def test_nanosecond_timestamp(self):
# GH 7610
expected = 1_293_840_000_000_000_005
t = Timestamp("2011-01-01") + offsets.Nano(5)
assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')"
assert t.value == expected
assert t.nanosecond == 5
t = Timestamp(t)
assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')"
assert t.value == expected
assert t.nanosecond == 5
t = Timestamp(np_datetime64_compat("2011-01-01 00:00:00.000000005Z"))
assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')"
assert t.value == expected
assert t.nanosecond == 5
expected = 1_293_840_000_000_000_010
t = t + offsets.Nano(5)
assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')"
assert t.value == expected
assert t.nanosecond == 10
t = Timestamp(t)
assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')"
assert t.value == expected
assert t.nanosecond == 10
t = Timestamp(np_datetime64_compat("2011-01-01 00:00:00.000000010Z"))
assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')"
assert t.value == expected
assert t.nanosecond == 10
def test_conversion(self):
rs = self.dtc.convert(["2012-1-1"], None, None)[0]
xp = dates.date2num(datetime(2012, 1, 1))
assert rs == xp
rs = self.dtc.convert("2012-1-1", None, None)
assert rs == xp
rs = self.dtc.convert(date(2012, 1, 1), None, None)
assert rs == xp
rs = self.dtc.convert("2012-1-1", None, None)
assert rs == xp
rs = self.dtc.convert(Timestamp("2012-1-1"), None, None)
assert rs == xp
# also testing datetime64 dtype (GH8614)
rs = self.dtc.convert(np_datetime64_compat("2012-01-01"), None, None)
assert rs == xp
rs = self.dtc.convert(np_datetime64_compat("2012-01-01 00:00:00+0000"),
None, None)
assert rs == xp
rs = self.dtc.convert(
np.array([
np_datetime64_compat("2012-01-01 00:00:00+0000"),
np_datetime64_compat("2012-01-02 00:00:00+0000"),
]),
None,
None,
)
assert rs[0] == xp
# we have a tz-aware date (constructed to that when we turn to utc it
# is the same as our sample)
ts = Timestamp("2012-01-01").tz_localize("UTC").tz_convert(
"US/Eastern")
rs = self.dtc.convert(ts, None, None)
assert rs == xp
rs = self.dtc.convert(ts.to_pydatetime(), None, None)
assert rs == xp
rs = self.dtc.convert(Index([ts - Day(1), ts]), None, None)
assert rs[1] == xp
rs = self.dtc.convert(
Index([ts - Day(1), ts]).to_pydatetime(), None, None)
assert rs[1] == xp
def setup_method(self, method):
self.d = datetime(2008, 1, 1)
self.nd = np_datetime64_compat("2008-01-01 00:00:00Z")
self.offset = CDay()
self.offset1 = self.offset
self.offset2 = CDay(2)
def test_rollforward(self, offset_types):
expecteds = self.expecteds.copy()
# result will not be changed if the target is on the offset
no_changes = [
"Day",
"MonthBegin",
"SemiMonthBegin",
"YearBegin",
"Week",
"Hour",
"Minute",
"Second",
"Milli",
"Micro",
"Nano",
"DateOffset",
]
for n in no_changes:
expecteds[n] = Timestamp("2011/01/01 09:00")
expecteds["BusinessHour"] = Timestamp("2011-01-03 09:00:00")
expecteds["CustomBusinessHour"] = Timestamp("2011-01-03 09:00:00")
# but be changed when normalize=True
norm_expected = expecteds.copy()
for k in norm_expected:
norm_expected[k] = Timestamp(norm_expected[k].date())
normalized = {
"Day": Timestamp("2011-01-02 00:00:00"),
"DateOffset": Timestamp("2011-01-02 00:00:00"),
"MonthBegin": Timestamp("2011-02-01 00:00:00"),
"SemiMonthBegin": Timestamp("2011-01-15 00:00:00"),
"YearBegin": Timestamp("2012-01-01 00:00:00"),
