adding asv-benchmark hash_functions

Author: realead

asv_bench/benchmarks/hash_functions.py

@@ -0,0 +1,164 @@
+import numpy as np
+
+import pandas as pd
+
+
+class IsinAlmostFullWithRandomInt:
+    params = [
+        [np.float64, np.int64, np.uint64, np.object],
+        range(10, 21),
+    ]
+    param_names = ["dtype", "exponent"]
+
+    def setup(self, dtype, exponent):
+        M = 3 * 2 ** (exponent - 2)
+        # 0.77-the maximal share of occupied buckets
+        np.random.seed(42)
+        self.s = pd.Series(np.random.randint(0, M, M)).astype(dtype)
+        self.values = np.random.randint(0, M, M).astype(dtype)
+        self.values_outside = self.values + M
+
+    def time_isin(self, dtype, exponent):
+        self.s.isin(self.values)
+
+    def time_isin_outside(self, dtype, exponent):
+        self.s.isin(self.values_outside)
+
+
+class IsinWithRandomFloat:
+    params = [
+        [np.float64, np.object],
+        [
+            1_300,
+            2_000,
+            7_000,
+            8_000,
+            70_000,
+            80_000,
+            750_000,
+            900_000,
+        ],
+    ]
+    param_names = ["dtype", "M"]
+
+    def setup(self, dtype, M):
+        np.random.seed(42)
+        self.values = np.random.rand(M)
+        self.s = pd.Series(self.values).astype(dtype)
+        np.random.shuffle(self.values)
+        self.values_outside = self.values + 0.1
+
+    def time_isin(self, dtype, M):
+        self.s.isin(self.values)
+
+    def time_isin_outside(self, dtype, M):
+        self.s.isin(self.values_outside)
+
+
+class IsinWithArangeSorted:
+    params = [
+        [np.float64, np.int64, np.uint64, np.object],
+        [
+            1_000,
+            2_000,
+            8_000,
+            100_000,
+            1_000_000,
+        ],
+    ]
+    param_names = ["dtype", "M"]
+
+    def setup(self, dtype, M):
+        self.s = pd.Series(np.arange(M)).astype(dtype)
+        self.values = np.arange(M).astype(dtype)
+
+    def time_isin(self, dtype, M):
+        self.s.isin(self.values)
+
+
+class IsinWithArange:
+    params = [
+        [np.float64, np.int64, np.uint64, np.object],
+        [
+            1_000,
+            2_000,
+            8_000,
+        ],
+        [-2, 0, 2],
+    ]
+    param_names = ["dtype", "M", "offset_factor"]
+
+    def setup(self, dtype, M, offset_factor):
+        offset = int(M * offset_factor)
+        np.random.seed(42)
+        tmp = pd.Series(np.random.randint(offset, M + offset, 10 ** 6))
+        self.s = tmp.astype(dtype)
+        self.values = np.arange(M).astype(dtype)
+
+    def time_isin(self, dtype, M, offset_factor):
+        self.s.isin(self.values)
+
+
+class Float64GroupIndex:
+    # GH28303
+    def setup(self):
+        self.df = pd.date_range(
+            start="1/1/2018", end="1/2/2018", periods=1e6
+        ).to_frame()
+        self.group_index = np.round(self.df.index.astype(int) / 1e9)
+
+    def time_groupby(self):
+        self.df.groupby(self.group_index).last()
+
+
+class UniqueAndFactorizeArange:
+    params = range(4, 16)
+    param_names = ["exponent"]
+
+    def setup(self, exponent):
+        a = np.arange(10 ** 4, dtype="float64")
+        self.a2 = (a + 10 ** exponent).repeat(100)
+
+    def time_factorize(self, exponent):
+        pd.factorize(self.a2)
+
+    def time_unique(self, exponent):
+        pd.unique(self.a2)
+
+
+class NumericSeriesIndexing:
+
+    params = [
+        (pd.Int64Index, pd.UInt64Index, pd.Float64Index),
+        (10 ** 4, 10 ** 5, 5 * 10 ** 5, 10 ** 6, 5 * 10 ** 6),
+    ]
+    param_names = ["index_dtype", "N"]
+
+    def setup(self, index, N):
+        vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)))
+        indices = index(vals)
+        self.data = pd.Series(np.arange(N), index=indices)
+
+    def time_loc_slice(self, index, N):
+        # trigger building of mapping
+        self.data.loc[:800]
+
+
+class NumericSeriesIndexingShuffled:
+
+    params = [
+        (pd.Int64Index, pd.UInt64Index, pd.Float64Index),
+        (10 ** 4, 10 ** 5, 5 * 10 ** 5, 10 ** 6, 5 * 10 ** 6),
+    ]
+    param_names = ["index_dtype", "N"]
+
+    def setup(self, index, N):
+        vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)))
+        np.random.seed(42)
+        np.random.shuffle(vals)
+        indices = index(vals)
+        self.data = pd.Series(np.arange(N), index=indices)
+
+    def time_loc_slice(self, index, N):
+        # trigger building of mapping
+        self.data.loc[:800]