def test_get_offset_name(self): assert BDay().freqstr == "B" assert BDay(2).freqstr == "2B" assert BMonthEnd().freqstr == "BM" assert Week(weekday=0).freqstr == "W-MON" assert Week(weekday=1).freqstr == "W-TUE" assert Week(weekday=2).freqstr == "W-WED" assert Week(weekday=3).freqstr == "W-THU" assert Week(weekday=4).freqstr == "W-FRI" assert LastWeekOfMonth(weekday=WeekDay.SUN).freqstr == "LWOM-SUN"
def test_last_week_of_month_on_offset(): # GH#19036, GH#18977 _adjust_dst was incorrect for LastWeekOfMonth offset = LastWeekOfMonth(n=4, weekday=6) ts = Timestamp("1917-05-27 20:55:27.084284178+0200", tz="Europe/Warsaw") slow = (ts + offset) - offset == ts fast = offset.is_on_offset(ts) assert fast == slow # negative n offset = LastWeekOfMonth(n=-4, weekday=5) ts = Timestamp("2005-08-27 05:01:42.799392561-0500", tz="America/Rainy_River") slow = (ts + offset) - offset == ts fast = offset.is_on_offset(ts) assert fast == slow
def create_data(): """ create the pickle/msgpack data """ data = { 'A': [0., 1., 2., 3., np.nan], 'B': [0, 1, 0, 1, 0], 'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'], 'D': date_range('1/1/2009', periods=5), 'E': [0., 1, Timestamp('20100101'), 'foo', 2.] } scalars = dict(timestamp=Timestamp('20130101'), period=Period('2012', 'M')) index = dict(int=Index(np.arange(10)), date=date_range('20130101', periods=10), period=period_range('2013-01-01', freq='M', periods=10), float=Index(np.arange(10, dtype=np.float64)), uint=Index(np.arange(10, dtype=np.uint64)), timedelta=timedelta_range('00:00:00', freq='30T', periods=10)) if _loose_version >= LooseVersion('0.18'): from pandas import RangeIndex index['range'] = RangeIndex(10) if _loose_version >= LooseVersion('0.21'): from pandas import interval_range index['interval'] = interval_range(0, periods=10) mi = dict(reg2=MultiIndex.from_tuples(tuple( zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])), names=['first', 'second'])) series = dict(float=Series(data['A']), int=Series(data['B']), mixed=Series(data['E']), ts=Series(np.arange(10).astype(np.int64), index=date_range('20130101', periods=10)), mi=Series(np.arange(5).astype(np.float64), index=MultiIndex.from_tuples(tuple( zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=['one', 'two'])), dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']), cat=Series(Categorical(['foo', 'bar', 'baz'])), dt=Series(date_range('20130101', periods=5)), dt_tz=Series( date_range('20130101', periods=5, tz='US/Eastern')), period=Series([Period('2000Q1')] * 5)) mixed_dup_df = DataFrame(data) mixed_dup_df.columns = list("ABCDA") frame = dict(float=DataFrame({ 'A': series['float'], 'B': series['float'] + 1 }), int=DataFrame({ 'A': series['int'], 'B': series['int'] + 1 }), mixed=DataFrame({k: data[k] for k in ['A', 'B', 'C', 'D']}), mi=DataFrame( { 'A': np.arange(5).astype(np.float64), 'B': np.arange(5).astype(np.int64) }, index=MultiIndex.from_tuples(tuple( zip(*[['bar', 'bar', 'baz', 'baz', 'baz'], ['one', 'two', 'one', 'two', 'three']])), names=['first', 'second'])), dup=DataFrame(np.arange(15).reshape(5, 3).astype(np.float64), columns=['A', 'B', 'A']), cat_onecol=DataFrame({'A': Categorical(['foo', 'bar'])}), cat_and_float=DataFrame({ 'A': Categorical(['foo', 'bar', 'baz']), 'B': np.arange(3).astype(np.int64) }), mixed_dup=mixed_dup_df, dt_mixed_tzs=DataFrame( { 'A': Timestamp('20130102', tz='US/Eastern'), 'B': Timestamp('20130603', tz='CET') }, index=range(5)), dt_mixed2_tzs=DataFrame( { 'A': Timestamp('20130102', tz='US/Eastern'), 'B': Timestamp('20130603', tz='CET'), 