"Week": Timestamp("2011-01-08 00:00:00"),
"Hour": Timestamp("2011-01-01 00:00:00"),
"Minute": Timestamp("2011-01-01 00:00:00"),
"Second": Timestamp("2011-01-01 00:00:00"),
"Milli": Timestamp("2011-01-01 00:00:00"),
"Micro": Timestamp("2011-01-01 00:00:00"),
}
norm_expected.update(normalized)
sdt = datetime(2011, 1, 1, 9, 0)
ndt = np_datetime64_compat("2011-01-01 09:00Z")
for dt in [sdt, ndt]:
expected = expecteds[offset_types.__name__]
self._check_offsetfunc_works(offset_types, "rollforward", dt,
expected)
expected = norm_expected[offset_types.__name__]
self._check_offsetfunc_works(offset_types,
"rollforward",
dt,
expected,
normalize=True)
def test_apply(self, offset_types):
sdt = datetime(2011, 1, 1, 9, 0)
ndt = np_datetime64_compat("2011-01-01 09:00Z")
for dt in [sdt, ndt]:
expected = self.expecteds[offset_types.__name__]
self._check_offsetfunc_works(offset_types, "apply", dt, expected)
expected = Timestamp(expected.date())
self._check_offsetfunc_works(
offset_types, "apply", dt, expected, normalize=True
)
def test_asof_nanosecond_index_access(self):
ts = Timestamp("20130101").value
dti = DatetimeIndex([ts + 50 + i for i in range(100)])
ser = Series(np.random.randn(100), index=dti)
first_value = ser.asof(ser.index[0])
# this used to not work bc parsing was done by dateutil that didn't
# handle nanoseconds
assert first_value == ser["2013-01-01 00:00:00.000000050+0000"]
expected_ts = np_datetime64_compat(
"2013-01-01 00:00:00.000000050+0000", "ns")
assert first_value == ser[Timestamp(expected_ts)]
def test_conversion(self):
rs = self.pc.convert(["2012-1-1"], None, self.axis)[0]
xp = Period("2012-1-1").ordinal
assert rs == xp
rs = self.pc.convert("2012-1-1", None, self.axis)
assert rs == xp
rs = self.pc.convert([date(2012, 1, 1)], None, self.axis)[0]
assert rs == xp
rs = self.pc.convert(date(2012, 1, 1), None, self.axis)
assert rs == xp
rs = self.pc.convert([Timestamp("2012-1-1")], None, self.axis)[0]
assert rs == xp
rs = self.pc.convert(Timestamp("2012-1-1"), None, self.axis)
assert rs == xp
rs = self.pc.convert(np_datetime64_compat("2012-01-01"), None,
self.axis)
assert rs == xp
rs = self.pc.convert(np_datetime64_compat("2012-01-01 00:00:00+0000"),
None, self.axis)
assert rs == xp
rs = self.pc.convert(
np.array([
np_datetime64_compat("2012-01-01 00:00:00+0000"),
np_datetime64_compat("2012-01-02 00:00:00+0000"),
]),
None,
self.axis,
)
assert rs[0] == xp
class TestCommon(Base):
# executed value created by Base._get_offset
# are applied to 2011/01/01 09:00 (Saturday)
# used for .apply and .rollforward
expecteds = {
"Day": Timestamp("2011-01-02 09:00:00"),
"DateOffset": Timestamp("2011-01-02 09:00:00"),
"BusinessDay": Timestamp("2011-01-03 09:00:00"),
"CustomBusinessDay": Timestamp("2011-01-03 09:00:00"),
"CustomBusinessMonthEnd": Timestamp("2011-01-31 09:00:00"),
"CustomBusinessMonthBegin": Timestamp("2011-01-03 09:00:00"),
"MonthBegin": Timestamp("2011-02-01 09:00:00"),
"BusinessMonthBegin": Timestamp("2011-01-03 09:00:00"),
"MonthEnd": Timestamp("2011-01-31 09:00:00"),
"SemiMonthEnd": Timestamp("2011-01-15 09:00:00"),
"SemiMonthBegin": Timestamp("2011-01-15 09:00:00"),
"BusinessMonthEnd": Timestamp("2011-01-31 09:00:00"),
"YearBegin": Timestamp("2012-01-01 09:00:00"),
"BYearBegin": Timestamp("2011-01-03 09:00:00"),
"YearEnd": Timestamp("2011-12-31 09:00:00"),
"BYearEnd": Timestamp("2011-12-30 09:00:00"),
"QuarterBegin": Timestamp("2011-03-01 09:00:00"),
