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/ tests / arithmetic / test_timedelta64.py
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
NaT,
Series,
Timedelta,
TimedeltaIndex,
Timestamp,
timedelta_range,
)
import pandas.util.testing as tm
def get_upcast_box(box, vector):
"""
Given two box-types, find the one that takes priority
"""
if box is DataFrame or isinstance(vector, DataFrame):
return DataFrame
if box is Series or isinstance(vector, Series):
return Series
if box is pd.Index or isinstance(vector, pd.Index):
return pd.Index
return box
# ------------------------------------------------------------------
# Timedelta64[ns] dtype Comparisons
class TestTimedelta64ArrayLikeComparisons:
# Comparison tests for timedelta64[ns] vectors fully parametrized over
# DataFrame/Series/TimedeltaIndex/TimedeltaArray. Ideally all comparison
# tests will eventually end up here.
def test_compare_timedelta64_zerodim(self, box_with_array):
# GH#26689 should unbox when comparing with zerodim array
box = box_with_array
xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
tdi = pd.timedelta_range("2H", periods=4)
other = np.array(tdi.to_numpy()[0])
tdi = tm.box_expected(tdi, box)
res = tdi <= other
expected = np.array([True, False, False, False])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(res, expected)
with pytest.raises(TypeError):
# zero-dim of wrong dtype should still raise
tdi >= np.array(4)
class TestTimedelta64ArrayComparisons:
# TODO: All of these need to be parametrized over box
def test_compare_timedelta_series(self):
# regression test for GH#5963
s = pd.Series([timedelta(days=1), timedelta(days=2)])
actual = s > timedelta(days=1)
expected = pd.Series([False, True])
tm.assert_series_equal(actual, expected)
def test_tdi_cmp_str_invalid(self, box_with_array):
# GH#13624
xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
tdi = TimedeltaIndex(["1 day", "2 days"])
tdarr = tm.box_expected(tdi, box_with_array)
for left, right in [(tdarr, "a"), ("a", tdarr)]:
with pytest.raises(TypeError):
left > right
with pytest.raises(TypeError):
left >= right
with pytest.raises(TypeError):
left < right
with pytest.raises(TypeError):
left <= right
result = left == right
expected = np.array([False, False], dtype=bool)
expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
result = left != right
expected = np.array([True, True], dtype=bool)
expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
@pytest.mark.parametrize("dtype", [None, object])
def test_comp_nat(self, dtype):
left = pd.TimedeltaIndex(
[pd.Timedelta("1 days"), pd.NaT, pd.Timedelta("3 days")]
)
right = pd.TimedeltaIndex([pd.NaT, pd.NaT, pd.Timedelta("3 days")])
lhs, rhs = left, right
if dtype is object:
lhs, rhs = left.astype(object), right.astype(object)
result = rhs == lhs
expected = np.array([False, False, True])
tm.assert_numpy_array_equal(result, expected)
result = rhs != lhs
expected = np.array([True, True, False])
tm.assert_numpy_array_equal(result, expected)
expected = np.array([False, False, False])
tm.assert_numpy_array_equal(lhs == pd.NaT, expected)
tm.assert_numpy_array_equal(pd.NaT == rhs, expected)
expected = np.array([True, True, True])
tm.assert_numpy_array_equal(lhs != pd.NaT, expected)
tm.assert_numpy_array_equal(pd.NaT != lhs, expected)
expected = np.array([False, False, False])
tm.assert_numpy_array_equal(lhs < pd.NaT, expected)
tm.assert_numpy_array_equal(pd.NaT > lhs, expected)
def test_comparisons_nat(self):
tdidx1 = pd.TimedeltaIndex(
[
"1 day",
pd.NaT,
"1 day 00:00:01",
pd.NaT,
"1 day 00:00:01",
"5 day 00:00:03",
]
)
tdidx2 = pd.TimedeltaIndex(
["2 day", "2 day", pd.NaT, pd.NaT, "1 day 00:00:02", "5 days 00:00:03"]
)
tdarr = np.array(
[
np.timedelta64(2, "D"),
np.timedelta64(2, "D"),
np.timedelta64("nat"),
np.timedelta64("nat"),
np.timedelta64(1, "D") + np.timedelta64(2, "s"),
np.timedelta64(5, "D") + np.timedelta64(3, "s"),
]
)
cases = [(tdidx1, tdidx2), (tdidx1, tdarr)]
# Check pd.NaT is handles as the same as np.nan
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)
# TODO: better name
def test_comparisons_coverage(self):
rng = timedelta_range("1 days", periods=10)
result = rng < rng[3]
expected = np.array([True, True, True] + [False] * 7)
tm.assert_numpy_array_equal(result, expected)
# raise TypeError for now
with pytest.raises(TypeError):
rng < rng[3].value
result = rng == list(rng)
exp = rng == rng
tm.assert_numpy_array_equal(result, exp)
# ------------------------------------------------------------------
# Timedelta64[ns] dtype Arithmetic Operations
class TestTimedelta64ArithmeticUnsorted:
# Tests moved from type-specific test files but not
# yet sorted/parametrized/de-duplicated
def test_ufunc_coercions(self):
# normal ops are also tested in tseries/test_timedeltas.py
idx = TimedeltaIndex(["2H", "4H", "6H", "8H", "10H"], freq="2H", name="x")
for result in [idx * 2, np.multiply(idx, 2)]:
assert isinstance(result, TimedeltaIndex)
exp = TimedeltaIndex(["4H", "8H", "12H", "16H", "20H"], freq="4H", name="x")
tm.assert_index_equal(result, exp)
assert result.freq == "4H"
for result in [idx / 2, np.divide(idx, 2)]:
assert isinstance(result, TimedeltaIndex)
exp = TimedeltaIndex(["1H", "2H", "3H", "4H", "5H"], freq="H", name="x")
tm.assert_index_equal(result, exp)
assert result.freq == "H"
idx = TimedeltaIndex(["2H", "4H", "6H", "8H", "10H"], freq="2H", name="x")
for result in [-idx, np.negative(idx)]:
assert isinstance(result, TimedeltaIndex)
exp = TimedeltaIndex(
["-2H", "-4H", "-6H", "-8H", "-10H"], freq="-2H", name="x"
)
tm.assert_index_equal(result, exp)
assert result.freq == "-2H"
idx = TimedeltaIndex(["-2H", "-1H", "0H", "1H", "2H"], freq="H", name="x")
for result in [abs(idx), np.absolute(idx)]:
assert isinstance(result, TimedeltaIndex)
