def test_contains_freq_mismatch(self):
rng = period_range("2007-01", freq="M", periods=10)
assert Period("2007-01", freq="M") in rng
assert not Period("2007-01", freq="D") in rng
assert not Period("2007-01", freq="2M") in rng
def test_getitem_list_periods(self):
# GH 7710
rng = period_range(start="2012-01-01", periods=10, freq="D")
ts = Series(range(len(rng)), index=rng)
exp = ts.iloc[[1]]
tm.assert_series_equal(ts[[Period("2012-01-02", freq="D")]], exp)
def test_constructor_invalid(self):
with tm.assert_raises_regex(TypeError, 'Cannot convert input'):
Timestamp(slice(2))
with tm.assert_raises_regex(ValueError, 'Cannot convert Period'):
Timestamp(Period('1000-01-01'))
def test_freq_str(self):
i1 = Period("1982", freq="Min")
assert i1.freq == offsets.Minute()
assert i1.freqstr == "T"
def setup(self, freq):
self.per = Period('2012-06-01', freq=freq)
def test_repr_nat(self):
p = Period("nat", freq="M")
assert repr(NaT) in repr(p)
def test_microsecond_repr(self):
p = Period("2000-01-01 12:15:02.123567")
assert repr(p) == "Period('2000-01-01 12:15:02.123567', 'U')"
def test_sub_offset(self):
# freq is DateOffset
msg = "Input has different freq|Input cannot be converted to Period"
for freq in ["A", "2A", "3A"]:
p = Period("2011", freq=freq)
assert p - offsets.YearEnd(2) == Period("2009", freq=freq)
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(365, "D"),
timedelta(365),
]:
with pytest.raises(IncompatibleFrequency, match=msg):
p - o
for freq in ["M", "2M", "3M"]:
p = Period("2011-03", freq=freq)
assert p - offsets.MonthEnd(2) == Period("2011-01", freq=freq)
assert p - offsets.MonthEnd(12) == Period("2010-03", freq=freq)
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(365, "D"),
timedelta(365),
]:
with pytest.raises(IncompatibleFrequency, match=msg):
p - o
# freq is Tick
for freq in ["D", "2D", "3D"]:
p = Period("2011-04-01", freq=freq)
assert p - offsets.Day(5) == Period("2011-03-27", freq=freq)
assert p - offsets.Hour(24) == Period("2011-03-31", freq=freq)
assert p - np.timedelta64(2, "D") == Period("2011-03-30", freq=freq)
assert p - np.timedelta64(3600 * 24, "s") == Period("2011-03-31", freq=freq)
assert p - timedelta(-2) == Period("2011-04-03", freq=freq)
assert p - timedelta(hours=48) == Period("2011-03-30", freq=freq)
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(4, "h"),
timedelta(hours=23),
]:
with pytest.raises(IncompatibleFrequency, match=msg):
p - o
for freq in ["H", "2H", "3H"]:
p = Period("2011-04-01 09:00", freq=freq)
assert p - offsets.Day(2) == Period("2011-03-30 09:00", freq=freq)
assert p - offsets.Hour(3) == Period("2011-04-01 06:00", freq=freq)
assert p - np.timedelta64(3, "h") == Period("2011-04-01 06:00", freq=freq)
assert p - np.timedelta64(3600, "s") == Period(
"2011-04-01 08:00", freq=freq
)
assert p - timedelta(minutes=120) == Period("2011-04-01 07:00", freq=freq)
assert p - timedelta(days=4, minutes=180) == Period(
"2011-03-28 06:00", freq=freq
)
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(3200, "s"),
timedelta(hours=23, minutes=30),
]:
with pytest.raises(IncompatibleFrequency, match=msg):
p - o
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_add_integer(self):
per1 = Period(freq="D", year=2008, month=1, day=1)
per2 = Period(freq="D", year=2008, month=1, day=2)
assert per1 + 1 == per2
assert 1 + per1 == per2
def test_add_offset(self):
# freq is DateOffset
for freq in ["A", "2A", "3A"]:
p = Period("2011", freq=freq)
exp = Period("2013", freq=freq)
assert p + offsets.YearEnd(2) == exp
assert offsets.YearEnd(2) + p == exp
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(365, "D"),
timedelta(365),
]:
msg = "Input has different freq|Input cannot be converted to Period"
with pytest.raises(IncompatibleFrequency, match=msg):
p + o
if isinstance(o, np.timedelta64):
msg = "cannot use operands with types"