"BQuarterBegin": Timestamp("2011-03-01 09:00:00"),
"QuarterEnd": Timestamp("2011-03-31 09:00:00"),
"BQuarterEnd": Timestamp("2011-03-31 09:00:00"),
"BusinessHour": Timestamp("2011-01-03 10:00:00"),
"CustomBusinessHour": Timestamp("2011-01-03 10:00:00"),
"WeekOfMonth": Timestamp("2011-01-08 09:00:00"),
"LastWeekOfMonth": Timestamp("2011-01-29 09:00:00"),
"FY5253Quarter": Timestamp("2011-01-25 09:00:00"),
"FY5253": Timestamp("2011-01-25 09:00:00"),
"Week": Timestamp("2011-01-08 09:00:00"),
"Easter": Timestamp("2011-04-24 09:00:00"),
"Hour": Timestamp("2011-01-01 10:00:00"),
"Minute": Timestamp("2011-01-01 09:01:00"),
"Second": Timestamp("2011-01-01 09:00:01"),
"Milli": Timestamp("2011-01-01 09:00:00.001000"),
"Micro": Timestamp("2011-01-01 09:00:00.000001"),
"Nano":
Timestamp(np_datetime64_compat("2011-01-01T09:00:00.000000001Z")),
}
def test_immutable(self, offset_types):
# GH#21341 check that __setattr__ raises
offset = self._get_offset(offset_types)
msg = "objects is not writable|DateOffset objects are immutable"
with pytest.raises(AttributeError, match=msg):
offset.normalize = True
with pytest.raises(AttributeError, match=msg):
offset.n = 91
def test_return_type(self, offset_types):
offset = self._get_offset(offset_types)
# make sure that we are returning a Timestamp
result = Timestamp("20080101") + offset
assert isinstance(result, Timestamp)
# make sure that we are returning NaT
assert NaT + offset is NaT
assert offset + NaT is NaT
assert NaT - offset is NaT
assert (-offset)._apply(NaT) is NaT
def test_offset_n(self, offset_types):
offset = self._get_offset(offset_types)
assert offset.n == 1
neg_offset = offset * -1
assert neg_offset.n == -1
mul_offset = offset * 3
assert mul_offset.n == 3
def test_offset_timedelta64_arg(self, offset_types):
# check that offset._validate_n raises TypeError on a timedelt64
# object
off = self._get_offset(offset_types)
td64 = np.timedelta64(4567, "s")
with pytest.raises(TypeError, match="argument must be an integer"):
type(off)(n=td64, **off.kwds)
def test_offset_mul_ndarray(self, offset_types):
off = self._get_offset(offset_types)
expected = np.array([[off, off * 2], [off * 3, off * 4]])
result = np.array([[1, 2], [3, 4]]) * off
tm.assert_numpy_array_equal(result, expected)
result = off * np.array([[1, 2], [3, 4]])
tm.assert_numpy_array_equal(result, expected)
def test_offset_freqstr(self, offset_types):
offset = self._get_offset(offset_types)
freqstr = offset.freqstr
if freqstr not in ("<Easter>", "<DateOffset: days=1>", "LWOM-SAT"):
code = _get_offset(freqstr)
assert offset.rule_code == code
def _check_offsetfunc_works(self,
offset,
funcname,
dt,
expected,
normalize=False):
if normalize and issubclass(offset, Tick):
# normalize=True disallowed for Tick subclasses GH#21427
return
offset_s = self._get_offset(offset, normalize=normalize)
func = getattr(offset_s, funcname)
result = func(dt)
assert isinstance(result, Timestamp)
assert result == expected
result = func(Timestamp(dt))
assert isinstance(result, Timestamp)
assert result == expected
# see gh-14101
exp_warning = None
ts = Timestamp(dt) + Nano(5)
if (type(offset_s).__name__ == "DateOffset"
and (funcname in ["apply", "_apply"] or normalize)
and ts.nanosecond > 0):
exp_warning = UserWarning
# test nanosecond is preserved
with tm.assert_produces_warning(exp_warning):
result = func(ts)
if exp_warning is None and funcname == "_apply":