exp = TimedeltaIndex(["2H", "1H", "0H", "1H", "2H"], freq=None, name="x")
tm.assert_index_equal(result, exp)
assert result.freq is None
def test_subtraction_ops(self):
# with datetimes/timedelta and tdi/dti
tdi = TimedeltaIndex(["1 days", pd.NaT, "2 days"], name="foo")
dti = pd.date_range("20130101", periods=3, name="bar")
td = Timedelta("1 days")
dt = Timestamp("20130101")
msg = "cannot subtract a datelike from a TimedeltaArray"
with pytest.raises(TypeError, match=msg):
tdi - dt
with pytest.raises(TypeError, match=msg):
tdi - dti
msg = (
r"descriptor '__sub__' requires a 'datetime\.datetime' object"
" but received a 'Timedelta'"
)
with pytest.raises(TypeError, match=msg):
td - dt
msg = "bad operand type for unary -: 'DatetimeArray'"
with pytest.raises(TypeError, match=msg):
td - dti
result = dt - dti
expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"], name="bar")
tm.assert_index_equal(result, expected)
result = dti - dt
expected = TimedeltaIndex(["0 days", "1 days", "2 days"], name="bar")
tm.assert_index_equal(result, expected)
result = tdi - td
expected = TimedeltaIndex(["0 days", pd.NaT, "1 days"], name="foo")
tm.assert_index_equal(result, expected, check_names=False)
result = td - tdi
expected = TimedeltaIndex(["0 days", pd.NaT, "-1 days"], name="foo")
tm.assert_index_equal(result, expected, check_names=False)
result = dti - td
expected = DatetimeIndex(["20121231", "20130101", "20130102"], name="bar")
tm.assert_index_equal(result, expected, check_names=False)
result = dt - tdi
expected = DatetimeIndex(["20121231", pd.NaT, "20121230"], name="foo")
tm.assert_index_equal(result, expected)
def test_subtraction_ops_with_tz(self):
# check that dt/dti subtraction ops with tz are validated
dti = pd.date_range("20130101", periods=3)
ts = Timestamp("20130101")
dt = ts.to_pydatetime()
dti_tz = pd.date_range("20130101", periods=3).tz_localize("US/Eastern")
ts_tz = Timestamp("20130101").tz_localize("US/Eastern")
ts_tz2 = Timestamp("20130101").tz_localize("CET")
dt_tz = ts_tz.to_pydatetime()
td = Timedelta("1 days")
def _check(result, expected):
assert result == expected
assert isinstance(result, Timedelta)
# scalars
result = ts - ts
expected = Timedelta("0 days")
_check(result, expected)
result = dt_tz - ts_tz
expected = Timedelta("0 days")
_check(result, expected)
result = ts_tz - dt_tz
expected = Timedelta("0 days")
_check(result, expected)
# tz mismatches
msg = "Timestamp subtraction must have the same timezones or no timezones"
with pytest.raises(TypeError, match=msg):
dt_tz - ts
msg = "can't subtract offset-naive and offset-aware datetimes"
with pytest.raises(TypeError, match=msg):
dt_tz - dt
msg = "Timestamp subtraction must have the same timezones or no timezones"
with pytest.raises(TypeError, match=msg):
dt_tz - ts_tz2
msg = "can't subtract offset-naive and offset-aware datetimes"
with pytest.raises(TypeError, match=msg):
dt - dt_tz
msg = "Timestamp subtraction must have the same timezones or no timezones"
with pytest.raises(TypeError, match=msg):
ts - dt_tz
with pytest.raises(TypeError, match=msg):
ts_tz2 - ts
with pytest.raises(TypeError, match=msg):
ts_tz2 - dt
with pytest.raises(TypeError, match=msg):
ts_tz - ts_tz2
# with dti
with pytest.raises(TypeError, match=msg):
dti - ts_tz
with pytest.raises(TypeError, match=msg):
dti_tz - ts
with pytest.raises(TypeError, match=msg):
dti_tz - ts_tz2
result = dti_tz - dt_tz
expected = TimedeltaIndex(["0 days", "1 days", "2 days"])
tm.assert_index_equal(result, expected)
result = dt_tz - dti_tz
expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"])
tm.assert_index_equal(result, expected)
result = dti_tz - ts_tz
expected = TimedeltaIndex(["0 days", "1 days", "2 days"])
tm.assert_index_equal(result, expected)
result = ts_tz - dti_tz
expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"])
tm.assert_index_equal(result, expected)
result = td - td
expected = Timedelta("0 days")
_check(result, expected)
result = dti_tz - td
expected = DatetimeIndex(["20121231", "20130101", "20130102"], tz="US/Eastern")
tm.assert_index_equal(result, expected)
def test_dti_tdi_numeric_ops(self):
# These are normally union/diff set-like ops
tdi = TimedeltaIndex(["1 days", pd.NaT, "2 days"], name="foo")
dti = pd.date_range("20130101", periods=3, name="bar")
# TODO(wesm): unused?
# td = Timedelta('1 days')
# dt = Timestamp('20130101')
result = tdi - tdi
expected = TimedeltaIndex(["0 days", pd.NaT, "0 days"], name="foo")
tm.assert_index_equal(result, expected)
result = tdi + tdi
expected = TimedeltaIndex(["2 days", pd.NaT, "4 days"], name="foo")
tm.assert_index_equal(result, expected)
result = dti - tdi # name will be reset
expected = DatetimeIndex(["20121231", pd.NaT, "20130101"])
tm.assert_index_equal(result, expected)
def test_addition_ops(self):
# with datetimes/timedelta and tdi/dti
tdi = TimedeltaIndex(["1 days", pd.NaT, "2 days"], name="foo")
dti = pd.date_range("20130101", periods=3, name="bar")
td = Timedelta("1 days")
dt = Timestamp("20130101")
result = tdi + dt
expected = DatetimeIndex(["20130102", pd.NaT, "20130103"], name="foo")
tm.assert_index_equal(result, expected)
result = dt + tdi
expected = DatetimeIndex(["20130102", pd.NaT, "20130103"], name="foo")
tm.assert_index_equal(result, expected)
result = td + tdi
expected = TimedeltaIndex(["2 days", pd.NaT, "3 days"], name="foo")
tm.assert_index_equal(result, expected)
result = tdi + td
expected = TimedeltaIndex(["2 days", pd.NaT, "3 days"], name="foo")
tm.assert_index_equal(result, expected)
# unequal length
msg = "cannot add indices of unequal length"
with pytest.raises(ValueError, match=msg):
tdi + dti[0:1]
with pytest.raises(ValueError, match=msg):
tdi[0:1] + dti
# random indexes
with pytest.raises(NullFrequencyError):
tdi + pd.Int64Index([1, 2, 3])
