def test_construction_month(self):
expected = Period('2007-01', freq='M')
i1 = Period('200701', freq='M')
assert i1 == expected
i1 = Period('200701', freq='M')
assert i1 == expected
i1 = Period(200701, freq='M')
assert i1 == expected
i1 = Period(ordinal=200701, freq='M')
assert i1.year == 18695
i1 = Period(datetime(2007, 1, 1), freq='M')
i2 = Period('200701', freq='M')
assert i1 == i2
i1 = Period(date(2007, 1, 1), freq='M')
i2 = Period(datetime(2007, 1, 1), freq='M')
i3 = Period(np.datetime64('2007-01-01'), freq='M')
i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M')
i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M')
assert i1 == i2
assert i1 == i3
assert i1 == i4
assert i1 == i5
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
def test_conversion(self):
rs = self.dtc.convert(['2012-1-1'], None, None)[0]
xp = datetime(2012, 1, 1).toordinal()
self.assertEqual(rs, xp)
rs = self.dtc.convert('2012-1-1', None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(date(2012, 1, 1), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(datetime(2012, 1, 1).toordinal(), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert('2012-1-1', None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(Timestamp('2012-1-1'), None, None)
self.assertEqual(rs, xp)
# also testing datetime64 dtype (GH8614)
rs = self.dtc.convert(np_datetime64_compat('2012-01-01'), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(np_datetime64_compat(
'2012-01-01 00:00:00+0000'), None, None)
self.assertEqual(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)
self.assertEqual(rs[0], xp)
def test_construction_month(self):
expected = Period('2007-01', freq='M')
i1 = Period('200701', freq='M')
assert i1 == expected
i1 = Period('200701', freq='M')
assert i1 == expected
i1 = Period(200701, freq='M')
assert i1 == expected
i1 = Period(ordinal=200701, freq='M')
assert i1.year == 18695
i1 = Period(datetime(2007, 1, 1), freq='M')
i2 = Period('200701', freq='M')
assert i1 == i2
i1 = Period(date(2007, 1, 1), freq='M')
i2 = Period(datetime(2007, 1, 1), freq='M')
i3 = Period(np.datetime64('2007-01-01'), freq='M')
i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M')
i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M')
assert i1 == i2
assert i1 == i3
assert i1 == i4
assert i1 == i5
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
def test_nanosecond_timestamp(self):
# GH 7610
expected = 1293840000000000005
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 = 1293840000000000010
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 = datetime(2012, 1, 1).toordinal()
self.assertEqual(rs, xp)
rs = self.dtc.convert('2012-1-1', None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(date(2012, 1, 1), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(datetime(2012, 1, 1).toordinal(), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert('2012-1-1', None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(Timestamp('2012-1-1'), None, None)
self.assertEqual(rs, xp)
# also testing datetime64 dtype (GH8614)
rs = self.dtc.convert(np_datetime64_compat('2012-01-01'), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(np_datetime64_compat('2012-01-01 00:00:00+0000'),
None, None)
self.assertEqual(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)
self.assertEqual(rs[0], xp)
def test_nanosecond_timestamp(self):
# GH 7610
expected = 1293840000000000005
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 = 1293840000000000010
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_construction(self):
i1 = Period('1/1/2005', freq='M')
i2 = Period('Jan 2005')
assert i1 == i2
i1 = Period('2005', freq='A')
i2 = Period('2005')
i3 = Period('2005', freq='a')
assert i1 == i2
assert i1 == i3
i4 = Period('2005', freq='M')
i5 = Period('2005', freq='m')
pytest.raises(ValueError, i1.__ne__, i4)
assert i4 == i5
