CLN: Move test_parallel to gil.py

asv_bench/benchmarks/gil.py

@@ -1,3 +1,6 @@
+from functools import wraps
+import threading
+
 import numpy as np
 
 from pandas import (
@@ -30,21 +33,57 @@
     from pandas._libs import algos
 except ImportError:
     from pandas import algos
-try:
-    from pandas._testing import test_parallel  # noqa: PDF014
 
-    have_real_test_parallel = True
-except ImportError:
-    have_real_test_parallel = False
 
-    def test_parallel(num_threads=1):
-        def wrapper(fname):
-            return fname
+from .pandas_vb_common import BaseIO  # isort:skip
 
-        return wrapper
 
+def test_parallel(num_threads=2, kwargs_list=None):
+    """
+    Decorator to run the same function multiple times in parallel.
 
-from .pandas_vb_common import BaseIO  # isort:skip
+    Parameters
+    ----------
+    num_threads : int, optional
+        The number of times the function is run in parallel.
+    kwargs_list : list of dicts, optional
+        The list of kwargs to update original
+        function kwargs on different threads.
+
+    Notes
+    -----
+    This decorator does not pass the return value of the decorated function.
+
+    Original from scikit-image:
+
+    https://github.com/scikit-image/scikit-image/pull/1519
+
+    """
+    assert num_threads > 0
+    has_kwargs_list = kwargs_list is not None
+    if has_kwargs_list:
+        assert len(kwargs_list) == num_threads
+
+    def wrapper(func):
+        @wraps(func)
+        def inner(*args, **kwargs):
+            if has_kwargs_list:
+                update_kwargs = lambda i: dict(kwargs, **kwargs_list[i])
+            else:
+                update_kwargs = lambda i: kwargs
+            threads = []
+            for i in range(num_threads):
+                updated_kwargs = update_kwargs(i)
+                thread = threading.Thread(target=func, args=args, kwargs=updated_kwargs)
+                threads.append(thread)
+            for thread in threads:
+                thread.start()
+            for thread in threads:
+                thread.join()
+
+        return inner
+
+    return wrapper
 
 
 class ParallelGroupbyMethods:
@@ -53,8 +92,7 @@ class ParallelGroupbyMethods:
     param_names = ["threads", "method"]
 
     def setup(self, threads, method):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         N = 10**6
         ngroups = 10**3
         df = DataFrame(
@@ -86,8 +124,7 @@ class ParallelGroups:
     param_names = ["threads"]
 
     def setup(self, threads):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         size = 2**22
         ngroups = 10**3
         data = Series(np.random.randint(0, ngroups, size=size))
@@ -108,8 +145,7 @@ class ParallelTake1D:
     param_names = ["dtype"]
 
     def setup(self, dtype):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         N = 10**6
         df = DataFrame({"col": np.arange(N, dtype=dtype)})
         indexer = np.arange(100, len(df) - 100)
@@ -131,8 +167,7 @@ class ParallelKth:
     repeat = 5
 
     def setup(self):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         N = 10**7
         k = 5 * 10**5
         kwargs_list = [{"arr": np.random.randn(N)}, {"arr": np.random.randn(N)}]
@@ -149,8 +184,7 @@ def time_kth_smallest(self):
 
 class ParallelDatetimeFields:
     def setup(self):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         N = 10**6
         self.dti = date_range("1900-01-01", periods=N, freq="T")
         self.period = self.dti.to_period("D")
@@ -204,8 +238,7 @@ class ParallelRolling:
     param_names = ["method"]
 
     def setup(self, method):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         win = 100
         arr = np.random.rand(100000)
         if hasattr(DataFrame, "rolling"):
@@ -248,8 +281,7 @@ class ParallelReadCSV(BaseIO):
     param_names = ["dtype"]
 
     def setup(self, dtype):
-        if not have_real_test_parallel:
-            raise NotImplementedError
+
         rows = 10000
         cols = 50
         data = {
@@ -284,8 +316,6 @@ class ParallelFactorize:
     param_names = ["threads"]
 
     def setup(self, threads):
-        if not have_real_test_parallel:
-            raise NotImplementedError
 
         strings = tm.makeStringIndex(100000)
 

pandas/_testing/__init__.py

@@ -3,7 +3,6 @@
 import collections
 from datetime import datetime
 from decimal import Decimal
-from functools import wraps
 import operator
 import os
 import re
@@ -749,55 +748,6 @@ def makeMissingDataframe(density=0.9, random_state=None):
     return df
 
 
-def test_parallel(num_threads=2, kwargs_list=None):
-    """
-    Decorator to run the same function multiple times in parallel.
-
-    Parameters
-    ----------
-    num_threads : int, optional
-        The number of times the function is run in parallel.
-    kwargs_list : list of dicts, optional
-        The list of kwargs to update original
-        function kwargs on different threads.
-
-    Notes
-    -----
-    This decorator does not pass the return value of the decorated function.
-
-    Original from scikit-image:
-
-    https://github.com/scikit-image/scikit-image/pull/1519
-
-    """
-    assert num_threads > 0
-    has_kwargs_list = kwargs_list is not None
-    if has_kwargs_list:
-        assert len(kwargs_list) == num_threads
-    import threading
-
-    def wrapper(func):
-        @wraps(func)
-        def inner(*args, **kwargs):
-            if has_kwargs_list:
-                update_kwargs = lambda i: dict(kwargs, **kwargs_list[i])
-            else:
-                update_kwargs = lambda i: kwargs
-            threads = []
-            for i in range(num_threads):
-                updated_kwargs = update_kwargs(i)
-                thread = threading.Thread(target=func, args=args, kwargs=updated_kwargs)
-                threads.append(thread)
-            for thread in threads:
-                thread.start()
-            for thread in threads:
-                thread.join()
-
-        return inner
-
-    return wrapper
-
-
 class SubclassedSeries(Series):
     _metadata = ["testattr", "name"]