# this is a union!
# pytest.raises(TypeError, lambda : Int64Index([1,2,3]) + tdi)
result = tdi + dti # name will be reset
expected = DatetimeIndex(["20130102", pd.NaT, "20130105"])
tm.assert_index_equal(result, expected)
result = dti + tdi # name will be reset
expected = DatetimeIndex(["20130102", pd.NaT, "20130105"])
tm.assert_index_equal(result, expected)
result = dt + td
expected = Timestamp("20130102")
assert result == expected
result = td + dt
expected = Timestamp("20130102")
assert result == expected
# TODO: Needs more informative name, probably split up into
# more targeted tests
@pytest.mark.parametrize("freq", ["D", "B"])
def test_timedelta(self, freq):
index = pd.date_range("1/1/2000", periods=50, freq=freq)
shifted = index + timedelta(1)
back = shifted + timedelta(-1)
tm.assert_index_equal(index, back)
if freq == "D":
expected = pd.tseries.offsets.Day(1)
assert index.freq == expected
assert shifted.freq == expected
assert back.freq == expected
else: # freq == 'B'
assert index.freq == pd.tseries.offsets.BusinessDay(1)
assert shifted.freq is None
assert back.freq == pd.tseries.offsets.BusinessDay(1)
result = index - timedelta(1)
expected = index + timedelta(-1)
tm.assert_index_equal(result, expected)
# GH#4134, buggy with timedeltas
rng = pd.date_range("2013", "2014")
s = Series(rng)
result1 = rng - pd.offsets.Hour(1)
result2 = DatetimeIndex(s - np.timedelta64(100000000))
result3 = rng - np.timedelta64(100000000)
result4 = DatetimeIndex(s - pd.offsets.Hour(1))
tm.assert_index_equal(result1, result4)
tm.assert_index_equal(result2, result3)
class TestAddSubNaTMasking:
# TODO: parametrize over boxes
def test_tdi_add_timestamp_nat_masking(self):
# GH#17991 checking for overflow-masking with NaT
tdinat = pd.to_timedelta(["24658 days 11:15:00", "NaT"])
tsneg = Timestamp("1950-01-01")
ts_neg_variants = [
tsneg,
tsneg.to_pydatetime(),
tsneg.to_datetime64().astype("datetime64[ns]"),
tsneg.to_datetime64().astype("datetime64[D]"),
]
tspos = Timestamp("1980-01-01")
ts_pos_variants = [
tspos,
tspos.to_pydatetime(),
tspos.to_datetime64().astype("datetime64[ns]"),
tspos.to_datetime64().astype("datetime64[D]"),
]
for variant in ts_neg_variants + ts_pos_variants:
res = tdinat + variant
assert res[1] is pd.NaT
def test_tdi_add_overflow(self):
# See GH#14068
# preliminary test scalar analogue of vectorized tests below
with pytest.raises(OutOfBoundsDatetime):
pd.to_timedelta(106580, "D") + Timestamp("2000")
with pytest.raises(OutOfBoundsDatetime):
Timestamp("2000") + pd.to_timedelta(106580, "D")
_NaT = int(pd.NaT) + 1
msg = "Overflow in int64 addition"
with pytest.raises(OverflowError, match=msg):
pd.to_timedelta([106580], "D") + Timestamp("2000")
with pytest.raises(OverflowError, match=msg):
Timestamp("2000") + pd.to_timedelta([106580], "D")
with pytest.raises(OverflowError, match=msg):
pd.to_timedelta([_NaT]) - Timedelta("1 days")
with pytest.raises(OverflowError, match=msg):
pd.to_timedelta(["5 days", _NaT]) - Timedelta("1 days")
with pytest.raises(OverflowError, match=msg):
(
pd.to_timedelta([_NaT, "5 days", "1 hours"])
- pd.to_timedelta(["7 seconds", _NaT, "4 hours"])
)