with pytest.raises(TypeError, match=msg):
o + p
else:
msg = "|".join(
[
"Input has different freq",
"Input cannot be converted to Period",
]
)
with pytest.raises(IncompatibleFrequency, match=msg):
o + p
for freq in ["M", "2M", "3M"]:
p = Period("2011-03", freq=freq)
exp = Period("2011-05", freq=freq)
assert p + offsets.MonthEnd(2) == exp
assert offsets.MonthEnd(2) + p == exp
exp = Period("2012-03", freq=freq)
assert p + offsets.MonthEnd(12) == exp
assert offsets.MonthEnd(12) + p == exp
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(365, "D"),
timedelta(365),
]:
msg = "Input has different freq|Input cannot be converted to Period"
with pytest.raises(IncompatibleFrequency, match=msg):
p + o
if isinstance(o, np.timedelta64):
msg = "cannot use operands with types"
with pytest.raises(TypeError, match=msg):
o + p
else:
msg = "|".join(
[
"Input has different freq",
"Input cannot be converted to Period",
]
)
with pytest.raises(IncompatibleFrequency, match=msg):
o + p
# freq is Tick
for freq in ["D", "2D", "3D"]:
p = Period("2011-04-01", freq=freq)
exp = Period("2011-04-06", freq=freq)
assert p + offsets.Day(5) == exp
assert offsets.Day(5) + p == exp
exp = Period("2011-04-02", freq=freq)
assert p + offsets.Hour(24) == exp
assert offsets.Hour(24) + p == exp
exp = Period("2011-04-03", freq=freq)
assert p + np.timedelta64(2, "D") == exp
msg = "cannot use operands with types"
with pytest.raises(TypeError, match=msg):
np.timedelta64(2, "D") + p
exp = Period("2011-04-02", freq=freq)
assert p + np.timedelta64(3600 * 24, "s") == exp
with pytest.raises(TypeError, match=msg):
np.timedelta64(3600 * 24, "s") + p
exp = Period("2011-03-30", freq=freq)
assert p + timedelta(-2) == exp
assert timedelta(-2) + p == exp
exp = Period("2011-04-03", freq=freq)
assert p + timedelta(hours=48) == exp
assert timedelta(hours=48) + p == exp
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(4, "h"),
timedelta(hours=23),
]:
msg = "Input has different freq|Input cannot be converted to Period"
with pytest.raises(IncompatibleFrequency, match=msg):
p + o
if isinstance(o, np.timedelta64):
msg = "cannot use operands with types"
with pytest.raises(TypeError, match=msg):
o + p
else:
msg = "|".join(
[
"Input has different freq",
"Input cannot be converted to Period",
]
)
with pytest.raises(IncompatibleFrequency, match=msg):
o + p
for freq in ["H", "2H", "3H"]:
p = Period("2011-04-01 09:00", freq=freq)
exp = Period("2011-04-03 09:00", freq=freq)
assert p + offsets.Day(2) == exp
assert offsets.Day(2) + p == exp
exp = Period("2011-04-01 12:00", freq=freq)
assert p + offsets.Hour(3) == exp
assert offsets.Hour(3) + p == exp
msg = "cannot use operands with types"
exp = Period("2011-04-01 12:00", freq=freq)
assert p + np.timedelta64(3, "h") == exp
with pytest.raises(TypeError, match=msg):
np.timedelta64(3, "h") + p
exp = Period("2011-04-01 10:00", freq=freq)
assert p + np.timedelta64(3600, "s") == exp
with pytest.raises(TypeError, match=msg):
np.timedelta64(3600, "s") + p
exp = Period("2011-04-01 11:00", freq=freq)
assert p + timedelta(minutes=120) == exp
assert timedelta(minutes=120) + p == exp
exp = Period("2011-04-05 12:00", freq=freq)
assert p + timedelta(days=4, minutes=180) == exp
assert timedelta(days=4, minutes=180) + p == exp
for o in [
offsets.YearBegin(2),
offsets.MonthBegin(1),
offsets.Minute(),
np.timedelta64(3200, "s"),
timedelta(hours=23, minutes=30),
]:
msg = "Input has different freq|Input cannot be converted to Period"
with pytest.raises(IncompatibleFrequency, match=msg):
p + o
if isinstance(o, np.timedelta64):
msg = "cannot use operands with types"
with pytest.raises(TypeError, match=msg):
o + p
else:
msg = "|".join(
[
"Input has different freq",
"Input cannot be converted to Period",
]
)
with pytest.raises(IncompatibleFrequency, match=msg):
o + p