i1 = Period.now('Q')
i2 = Period(datetime.now(), freq='Q')
i3 = Period.now('q')
assert i1 == i2
assert i1 == i3
i1 = Period('1982', freq='min')
i2 = Period('1982', freq='MIN')
assert i1 == i2
i2 = Period('1982', freq=('Min', 1))
assert i1 == i2
i1 = Period(year=2005, month=3, day=1, freq='D')
i2 = Period('3/1/2005', freq='D')
assert i1 == i2
i3 = Period(year=2005, month=3, day=1, freq='d')
assert i1 == i3
i1 = Period('2007-01-01 09:00:00.001')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L')
assert i1 == expected
expected = Period(np_datetime64_compat(
'2007-01-01 09:00:00.001Z'), freq='L')
assert i1 == expected
i1 = Period('2007-01-01 09:00:00.00101')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U')
assert i1 == expected
expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'),
freq='U')
assert i1 == expected
pytest.raises(ValueError, Period, ordinal=200701)
pytest.raises(ValueError, Period, '2007-1-1', freq='X')
def test_construction(self):
i1 = Period('1/1/2005', freq='M')
i2 = Period('Jan 2005')
assert i1 == i2
i1 = Period('2005', freq='A')
i2 = Period('2005')
i3 = Period('2005', freq='a')
assert i1 == i2
assert i1 == i3
i4 = Period('2005', freq='M')
i5 = Period('2005', freq='m')
pytest.raises(ValueError, i1.__ne__, i4)
assert i4 == i5
i1 = Period.now('Q')
i2 = Period(datetime.now(), freq='Q')
i3 = Period.now('q')
assert i1 == i2
assert i1 == i3
i1 = Period('1982', freq='min')
i2 = Period('1982', freq='MIN')
assert i1 == i2
i2 = Period('1982', freq=('Min', 1))
assert i1 == i2
i1 = Period(year=2005, month=3, day=1, freq='D')
i2 = Period('3/1/2005', freq='D')
assert i1 == i2
i3 = Period(year=2005, month=3, day=1, freq='d')
assert i1 == i3
i1 = Period('2007-01-01 09:00:00.001')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L')
assert i1 == expected
expected = Period(np_datetime64_compat(
'2007-01-01 09:00:00.001Z'), freq='L')
assert i1 == expected
i1 = Period('2007-01-01 09:00:00.00101')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U')
assert i1 == expected
expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'),
freq='U')
assert i1 == expected
pytest.raises(ValueError, Period, ordinal=200701)
pytest.raises(ValueError, Period, '2007-1-1', freq='X')
def test_conversion(self):
rs = self.dtc.convert(["2012-1-1"], None, None)[0]
xp = datetime(2012, 1, 1).toordinal()
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(datetime(2012, 1, 1).toordinal(), 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 test_conversion(self):
rs = self.dtc.convert(['2012-1-1'], None, None)[0]
xp = datetime(2012, 1, 1).toordinal()
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(datetime(2012, 1, 1).toordinal(), 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 test_conversion(self):
rs = self.dtc.convert(['2012-1-1'], None, None)[0]
xp = datetime(2012, 1, 1).toordinal()
self.assertEqual(rs, xp)
rs = self.dtc.convert('2012-1-1', None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(date(2012, 1, 1), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(datetime(2012, 1, 1).toordinal(), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert('2012-1-1', None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(Timestamp('2012-1-1'), None, None)
self.assertEqual(rs, xp)
# also testing datetime64 dtype (GH8614)
rs = self.dtc.convert(np_datetime64_compat('2012-01-01'), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(np_datetime64_compat('2012-01-01 00:00:00+0000'),
None, None)
self.assertEqual(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)
self.assertEqual(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)
self.assertEqual(rs, xp)
rs = self.dtc.convert(ts.to_pydatetime(), None, None)
self.assertEqual(rs, xp)
rs = self.dtc.convert(Index([ts - Day(1), ts]), None, None)
self.assertEqual(rs[1], xp)
rs = self.dtc.convert(