# GH#44522
# Check in this particular case to avoid headaches with
# testing for multiple warnings produced by the same call.
with tm.assert_produces_warning(FutureWarning,
match="apply is deprecated"):
res2 = offset_s.apply(ts)
assert type(res2) is type(result)
assert res2 == result
assert isinstance(result, Timestamp)
if normalize is False:
assert result == expected + Nano(5)
else:
assert result == expected
if isinstance(dt, np.datetime64):
# test tz when input is datetime or Timestamp
return
for tz in self.timezones:
expected_localize = expected.tz_localize(tz)
tz_obj = timezones.maybe_get_tz(tz)
dt_tz = conversion.localize_pydatetime(dt, tz_obj)
result = func(dt_tz)
assert isinstance(result, Timestamp)
assert result == expected_localize
result = func(Timestamp(dt, tz=tz))
assert isinstance(result, Timestamp)
assert result == expected_localize
# see gh-14101
exp_warning = None
ts = Timestamp(dt, tz=tz) + Nano(5)
if (type(offset_s).__name__ == "DateOffset"
and (funcname in ["apply", "_apply"] or normalize)
and ts.nanosecond > 0):
exp_warning = UserWarning
# test nanosecond is preserved
with tm.assert_produces_warning(exp_warning):
result = func(ts)
assert isinstance(result, Timestamp)
if normalize is False:
assert result == expected_localize + Nano(5)
else:
assert result == expected_localize
def test_apply(self, offset_types):
sdt = datetime(2011, 1, 1, 9, 0)
ndt = np_datetime64_compat("2011-01-01 09:00Z")
expected = self.expecteds[offset_types.__name__]
expected_norm = Timestamp(expected.date())
for dt in [sdt, ndt]:
self._check_offsetfunc_works(offset_types, "_apply", dt, expected)
self._check_offsetfunc_works(offset_types,
"_apply",
dt,
expected_norm,
normalize=True)
def test_rollforward(self, offset_types):
expecteds = self.expecteds.copy()
# result will not be changed if the target is on the offset
no_changes = [
"Day",
"MonthBegin",
"SemiMonthBegin",
"YearBegin",
"Week",
"Hour",
"Minute",
"Second",
"Milli",
"Micro",
"Nano",
"DateOffset",
]
for n in no_changes:
expecteds[n] = Timestamp("2011/01/01 09:00")
expecteds["BusinessHour"] = Timestamp("2011-01-03 09:00:00")
expecteds["CustomBusinessHour"] = Timestamp("2011-01-03 09:00:00")
# but be changed when normalize=True
norm_expected = expecteds.copy()
for k in norm_expected:
norm_expected[k] = Timestamp(norm_expected[k].date())
normalized = {
"Day": Timestamp("2011-01-02 00:00:00"),
"DateOffset": Timestamp("2011-01-02 00:00:00"),
"MonthBegin": Timestamp("2011-02-01 00:00:00"),
"SemiMonthBegin": Timestamp("2011-01-15 00:00:00"),
"YearBegin": Timestamp("2012-01-01 00:00:00"),
"Week": Timestamp("2011-01-08 00:00:00"),
"Hour": Timestamp("2011-01-01 00:00:00"),
"Minute": Timestamp("2011-01-01 00:00:00"),
"Second": Timestamp("2011-01-01 00:00:00"),
"Milli": Timestamp("2011-01-01 00:00:00"),
"Micro": Timestamp("2011-01-01 00:00:00"),
}
norm_expected.update(normalized)
sdt = datetime(2011, 1, 1, 9, 0)
ndt = np_datetime64_compat("2011-01-01 09:00Z")
for dt in [sdt, ndt]:
expected = expecteds[offset_types.__name__]
self._check_offsetfunc_works(offset_types, "rollforward", dt,
expected)
expected = norm_expected[offset_types.__name__]
self._check_offsetfunc_works(offset_types,
"rollforward",
dt,
expected,
normalize=True)
def test_rollback(self, offset_types):
expecteds = {