# These should not overflow!
exp = TimedeltaIndex([pd.NaT])
result = pd.to_timedelta([pd.NaT]) - Timedelta("1 days")
tm.assert_index_equal(result, exp)
exp = TimedeltaIndex(["4 days", pd.NaT])
result = pd.to_timedelta(["5 days", pd.NaT]) - Timedelta("1 days")
tm.assert_index_equal(result, exp)
exp = TimedeltaIndex([pd.NaT, pd.NaT, "5 hours"])
result = pd.to_timedelta([pd.NaT, "5 days", "1 hours"]) + pd.to_timedelta(
["7 seconds", pd.NaT, "4 hours"]
)
tm.assert_index_equal(result, exp)
class TestTimedeltaArraylikeAddSubOps:
# Tests for timedelta64[ns] __add__, __sub__, __radd__, __rsub__
# TODO: moved from frame tests; needs parametrization/de-duplication
def test_td64_df_add_int_frame(self):
# GH#22696 Check that we don't dispatch to numpy implementation,
# which treats int64 as m8[ns]
tdi = pd.timedelta_range("1", periods=3)
df = tdi.to_frame()
other = pd.DataFrame([1, 2, 3], index=tdi) # indexed like `df`
with pytest.raises(TypeError):
df + other
with pytest.raises(TypeError):
other + df
with pytest.raises(TypeError):
df - other
with pytest.raises(TypeError):
other - df
# TODO: moved from tests.indexes.timedeltas.test_arithmetic; needs
# parametrization+de-duplication
def test_timedelta_ops_with_missing_values(self):
# setup
s1 = pd.to_timedelta(Series(["00:00:01"]))
s2 = pd.to_timedelta(Series(["00:00:02"]))
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# Passing datetime64-dtype data to TimedeltaIndex is deprecated
sn = pd.to_timedelta(Series([pd.NaT]))
df1 = pd.DataFrame(["00:00:01"]).apply(pd.to_timedelta)
df2 = pd.DataFrame(["00:00:02"]).apply(pd.to_timedelta)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# Passing datetime64-dtype data to TimedeltaIndex is deprecated
dfn = pd.DataFrame([pd.NaT]).apply(pd.to_timedelta)
scalar1 = pd.to_timedelta("00:00:01")
scalar2 = pd.to_timedelta("00:00:02")
timedelta_NaT = pd.to_timedelta("NaT")
actual = scalar1 + scalar1
assert actual == scalar2
actual = scalar2 - scalar1
assert actual == scalar1
actual = s1 + s1
tm.assert_series_equal(actual, s2)
actual = s2 - s1
tm.assert_series_equal(actual, s1)
actual = s1 + scalar1
tm.assert_series_equal(actual, s2)
actual = scalar1 + s1
tm.assert_series_equal(actual, s2)
actual = s2 - scalar1
tm.assert_series_equal(actual, s1)
actual = -scalar1 + s2
tm.assert_series_equal(actual, s1)
actual = s1 + timedelta_NaT
tm.assert_series_equal(actual, sn)
actual = timedelta_NaT + s1
tm.assert_series_equal(actual, sn)
actual = s1 - timedelta_NaT
tm.assert_series_equal(actual, sn)
actual = -timedelta_NaT + s1
tm.assert_series_equal(actual, sn)
with pytest.raises(TypeError):
s1 + np.nan
with pytest.raises(TypeError):
np.nan + s1
with pytest.raises(TypeError):
s1 - np.nan
with pytest.raises(TypeError):
-np.nan + s1
actual = s1 + pd.NaT
tm.assert_series_equal(actual, sn)
actual = s2 - pd.NaT
tm.assert_series_equal(actual, sn)
actual = s1 + df1
tm.assert_frame_equal(actual, df2)
actual = s2 - df1
tm.assert_frame_equal(actual, df1)
actual = df1 + s1
tm.assert_frame_equal(actual, df2)
actual = df2 - s1
tm.assert_frame_equal(actual, df1)
actual = df1 + df1
tm.assert_frame_equal(actual, df2)
actual = df2 - df1
tm.assert_frame_equal(actual, df1)
actual = df1 + scalar1
tm.assert_frame_equal(actual, df2)
actual = df2 - scalar1
tm.assert_frame_equal(actual, df1)
actual = df1 + timedelta_NaT
tm.assert_frame_equal(actual, dfn)
actual = df1 - timedelta_NaT
tm.assert_frame_equal(actual, dfn)
with pytest.raises(TypeError):
df1 + np.nan
with pytest.raises(TypeError):
df1 - np.nan
actual = df1 + pd.NaT # NaT is datetime, not timedelta
tm.assert_frame_equal(actual, dfn)
actual = df1 - pd.NaT
tm.assert_frame_equal(actual, dfn)
# TODO: moved from tests.series.test_operators, needs splitting, cleanup,
# de-duplication, box-parametrization...
def test_operators_timedelta64(self):
# series ops
v1 = pd.date_range("2012-1-1", periods=3, freq="D")
v2 = pd.date_range("2012-1-2", periods=3, freq="D")
rs = Series(v2) - Series(v1)
xp = Series(1e9 * 3600 * 24, rs.index).astype("int64").astype("timedelta64[ns]")
tm.assert_series_equal(rs, xp)
assert rs.dtype == "timedelta64[ns]"
df = DataFrame(dict(A=v1))
td = Series([timedelta(days=i) for i in range(3)])
assert td.dtype == "timedelta64[ns]"
# series on the rhs
result = df["A"] - df["A"].shift()
assert result.dtype == "timedelta64[ns]"
result = df["A"] + td
assert result.dtype == "M8[ns]"
# scalar Timestamp on rhs
maxa = df["A"].max()
assert isinstance(maxa, Timestamp)
resultb = df["A"] - df["A"].max()
assert resultb.dtype == "timedelta64[ns]"
# timestamp on lhs
result = resultb + df["A"]
values = [Timestamp("20111230"), Timestamp("20120101"), Timestamp("20120103")]
expected = Series(values, name="A")
tm.assert_series_equal(result, expected)
# datetimes on rhs
result = df["A"] - datetime(2001, 1, 1)
expected = Series([timedelta(days=4017 + i) for i in range(3)], name="A")
tm.assert_series_equal(result, expected)
assert result.dtype == "m8[ns]"
d = datetime(2001, 1, 1, 3, 4)
resulta = df["A"] - d
assert resulta.dtype == "m8[ns]"
# roundtrip
resultb = resulta + d
tm.assert_series_equal(df["A"], resultb)
# timedeltas on rhs
td = timedelta(days=1)
resulta = df["A"] + td
resultb = resulta - td
tm.assert_series_equal(resultb, df["A"])
assert resultb.dtype == "M8[ns]"
# roundtrip
td = timedelta(minutes=5, seconds=3)
resulta = df["A"] + td
resultb = resulta - td