Index([ts - Day(1), ts]).to_pydatetime(), None, None)
self.assertEqual(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_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
def test_period_constructor_offsets(self):
assert (Period('1/1/2005', freq=offsets.MonthEnd()) ==
Period('1/1/2005', freq='M'))
assert (Period('2005', freq=offsets.YearEnd()) ==
Period('2005', freq='A'))
assert (Period('2005', freq=offsets.MonthEnd()) ==
Period('2005', freq='M'))
assert (Period('3/10/12', freq=offsets.BusinessDay()) ==
Period('3/10/12', freq='B'))
assert (Period('3/10/12', freq=offsets.Day()) ==
Period('3/10/12', freq='D'))
assert (Period(year=2005, quarter=1,
freq=offsets.QuarterEnd(startingMonth=12)) ==
Period(year=2005, quarter=1, freq='Q'))
assert (Period(year=2005, quarter=2,
freq=offsets.QuarterEnd(startingMonth=12)) ==
Period(year=2005, quarter=2, freq='Q'))
assert (Period(year=2005, month=3, day=1, freq=offsets.Day()) ==
Period(year=2005, month=3, day=1, freq='D'))
assert (Period(year=2012, month=3, day=10, freq=offsets.BDay()) ==
Period(year=2012, month=3, day=10, freq='B'))
expected = Period('2005-03-01', freq='3D')
assert (Period(year=2005, month=3, day=1,
freq=offsets.Day(3)) == expected)
assert Period(year=2005, month=3, day=1, freq='3D') == expected
assert (Period(year=2012, month=3, day=10,
freq=offsets.BDay(3)) ==
Period(year=2012, month=3, day=10, freq='3B'))
assert (Period(200701, freq=offsets.MonthEnd()) ==
Period(200701, freq='M'))
i1 = Period(ordinal=200701, freq=offsets.MonthEnd())
i2 = Period(ordinal=200701, freq='M')
assert i1 == i2
assert i1.year == 18695
assert i2.year == 18695
i1 = Period(datetime(2007, 1, 1), freq='M')
i2 = Period('200701', freq='M')
assert i1 == i2
i1 = Period(date(2007, 1, 1), freq='M')
i2 = Period(datetime(2007, 1, 1), freq='M')
i3 = Period(np.datetime64('2007-01-01'), freq='M')
i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M')
i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M')
assert i1 == i2
assert i1 == i3
assert i1 == i4
assert i1 == i5
i1 = Period('2007-01-01 09:00:00.001')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L')
assert i1 == expected
expected = Period(np_datetime64_compat(
'2007-01-01 09:00:00.001Z'), freq='L')
assert i1 == expected
i1 = Period('2007-01-01 09:00:00.00101')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U')
assert i1 == expected
expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'),
freq='U')
assert i1 == expected
pytest.raises(ValueError, Period, ordinal=200701)
pytest.raises(ValueError, Period, '2007-1-1', freq='X')
def test_comparisons_nat(self):
fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
didx1 = pd.DatetimeIndex(['2014-01-01', pd.NaT, '2014-03-01', pd.NaT,
'2014-05-01', '2014-07-01'])
didx2 = pd.DatetimeIndex(['2014-02-01', '2014-03-01', pd.NaT, pd.NaT,
'2014-06-01', '2014-07-01'])
darr = np.array([np_datetime64_compat('2014-02-01 00:00Z'),
np_datetime64_compat('2014-03-01 00:00Z'),
np_datetime64_compat('nat'), np.datetime64('nat'),
np_datetime64_compat('2014-06-01 00:00Z'),
np_datetime64_compat('2014-07-01 00:00Z')])
if _np_version_under1p8:
# cannot test array because np.datetime('nat') returns today's date
cases = [(fidx1, fidx2), (didx1, didx2)]
else:
cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, idx2 in cases:
result = idx1 < idx2
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx2 > idx1
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= idx2
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx2 >= idx1
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == idx2
expected = np.array([False, False, False, False, False, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != idx2
expected = np.array([True, True, True, True, True, False])
tm.assert_numpy_array_equal(result, expected)
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
result = idx1 < val