"BusinessDay": Timestamp("2010-12-31 09:00:00"),
"CustomBusinessDay": Timestamp("2010-12-31 09:00:00"),
"CustomBusinessMonthEnd": Timestamp("2010-12-31 09:00:00"),
"CustomBusinessMonthBegin": Timestamp("2010-12-01 09:00:00"),
"BusinessMonthBegin": Timestamp("2010-12-01 09:00:00"),
"MonthEnd": Timestamp("2010-12-31 09:00:00"),
"SemiMonthEnd": Timestamp("2010-12-31 09:00:00"),
"BusinessMonthEnd": Timestamp("2010-12-31 09:00:00"),
"BYearBegin": Timestamp("2010-01-01 09:00:00"),
"YearEnd": Timestamp("2010-12-31 09:00:00"),
"BYearEnd": Timestamp("2010-12-31 09:00:00"),
"QuarterBegin": Timestamp("2010-12-01 09:00:00"),
"BQuarterBegin": Timestamp("2010-12-01 09:00:00"),
"QuarterEnd": Timestamp("2010-12-31 09:00:00"),
"BQuarterEnd": Timestamp("2010-12-31 09:00:00"),
"BusinessHour": Timestamp("2010-12-31 17:00:00"),
"CustomBusinessHour": Timestamp("2010-12-31 17:00:00"),
"WeekOfMonth": Timestamp("2010-12-11 09:00:00"),
"LastWeekOfMonth": Timestamp("2010-12-25 09:00:00"),
"FY5253Quarter": Timestamp("2010-10-26 09:00:00"),
"FY5253": Timestamp("2010-01-26 09:00:00"),
"Easter": Timestamp("2010-04-04 09:00:00"),
}
# result will not be changed if the target is on the offset
for n in [
"Day",
"MonthBegin",
"SemiMonthBegin",
"YearBegin",
"Week",
"Hour",
"Minute",
"Second",
"Milli",
"Micro",
"Nano",
"DateOffset",
]:
expecteds[n] = Timestamp("2011/01/01 09:00")
# but be changed when normalize=True
norm_expected = expecteds.copy()
for k in norm_expected:
norm_expected[k] = Timestamp(norm_expected[k].date())
normalized = {
"Day": Timestamp("2010-12-31 00:00:00"),
"DateOffset": Timestamp("2010-12-31 00:00:00"),
"MonthBegin": Timestamp("2010-12-01 00:00:00"),
"SemiMonthBegin": Timestamp("2010-12-15 00:00:00"),
"YearBegin": Timestamp("2010-01-01 00:00:00"),
"Week": Timestamp("2010-12-25 00:00:00"),
"Hour": Timestamp("2011-01-01 00:00:00"),
"Minute": Timestamp("2011-01-01 00:00:00"),
"Second": Timestamp("2011-01-01 00:00:00"),
"Milli": Timestamp("2011-01-01 00:00:00"),
"Micro": Timestamp("2011-01-01 00:00:00"),
}
norm_expected.update(normalized)
sdt = datetime(2011, 1, 1, 9, 0)
ndt = np_datetime64_compat("2011-01-01 09:00Z")
for dt in [sdt, ndt]:
expected = expecteds[offset_types.__name__]
self._check_offsetfunc_works(offset_types, "rollback", dt,
expected)
expected = norm_expected[offset_types.__name__]
self._check_offsetfunc_works(offset_types,
"rollback",
dt,
expected,
normalize=True)
def test_is_on_offset(self, offset_types):
dt = self.expecteds[offset_types.__name__]
offset_s = self._get_offset(offset_types)
assert offset_s.is_on_offset(dt)
# when normalize=True, is_on_offset checks time is 00:00:00
if issubclass(offset_types, Tick):
# normalize=True disallowed for Tick subclasses GH#21427
return
offset_n = self._get_offset(offset_types, normalize=True)
assert not offset_n.is_on_offset(dt)
if offset_types in (BusinessHour, CustomBusinessHour):
# In default BusinessHour (9:00-17:00), normalized time
# cannot be in business hour range
return
date = datetime(dt.year, dt.month, dt.day)
assert offset_n.is_on_offset(date)
def test_add(self, offset_types, tz_naive_fixture):
tz = tz_naive_fixture
dt = datetime(2011, 1, 1, 9, 0)
offset_s = self._get_offset(offset_types)
expected = self.expecteds[offset_types.__name__]
result_dt = dt + offset_s
result_ts = Timestamp(dt) + offset_s