tm.assert_series_equal(df["A"], resultb)
assert resultb.dtype == "M8[ns]"
# inplace
value = rs[2] + np.timedelta64(timedelta(minutes=5, seconds=1))
rs[2] += np.timedelta64(timedelta(minutes=5, seconds=1))
assert rs[2] == value
def test_timedelta64_ops_nat(self):
# GH 11349
timedelta_series = Series([NaT, Timedelta("1s")])
nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]")
single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]")
# subtraction
tm.assert_series_equal(timedelta_series - NaT, nat_series_dtype_timedelta)
tm.assert_series_equal(-NaT + timedelta_series, nat_series_dtype_timedelta)
tm.assert_series_equal(
timedelta_series - single_nat_dtype_timedelta, nat_series_dtype_timedelta
)
tm.assert_series_equal(
-single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta
)
# addition
tm.assert_series_equal(
nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta
)
tm.assert_series_equal(
NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta
)
tm.assert_series_equal(
nat_series_dtype_timedelta + single_nat_dtype_timedelta,
nat_series_dtype_timedelta,
)
tm.assert_series_equal(
single_nat_dtype_timedelta + nat_series_dtype_timedelta,
nat_series_dtype_timedelta,
)
tm.assert_series_equal(timedelta_series + NaT, nat_series_dtype_timedelta)
tm.assert_series_equal(NaT + timedelta_series, nat_series_dtype_timedelta)
tm.assert_series_equal(
timedelta_series + single_nat_dtype_timedelta, nat_series_dtype_timedelta
)
tm.assert_series_equal(
single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta
)
tm.assert_series_equal(
nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta
)
tm.assert_series_equal(
NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta
)
tm.assert_series_equal(
nat_series_dtype_timedelta + single_nat_dtype_timedelta,
nat_series_dtype_timedelta,
)
tm.assert_series_equal(
single_nat_dtype_timedelta + nat_series_dtype_timedelta,
nat_series_dtype_timedelta,
)
# multiplication
tm.assert_series_equal(
nat_series_dtype_timedelta * 1.0, nat_series_dtype_timedelta
)
tm.assert_series_equal(
1.0 * nat_series_dtype_timedelta, nat_series_dtype_timedelta
)
tm.assert_series_equal(timedelta_series * 1, timedelta_series)
tm.assert_series_equal(1 * timedelta_series, timedelta_series)
tm.assert_series_equal(timedelta_series * 1.5, Series([NaT, Timedelta("1.5s")]))
tm.assert_series_equal(1.5 * timedelta_series, Series([NaT, Timedelta("1.5s")]))
tm.assert_series_equal(timedelta_series * np.nan, nat_series_dtype_timedelta)
tm.assert_series_equal(np.nan * timedelta_series, nat_series_dtype_timedelta)
# division
tm.assert_series_equal(timedelta_series / 2, Series([NaT, Timedelta("0.5s")]))
tm.assert_series_equal(timedelta_series / 2.0, Series([NaT, Timedelta("0.5s")]))
tm.assert_series_equal(timedelta_series / np.nan, nat_series_dtype_timedelta)
# -------------------------------------------------------------
# Invalid Operations
def test_td64arr_add_str_invalid(self, box_with_array):
# GH#13624
tdi = TimedeltaIndex(["1 day", "2 days"])
tdi = tm.box_expected(tdi, box_with_array)
with pytest.raises(TypeError):
tdi + "a"
with pytest.raises(TypeError):
"a" + tdi
@pytest.mark.parametrize("other", [3.14, np.array([2.0, 3.0])])
def test_td64arr_add_sub_float(self, box_with_array, other):
tdi = TimedeltaIndex(["-1 days", "-1 days"])
tdarr = tm.box_expected(tdi, box_with_array)
with pytest.raises(TypeError):
tdarr + other
with pytest.raises(TypeError):
other + tdarr
with pytest.raises(TypeError):
tdarr - other
with pytest.raises(TypeError):
other - tdarr
@pytest.mark.parametrize("freq", [None, "H"])
def test_td64arr_sub_period(self, box_with_array, freq):
# GH#13078
# not supported, check TypeError
p = pd.Period("2011-01-01", freq="D")
idx = TimedeltaIndex(["1 hours", "2 hours"], freq=freq)
idx = tm.box_expected(idx, box_with_array)
with pytest.raises(TypeError):
idx - p
with pytest.raises(TypeError):
p - idx
@pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "H"])
@pytest.mark.parametrize("tdi_freq", [None, "H"])
def test_td64arr_sub_pi(self, box_with_array, tdi_freq, pi_freq):
# GH#20049 subtracting PeriodIndex should raise TypeError
tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq)
dti = Timestamp("2018-03-07 17:16:40") + tdi
pi = dti.to_period(pi_freq)
# TODO: parametrize over box for pi?
tdi = tm.box_expected(tdi, box_with_array)
with pytest.raises(TypeError):
tdi - pi
# -------------------------------------------------------------
# Binary operations td64 arraylike and datetime-like
def test_td64arr_sub_timestamp_raises(self, box_with_array):
idx = TimedeltaIndex(["1 day", "2 day"])
idx = tm.box_expected(idx, box_with_array)
msg = (
"cannot subtract a datelike from|"
"Could not operate|"
"cannot perform operation"
)
with pytest.raises(TypeError, match=msg):
idx - Timestamp("2011-01-01")
def test_td64arr_add_timestamp(self, box_with_array, tz_naive_fixture):
# GH#23215
# TODO: parametrize over scalar datetime types?
tz = tz_naive_fixture
other = Timestamp("2011-01-01", tz=tz)
idx = TimedeltaIndex(["1 day", "2 day"])
expected = DatetimeIndex(["2011-01-02", "2011-01-03"], tz=tz)
idx = tm.box_expected(idx, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = idx + other
tm.assert_equal(result, expected)
result = other + idx
tm.assert_equal(result, expected)
def test_td64arr_add_sub_timestamp(self, box_with_array):
# GH#11925
ts = Timestamp("2012-01-01")