expected = np.array([False, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, True, True, True, True])
tm.assert_numpy_array_equal(result, expected)
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
result = idx1 < val
expected = np.array([True, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
expected = np.array([False, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
expected = np.array([False, False, True, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
expected = np.array([False, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, False, True, True, True])
tm.assert_numpy_array_equal(result, expected)
def test_period_constructor_offsets(self):
assert Period("1/1/2005", freq=offsets.MonthEnd()) == Period(
"1/1/2005", freq="M"
)
assert Period("2005", freq=offsets.YearEnd()) == Period("2005", freq="A")
assert Period("2005", freq=offsets.MonthEnd()) == Period("2005", freq="M")
assert Period("3/10/12", freq=offsets.BusinessDay()) == Period(
"3/10/12", freq="B"
)
assert Period("3/10/12", freq=offsets.Day()) == Period("3/10/12", freq="D")
assert Period(
year=2005, quarter=1, freq=offsets.QuarterEnd(startingMonth=12)
) == Period(year=2005, quarter=1, freq="Q")
assert Period(
year=2005, quarter=2, freq=offsets.QuarterEnd(startingMonth=12)
) == Period(year=2005, quarter=2, freq="Q")
assert Period(year=2005, month=3, day=1, freq=offsets.Day()) == Period(
year=2005, month=3, day=1, freq="D"
)
assert Period(year=2012, month=3, day=10, freq=offsets.BDay()) == Period(
year=2012, month=3, day=10, freq="B"
)
expected = Period("2005-03-01", freq="3D")
assert Period(year=2005, month=3, day=1, freq=offsets.Day(3)) == expected
assert Period(year=2005, month=3, day=1, freq="3D") == expected
assert Period(year=2012, month=3, day=10, freq=offsets.BDay(3)) == Period(
year=2012, month=3, day=10, freq="3B"
)
assert Period(200701, freq=offsets.MonthEnd()) == Period(200701, freq="M")
i1 = Period(ordinal=200701, freq=offsets.MonthEnd())
i2 = Period(ordinal=200701, freq="M")
assert i1 == i2
assert i1.year == 18695
assert i2.year == 18695
i1 = Period(datetime(2007, 1, 1), freq="M")
i2 = Period("200701", freq="M")
assert i1 == i2
i1 = Period(date(2007, 1, 1), freq="M")
i2 = Period(datetime(2007, 1, 1), freq="M")
i3 = Period(np.datetime64("2007-01-01"), freq="M")
i4 = Period(np_datetime64_compat("2007-01-01 00:00:00Z"), freq="M")
i5 = Period(np_datetime64_compat("2007-01-01 00:00:00.000Z"), freq="M")
assert i1 == i2
assert i1 == i3
assert i1 == i4
assert i1 == i5
i1 = Period("2007-01-01 09:00:00.001")
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq="L")
assert i1 == expected
expected = Period(np_datetime64_compat("2007-01-01 09:00:00.001Z"), freq="L")
assert i1 == expected
i1 = Period("2007-01-01 09:00:00.00101")
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq="U")
assert i1 == expected
expected = Period(np_datetime64_compat("2007-01-01 09:00:00.00101Z"), freq="U")
assert i1 == expected
def test_construction(self):
i1 = Period("1/1/2005", freq="M")
i2 = Period("Jan 2005")
assert i1 == i2
i1 = Period("2005", freq="A")
i2 = Period("2005")
i3 = Period("2005", freq="a")
assert i1 == i2
assert i1 == i3
i4 = Period("2005", freq="M")
i5 = Period("2005", freq="m")
msg = r"Input has different freq=M from Period\(freq=A-DEC\)"
with pytest.raises(IncompatibleFrequency, match=msg):
i1 != i4
assert i4 == i5
i1 = Period.now("Q")
i2 = Period(datetime.now(), freq="Q")
i3 = Period.now("q")
assert i1 == i2
assert i1 == i3
i1 = Period("1982", freq="min")
i2 = Period("1982", freq="MIN")
assert i1 == i2
i2 = Period("1982", freq=("Min", 1))
assert i1 == i2
i1 = Period(year=2005, month=3, day=1, freq="D")
i2 = Period("3/1/2005", freq="D")
assert i1 == i2
i3 = Period(year=2005, month=3, day=1, freq="d")
assert i1 == i3
i1 = Period("2007-01-01 09:00:00.001")
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq="L")