def test_construction_quarter(self):
i1 = Period(year=2005, quarter=1, freq="Q")
i2 = Period("1/1/2005", freq="Q")
assert i1 == i2
i1 = Period(year=2005, quarter=3, freq="Q")
i2 = Period("9/1/2005", freq="Q")
assert i1 == i2
i1 = Period("2005Q1")
i2 = Period(year=2005, quarter=1, freq="Q")
i3 = Period("2005q1")
assert i1 == i2
assert i1 == i3
i1 = Period("05Q1")
assert i1 == i2
lower = Period("05q1")
assert i1 == lower
i1 = Period("1Q2005")
assert i1 == i2
lower = Period("1q2005")
assert i1 == lower
i1 = Period("1Q05")
assert i1 == i2
lower = Period("1q05")
assert i1 == lower
i1 = Period("4Q1984")
assert i1.year == 1984
lower = Period("4q1984")
assert i1 == lower
def test_isscalar_pandas_scalars(self):
assert is_scalar(Timestamp('2014-01-01'))
assert is_scalar(Timedelta(hours=1))
assert is_scalar(Period('2014-01-01'))
def test_constructor(self):
pi = period_range(freq="A", start="1/1/2001", end="12/1/2009")
assert len(pi) == 9
pi = period_range(freq="Q", start="1/1/2001", end="12/1/2009")
assert len(pi) == 4 * 9
pi = period_range(freq="M", start="1/1/2001", end="12/1/2009")
assert len(pi) == 12 * 9
pi = period_range(freq="D", start="1/1/2001", end="12/31/2009")
assert len(pi) == 365 * 9 + 2
pi = period_range(freq="B", start="1/1/2001", end="12/31/2009")
assert len(pi) == 261 * 9
pi = period_range(freq="H", start="1/1/2001", end="12/31/2001 23:00")
assert len(pi) == 365 * 24
pi = period_range(freq="Min", start="1/1/2001", end="1/1/2001 23:59")
assert len(pi) == 24 * 60
pi = period_range(freq="S", start="1/1/2001", end="1/1/2001 23:59:59")
assert len(pi) == 24 * 60 * 60
start = Period("02-Apr-2005", "B")
i1 = period_range(start=start, periods=20)
assert len(i1) == 20
assert i1.freq == start.freq
assert i1[0] == start
end_intv = Period("2006-12-31", "W")
i1 = period_range(end=end_intv, periods=10)
assert len(i1) == 10
assert i1.freq == end_intv.freq
assert i1[-1] == end_intv
end_intv = Period("2006-12-31", "1w")
i2 = period_range(end=end_intv, periods=10)
assert len(i1) == len(i2)
assert (i1 == i2).all()
assert i1.freq == i2.freq
end_intv = Period("2005-05-01", "B")
i1 = period_range(start=start, end=end_intv)
# infer freq from first element
i2 = PeriodIndex([end_intv, Period("2005-05-05", "B")])
assert len(i2) == 2
assert i2[0] == end_intv
i2 = PeriodIndex(np.array([end_intv, Period("2005-05-05", "B")]))
assert len(i2) == 2
assert i2[0] == end_intv
# Mixed freq should fail
vals = [end_intv, Period("2006-12-31", "w")]
msg = r"Input has different freq=W-SUN from PeriodIndex\(freq=B\)"
with pytest.raises(IncompatibleFrequency, match=msg):
PeriodIndex(vals)
vals = np.array(vals)
with pytest.raises(IncompatibleFrequency, match=msg):
PeriodIndex(vals)
# tuple freq disallowed GH#34703
with pytest.raises(TypeError, match="pass as a string instead"):
Period("2006-12-31", ("w", 1))
def test_to_timestamp_tz_arg_dateutil_from_string(self):
p = Period("1/1/2005", freq="M").to_timestamp(tz="dateutil/Europe/Brussels")
assert p.tz == dateutil_gettz("Europe/Brussels")
def test_period_addsub_nat(self, freq):
assert NaT - Period("2011-01", freq=freq) is NaT
assert Period("2011-01", freq=freq) - NaT is NaT
def test_repr(self):
p = Period("Jan-2000")
assert "2000-01" in repr(p)
p = Period("2000-12-15")
assert "2000-12-15" in repr(p)
def test_small_year_parsing():
per1 = Period("0001-01-07", "D")
assert per1.year == 1
assert per1.day == 7
def test_millisecond_repr(self):
p = Period("2000-01-01 12:15:02.123")
assert repr(p) == "Period('2000-01-01 12:15:02.123', 'L')"
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_strftime(self):
# GH#3363
p = Period("2000-1-1 12:34:12", freq="S")
res = p.strftime("%Y-%m-%d %H:%M:%S")
assert res == "2000-01-01 12:34:12"
assert isinstance(res, str)
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_properties_annually(self):
