REF/BENCH: tslibs-specific parts of asvs

asv_bench/benchmarks/offset.py

@@ -1,8 +1,5 @@
-from datetime import datetime
 import warnings
 
-import numpy as np
-
 import pandas as pd
 
 try:
@@ -54,24 +51,6 @@ def time_apply_index(self, offset):
         offset.apply_index(self.rng)
 
 
-class OnOffset:
-
-    params = offsets
-    param_names = ["offset"]
-
-    def setup(self, offset):
-        self.dates = [
-            datetime(2016, m, d)
-            for m in [10, 11, 12]
-            for d in [1, 2, 3, 28, 29, 30, 31]
-            if not (m == 11 and d == 31)
-        ]
-
-    def time_on_offset(self, offset):
-        for date in self.dates:
-            offset.onOffset(date)
-
-
 class OffsetSeriesArithmetic:
 
     params = offsets
@@ -99,31 +78,3 @@ def setup(self, offset):
     def time_add_offset(self, offset):
         with warnings.catch_warnings(record=True):
             self.data + offset
-
-
-class OffestDatetimeArithmetic:
-
-    params = offsets
-    param_names = ["offset"]
-
-    def setup(self, offset):
-        self.date = datetime(2011, 1, 1)
-        self.dt64 = np.datetime64("2011-01-01 09:00Z")
-
-    def time_apply(self, offset):
-        offset.apply(self.date)
-
-    def time_apply_np_dt64(self, offset):
-        offset.apply(self.dt64)
-
-    def time_add(self, offset):
-        self.date + offset
-
-    def time_add_10(self, offset):
-        self.date + (10 * offset)
-
-    def time_subtract(self, offset):
-        self.date - offset
-
-    def time_subtract_10(self, offset):
-        self.date - (10 * offset)

asv_bench/benchmarks/period.py

@@ -1,71 +1,12 @@
+"""
+Period benchmarks with non-tslibs dependencies.  See
+benchmarks.tslibs.period for benchmarks that rely only on tslibs.
+"""
 from pandas import DataFrame, Period, PeriodIndex, Series, date_range, period_range
 
 from pandas.tseries.frequencies import to_offset
 
 
-class PeriodProperties:
-
-    params = (
-        ["M", "min"],
-        [
-            "year",
-            "month",
-            "day",
-            "hour",
-            "minute",
-            "second",
-            "is_leap_year",
-            "quarter",
-            "qyear",
-            "week",
-            "daysinmonth",
-            "dayofweek",
-            "dayofyear",
-            "start_time",
-            "end_time",
-        ],
-    )
-    param_names = ["freq", "attr"]
-
-    def setup(self, freq, attr):
-        self.per = Period("2012-06-01", freq=freq)
-
-    def time_property(self, freq, attr):
-        getattr(self.per, attr)
-
-
-class PeriodUnaryMethods:
-
-    params = ["M", "min"]
-    param_names = ["freq"]
-
-    def setup(self, freq):
-        self.per = Period("2012-06-01", freq=freq)
-
-    def time_to_timestamp(self, freq):
-        self.per.to_timestamp()
-
-    def time_now(self, freq):
-        self.per.now(freq)
-
-    def time_asfreq(self, freq):
-        self.per.asfreq("A")
-
-
-class PeriodConstructor:
-    params = [["D"], [True, False]]
-    param_names = ["freq", "is_offset"]
-
-    def setup(self, freq, is_offset):
-        if is_offset:
-            self.freq = to_offset(freq)
-        else:
-            self.freq = freq
-
-    def time_period_constructor(self, freq, is_offset):
-        Period("2012-06-01", freq=freq)
-
-
 class PeriodIndexConstructor:
 
     params = [["D"], [True, False]]

asv_bench/benchmarks/timedelta.py

@@ -1,49 +1,11 @@
-import datetime
+"""
+Timedelta benchmarks with non-tslibs dependencies.  See
+benchmarks.tslibs.timedelta for benchmarks that rely only on tslibs.
+"""
 
 import numpy as np
 
-from pandas import (
-    DataFrame,
-    Series,
-    Timedelta,
-    Timestamp,
-    timedelta_range,
-    to_timedelta,
-)
-
-
-class TimedeltaConstructor:
-    def time_from_int(self):
-        Timedelta(123456789)
-
-    def time_from_unit(self):
-        Timedelta(1, unit="d")
-
-    def time_from_components(self):
-        Timedelta(
-            days=1,
-            hours=2,
-            minutes=3,
-            seconds=4,
-            milliseconds=5,
-            microseconds=6,
-            nanoseconds=7,
-        )
-
-    def time_from_datetime_timedelta(self):
-        Timedelta(datetime.timedelta(days=1, seconds=1))
-
-    def time_from_np_timedelta(self):
-        Timedelta(np.timedelta64(1, "ms"))
-
-    def time_from_string(self):
-        Timedelta("1 days")
-
-    def time_from_iso_format(self):
-        Timedelta("P4DT12H30M5S")
-
-    def time_from_missing(self):
-        Timedelta("nat")
+from pandas import DataFrame, Series, Timestamp, timedelta_range, to_timedelta
 
 
 class ToTimedelta:
@@ -88,24 +50,6 @@ def time_add_td_ts(self):
         self.td + self.ts
 