for result in [result_dt, result_ts]:
assert isinstance(result, Timestamp)
assert result == expected
expected_localize = expected.tz_localize(tz)
result = Timestamp(dt, tz=tz) + offset_s
assert isinstance(result, Timestamp)
assert result == expected_localize
# normalize=True, disallowed for Tick subclasses GH#21427
if issubclass(offset_types, Tick):
return
offset_s = self._get_offset(offset_types, normalize=True)
expected = Timestamp(expected.date())
result_dt = dt + offset_s
result_ts = Timestamp(dt) + offset_s
for result in [result_dt, result_ts]:
assert isinstance(result, Timestamp)
assert result == expected
expected_localize = expected.tz_localize(tz)
result = Timestamp(dt, tz=tz) + offset_s
assert isinstance(result, Timestamp)
assert result == expected_localize
def test_add_empty_datetimeindex(self, offset_types, tz_naive_fixture):
# GH#12724, GH#30336
offset_s = self._get_offset(offset_types)
dti = DatetimeIndex([], tz=tz_naive_fixture)
warn = None
if isinstance(
offset_s,
(
Easter,
WeekOfMonth,
LastWeekOfMonth,
CustomBusinessDay,
BusinessHour,
CustomBusinessHour,
CustomBusinessMonthBegin,
CustomBusinessMonthEnd,
FY5253,
FY5253Quarter,
),
):
# We don't have an optimized apply_index
warn = PerformanceWarning
with tm.assert_produces_warning(warn):
result = dti + offset_s
tm.assert_index_equal(result, dti)
with tm.assert_produces_warning(warn):
result = offset_s + dti
tm.assert_index_equal(result, dti)
dta = dti._data
with tm.assert_produces_warning(warn):
result = dta + offset_s
tm.assert_equal(result, dta)
with tm.assert_produces_warning(warn):
result = offset_s + dta
tm.assert_equal(result, dta)
def test_pickle_roundtrip(self, offset_types):
off = self._get_offset(offset_types)
res = tm.round_trip_pickle(off)
assert off == res
if type(off) is not DateOffset:
for attr in off._attributes:
if attr == "calendar":
# np.busdaycalendar __eq__ will return False;
# we check holidays and weekmask attrs so are OK
continue
# Make sure nothings got lost from _params (which __eq__) is based on
assert getattr(off, attr) == getattr(res, attr)
def test_pickle_dateoffset_odd_inputs(self):
# GH#34511
off = DateOffset(months=12)
res = tm.round_trip_pickle(off)
assert off == res
base_dt = datetime(2020, 1, 1)
assert base_dt + off == base_dt + res
def test_onOffset_deprecated(self, offset_types, fixed_now_ts):
# GH#30340 use idiomatic naming
off = self._get_offset(offset_types)
ts = fixed_now_ts
with tm.assert_produces_warning(FutureWarning):
result = off.onOffset(ts)
expected = off.is_on_offset(ts)
assert result == expected
def test_isAnchored_deprecated(self, offset_types):
# GH#30340 use idiomatic naming
off = self._get_offset(offset_types)
with tm.assert_produces_warning(FutureWarning):
result = off.isAnchored()
expected = off.is_anchored()
assert result == expected
def test_offsets_hashable(self, offset_types):
# GH: 37267
off = self._get_offset(offset_types)
assert hash(off) is not None
def test_rollback(self, offset_types):
expecteds = {
"BusinessDay": Timestamp("2010-12-31 09:00:00"),
"CustomBusinessDay": Timestamp("2010-12-31 09:00:00"),
"CustomBusinessMonthEnd": Timestamp("2010-12-31 09:00:00"),
"CustomBusinessMonthBegin": Timestamp("2010-12-01 09:00:00"),
"BusinessMonthBegin": Timestamp("2010-12-01 09:00:00"),
"MonthEnd": Timestamp("2010-12-31 09:00:00"),