# TODO: parametrize over types of datetime scalar?
tdi = timedelta_range("1 day", periods=3)
expected = pd.date_range("2012-01-02", periods=3)
tdarr = tm.box_expected(tdi, box_with_array)
expected = tm.box_expected(expected, box_with_array)
tm.assert_equal(ts + tdarr, expected)
tm.assert_equal(tdarr + ts, expected)
expected2 = pd.date_range("2011-12-31", periods=3, freq="-1D")
expected2 = tm.box_expected(expected2, box_with_array)
tm.assert_equal(ts - tdarr, expected2)
tm.assert_equal(ts + (-tdarr), expected2)
with pytest.raises(TypeError):
tdarr - ts
def test_tdi_sub_dt64_array(self, box_with_array):
dti = pd.date_range("2016-01-01", periods=3)
tdi = dti - dti.shift(1)
dtarr = dti.values
expected = pd.DatetimeIndex(dtarr) - tdi
tdi = tm.box_expected(tdi, box_with_array)
expected = tm.box_expected(expected, box_with_array)
with pytest.raises(TypeError):
tdi - dtarr
# TimedeltaIndex.__rsub__
result = dtarr - tdi
tm.assert_equal(result, expected)
def test_tdi_add_dt64_array(self, box_with_array):
dti = pd.date_range("2016-01-01", periods=3)
tdi = dti - dti.shift(1)
dtarr = dti.values
expected = pd.DatetimeIndex(dtarr) + tdi
tdi = tm.box_expected(tdi, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = tdi + dtarr
tm.assert_equal(result, expected)
result = dtarr + tdi
tm.assert_equal(result, expected)
def test_td64arr_add_datetime64_nat(self, box_with_array):
# GH#23215
other = np.datetime64("NaT")
tdi = timedelta_range("1 day", periods=3)
expected = pd.DatetimeIndex(["NaT", "NaT", "NaT"])
tdser = tm.box_expected(tdi, box_with_array)
expected = tm.box_expected(expected, box_with_array)
tm.assert_equal(tdser + other, expected)
tm.assert_equal(other + tdser, expected)
# ------------------------------------------------------------------
# Operations with int-like others
def test_td64arr_add_int_series_invalid(self, box):
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
tdser = tm.box_expected(tdser, box)
err = TypeError if box is not pd.Index else NullFrequencyError
int_ser = Series([2, 3, 4])
with pytest.raises(err):
tdser + int_ser
with pytest.raises(err):
int_ser + tdser
with pytest.raises(err):
tdser - int_ser
with pytest.raises(err):
int_ser - tdser
def test_td64arr_add_intlike(self, box_with_array):
# GH#19123
tdi = TimedeltaIndex(["59 days", "59 days", "NaT"])
ser = tm.box_expected(tdi, box_with_array)
err = TypeError
if box_with_array in [pd.Index, tm.to_array]:
err = NullFrequencyError
other = Series([20, 30, 40], dtype="uint8")
# TODO: separate/parametrize
with pytest.raises(err):
ser + 1
with pytest.raises(err):
ser - 1
with pytest.raises(err):
ser + other
with pytest.raises(err):
ser - other
with pytest.raises(err):
ser + np.array(other)
with pytest.raises(err):
ser - np.array(other)
with pytest.raises(err):
ser + pd.Index(other)
with pytest.raises(err):
ser - pd.Index(other)
@pytest.mark.parametrize("scalar", [1, 1.5, np.array(2)])
def test_td64arr_add_sub_numeric_scalar_invalid(self, box_with_array, scalar):
box = box_with_array
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
tdser = tm.box_expected(tdser, box)
err = TypeError
if box in [pd.Index, tm.to_array] and not isinstance(scalar, float):
err = NullFrequencyError
with pytest.raises(err):
tdser + scalar
with pytest.raises(err):
scalar + tdser
with pytest.raises(err):
tdser - scalar
with pytest.raises(err):
scalar - tdser
@pytest.mark.parametrize(
"dtype",
[
"int64",
"int32",
"int16",
"uint64",
"uint32",
"uint16",
"uint8",
"float64",
"float32",
"float16",
],
)
@pytest.mark.parametrize(
"vec",
[
np.array([1, 2, 3]),
pd.Index([1, 2, 3]),
Series([1, 2, 3])
# TODO: Add DataFrame in here?
],
ids=lambda x: type(x).__name__,
)
def test_td64arr_add_sub_numeric_arr_invalid(self, box, vec, dtype):
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
tdser = tm.box_expected(tdser, box)
err = TypeError
if box is pd.Index and not dtype.startswith("float"):
err = NullFrequencyError
vector = vec.astype(dtype)
with pytest.raises(err):
tdser + vector
with pytest.raises(err):
vector + tdser
with pytest.raises(err):
tdser - vector
with pytest.raises(err):
vector - tdser
# ------------------------------------------------------------------
# Operations with timedelta-like others
# TODO: this was taken from tests.series.test_ops; de-duplicate
@pytest.mark.parametrize(
"scalar_td",
[
timedelta(minutes=5, seconds=4),
Timedelta(minutes=5, seconds=4),
Timedelta("5m4s").to_timedelta64(),
],
)
def test_operators_timedelta64_with_timedelta(self, scalar_td):
# smoke tests
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
td1 + scalar_td
scalar_td + td1
td1 - scalar_td
scalar_td - td1
td1 / scalar_td
scalar_td / td1
# TODO: this was taken from tests.series.test_ops; de-duplicate
def test_timedelta64_operations_with_timedeltas(self):
# td operate with td
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td2 = timedelta(minutes=5, seconds=4)
result = td1 - td2
expected = Series([timedelta(seconds=0)] * 3) - Series(
[timedelta(seconds=1)] * 3
)
assert result.dtype == "m8[ns]"
tm.assert_series_equal(result, expected)
result2 = td2 - td1
expected = Series([timedelta(seconds=1)] * 3) - Series(
[timedelta(seconds=0)] * 3
)
tm.assert_series_equal(result2, expected)
# roundtrip
tm.assert_series_equal(result + td2, td1)
# Now again, using pd.to_timedelta, which should build
# a Series or a scalar, depending on input.
td1 = Series(pd.to_timedelta(["00:05:03"] * 3))
td2 = pd.to_timedelta("00:05:04")
result = td1 - td2
expected = Series([timedelta(seconds=0)] * 3) - Series(
[timedelta(seconds=1)] * 3
)
assert result.dtype == "m8[ns]"
tm.assert_series_equal(result, expected)
result2 = td2 - td1
expected = Series([timedelta(seconds=1)] * 3) - Series(
[timedelta(seconds=0)] * 3
)
tm.assert_series_equal(result2, expected)
# roundtrip
tm.assert_series_equal(result + td2, td1)
def test_td64arr_add_td64_array(self, box):
dti = pd.date_range("2016-01-01", periods=3)
tdi = dti - dti.shift(1)
tdarr = tdi.values
expected = 2 * tdi
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, box)
result = tdi + tdarr
tm.assert_equal(result, expected)
result = tdarr + tdi
tm.assert_equal(result, expected)
def test_td64arr_sub_td64_array(self, box):
dti = pd.date_range("2016-01-01", periods=3)
tdi = dti - dti.shift(1)
tdarr = tdi.values
expected = 0 * tdi
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, box)
result = tdi - tdarr
tm.assert_equal(result, expected)
result = tdarr - tdi
tm.assert_equal(result, expected)