'C': Timestamp('20130603', tz='UTC') }, index=range(5))) cat = dict(int8=Categorical(list('abcdefg')), int16=Categorical(np.arange(1000)), int32=Categorical(np.arange(10000))) timestamp = dict(normal=Timestamp('2011-01-01'), nat=NaT, tz=Timestamp('2011-01-01', tz='US/Eastern')) if _loose_version < LooseVersion('0.19.2'): timestamp['freq'] = Timestamp('2011-01-01', offset='D') timestamp['both'] = Timestamp('2011-01-01', tz='Asia/Tokyo', offset='M') else: timestamp['freq'] = Timestamp('2011-01-01', freq='D') timestamp['both'] = Timestamp('2011-01-01', tz='Asia/Tokyo', freq='M') off = { 'DateOffset': DateOffset(years=1), 'DateOffset_h_ns': DateOffset(hour=6, nanoseconds=5824), 'BusinessDay': BusinessDay(offset=timedelta(seconds=9)), 'BusinessHour': BusinessHour(normalize=True, n=6, end='15:14'), 'CustomBusinessDay': CustomBusinessDay(weekmask='Mon Fri'), 'SemiMonthBegin': SemiMonthBegin(day_of_month=9), 'SemiMonthEnd': SemiMonthEnd(day_of_month=24), 'MonthBegin': MonthBegin(1), 'MonthEnd': MonthEnd(1), 'QuarterBegin': QuarterBegin(1), 'QuarterEnd': QuarterEnd(1), 'Day': Day(1), 'YearBegin': YearBegin(1), 'YearEnd': YearEnd(1), 'Week': Week(1), 'Week_Tues': Week(2, normalize=False, weekday=1), 'WeekOfMonth': WeekOfMonth(week=3, weekday=4), 'LastWeekOfMonth': LastWeekOfMonth(n=1, weekday=3), 'FY5253': FY5253(n=2, weekday=6, startingMonth=7, variation="last"), 'Easter': Easter(), 'Hour': Hour(1), 'Minute': Minute(1) } return dict(series=series, frame=frame, index=index, scalars=scalars, mi=mi, sp_series=dict(float=_create_sp_series(), ts=_create_sp_tsseries()), sp_frame=dict(float=_create_sp_frame()), cat=cat, timestamp=timestamp, offsets=off)
def create_data(): """ create the pickle/msgpack data """ data = { "A": [0.0, 1.0, 2.0, 3.0, np.nan], "B": [0, 1, 0, 1, 0], "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], "D": date_range("1/1/2009", periods=5), "E": [0.0, 1, Timestamp("20100101"), "foo", 2.0], } scalars = dict(timestamp=Timestamp("20130101"), period=Period("2012", "M")) index = dict( int=Index(np.arange(10)), date=date_range("20130101", periods=10), period=period_range("2013-01-01", freq="M", periods=10), float=Index(np.arange(10, dtype=np.float64)), uint=Index(np.arange(10, dtype=np.uint64)), timedelta=timedelta_range("00:00:00", freq="30T", periods=10), ) index["range"] = RangeIndex(10) if _loose_version >= LooseVersion("0.21"): from pandas import interval_range index["interval"] = interval_range(0, periods=10) mi = dict(reg2=MultiIndex.from_tuples( tuple( zip(*[ ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ["one", "two", "one", "two", "one", "two", "one", "two"], ])), names=["first", "second"], )) series = dict( float=Series(data["A"]), int=Series(data["B"]), mixed=Series(data["E"]), ts=Series(np.arange(10).astype(np.int64), index=date_range("20130101", periods=10)), mi=Series( np.arange(5).astype(np.float64), index=MultiIndex.from_tuples(tuple( zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=["one", "two"]), ), dup=Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]), cat=Series(Categorical(["foo", "bar", "baz"])), dt=Series(date_range("20130101", periods=5)), dt_tz=Series(date_range("20130101", periods=5, tz="US/Eastern")), period=Series([Period("2000Q1")] * 5), ) mixed_dup_df = DataFrame(data) mixed_dup_df.columns = list("ABCDA") frame = dict( float=DataFrame({ "A": series["float"], "B": series["float"] + 1 }), int=DataFrame({ "A": series["int"], "B": series["int"] + 1 }), mixed=DataFrame({k: data[k] for k in ["A", "B", "C", "D"]}), mi=DataFrame( { "A": np.arange(5).astype(np.float64), "B": np.arange(5).astype(np.int64) }, index=MultiIndex.from_tuples( tuple( zip(*[ ["bar", "bar", "baz", "baz", "baz"], ["one", "two", "one", "two", "three"], ])), names=["first", "second"], ), ), dup=DataFrame(np.arange(15).reshape(5, 3).astype(np.float64), columns=["A", "B", "A"]), cat_onecol=DataFrame({"A": Categorical(["foo", "bar"])}), cat_and_float=DataFrame({ "A": Categorical(["foo", "bar", "baz"]), "B": np.arange(3).astype(np.int64), }), mixed_dup=mixed_dup_df, dt_mixed_tzs=DataFrame( { "A": Timestamp("20130102", tz="US/Eastern"), "B": Timestamp("20130603", tz="CET"), }, index=range(5), ), dt_mixed2_tzs=DataFrame( { "A": Timestamp("20130102", tz="US/Eastern"), "B": Timestamp("20130603", tz="CET"), "C": Timestamp("20130603", tz="UTC"), }, index=range(5), ), ) cat = dict( int8=Categorical(list("abcdefg")), int16=Categorical(np.arange(1000)), int32=Categorical(np.arange(10000)), ) timestamp = dict( normal=Timestamp("2011-01-01"), nat=NaT, tz=Timestamp("2011-01-01", tz="US/Eastern"), ) timestamp["freq"] = Timestamp("2011-01-01", freq="D") timestamp["both"] = Timestamp("2011-01-01", tz="Asia/Tokyo", freq="M") off = { "DateOffset": DateOffset(years=1), "DateOffset_h_ns": DateOffset(hour=6, nanoseconds=5824), "BusinessDay": BusinessDay(offset=timedelta(seconds=9)), "BusinessHour": BusinessHour(normalize=True, n=6, end="15:14"), "CustomBusinessDay": CustomBusinessDay(weekmask="Mon Fri"), "SemiMonthBegin": SemiMonthBegin(day_of_month=9), "SemiMonthEnd": SemiMonthEnd(day_of_month=24), "MonthBegin": MonthBegin(1), "MonthEnd": MonthEnd(1), "QuarterBegin": QuarterBegin(1), "QuarterEnd": QuarterEnd(1), "Day": Day(1), "YearBegin": YearBegin(1), "YearEnd": YearEnd(1), "Week": Week(1), "Week_Tues": Week(2, normalize=False, weekday=1), "WeekOfMonth": WeekOfMonth(week=3, weekday=4), "LastWeekOfMonth": LastWeekOfMonth(n=1, weekday=3), "FY5253": FY5253(n=2, weekday=6, startingMonth=7, variation="last"), "Easter": Easter(), "Hour": Hour(1), "Minute": Minute(1), } return dict( series=series, frame=frame, index=index, scalars=scalars, mi=mi, sp_series=dict(float=_create_sp_series(), ts=_create_sp_tsseries()), sp_frame=dict(float=_create_sp_frame()), cat=cat, timestamp=timestamp, offsets=off, )
start_date=Timestamp('1983-01-01'), end_date=Timestamp('1997-06-01')) QueenBirthday2 = Holiday( name="Queen's Birthday", # 英女王生日 4月 month=4, day=21, observance=process_queen_birthday, start_date=Timestamp('1926-04-21'), end_date=Timestamp('1983-01-01')) CommemoratingAlliedVictory = Holiday( name="Commemorating the allied victory", # 重光纪念日 8月最后一个星期一 month=8, day=20, offset=LastWeekOfMonth(weekday=0), start_date=Timestamp('1945-08-30'), end_date=Timestamp('1997-07-01')) IDontKnow = Holiday( name="I dont know these days, please tell me", # 8月第一个星期一 month=7, day=31, offset=WeekOfMonth(week=0, weekday=0), start_date=Timestamp('1960-08-01'), end_date=Timestamp('1983-01-01')) HKClosedDay = [ # I dont know these days Timestamp('1970-07-01', tz='UTC'), Timestamp('1971-07-01', tz='UTC'),
def create_data(): """create the pickle data""" data = { "A": [0.0, 1.0, 2.0, 3.0, np.nan], "B": [0, 1, 0, 1, 0], "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], "D": date_range("1/1/2009", periods=5), "E": [0.0, 1, Timestamp("20100101"), "foo", 2.0], } scalars = { "timestamp": Timestamp("20130101"), "period": Period("2012", "M") } index = { "int": Index(np.arange(10)), "date": date_range("20130101", periods=10), "period": period_range("2013-01-01", freq="M", periods=10), "float": Index(np.arange(10, dtype=np.float64)), "uint": Index(np.arange(10, dtype=np.uint64)), "timedelta": timedelta_range("00:00:00", freq="30T", periods=10), } index["range"] = RangeIndex(10) index["interval"] = interval_range(0, periods=10) mi = { "reg2": MultiIndex.from_tuples( tuple( zip(*[ ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ["one", "two", "one", "two", "one", "two", "one", "two"], ])), names=["first", "second"], ) } series = { "float": Series(data["A"]), "int": Series(data["B"]), "mixed": Series(data["E"]), "ts": Series(np.arange(10).astype(np.int64), index=date_range("20130101", periods=10)), "mi": Series( np.arange(5).astype(np.float64), index=MultiIndex.from_tuples(tuple( zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=["one", "two"]), ), "dup": Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]), "cat": Series(Categorical(["foo", "bar", "baz"])), "dt": Series(date_range("20130101", periods=5)), "dt_tz": Series(date_range("20130101", periods=5, tz="US/Eastern")), "period": Series([Period("2000Q1")] * 5), } mixed_dup_df = DataFrame(data) mixed_dup_df.columns = list("ABCDA") frame = { "float": DataFrame({ "A": series["float"], "B": series["float"] + 1 }), "int": DataFrame({ "A": series["int"], "B": series["int"] + 1 }), "mixed": DataFrame({k: data[k] for k in ["A", "B", "C", "D"]}), "mi": DataFrame( { "A": np.arange(5).astype(np.float64), "B": np.arange(5).astype(np.int64) }, index=MultiIndex.from_tuples( tuple( zip(*[ ["bar", "bar", "baz", "baz", "baz"], ["one", "two", "one", "two", "three"], ])), names=["first", "second"], ), ), "dup": DataFrame(np.arange(15).reshape(5, 3).astype(np.float64), columns=["A", "B", "A"]), "cat_onecol": DataFrame({"A": Categorical(["foo", "bar"])}), "cat_and_float": DataFrame({ "A": Categorical(["foo", "bar", "baz"]), "B": np.arange(3).astype(np.int64), }), "mixed_dup": mixed_dup_df, "dt_mixed_tzs": DataFrame( { "A": Timestamp("20130102", tz="US/Eastern"), "B": Timestamp("20130603", tz="CET"), }, index=range(5), ), "dt_mixed2_tzs": DataFrame( { "A": Timestamp("20130102", tz="US/Eastern"), "B": Timestamp("20130603", tz="CET"), "C": Timestamp("20130603", tz="UTC"), }, index=range(5), ), } cat = { "int8": Categorical(list("abcdefg")), "int16": Categorical(np.arange(1000)), "int32": Categorical(np.arange(10000)), } timestamp = { "normal": Timestamp("2011-01-01"), "nat": NaT, "tz": Timestamp("2011-01-01", tz="US/Eastern"), } timestamp["freq"] = Timestamp("2011-01-01", freq="D") timestamp["both"] = Timestamp("2011-01-01", tz="Asia/Tokyo", freq="M") off = { "DateOffset": DateOffset(years=1), "DateOffset_h_ns": DateOffset(hour=6, nanoseconds=5824), "BusinessDay": BusinessDay(offset=timedelta(seconds=9)), "BusinessHour": BusinessHour(normalize=True, n=6, end="15:14"), "CustomBusinessDay": CustomBusinessDay(weekmask="Mon Fri"), "SemiMonthBegin": SemiMonthBegin(day_of_month=9), "SemiMonthEnd": SemiMonthEnd(day_of_month=24), "MonthBegin": MonthBegin(1), "MonthEnd": MonthEnd(1), "QuarterBegin": QuarterBegin(1), "QuarterEnd": QuarterEnd(1), "Day": Day(1), "YearBegin": YearBegin(1), "YearEnd": YearEnd(1), "Week": Week(1), "Week_Tues": Week(2, normalize=False, weekday=1), "WeekOfMonth": WeekOfMonth(week=3, weekday=4), "LastWeekOfMonth": LastWeekOfMonth(n=1, weekday=3), "FY5253": FY5253(n=2, weekday=6, startingMonth=7, variation="last"), "Easter": Easter(), "Hour": Hour(1), "Minute": Minute(1), } return { "series": series, "frame": frame, "index": index, "scalars": scalars, "mi": mi, "sp_series": { "float": _create_sp_series(), "ts": _create_sp_tsseries() }, "sp_frame": { "float": _create_sp_frame() }, "cat": cat, "timestamp": timestamp, "offsets": off, }