"SemiMonthEnd": Timestamp("2010-12-31 09:00:00"),
"BusinessMonthEnd": Timestamp("2010-12-31 09:00:00"),
"BYearBegin": Timestamp("2010-01-01 09:00:00"),
"YearEnd": Timestamp("2010-12-31 09:00:00"),
"BYearEnd": Timestamp("2010-12-31 09:00:00"),
"QuarterBegin": Timestamp("2010-12-01 09:00:00"),
"BQuarterBegin": Timestamp("2010-12-01 09:00:00"),
"QuarterEnd": Timestamp("2010-12-31 09:00:00"),
"BQuarterEnd": Timestamp("2010-12-31 09:00:00"),
"BusinessHour": Timestamp("2010-12-31 17:00:00"),
"CustomBusinessHour": Timestamp("2010-12-31 17:00:00"),
"WeekOfMonth": Timestamp("2010-12-11 09:00:00"),
"LastWeekOfMonth": Timestamp("2010-12-25 09:00:00"),
"FY5253Quarter": Timestamp("2010-10-26 09:00:00"),
"FY5253": Timestamp("2010-01-26 09:00:00"),
"Easter": Timestamp("2010-04-04 09:00:00"),
}
# result will not be changed if the target is on the offset
for n in [
"Day",
"MonthBegin",
"SemiMonthBegin",
"YearBegin",
"Week",
"Hour",
"Minute",
"Second",
"Milli",
"Micro",
"Nano",
"DateOffset",
]:
expecteds[n] = Timestamp("2011/01/01 09:00")
# but be changed when normalize=True
norm_expected = expecteds.copy()
for k in norm_expected:
norm_expected[k] = Timestamp(norm_expected[k].date())
normalized = {
"Day": Timestamp("2010-12-31 00:00:00"),
"DateOffset": Timestamp("2010-12-31 00:00:00"),
"MonthBegin": Timestamp("2010-12-01 00:00:00"),
"SemiMonthBegin": Timestamp("2010-12-15 00:00:00"),
"YearBegin": Timestamp("2010-01-01 00:00:00"),
"Week": Timestamp("2010-12-25 00:00:00"),
"Hour": Timestamp("2011-01-01 00:00:00"),
"Minute": Timestamp("2011-01-01 00:00:00"),
"Second": Timestamp("2011-01-01 00:00:00"),
"Milli": Timestamp("2011-01-01 00:00:00"),
"Micro": Timestamp("2011-01-01 00:00:00"),
}
norm_expected.update(normalized)
sdt = datetime(2011, 1, 1, 9, 0)
ndt = np_datetime64_compat("2011-01-01 09:00Z")
for dt in [sdt, ndt]:
expected = expecteds[offset_types.__name__]
self._check_offsetfunc_works(offset_types, "rollback", dt,
expected)
expected = norm_expected[offset_types.__name__]
self._check_offsetfunc_works(offset_types,
"rollback",
dt,
expected,
normalize=True)
class TestDtypeEnforced:
# check we don't silently ignore the dtype keyword
@pytest.mark.parametrize("dtype",
[object, "float64", "uint64", "category"])
def test_constructor_range_values_mismatched_dtype(self, dtype):
rng = Index(range(5))
result = Index(rng, dtype=dtype)
assert result.dtype == dtype
result = Index(range(5), dtype=dtype)
assert result.dtype == dtype
@pytest.mark.parametrize("dtype",
[object, "float64", "uint64", "category"])
def test_constructor_categorical_values_mismatched_non_ea_dtype(
self, dtype):
cat = Categorical([1, 2, 3])
result = Index(cat, dtype=dtype)
assert result.dtype == dtype
def test_constructor_categorical_values_mismatched_dtype(self):
dti = date_range("2016-01-01", periods=3)
cat = Categorical(dti)
result = Index(cat, dti.dtype)
tm.assert_index_equal(result, dti)
dti2 = dti.tz_localize("Asia/Tokyo")
cat2 = Categorical(dti2)
result = Index(cat2, dti2.dtype)
tm.assert_index_equal(result, dti2)
ii = IntervalIndex.from_breaks(range(5))
cat3 = Categorical(ii)
result = Index(cat3, dtype=ii.dtype)
tm.assert_index_equal(result, ii)
def test_constructor_ea_values_mismatched_categorical_dtype(self):
dti = date_range("2016-01-01", periods=3)
result = Index(dti, dtype="category")