 
-class TimedeltaProperties:
-    def setup_cache(self):
-        td = Timedelta(days=365, minutes=35, seconds=25, milliseconds=35)
-        return td
-
-    def time_timedelta_days(self, td):
-        td.days
-
-    def time_timedelta_seconds(self, td):
-        td.seconds
-
-    def time_timedelta_microseconds(self, td):
-        td.microseconds
-
-    def time_timedelta_nanoseconds(self, td):
-        td.nanoseconds
-
-
 class DatetimeAccessor:
     def setup_cache(self):
         N = 100000

asv_bench/benchmarks/tslibs/__init__.py

@@ -0,0 +1,7 @@
+"""
+Benchmarks in this directory should depend only on tslibs, tseries.offsets,
+and to_offset.
+
+i.e. any code changes that do not touch those files should not need to
+run these benchmarks.
+"""

asv_bench/benchmarks/tslibs/offsets.py

@@ -0,0 +1,90 @@
+"""
+offsets benchmarks that rely only on tslibs.  See benchmarks.offset for
+offsets benchmarks that rely on other parts of pandas.
+"""
+from datetime import datetime
+
+import numpy as np
+
+from pandas import offsets
+
+try:
+    import pandas.tseries.holiday  # noqa
+except ImportError:
+    pass
+
+hcal = pandas.tseries.holiday.USFederalHolidayCalendar()
+# These offsets currently raise a NotImplimentedError with .apply_index()
+non_apply = [
+    offsets.Day(),
+    offsets.BYearEnd(),
+    offsets.BYearBegin(),
+    offsets.BQuarterEnd(),
+    offsets.BQuarterBegin(),
+    offsets.BMonthEnd(),
+    offsets.BMonthBegin(),
+    offsets.CustomBusinessDay(),
+    offsets.CustomBusinessDay(calendar=hcal),
+    offsets.CustomBusinessMonthBegin(calendar=hcal),
+    offsets.CustomBusinessMonthEnd(calendar=hcal),
+    offsets.CustomBusinessMonthEnd(calendar=hcal),
+]
+other_offsets = [
+    offsets.YearEnd(),
+    offsets.YearBegin(),
+    offsets.QuarterEnd(),
+    offsets.QuarterBegin(),
+    offsets.MonthEnd(),
+    offsets.MonthBegin(),
+    offsets.DateOffset(months=2, days=2),
+    offsets.BusinessDay(),
+    offsets.SemiMonthEnd(),
+    offsets.SemiMonthBegin(),
+]
+offset_objs = non_apply + other_offsets
+
+
+class OnOffset:
+
+    params = offset_objs
+    param_names = ["offset"]
+
+    def setup(self, offset):
+        self.dates = [
+            datetime(2016, m, d)
+            for m in [10, 11, 12]
+            for d in [1, 2, 3, 28, 29, 30, 31]
+            if not (m == 11 and d == 31)
+        ]
+
+    def time_on_offset(self, offset):
+        for date in self.dates:
+            offset.onOffset(date)
+
+
+class OffestDatetimeArithmetic:
+
+    params = offset_objs
+    param_names = ["offset"]
+
+    def setup(self, offset):
+        self.date = datetime(2011, 1, 1)
+        self.dt64 = np.datetime64("2011-01-01 09:00Z")
+
+    def time_apply(self, offset):
+        offset.apply(self.date)
+
+    def time_apply_np_dt64(self, offset):
+        offset.apply(self.dt64)
+
+    def time_add(self, offset):
+        self.date + offset
+
+    def time_add_10(self, offset):
+        self.date + (10 * offset)
+
+    def time_subtract(self, offset):
+        self.date - offset
+
+    def time_subtract_10(self, offset):
+        self.date - (10 * offset)

asv_bench/benchmarks/tslibs/period.py

@@ -0,0 +1,70 @@
+"""
+Period benchmarks that rely only on tslibs.  See benchmarks.period for
+Period benchmarks that rely on other parts fo pandas.
+"""
+from pandas import Period
+
+from pandas.tseries.frequencies import to_offset
+
+
+class PeriodProperties:
+
+    params = (
+        ["M", "min"],
+        [
+            "year",
+            "month",
+            "day",
+            "hour",
+            "minute",
+            "second",
+            "is_leap_year",
+            "quarter",
+            "qyear",
+            "week",
+            "daysinmonth",
+            "dayofweek",
+            "dayofyear",
+            "start_time",
+            "end_time",
+        ],
+    )
+    param_names = ["freq", "attr"]
+
+    def setup(self, freq, attr):
+        self.per = Period("2012-06-01", freq=freq)
+
+    def time_property(self, freq, attr):
+        getattr(self.per, attr)
+
+
+class PeriodUnaryMethods:
+
+    params = ["M", "min"]
+    param_names = ["freq"]
+
+    def setup(self, freq):
+        self.per = Period("2012-06-01", freq=freq)
+
+    def time_to_timestamp(self, freq):
+        self.per.to_timestamp()
+
+    def time_now(self, freq):
+        self.per.now(freq)
+
+    def time_asfreq(self, freq):
+        self.per.asfreq("A")
+
+
+class PeriodConstructor:
+    params = [["D"], [True, False]]
+    param_names = ["freq", "is_offset"]
+
+    def setup(self, freq, is_offset):
+        if is_offset:
+            self.freq = to_offset(freq)
+        else:
+            self.freq = freq
+
+    def time_period_constructor(self, freq, is_offset):
+        Period("2012-06-01", freq=freq)