# TODO: parametrize over [add, sub, radd, rsub]?
@pytest.mark.parametrize(
"names",
[
(None, None, None),
("Egon", "Venkman", None),
("NCC1701D", "NCC1701D", "NCC1701D"),
],
)
def test_td64arr_add_sub_tdi(self, box, names):
# GH#17250 make sure result dtype is correct
# GH#19043 make sure names are propagated correctly
if box is pd.DataFrame and names[1] == "Venkman":
pytest.skip(
"Name propagation for DataFrame does not behave like "
"it does for Index/Series"
)
tdi = TimedeltaIndex(["0 days", "1 day"], name=names[0])
ser = Series([Timedelta(hours=3), Timedelta(hours=4)], name=names[1])
expected = Series(
[Timedelta(hours=3), Timedelta(days=1, hours=4)], name=names[2]
)
ser = tm.box_expected(ser, box)
expected = tm.box_expected(expected, box)
result = tdi + ser
tm.assert_equal(result, expected)
if box is not pd.DataFrame:
assert result.dtype == "timedelta64[ns]"
else:
assert result.dtypes[0] == "timedelta64[ns]"
result = ser + tdi
tm.assert_equal(result, expected)
if box is not pd.DataFrame:
assert result.dtype == "timedelta64[ns]"
else:
assert result.dtypes[0] == "timedelta64[ns]"
expected = Series(
[Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=names[2]
)
expected = tm.box_expected(expected, box)
result = tdi - ser
tm.assert_equal(result, expected)
if box is not pd.DataFrame:
assert result.dtype == "timedelta64[ns]"
else:
assert result.dtypes[0] == "timedelta64[ns]"
result = ser - tdi
tm.assert_equal(result, -expected)
if box is not pd.DataFrame:
assert result.dtype == "timedelta64[ns]"
else:
assert result.dtypes[0] == "timedelta64[ns]"
def test_td64arr_add_sub_td64_nat(self, box):
# GH#23320 special handling for timedelta64("NaT")
tdi = pd.TimedeltaIndex([NaT, Timedelta("1s")])
other = np.timedelta64("NaT")
expected = pd.TimedeltaIndex(["NaT"] * 2)
obj = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, box)
result = obj + other
tm.assert_equal(result, expected)
result = other + obj
tm.assert_equal(result, expected)
result = obj - other
tm.assert_equal(result, expected)
result = other - obj
tm.assert_equal(result, expected)
def test_td64arr_sub_NaT(self, box):
# GH#18808
ser = Series([NaT, Timedelta("1s")])
expected = Series([NaT, NaT], dtype="timedelta64[ns]")
ser = tm.box_expected(ser, box)
expected = tm.box_expected(expected, box)
res = ser - pd.NaT
tm.assert_equal(res, expected)
def test_td64arr_add_timedeltalike(self, two_hours, box):
# only test adding/sub offsets as + is now numeric
rng = timedelta_range("1 days", "10 days")
expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
rng = tm.box_expected(rng, box)
expected = tm.box_expected(expected, box)
result = rng + two_hours
tm.assert_equal(result, expected)
def test_td64arr_sub_timedeltalike(self, two_hours, box):
# only test adding/sub offsets as - is now numeric
rng = timedelta_range("1 days", "10 days")
expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
rng = tm.box_expected(rng, box)
expected = tm.box_expected(expected, box)
result = rng - two_hours
tm.assert_equal(result, expected)
# ------------------------------------------------------------------
# __add__/__sub__ with DateOffsets and arrays of DateOffsets
# TODO: this was taken from tests.series.test_operators; de-duplicate
def test_timedelta64_operations_with_DateOffset(self):
# GH#10699
td = Series([timedelta(minutes=5, seconds=3)] * 3)
result = td + pd.offsets.Minute(1)
expected = Series([timedelta(minutes=6, seconds=3)] * 3)
tm.assert_series_equal(result, expected)
result = td - pd.offsets.Minute(1)
expected = Series([timedelta(minutes=4, seconds=3)] * 3)
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(PerformanceWarning):
result = td + Series(
[pd.offsets.Minute(1), pd.offsets.Second(3), pd.offsets.Hour(2)]
)
expected = Series(
[
timedelta(minutes=6, seconds=3),
timedelta(minutes=5, seconds=6),
timedelta(hours=2, minutes=5, seconds=3),
]
)
tm.assert_series_equal(result, expected)
result = td + pd.offsets.Minute(1) + pd.offsets.Second(12)
expected = Series([timedelta(minutes=6, seconds=15)] * 3)
tm.assert_series_equal(result, expected)
# valid DateOffsets
for do in ["Hour", "Minute", "Second", "Day", "Micro", "Milli", "Nano"]:
op = getattr(pd.offsets, do)
td + op(5)
op(5) + td
td - op(5)
op(5) - td
@pytest.mark.parametrize(
"names", [(None, None, None), ("foo", "bar", None), ("foo", "foo", "foo")]
)
def test_td64arr_add_offset_index(self, names, box):
# GH#18849, GH#19744
if box is pd.DataFrame and names[1] == "bar":
pytest.skip(
"Name propagation for DataFrame does not behave like "
"it does for Index/Series"
)
tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
other = pd.Index([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)], name=names[1])
expected = TimedeltaIndex(
[tdi[n] + other[n] for n in range(len(tdi))], freq="infer", name=names[2]
)
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, box)
# The DataFrame operation is transposed and so operates as separate
# scalar operations, which do not issue a PerformanceWarning
warn = PerformanceWarning if box is not pd.DataFrame else None
with tm.assert_produces_warning(warn):
res = tdi + other
tm.assert_equal(res, expected)
with tm.assert_produces_warning(warn):
res2 = other + tdi
tm.assert_equal(res2, expected)