assert i1 == expected
expected = Period(np_datetime64_compat("2007-01-01 09:00:00.001Z"), freq="L")
assert i1 == expected
i1 = Period("2007-01-01 09:00:00.00101")
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq="U")
assert i1 == expected
expected = Period(np_datetime64_compat("2007-01-01 09:00:00.00101Z"), freq="U")
assert i1 == expected
msg = "Must supply freq for ordinal value"
with pytest.raises(ValueError, match=msg):
Period(ordinal=200701)
msg = "Invalid frequency: X"
with pytest.raises(ValueError, match=msg):
Period("2007-1-1", freq="X")
def test_dti_cmp_nat_behaves_like_float_cmp_nan(self):
fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
didx1 = pd.DatetimeIndex([
'2014-01-01', pd.NaT, '2014-03-01', pd.NaT, '2014-05-01',
'2014-07-01'
])
didx2 = pd.DatetimeIndex([
'2014-02-01', '2014-03-01', pd.NaT, pd.NaT, '2014-06-01',
'2014-07-01'
])
darr = np.array([
np_datetime64_compat('2014-02-01 00:00Z'),
np_datetime64_compat('2014-03-01 00:00Z'),
np_datetime64_compat('nat'),
np.datetime64('nat'),
np_datetime64_compat('2014-06-01 00:00Z'),
np_datetime64_compat('2014-07-01 00:00Z')
])
cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, idx2 in cases:
result = idx1 < idx2
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx2 > idx1
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= idx2
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx2 >= idx1
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == idx2
expected = np.array([False, False, False, False, False, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != idx2
expected = np.array([True, True, True, True, True, False])
tm.assert_numpy_array_equal(result, expected)
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
result = idx1 < val
expected = np.array([False, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, True, True, True, True])
tm.assert_numpy_array_equal(result, expected)
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
result = idx1 < val
expected = np.array([True, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
expected = np.array([False, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
expected = np.array([False, False, True, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
expected = np.array([False, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, False, True, True, True])
tm.assert_numpy_array_equal(result, expected)
def test_should_return_credentials_path_set_by_env_var():
env = {'PANDAS_GBQ_CREDENTIALS_FILE': '/tmp/dummy.dat'}
with mock.patch.dict('os.environ', env):
assert gbq._get_credentials_file() == '/tmp/dummy.dat'
@pytest.mark.parametrize(
('input', 'type_', 'expected'), [
(1, 'INTEGER', int(1)),
(1, 'FLOAT', float(1)),
pytest.param('false', 'BOOLEAN', False, marks=pytest.mark.xfail),
pytest.param(
'0e9', 'TIMESTAMP',
np_datetime64_compat('1970-01-01T00:00:00Z'),
marks=pytest.mark.xfail),
('STRING', 'STRING', 'STRING'),
])
def test_should_return_bigquery_correctly_typed(
input, type_, expected):
result = gbq._parse_data(
dict(fields=[dict(name='x', type=type_, mode='NULLABLE')]),
rows=[[input]]).iloc[0, 0]
assert result == expected
def test_to_gbq_should_fail_if_invalid_table_name_passed():
with pytest.raises(gbq.NotFoundException):
gbq.to_gbq(DataFrame(), 'invalid_table_name', project_id="1234")
def test_should_return_credentials_path_set_by_env_var():
env = {"PANDAS_GBQ_CREDENTIALS_FILE": "/tmp/dummy.dat"}
with mock.patch.dict("os.environ", env):
assert gbq._get_credentials_file() == "/tmp/dummy.dat"
@pytest.mark.parametrize(
("input", "type_", "expected"),
[
(1, "INTEGER", int(1)),
(1, "FLOAT", float(1)),
pytest.param("false", "BOOLEAN", False, marks=pytest.mark.xfail),
pytest.param(
"0e9",
"TIMESTAMP",
np_datetime64_compat("1970-01-01T00:00:00Z"),
marks=pytest.mark.xfail,
),
("STRING", "STRING", "STRING"),
],
)
