REF: collect arithmetic benchmarks

asv_bench/benchmarks/binary_ops.py → asv_bench/benchmarks/arithmetic.py

@@ -1,14 +1,23 @@
 import operator
+import warnings
 
 import numpy as np
 
-from pandas import DataFrame, Series, date_range
+import pandas as pd
+from pandas import DataFrame, Series, Timestamp, date_range, to_timedelta
+import pandas._testing as tm
 from pandas.core.algorithms import checked_add_with_arr
 
+from .pandas_vb_common import numeric_dtypes
+
 try:
     import pandas.core.computation.expressions as expr
 except ImportError:
     import pandas.computation.expressions as expr
+try:
+    import pandas.tseries.holiday
+except ImportError:
+    pass
 
 
 class IntFrameWithScalar:
@@ -151,6 +160,110 @@ def time_timestamp_ops_diff_with_shift(self, tz):
         self.s - self.s.shift()
 
 
+class IrregularOps:
+    def setup(self):
+        N = 10 ** 5
+        idx = date_range(start="1/1/2000", periods=N, freq="s")
+        s = Series(np.random.randn(N), index=idx)
+        self.left = s.sample(frac=1)
+        self.right = s.sample(frac=1)
+
+    def time_add(self):
+        self.left + self.right
+
+
+class TimedeltaOps:
+    def setup(self):
+        self.td = to_timedelta(np.arange(1000000))
+        self.ts = Timestamp("2000")
+
+    def time_add_td_ts(self):
+        self.td + self.ts
+
+
+class CategoricalComparisons:
+    params = ["__lt__", "__le__", "__eq__", "__ne__", "__ge__", "__gt__"]
+    param_names = ["op"]
+
+    def setup(self, op):
+        N = 10 ** 5
+        self.cat = pd.Categorical(list("aabbcd") * N, ordered=True)
+
+    def time_categorical_op(self, op):
+        getattr(self.cat, op)("b")
+
+
+class IndexArithmetic:
+
+    params = ["float", "int"]
+    param_names = ["dtype"]
+
+    def setup(self, dtype):
+        N = 10 ** 6
+        indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"}
+        self.index = getattr(tm, indexes[dtype])(N)
+
+    def time_add(self, dtype):
+        self.index + 2
+
+    def time_subtract(self, dtype):
+        self.index - 2
+
+    def time_multiply(self, dtype):
+        self.index * 2
+
+    def time_divide(self, dtype):
+        self.index / 2
+
+    def time_modulo(self, dtype):
+        self.index % 2
+
+
+class NumericInferOps:
+    # from GH 7332
+    params = numeric_dtypes
+    param_names = ["dtype"]
+
+    def setup(self, dtype):
+        N = 5 * 10 ** 5
+        self.df = DataFrame(
+            {"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)}
+        )
+
+    def time_add(self, dtype):
+        self.df["A"] + self.df["B"]
+
+    def time_subtract(self, dtype):
+        self.df["A"] - self.df["B"]
+
+    def time_multiply(self, dtype):
+        self.df["A"] * self.df["B"]
+
+    def time_divide(self, dtype):
+        self.df["A"] / self.df["B"]
+
+    def time_modulo(self, dtype):
+        self.df["A"] % self.df["B"]
+
+
+class DateInferOps:
+    # from GH 7332
+    def setup_cache(self):
+        N = 5 * 10 ** 5
+        df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")})
+        df["timedelta"] = df["datetime64"] - df["datetime64"]
+        return df
+
+    def time_subtract_datetimes(self, df):
+        df["datetime64"] - df["datetime64"]
+
+    def time_timedelta_plus_datetime(self, df):
+        df["timedelta"] + df["datetime64"]
+
+    def time_add_timedeltas(self, df):
+        df["timedelta"] + df["timedelta"]
+
+
 class AddOverflowScalar:
 
     params = [1, -1, 0]
@@ -188,4 +301,68 @@ def time_add_overflow_both_arg_nan(self):
         )
 