# Test properties on Periods with annually frequency.
a_date = Period(freq="A", year=2007)
assert a_date.year == 2007
def test_period_from_ordinal(self):
p = Period("2011-01", freq="M")
res = Period._from_ordinal(p.ordinal, freq="M")
assert p == res
assert isinstance(res, Period)
def time_period_constructor(self, freq, is_offset):
Period('2012-06-01', freq=freq)
def test_period_cons_combined(self):
p = [
(
Period("2011-01", freq="1D1H"),
Period("2011-01", freq="1H1D"),
Period("2011-01", freq="H"),
),
(
Period(ordinal=1, freq="1D1H"),
Period(ordinal=1, freq="1H1D"),
Period(ordinal=1, freq="H"),
),
]
for p1, p2, p3 in p:
assert p1.ordinal == p3.ordinal
assert p2.ordinal == p3.ordinal
assert p1.freq == offsets.Hour(25)
assert p1.freqstr == "25H"
assert p2.freq == offsets.Hour(25)
assert p2.freqstr == "25H"
assert p3.freq == offsets.Hour()
assert p3.freqstr == "H"
result = p1 + 1
assert result.ordinal == (p3 + 25).ordinal
assert result.freq == p1.freq
assert result.freqstr == "25H"
result = p2 + 1
assert result.ordinal == (p3 + 25).ordinal
assert result.freq == p2.freq
assert result.freqstr == "25H"
result = p1 - 1
assert result.ordinal == (p3 - 25).ordinal
assert result.freq == p1.freq
assert result.freqstr == "25H"
result = p2 - 1
assert result.ordinal == (p3 - 25).ordinal
assert result.freq == p2.freq
assert result.freqstr == "25H"
msg = "Frequency must be positive, because it represents span: -25H"
with pytest.raises(ValueError, match=msg):
Period("2011-01", freq="-1D1H")
with pytest.raises(ValueError, match=msg):
Period("2011-01", freq="-1H1D")
with pytest.raises(ValueError, match=msg):
Period(ordinal=1, freq="-1D1H")
with pytest.raises(ValueError, match=msg):
Period(ordinal=1, freq="-1H1D")
msg = "Frequency must be positive, because it represents span: 0D"
with pytest.raises(ValueError, match=msg):
Period("2011-01", freq="0D0H")
with pytest.raises(ValueError, match=msg):
Period(ordinal=1, freq="0D0H")
# You can only combine together day and intraday offsets
msg = "Invalid frequency: 1W1D"
with pytest.raises(ValueError, match=msg):
Period("2011-01", freq="1W1D")
msg = "Invalid frequency: 1D1W"
with pytest.raises(ValueError, match=msg):
Period("2011-01", freq="1D1W")
class TestPartialSetting:
def test_partial_setting(self):
# GH2578, allow ix and friends to partially set
# series
s_orig = Series([1, 2, 3])
s = s_orig.copy()
s[5] = 5
expected = Series([1, 2, 3, 5], index=[0, 1, 2, 5])
tm.assert_series_equal(s, expected)
s = s_orig.copy()
s.loc[5] = 5
expected = Series([1, 2, 3, 5], index=[0, 1, 2, 5])
tm.assert_series_equal(s, expected)
s = s_orig.copy()
s[5] = 5.0
expected = Series([1, 2, 3, 5.0], index=[0, 1, 2, 5])
tm.assert_series_equal(s, expected)
s = s_orig.copy()
s.loc[5] = 5.0
expected = Series([1, 2, 3, 5.0], index=[0, 1, 2, 5])
tm.assert_series_equal(s, expected)
# iloc/iat raise
s = s_orig.copy()
msg = "iloc cannot enlarge its target object"
with pytest.raises(IndexError, match=msg):
s.iloc[3] = 5.0
msg = "index 3 is out of bounds for axis 0 with size 3"
with pytest.raises(IndexError, match=msg):
s.iat[3] = 5.0
# ## frame ##
df_orig = DataFrame(np.arange(6).reshape(3, 2),
columns=["A", "B"],
dtype="int64")
# iloc/iat raise
df = df_orig.copy()
msg = "iloc cannot enlarge its target object"
with pytest.raises(IndexError, match=msg):
df.iloc[4, 2] = 5.0
msg = "index 2 is out of bounds for axis 0 with size 2"
with pytest.raises(IndexError, match=msg):
df.iat[4, 2] = 5.0
# row setting where it exists
expected = DataFrame(dict({"A": [0, 4, 4], "B": [1, 5, 5]}))
df = df_orig.copy()
df.iloc[1] = df.iloc[2]
tm.assert_frame_equal(df, expected)
expected = DataFrame(dict({"A": [0, 4, 4], "B": [1, 5, 5]}))
df = df_orig.copy()
df.loc[1] = df.loc[2]
tm.assert_frame_equal(df, expected)
# like 2578, partial setting with dtype preservation
expected = DataFrame(dict({"A": [0, 2, 4, 4], "B": [1, 3, 5, 5]}))
df = df_orig.copy()
df.loc[3] = df.loc[2]
tm.assert_frame_equal(df, expected)
# single dtype frame, overwrite
expected = DataFrame(dict({"A": [0, 2, 4], "B": [0, 2, 4]}))
df = df_orig.copy()
df.loc[:, "B"] = df.loc[:, "A"]
tm.assert_frame_equal(df, expected)
# mixed dtype frame, overwrite
expected = DataFrame(dict({"A": [0, 2, 4], "B": Series([0, 2, 4])}))
df = df_orig.copy()
df["B"] = df["B"].astype(np.float64)
df.loc[:, "B"] = df.loc[:, "A"]
tm.assert_frame_equal(df, expected)
# single dtype frame, partial setting
expected = df_orig.copy()
expected["C"] = df["A"]
df = df_orig.copy()
df.loc[:, "C"] = df.loc[:, "A"]
tm.assert_frame_equal(df, expected)
# mixed frame, partial setting
expected = df_orig.copy()
expected["C"] = df["A"]
df = df_orig.copy()
df.loc[:, "C"] = df.loc[:, "A"]
tm.assert_frame_equal(df, expected)
# GH 8473
dates = date_range("1/1/2000", periods=8)
df_orig = DataFrame(np.random.randn(8, 4),
index=dates,
columns=["A", "B", "C", "D"])
expected = pd.concat(
[df_orig,
DataFrame({"A": 7}, index=dates[-1:] + dates.freq)],
sort=True)
df = df_orig.copy()
df.loc[dates[-1] + dates.freq, "A"] = 7
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.at[dates[-1] + dates.freq, "A"] = 7
tm.assert_frame_equal(df, expected)
exp_other = DataFrame({0: 7}, index=dates[-1:] + dates.freq)
expected = pd.concat([df_orig, exp_other], axis=1)
df = df_orig.copy()
df.loc[dates[-1] + dates.freq, 0] = 7
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.at[dates[-1] + dates.freq, 0] = 7
tm.assert_frame_equal(df, expected)
def test_partial_setting_mixed_dtype(self):
# in a mixed dtype environment, try to preserve dtypes
# by appending
df = DataFrame([[True, 1], [False, 2]], columns=["female", "fitness"])
s = df.loc[1].copy()
s.name = 2
expected = df.append(s)
df.loc[2] = df.loc[1]
tm.assert_frame_equal(df, expected)
# columns will align
df = DataFrame(columns=["A", "B"])
df.loc[0] = Series(1, index=range(4))
tm.assert_frame_equal(df, DataFrame(columns=["A", "B"], index=[0]))
# columns will align
df = DataFrame(columns=["A", "B"])
df.loc[0] = Series(1, index=["B"])
exp = DataFrame([[np.nan, 1]],
columns=["A", "B"],
index=[0],
dtype="float64")
tm.assert_frame_equal(df, exp)
# list-like must conform
df = DataFrame(columns=["A", "B"])
msg = "cannot set a row with mismatched columns"
with pytest.raises(ValueError, match=msg):
df.loc[0] = [1, 2, 3]
# TODO: #15657, these are left as object and not coerced
df = DataFrame(columns=["A", "B"])
df.loc[3] = [6, 7]
exp = DataFrame([[6, 7]],
index=[3],
columns=["A", "B"],
dtype="object")
tm.assert_frame_equal(df, exp)
def test_series_partial_set(self):
# partial set with new index
# Regression from GH4825
ser = Series([0.1, 0.2], index=[1, 2])
# loc equiv to .reindex
expected = Series([np.nan, 0.2, np.nan], index=[3, 2, 3])
with pytest.raises(KeyError, match="with any missing labels"):
result = ser.loc[[3, 2, 3]]
result = ser.reindex([3, 2, 3])
tm.assert_series_equal(result, expected, check_index_type=True)
expected = Series([np.nan, 0.2, np.nan, np.nan], index=[3, 2, 3, "x"])
with pytest.raises(KeyError, match="with any missing labels"):
result = ser.loc[[3, 2, 3, "x"]]
result = ser.reindex([3, 2, 3, "x"])
tm.assert_series_equal(result, expected, check_index_type=True)
expected = Series([0.2, 0.2, 0.1], index=[2, 2, 1])
result = ser.loc[[2, 2, 1]]
tm.assert_series_equal(result, expected, check_index_type=True)
expected = Series([0.2, 0.2, np.nan, 0.1], index=[2, 2, "x", 1])
with pytest.raises(KeyError, match="with any missing labels"):
result = ser.loc[[2, 2, "x", 1]]
result = ser.reindex([2, 2, "x", 1])
tm.assert_series_equal(result, expected, check_index_type=True)
# raises as nothing in in the index
msg = (r"\"None of \[Int64Index\(\[3, 3, 3\], dtype='int64'\)\] are "
r"in the \[index\]\"")
with pytest.raises(KeyError, match=msg):
ser.loc[[3, 3, 3]]
expected = Series([0.2, 0.2, np.nan], index=[2, 2, 3])
with pytest.raises(KeyError, match="with any missing labels"):
ser.loc[[2, 2, 3]]
result = ser.reindex([2, 2, 3])
tm.assert_series_equal(result, expected, check_index_type=True)
s = Series([0.1, 0.2, 0.3], index=[1, 2, 3])
expected = Series([0.3, np.nan, np.nan], index=[3, 4, 4])
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[[3, 4, 4]]
result = s.reindex([3, 4, 4])
tm.assert_series_equal(result, expected, check_index_type=True)
s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4])
expected = Series([np.nan, 0.3, 0.3], index=[5, 3, 3])
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[[5, 3, 3]]
result = s.reindex([5, 3, 3])
tm.assert_series_equal(result, expected, check_index_type=True)
s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4])
expected = Series([np.nan, 0.4, 0.4], index=[5, 4, 4])
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[[5, 4, 4]]
result = s.reindex([5, 4, 4])
tm.assert_series_equal(result, expected, check_index_type=True)
s = Series([0.1, 0.2, 0.3, 0.4], index=[4, 5, 6, 7])
expected = Series([0.4, np.nan, np.nan], index=[7, 2, 2])
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[[7, 2, 2]]
result = s.reindex([7, 2, 2])
tm.assert_series_equal(result, expected, check_index_type=True)
s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4])
expected = Series([0.4, np.nan, np.nan], index=[4, 5, 5])
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[[4, 5, 5]]
result = s.reindex([4, 5, 5])
tm.assert_series_equal(result, expected, check_index_type=True)
# iloc
expected = Series([0.2, 0.2, 0.1, 0.1], index=[2, 2, 1, 1])
result = ser.iloc[[1, 1, 0, 0]]
tm.assert_series_equal(result, expected, check_index_type=True)
def test_series_partial_set_with_name(self):
# GH 11497
idx = Index([1, 2], dtype="int64", name="idx")
ser = Series([0.1, 0.2], index=idx, name="s")
# loc
with pytest.raises(KeyError, match="with any missing labels"):
ser.loc[[3, 2, 3]]
with pytest.raises(KeyError, match="with any missing labels"):
ser.loc[[3, 2, 3, "x"]]
exp_idx = Index([2, 2, 1], dtype="int64", name="idx")
expected = Series([0.2, 0.2, 0.1], index=exp_idx, name="s")
result = ser.loc[[2, 2, 1]]
tm.assert_series_equal(result, expected, check_index_type=True)
with pytest.raises(KeyError, match="with any missing labels"):
ser.loc[[2, 2, "x", 1]]
# raises as nothing in in the index
msg = (r"\"None of \[Int64Index\(\[3, 3, 3\], dtype='int64', "
r"name='idx'\)\] are in the \[index\]\"")
with pytest.raises(KeyError, match=msg):
ser.loc[[3, 3, 3]]
with pytest.raises(KeyError, match="with any missing labels"):
ser.loc[[2, 2, 3]]
idx = Index([1, 2, 3], dtype="int64", name="idx")
with pytest.raises(KeyError, match="with any missing labels"):
Series([0.1, 0.2, 0.3], index=idx, name="s").loc[[3, 4, 4]]
idx = Index([1, 2, 3, 4], dtype="int64", name="idx")
with pytest.raises(KeyError, match="with any missing labels"):
Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[5, 3, 3]]
idx = Index([1, 2, 3, 4], dtype="int64", name="idx")
with pytest.raises(KeyError, match="with any missing labels"):
Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[5, 4, 4]]
idx = Index([4, 5, 6, 7], dtype="int64", name="idx")
with pytest.raises(KeyError, match="with any missing labels"):
Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[7, 2, 2]]
idx = Index([1, 2, 3, 4], dtype="int64", name="idx")
with pytest.raises(KeyError, match="with any missing labels"):
Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[4, 5, 5]]
# iloc
exp_idx = Index([2, 2, 1, 1], dtype="int64", name="idx")
expected = Series([0.2, 0.2, 0.1, 0.1], index=exp_idx, name="s")
result = ser.iloc[[1, 1, 0, 0]]
tm.assert_series_equal(result, expected, check_index_type=True)
def test_partial_set_invalid(self):
# GH 4940
# allow only setting of 'valid' values
orig = tm.makeTimeDataFrame()
df = orig.copy()
# don't allow not string inserts
msg = r"value should be a 'Timestamp' or 'NaT'\. Got '.*' instead\."
with pytest.raises(TypeError, match=msg):
df.loc[100.0, :] = df.iloc[0]
with pytest.raises(TypeError, match=msg):
df.loc[100, :] = df.iloc[0]
# allow object conversion here
df = orig.copy()
df.loc["a", :] = df.iloc[0]
exp = orig.append(Series(df.iloc[0], name="a"))
tm.assert_frame_equal(df, exp)
tm.assert_index_equal(df.index, Index(orig.index.tolist() + ["a"]))
assert df.index.dtype == "object"
def test_partial_set_empty_frame(self):
# partially set with an empty object
# frame
df = DataFrame()
msg = "cannot set a frame with no defined columns"
with pytest.raises(ValueError, match=msg):
df.loc[1] = 1
with pytest.raises(ValueError, match=msg):
df.loc[1] = Series([1], index=["foo"])
msg = "cannot set a frame with no defined index and a scalar"
with pytest.raises(ValueError, match=msg):
df.loc[:, 1] = 1
# these work as they don't really change
# anything but the index
# GH5632
expected = DataFrame(columns=["foo"], index=Index([], dtype="object"))
def f():
df = DataFrame(index=Index([], dtype="object"))
df["foo"] = Series([], dtype="object")
return df
tm.assert_frame_equal(f(), expected)
def f():
df = DataFrame()
df["foo"] = Series(df.index)
return df
tm.assert_frame_equal(f(), expected)
def f():
df = DataFrame()
df["foo"] = df.index
return df
tm.assert_frame_equal(f(), expected)
expected = DataFrame(columns=["foo"], index=Index([], dtype="int64"))
expected["foo"] = expected["foo"].astype("float64")
def f():
df = DataFrame(index=Index([], dtype="int64"))
df["foo"] = []
return df
tm.assert_frame_equal(f(), expected)
def f():
df = DataFrame(index=Index([], dtype="int64"))
df["foo"] = Series(np.arange(len(df)), dtype="float64")
return df
tm.assert_frame_equal(f(), expected)
def f():
df = DataFrame(index=Index([], dtype="int64"))
df["foo"] = range(len(df))
return df
expected = DataFrame(columns=["foo"], index=Index([], dtype="int64"))
expected["foo"] = expected["foo"].astype("float64")
tm.assert_frame_equal(f(), expected)
df = DataFrame()
tm.assert_index_equal(df.columns, Index([], dtype=object))
df2 = DataFrame()
df2[1] = Series([1], index=["foo"])
df.loc[:, 1] = Series([1], index=["foo"])
tm.assert_frame_equal(df, DataFrame([[1]], index=["foo"], columns=[1]))
tm.assert_frame_equal(df, df2)
# no index to start
expected = DataFrame({0: Series(1, index=range(4))},
columns=["A", "B", 0])
df = DataFrame(columns=["A", "B"])
df[0] = Series(1, index=range(4))
df.dtypes
str(df)
tm.assert_frame_equal(df, expected)
df = DataFrame(columns=["A", "B"])
df.loc[:, 0] = Series(1, index=range(4))
df.dtypes
str(df)
tm.assert_frame_equal(df, expected)
def test_partial_set_empty_frame_row(self):
# GH5720, GH5744
# don't create rows when empty
expected = DataFrame(columns=["A", "B", "New"],
index=Index([], dtype="int64"))
expected["A"] = expected["A"].astype("int64")
expected["B"] = expected["B"].astype("float64")
expected["New"] = expected["New"].astype("float64")
df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
y = df[df.A > 5]
y["New"] = np.nan
tm.assert_frame_equal(y, expected)
# tm.assert_frame_equal(y,expected)
expected = DataFrame(columns=["a", "b", "c c", "d"])
expected["d"] = expected["d"].astype("int64")
df = DataFrame(columns=["a", "b", "c c"])
df["d"] = 3
tm.assert_frame_equal(df, expected)
tm.assert_series_equal(df["c c"], Series(name="c c", dtype=object))
# reindex columns is ok
df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
y = df[df.A > 5]
result = y.reindex(columns=["A", "B", "C"])
expected = DataFrame(columns=["A", "B", "C"],
index=Index([], dtype="int64"))
expected["A"] = expected["A"].astype("int64")
expected["B"] = expected["B"].astype("float64")
expected["C"] = expected["C"].astype("float64")
tm.assert_frame_equal(result, expected)
def test_partial_set_empty_frame_set_series(self):
# GH 5756
# setting with empty Series
df = DataFrame(Series(dtype=object))
expected = DataFrame({0: Series(dtype=object)})
tm.assert_frame_equal(df, expected)
df = DataFrame(Series(name="foo", dtype=object))
expected = DataFrame({"foo": Series(dtype=object)})
tm.assert_frame_equal(df, expected)
def test_partial_set_empty_frame_empty_copy_assignment(self):
# GH 5932
# copy on empty with assignment fails
df = DataFrame(index=[0])
df = df.copy()
df["a"] = 0
expected = DataFrame(0, index=[0], columns=["a"])
tm.assert_frame_equal(df, expected)
def test_partial_set_empty_frame_empty_consistencies(self):
# GH 6171
# consistency on empty frames
df = DataFrame(columns=["x", "y"])
df["x"] = [1, 2]
expected = DataFrame(dict(x=[1, 2], y=[np.nan, np.nan]))
tm.assert_frame_equal(df, expected, check_dtype=False)
df = DataFrame(columns=["x", "y"])
df["x"] = ["1", "2"]
expected = DataFrame(dict(x=["1", "2"], y=[np.nan, np.nan]),