def test_should_return_bigquery_correctly_typed(input, type_, expected):
result = gbq._parse_data(
dict(fields=[dict(name="x", type=type_, mode="NULLABLE")]),
rows=[[input]],
).iloc[0, 0]
assert result == expected
def test_to_gbq_should_fail_if_invalid_table_name_passed():
with pytest.raises(gbq.NotFoundException):
def test_period_constructor_offsets(self):
assert (Period('1/1/2005', freq=offsets.MonthEnd()) ==
Period('1/1/2005', freq='M'))
assert (Period('2005', freq=offsets.YearEnd()) ==
Period('2005', freq='A'))
assert (Period('2005', freq=offsets.MonthEnd()) ==
Period('2005', freq='M'))
assert (Period('3/10/12', freq=offsets.BusinessDay()) ==
Period('3/10/12', freq='B'))
assert (Period('3/10/12', freq=offsets.Day()) ==
Period('3/10/12', freq='D'))
assert (Period(year=2005, quarter=1,
freq=offsets.QuarterEnd(startingMonth=12)) ==
Period(year=2005, quarter=1, freq='Q'))
assert (Period(year=2005, quarter=2,
freq=offsets.QuarterEnd(startingMonth=12)) ==
Period(year=2005, quarter=2, freq='Q'))
assert (Period(year=2005, month=3, day=1, freq=offsets.Day()) ==
Period(year=2005, month=3, day=1, freq='D'))
assert (Period(year=2012, month=3, day=10, freq=offsets.BDay()) ==
Period(year=2012, month=3, day=10, freq='B'))
expected = Period('2005-03-01', freq='3D')
assert (Period(year=2005, month=3, day=1,
freq=offsets.Day(3)) == expected)
assert Period(year=2005, month=3, day=1, freq='3D') == expected
assert (Period(year=2012, month=3, day=10,
freq=offsets.BDay(3)) ==
Period(year=2012, month=3, day=10, freq='3B'))
assert (Period(200701, freq=offsets.MonthEnd()) ==
Period(200701, freq='M'))
i1 = Period(ordinal=200701, freq=offsets.MonthEnd())
i2 = Period(ordinal=200701, freq='M')
assert i1 == i2
assert i1.year == 18695
assert i2.year == 18695
i1 = Period(datetime(2007, 1, 1), freq='M')
i2 = Period('200701', freq='M')
assert i1 == i2
i1 = Period(date(2007, 1, 1), freq='M')
i2 = Period(datetime(2007, 1, 1), freq='M')
i3 = Period(np.datetime64('2007-01-01'), freq='M')
i4 = Period(np_datetime64_compat('2007-01-01 00:00:00Z'), freq='M')
i5 = Period(np_datetime64_compat('2007-01-01 00:00:00.000Z'), freq='M')
assert i1 == i2
assert i1 == i3
assert i1 == i4
assert i1 == i5
i1 = Period('2007-01-01 09:00:00.001')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L')
assert i1 == expected
expected = Period(np_datetime64_compat(
'2007-01-01 09:00:00.001Z'), freq='L')
assert i1 == expected
i1 = Period('2007-01-01 09:00:00.00101')
expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq='U')
assert i1 == expected
expected = Period(np_datetime64_compat('2007-01-01 09:00:00.00101Z'),
freq='U')
assert i1 == expected
pytest.raises(ValueError, Period, ordinal=200701)
pytest.raises(ValueError, Period, '2007-1-1', freq='X')
def test_should_return_bigquery_timestamps_as_numpy_datetime(self):
result = gbq._parse_entry('0e9', 'TIMESTAMP')
tm.assert_equal(result, np_datetime64_compat('1970-01-01T00:00:00Z'))
def test_dti_cmp_nat_behaves_like_float_cmp_nan(self):
fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
didx1 = pd.DatetimeIndex(['2014-01-01', pd.NaT, '2014-03-01', pd.NaT,
'2014-05-01', '2014-07-01'])
didx2 = pd.DatetimeIndex(['2014-02-01', '2014-03-01', pd.NaT, pd.NaT,
'2014-06-01', '2014-07-01'])
darr = np.array([np_datetime64_compat('2014-02-01 00:00Z'),
np_datetime64_compat('2014-03-01 00:00Z'),
np_datetime64_compat('nat'), np.datetime64('nat'),
np_datetime64_compat('2014-06-01 00:00Z'),
np_datetime64_compat('2014-07-01 00:00Z')])
cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, idx2 in cases:
result = idx1 < idx2
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx2 > idx1
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= idx2
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx2 >= idx1
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == idx2