 
+hcal = pd.tseries.holiday.USFederalHolidayCalendar()
+# These offsets currently raise a NotImplimentedError with .apply_index()
+non_apply = [
+    pd.offsets.Day(),
+    pd.offsets.BYearEnd(),
+    pd.offsets.BYearBegin(),
+    pd.offsets.BQuarterEnd(),
+    pd.offsets.BQuarterBegin(),
+    pd.offsets.BMonthEnd(),
+    pd.offsets.BMonthBegin(),
+    pd.offsets.CustomBusinessDay(),
+    pd.offsets.CustomBusinessDay(calendar=hcal),
+    pd.offsets.CustomBusinessMonthBegin(calendar=hcal),
+    pd.offsets.CustomBusinessMonthEnd(calendar=hcal),
+    pd.offsets.CustomBusinessMonthEnd(calendar=hcal),
+]
+other_offsets = [
+    pd.offsets.YearEnd(),
+    pd.offsets.YearBegin(),
+    pd.offsets.QuarterEnd(),
+    pd.offsets.QuarterBegin(),
+    pd.offsets.MonthEnd(),
+    pd.offsets.MonthBegin(),
+    pd.offsets.DateOffset(months=2, days=2),
+    pd.offsets.BusinessDay(),
+    pd.offsets.SemiMonthEnd(),
+    pd.offsets.SemiMonthBegin(),
+]
+offsets = non_apply + other_offsets
+
+
+class OffsetArrayArithmetic:
+
+    params = offsets
+    param_names = ["offset"]
+
+    def setup(self, offset):
+        N = 10000
+        rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
+        self.rng = rng
+        self.ser = pd.Series(rng)
+
+    def time_add_series_offset(self, offset):
+        with warnings.catch_warnings(record=True):
+            self.ser + offset
+
+    def time_add_dti_offset(self, offset):
+        with warnings.catch_warnings(record=True):
+            self.rng + offset
+
+
+class ApplyIndex:
+    params = other_offsets
+    param_names = ["offset"]
+
+    def setup(self, offset):
+        N = 10000
+        rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
+        self.rng = rng
+
+    def time_apply_index(self, offset):
+        offset.apply_index(self.rng)
+
+
 from .pandas_vb_common import setup  # noqa: F401 isort:skip

asv_bench/benchmarks/categoricals.py

@@ -63,18 +63,6 @@ def time_existing_series(self):
         pd.Categorical(self.series)
 
 
-class CategoricalOps:
-    params = ["__lt__", "__le__", "__eq__", "__ne__", "__ge__", "__gt__"]
-    param_names = ["op"]
-
-    def setup(self, op):
-        N = 10 ** 5
-        self.cat = pd.Categorical(list("aabbcd") * N, ordered=True)
-
-    def time_categorical_op(self, op):
-        getattr(self.cat, op)("b")
-
-
 class Concat:
     def setup(self):
         N = 10 ** 5

asv_bench/benchmarks/index_object.py

@@ -63,32 +63,6 @@ def time_is_dates_only(self):
         self.dr._is_dates_only
 
 
-class Ops:
-
-    params = ["float", "int"]
-    param_names = ["dtype"]
-
-    def setup(self, dtype):
-        N = 10 ** 6
-        indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"}
-        self.index = getattr(tm, indexes[dtype])(N)
-
-    def time_add(self, dtype):
-        self.index + 2
-
-    def time_subtract(self, dtype):
-        self.index - 2
-
-    def time_multiply(self, dtype):
-        self.index * 2
-
-    def time_divide(self, dtype):
-        self.index / 2
-
-    def time_modulo(self, dtype):
-        self.index % 2
-
-
 class Range:
     def setup(self):
         self.idx_inc = RangeIndex(start=0, stop=10 ** 7, step=3)

asv_bench/benchmarks/inference.py

@@ -1,53 +1,8 @@
 import numpy as np
 
-from pandas import DataFrame, Series, to_numeric
+from pandas import Series, to_numeric
 
-from .pandas_vb_common import lib, numeric_dtypes, tm
-
-
-class NumericInferOps:
-    # from GH 7332
-    params = numeric_dtypes
-    param_names = ["dtype"]
-
-    def setup(self, dtype):
-        N = 5 * 10 ** 5
-        self.df = DataFrame(
-            {"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)}
-        )
-
-    def time_add(self, dtype):
-        self.df["A"] + self.df["B"]
-
-    def time_subtract(self, dtype):
-        self.df["A"] - self.df["B"]
-
-    def time_multiply(self, dtype):
-        self.df["A"] * self.df["B"]
-
-    def time_divide(self, dtype):
-        self.df["A"] / self.df["B"]
-
-    def time_modulo(self, dtype):
-        self.df["A"] % self.df["B"]
-
-
-class DateInferOps:
-    # from GH 7332
-    def setup_cache(self):
-        N = 5 * 10 ** 5
-        df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")})
-        df["timedelta"] = df["datetime64"] - df["datetime64"]
-        return df
-
-    def time_subtract_datetimes(self, df):
-        df["datetime64"] - df["datetime64"]
-
-    def time_timedelta_plus_datetime(self, df):
-        df["timedelta"] + df["datetime64"]
-
-    def time_add_timedeltas(self, df):
-        df["timedelta"] + df["timedelta"]
+from .pandas_vb_common import lib, tm
 
 
 class ToNumeric: