add ASVs

Author: topper-123

asv_bench/benchmarks/indexing_engines.py

@@ -0,0 +1,63 @@
+import numpy as np
+
+import pandas.util.testing as tm
+from pandas._libs.index import (Int64Engine, Int32Engine,
+                                Int16Engine, Int8Engine,
+                                UInt64Engine, UInt32Engine,
+                                UInt16Engine, UInt8Engine,
+                                Float64Engine, Float32Engine,
+                                ObjectEngine,
+                                )
+
+
+class NumericEngineIndexing(object):
+
+    goal_time = 0.2
+    params = [[Int64Engine, Int32Engine, Int16Engine, Int8Engine,
+               UInt64Engine, UInt32Engine, UInt16Engine, UInt8Engine,
+               Float64Engine, Float32Engine,
+               ],
+              ['monotonic_incr', 'monotonic_decr', 'non_monotonic']
+              ]
+    param_names = ['engine', 'index_type']
+
+    def setup(self, engine, index_type):
+        N = 10**5
+        values = list([1] * N + [2] * N + [3] * N)
+        array_ = {
+            'monotonic_incr': np.array(values, dtype=engine._dtype),
+            'monotonic_decr': np.array(list(reversed(values)),
+                                       dtype=engine._dtype),
+            'non_monotonic': np.array([1, 2, 3] * N, dtype=engine._dtype),
+        }[index_type]
+
+        self.data = engine(lambda: array_, len(array_))
+        self.int_scalar = 2
+
+    def time_get_loc(self, engine, index_type):
+        self.data.get_loc(self.int_scalar)
+
+
+class ObjectEngineIndexing(object):
+
+    goal_time = 0.2
+    params = [[ObjectEngine],
+              ['monotonic_incr', 'monotonic_decr', 'non_monotonic']
+              ]
+    param_names = ['engine', 'index_type']
+
+    def setup(self, engine, index_type):
+        N = 10**5
+        values = list('a' * N + 'b' * N + 'c' * N)
+        array_ = {
+            'monotonic_incr': np.array(values, dtype=engine._dtype),
+            'monotonic_decr': np.array(list(reversed(values)),
+                                       dtype=engine._dtype),
+            'non_monotonic': np.array(list('abc') * N, dtype=engine._dtype),
+        }[index_type]
+
+        self.data = engine(lambda: array_, len(array_))
+        self.int_scalar = 'b'
+
+    def time_get_loc(self, engine, index_type):
+        self.data.get_loc(self.int_scalar)

pandas/tests/indexes/test_engine.py

@@ -6,8 +6,6 @@
 import pandas as pd
 from pandas._libs.index import (Int64Engine, UInt64Engine,
                                 Float64Engine, ObjectEngine)
-from pandas._libs.lib import is_scalar
-import pandas.util.testing as tm
 
 
 class TestNumericEngine(object):
@@ -57,8 +55,8 @@ def test_is_unique(self, values, expected, num_engine):
     @pytest.mark.parametrize('values, value, expected', [
         ([1, 2, 3], 2, 1),
         ([1, 2, 2, 3], 2, slice(1, 3)),
-        ([3, 2, 2, 1], 2, np.array([False,  True,  True, False])),
-        ([1, 2, 2, 1], 2, np.array([False,  True,  True, False])),
+        ([3, 2, 2, 1], 2, np.array([False, True, True, False])),
+        ([1, 2, 2, 1], 2, np.array([False, True, True, False])),
         ([1, 3, 2], 2, 2),
     ])
     def test_get_loc(self, values, value, expected, num_engine):
@@ -125,8 +123,8 @@ def test_is_unique(self, values, expected):
     @pytest.mark.parametrize('values, value, expected', [
         (list('abc'), 'b', 1),
         (list('abbc'), 'b', slice(1, 3)),
-        (list('cbba'), 'b', np.array([False,  True,  True, False])),
-        (list('abba'), 'b', np.array([False,  True,  True, False])),
+        (list('cbba'), 'b', np.array([False, True, True, False])),
+        (list('abba'), 'b', np.array([False, True, True, False])),
         (list('acb'), 'b', 2),
     ])
     def test_get_loc(self, values, value, expected):