expected = np.array([False, False, False, False, False, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != idx2
expected = np.array([True, True, True, True, True, False])
tm.assert_numpy_array_equal(result, expected)
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
result = idx1 < val
expected = np.array([False, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, True, True, True, True])
tm.assert_numpy_array_equal(result, expected)
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
result = idx1 < val
expected = np.array([True, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
expected = np.array([False, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
expected = np.array([False, False, True, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
expected = np.array([False, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, False, True, True, True])
tm.assert_numpy_array_equal(result, expected)
def test_should_return_bigquery_timestamps_as_numpy_datetime(self):
result = gbq._parse_entry('0e9', 'TIMESTAMP')
tm.assert_equal(result, np_datetime64_compat('1970-01-01T00:00:00Z'))
def test_comparisons_nat(self):
fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
didx1 = pd.DatetimeIndex(['2014-01-01', pd.NaT, '2014-03-01', pd.NaT,
'2014-05-01', '2014-07-01'])
didx2 = pd.DatetimeIndex(['2014-02-01', '2014-03-01', pd.NaT, pd.NaT,
'2014-06-01', '2014-07-01'])
darr = np.array([np_datetime64_compat('2014-02-01 00:00Z'),
np_datetime64_compat('2014-03-01 00:00Z'),
np_datetime64_compat('nat'), np.datetime64('nat'),
np_datetime64_compat('2014-06-01 00:00Z'),
np_datetime64_compat('2014-07-01 00:00Z')])
if _np_version_under1p8:
# cannot test array because np.datetime('nat') returns today's date
cases = [(fidx1, fidx2), (didx1, didx2)]
else:
cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, idx2 in cases:
result = idx1 < idx2
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx2 > idx1
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= idx2
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx2 >= idx1
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == idx2
expected = np.array([False, False, False, False, False, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != idx2
expected = np.array([True, True, True, True, True, False])
tm.assert_numpy_array_equal(result, expected)
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
result = idx1 < val
expected = np.array([False, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, True, True, True, True])
tm.assert_numpy_array_equal(result, expected)
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
result = idx1 < val
expected = np.array([True, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
expected = np.array([False, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
expected = np.array([False, False, True, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
expected = np.array([False, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, False, True, True, True])
tm.assert_numpy_array_equal(result, expected)
class TestCommon(Base):
# exected 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 == "apply" or normalize)
and ts.nanosecond > 0
):
exp_warning = UserWarning
# test nanosecond is preserved
with tm.assert_produces_warning(exp_warning, check_stacklevel=False):
result = func(ts)
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 == "apply" or normalize)
and ts.nanosecond > 0
):
exp_warning = UserWarning
# test nanosecond is preserved
with tm.assert_produces_warning(exp_warning, check_stacklevel=False):
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")
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_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):
# GH#30340 use idiomatic naming
off = self._get_offset(offset_types)
ts = Timestamp.now()
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
)
