add tests for indexing engines and Uint64Engine (pandas-dev#23090)

Author: topper-123

asv_bench/benchmarks/indexing.py

@@ -2,16 +2,17 @@
 
 import numpy as np
 import pandas.util.testing as tm
-from pandas import (Series, DataFrame, Panel, MultiIndex, Int64Index,
-                    Float64Index, IntervalIndex, CategoricalIndex,
+from pandas import (Series, DataFrame, Panel, MultiIndex,
+                    Int64Index, UInt64Index, Float64Index,
+                    IntervalIndex, CategoricalIndex,
                     IndexSlice, concat, date_range)
 
 
 class NumericSeriesIndexing(object):
 
     goal_time = 0.2
     params = [
-        (Int64Index, Float64Index),
+        (Int64Index, UInt64Index, Float64Index),
         ('unique_monotonic_inc', 'nonunique_monotonic_inc'),
     ]
     param_names = ['index_dtype', 'index_structure']

asv_bench/benchmarks/indexing_engines.py

@@ -0,0 +1,54 @@
+import numpy as np
+
+from pandas._libs.index import (Int64Engine, UInt64Engine, Float64Engine,
+                                ObjectEngine)
+
+
+class NumericEngineIndexing(object):
+
+    goal_time = 0.2
+    params = [[Int64Engine, UInt64Engine, Float64Engine],
+              [np.int64, np.uint64, np.float64],
+              ['monotonic_incr', 'monotonic_decr', 'non_monotonic'],
+              ]
+    param_names = ['engine', 'dtype', 'index_type']
+
+    def setup(self, engine, dtype, index_type):
+        N = 10**5
+        values = list([1] * N + [2] * N + [3] * N)
+        arr = {
+            'monotonic_incr': np.array(values, dtype=dtype),
+            'monotonic_decr': np.array(list(reversed(values)),
+                                       dtype=dtype),
+            'non_monotonic': np.array([1, 2, 3] * N, dtype=dtype),
+        }[index_type]
+
+        self.data = engine(lambda: arr, len(arr))
+        # code belows avoids populating the mapping etc. while timing.
+        self.data.get_loc(2)
+
+    def time_get_loc(self, engine, dtype, index_type):
+        self.data.get_loc(2)
+
+
+class ObjectEngineIndexing(object):
+
+    goal_time = 0.2
+    params = [('monotonic_incr', 'monotonic_decr', 'non_monotonic')]
+    param_names = ['index_type']
+
+    def setup(self, index_type):
+        N = 10**5
+        values = list('a' * N + 'b' * N + 'c' * N)
+        arr = {
+            'monotonic_incr': np.array(values, dtype=object),
+            'monotonic_decr': np.array(list(reversed(values)), dtype=object),
+            'non_monotonic': np.array(list('abc') * N, dtype=object),
+        }[index_type]
+
+        self.data = ObjectEngine(lambda: arr, len(arr))
+        # code belows avoids populating the mapping etc. while timing.
+        self.data.get_loc('b')
+
+    def time_get_loc(self, index_type):
+        self.data.get_loc('b')