expected = CategoricalIndex(dti)
tm.assert_index_equal(result, expected)
dti2 = date_range("2016-01-01", periods=3, tz="US/Pacific")
result = Index(dti2, dtype="category")
expected = CategoricalIndex(dti2)
tm.assert_index_equal(result, expected)
def test_constructor_period_values_mismatched_dtype(self):
pi = period_range("2016-01-01", periods=3, freq="D")
result = Index(pi, dtype="category")
expected = CategoricalIndex(pi)
tm.assert_index_equal(result, expected)
def test_constructor_timedelta64_values_mismatched_dtype(self):
# check we don't silently ignore the dtype keyword
tdi = timedelta_range("4 Days", periods=5)
result = Index(tdi, dtype="category")
expected = CategoricalIndex(tdi)
tm.assert_index_equal(result, expected)
def test_constructor_interval_values_mismatched_dtype(self):
dti = date_range("2016-01-01", periods=3)
ii = IntervalIndex.from_breaks(dti)
result = Index(ii, dtype="category")
expected = CategoricalIndex(ii)
tm.assert_index_equal(result, expected)
def test_constructor_datetime64_values_mismatched_period_dtype(self):
dti = date_range("2016-01-01", periods=3)
result = Index(dti, dtype="Period[D]")
expected = dti.to_period("D")
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("dtype", ["int64", "uint64"])
def test_constructor_int_dtype_nan_raises(self, dtype):
# see GH#15187
data = [np.nan]
msg = "cannot convert"
with pytest.raises(ValueError, match=msg):
Index(data, dtype=dtype)
@pytest.mark.parametrize(
"vals",
[
[1, 2, 3],
np.array([1, 2, 3]),
np.array([1, 2, 3], dtype=int),
# below should coerce
[1.0, 2.0, 3.0],
np.array([1.0, 2.0, 3.0], dtype=float),
],
)
def test_constructor_dtypes_to_int64(self, vals):
index = Index(vals, dtype=int)
assert isinstance(index, Int64Index)
@pytest.mark.parametrize(
"vals",
[
[1, 2, 3],
[1.0, 2.0, 3.0],
np.array([1.0, 2.0, 3.0]),
np.array([1, 2, 3], dtype=int),
np.array([1.0, 2.0, 3.0], dtype=float),
],
)
def test_constructor_dtypes_to_float64(self, vals):
index = Index(vals, dtype=float)
assert isinstance(index, Float64Index)
@pytest.mark.parametrize(
"vals",
[
[1, 2, 3],
np.array([1, 2, 3], dtype=int),
np.array([
np_datetime64_compat("2011-01-01"),
np_datetime64_compat("2011-01-02")
]),
[datetime(2011, 1, 1), datetime(2011, 1, 2)],
],
)
def test_constructor_dtypes_to_categorical(self, vals):
index = Index(vals, dtype="category")
assert isinstance(index, CategoricalIndex)
@pytest.mark.parametrize("cast_index", [True, False])
@pytest.mark.parametrize(
"vals",
[
Index(
np.array([
np_datetime64_compat("2011-01-01"),
np_datetime64_compat("2011-01-02"),
])),
Index([datetime(2011, 1, 1),
datetime(2011, 1, 2)]),
],
)
def test_constructor_dtypes_to_datetime(self, cast_index, vals):
if cast_index:
index = Index(vals, dtype=object)
assert isinstance(index, Index)
assert index.dtype == object
else:
index = Index(vals)
assert isinstance(index, DatetimeIndex)
@pytest.mark.parametrize("cast_index", [True, False])
@pytest.mark.parametrize(
"vals",
[
np.array([np.timedelta64(1, "D"),
np.timedelta64(1, "D")]),
[timedelta(1), timedelta(1)],
],
)
def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
if cast_index:
index = Index(vals, dtype=object)
assert isinstance(index, Index)
assert index.dtype == object
else:
index = Index(vals)
assert isinstance(index, TimedeltaIndex)