dtype=object)
tm.assert_frame_equal(df, expected)
df = DataFrame(columns=["x", "y"])
df.loc[0, "x"] = 1
expected = DataFrame(dict(x=[1], y=[np.nan]))
tm.assert_frame_equal(df, expected, check_dtype=False)
@pytest.mark.parametrize(
"idx,labels,expected_idx",
[
(
period_range(start="2000", periods=20, freq="D"),
["2000-01-04", "2000-01-08", "2000-01-12"],
[
Period("2000-01-04", freq="D"),
Period("2000-01-08", freq="D"),
Period("2000-01-12", freq="D"),
],
),
(
date_range(start="2000", periods=20, freq="D"),
["2000-01-04", "2000-01-08", "2000-01-12"],
[
Timestamp("2000-01-04", freq="D"),
Timestamp("2000-01-08", freq="D"),
Timestamp("2000-01-12", freq="D"),
],
),
(
pd.timedelta_range(start="1 day", periods=20),
["4D", "8D", "12D"],
[
pd.Timedelta("4 day"),
pd.Timedelta("8 day"),
pd.Timedelta("12 day")
],
),
],
)
def test_loc_with_list_of_strings_representing_datetimes(
self, idx, labels, expected_idx, frame_or_series):
# GH 11278
obj = frame_or_series(range(20), index=idx)
expected_value = [3, 7, 11]
expected = frame_or_series(expected_value, expected_idx)
tm.assert_equal(expected, obj.loc[labels])
if frame_or_series is Series:
tm.assert_series_equal(expected, obj[labels])
@pytest.mark.parametrize(
"idx,labels",
[
(
period_range(start="2000", periods=20, freq="D"),
["2000-01-04", "2000-01-30"],
),
(
date_range(start="2000", periods=20, freq="D"),
["2000-01-04", "2000-01-30"],
),
(pd.timedelta_range(start="1 day",
periods=20), ["3 day", "30 day"]),
],
)
def test_loc_with_list_of_strings_representing_datetimes_missing_value(
self, idx, labels):
# GH 11278
s = Series(range(20), index=idx)
df = DataFrame(range(20), index=idx)
msg = r"with any missing labels"
with pytest.raises(KeyError, match=msg):
s.loc[labels]
with pytest.raises(KeyError, match=msg):
s[labels]
with pytest.raises(KeyError, match=msg):
df.loc[labels]
@pytest.mark.parametrize(
"idx,labels,msg",
[
(
period_range(start="2000", periods=20, freq="D"),
["4D", "8D"],
(r"None of \[Index\(\['4D', '8D'\], dtype='object'\)\] "
r"are in the \[index\]"),
),
(
date_range(start="2000", periods=20, freq="D"),
["4D", "8D"],
(r"None of \[Index\(\['4D', '8D'\], dtype='object'\)\] "
r"are in the \[index\]"),
),
(
pd.timedelta_range(start="1 day", periods=20),
["2000-01-04", "2000-01-08"],
(r"None of \[Index\(\['2000-01-04', '2000-01-08'\], "
r"dtype='object'\)\] are in the \[index\]"),
),
],
)
def test_loc_with_list_of_strings_representing_datetimes_not_matched_type(
self, idx, labels, msg):
# GH 11278
s = Series(range(20), index=idx)
df = DataFrame(range(20), index=idx)
with pytest.raises(KeyError, match=msg):
s.loc[labels]
with pytest.raises(KeyError, match=msg):
s[labels]
with pytest.raises(KeyError, match=msg):
df.loc[labels]
def test_index_name_empty(self):
# GH 31368
df = DataFrame({}, index=pd.RangeIndex(0, name="df_index"))
series = Series(1.23, index=pd.RangeIndex(4, name="series_index"))
df["series"] = series
expected = DataFrame({"series": [1.23] * 4},
index=pd.RangeIndex(4, name="df_index"))
tm.assert_frame_equal(df, expected)
# GH 36527
df = DataFrame()
series = Series(1.23, index=pd.RangeIndex(4, name="series_index"))
df["series"] = series
expected = DataFrame({"series": [1.23] * 4},
index=pd.RangeIndex(4, name="series_index"))
tm.assert_frame_equal(df, expected)
def test_slice_irregular_datetime_index_with_nan(self):
# GH36953
index = pd.to_datetime(
["2012-01-01", "2012-01-02", "2012-01-03", None])
df = DataFrame(range(len(index)), index=index)
expected = DataFrame(range(len(index[:3])), index=index[:3])
result = df["2012-01-01":"2012-01-04"]
tm.assert_frame_equal(result, expected)
def test_round_trip(self):
p = Period("2000Q1")
new_p = tm.round_trip_pickle(p)
assert new_p == p
return Series(np.random.randn(len(dates)), index=dates)
# ----------------------------------------------------------------
# Scalars
# ----------------------------------------------------------------
@pytest.fixture(params=[
(
Interval(left=0, right=5, inclusive="right"),
IntervalDtype("int64", inclusive="right"),
),
(
Interval(left=0.1, right=0.5, inclusive="right"),
IntervalDtype("float64", inclusive="right"),
),
(Period("2012-01", freq="M"), "period[M]"),
(Period("2012-02-01", freq="D"), "period[D]"),
(
Timestamp("2011-01-01", tz="US/Eastern"),
DatetimeTZDtype(tz="US/Eastern"),
),
(Timedelta(seconds=500), "timedelta64[ns]"),
])
def ea_scalar_and_dtype(request):
return request.param
# ----------------------------------------------------------------
# Operators & Operations
# ----------------------------------------------------------------
_all_arithmetic_operators = [
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 = 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,
)
