 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check):
         # required min elements (otherwise we are adding overhead)
         if np.prod(a.shape) > _MIN_ELEMENTS:
             # check for dtype compatibility
-            dtypes = set()
+            dtypes: Set[str] = set()
             for o in [a, b]:
                 # Series implements dtypes, check for dimension count as well
                 if hasattr(o, "dtypes") and o.ndim > 1:
@@ -247,25 +248,28 @@ def where(cond, a, b, use_numexpr=True):
     return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)
 
 
-def set_test_mode(v=True):
+def set_test_mode(v: bool = True) -> None:
     """
-    Keeps track of whether numexpr was used.  Stores an additional ``True``
-    for every successful use of evaluate with numexpr since the last
-    ``get_test_result``
+    Keeps track of whether numexpr was used.
+
+    Stores an additional ``True`` for every successful use of evaluate with
+    numexpr since the last ``get_test_result``.
     """
     global _TEST_MODE, _TEST_RESULT
     _TEST_MODE = v
     _TEST_RESULT = []
 
 
-def _store_test_result(used_numexpr):
+def _store_test_result(used_numexpr: bool) -> None:
     global _TEST_RESULT
     if used_numexpr:
         _TEST_RESULT.append(used_numexpr)
 
 
-def get_test_result():
-    """get test result and reset test_results"""
+def get_test_result() -> List[bool]:
+    """
+    Get test result and reset test_results.
+    """
     global _TEST_RESULT
     res = _TEST_RESULT
     _TEST_RESULT = []