# TODO: combine with test_td64arr_add_offset_index by parametrizing
# over second box?
def test_td64arr_add_offset_array(self, box):
# GH#18849
tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
other = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)])
expected = TimedeltaIndex(
[tdi[n] + other[n] for n in range(len(tdi))], freq="infer"
)
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, box)
# The DataFrame operation is transposed and so operates as separate
# scalar operations, which do not issue a PerformanceWarning
warn = PerformanceWarning if box is not pd.DataFrame else None
with tm.assert_produces_warning(warn):
res = tdi + other
tm.assert_equal(res, expected)
with tm.assert_produces_warning(warn):
res2 = other + tdi
tm.assert_equal(res2, expected)
@pytest.mark.parametrize(
"names", [(None, None, None), ("foo", "bar", None), ("foo", "foo", "foo")]
)
def test_td64arr_sub_offset_index(self, names, box):
# GH#18824, GH#19744
if box is pd.DataFrame and names[1] == "bar":
pytest.skip(
"Name propagation for DataFrame does not behave like "
"it does for Index/Series"
)
tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
other = pd.Index([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)], name=names[1])
expected = TimedeltaIndex(
[tdi[n] - other[n] for n in range(len(tdi))], freq="infer", name=names[2]
)
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, box)
# The DataFrame operation is transposed and so operates as separate
# scalar operations, which do not issue a PerformanceWarning
warn = PerformanceWarning if box is not pd.DataFrame else None
with tm.assert_produces_warning(warn):
res = tdi - other
tm.assert_equal(res, expected)
def test_td64arr_sub_offset_array(self, box_with_array):
# GH#18824
tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
other = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)])
expected = TimedeltaIndex(
[tdi[n] - other[n] for n in range(len(tdi))], freq="infer"
)
tdi = tm.box_expected(tdi, box_with_array)
expected = tm.box_expected(expected, box_with_array)
# The DataFrame operation is transposed and so operates as separate
# scalar operations, which do not issue a PerformanceWarning
warn = None if box_with_array is pd.DataFrame else PerformanceWarning
with tm.assert_produces_warning(warn):
res = tdi - other
tm.assert_equal(res, expected)
@pytest.mark.parametrize(
"names", [(None, None, None), ("foo", "bar", None), ("foo", "foo", "foo")]
)
def test_td64arr_with_offset_series(self, names, box_df_fail):
# GH#18849
box = box_df_fail
box2 = Series if box in [pd.Index, tm.to_array] else box
tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0])
other = Series([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)], name=names[1])
expected_add = Series(
[tdi[n] + other[n] for n in range(len(tdi))], name=names[2]
)
tdi = tm.box_expected(tdi, box)
expected_add = tm.box_expected(expected_add, box2)
with tm.assert_produces_warning(PerformanceWarning):
res = tdi + other
tm.assert_equal(res, expected_add)
with tm.assert_produces_warning(PerformanceWarning):
res2 = other + tdi
tm.assert_equal(res2, expected_add)
# TODO: separate/parametrize add/sub test?
expected_sub = Series(
[tdi[n] - other[n] for n in range(len(tdi))], name=names[2]
)
expected_sub = tm.box_expected(expected_sub, box2)
with tm.assert_produces_warning(PerformanceWarning):
res3 = tdi - other
tm.assert_equal(res3, expected_sub)
@pytest.mark.parametrize("obox", [np.array, pd.Index, pd.Series])
def test_td64arr_addsub_anchored_offset_arraylike(self, obox, box_with_array):
# GH#18824
tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"])
tdi = tm.box_expected(tdi, box_with_array)
anchored = obox([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)])
# addition/subtraction ops with anchored offsets should issue
# a PerformanceWarning and _then_ raise a TypeError.
with pytest.raises(TypeError):
with tm.assert_produces_warning(PerformanceWarning):
tdi + anchored
with pytest.raises(TypeError):
with tm.assert_produces_warning(PerformanceWarning):
anchored + tdi
with pytest.raises(TypeError):
with tm.assert_produces_warning(PerformanceWarning):
tdi - anchored
with pytest.raises(TypeError):
with tm.assert_produces_warning(PerformanceWarning):
anchored - tdi
class TestTimedeltaArraylikeMulDivOps:
# Tests for timedelta64[ns]
# __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
# TODO: Moved from tests.series.test_operators; needs cleanup
@pytest.mark.parametrize("m", [1, 3, 10])
@pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
def test_timedelta64_conversions(self, m, unit):
startdate = Series(pd.date_range("2013-01-01", "2013-01-03"))
enddate = Series(pd.date_range("2013-03-01", "2013-03-03"))
ser = enddate - startdate
ser[2] = np.nan
# op
expected = Series([x / np.timedelta64(m, unit) for x in ser])
result = ser / np.timedelta64(m, unit)
tm.assert_series_equal(result, expected)
# reverse op
expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in ser])
result = np.timedelta64(m, unit) / ser
tm.assert_series_equal(result, expected)
# ------------------------------------------------------------------
# Multiplication
# organized with scalar others first, then array-like
def test_td64arr_mul_int(self, box_with_array):
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
idx = tm.box_expected(idx, box_with_array)
result = idx * 1
tm.assert_equal(result, idx)
result = 1 * idx
tm.assert_equal(result, idx)
def test_td64arr_mul_tdlike_scalar_raises(self, two_hours, box_with_array):
rng = timedelta_range("1 days", "10 days", name="foo")
rng = tm.box_expected(rng, box_with_array)
with pytest.raises(TypeError):
rng * two_hours
def test_tdi_mul_int_array_zerodim(self, box_with_array):
rng5 = np.arange(5, dtype="int64")
idx = TimedeltaIndex(rng5)
expected = TimedeltaIndex(rng5 * 5)
idx = tm.box_expected(idx, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = idx * np.array(5, dtype="int64")
tm.assert_equal(result, expected)
def test_tdi_mul_int_array(self, box_with_array):
rng5 = np.arange(5, dtype="int64")
idx = TimedeltaIndex(rng5)
expected = TimedeltaIndex(rng5 ** 2)
idx = tm.box_expected(idx, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = idx * rng5
tm.assert_equal(result, expected)
def test_tdi_mul_int_series(self, box_with_array):
box = box_with_array
xbox = pd.Series if box in [pd.Index, tm.to_array] else box
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
expected = TimedeltaIndex(np.arange(5, dtype="int64") ** 2)
idx = tm.box_expected(idx, box)
expected = tm.box_expected(expected, xbox)
result = idx * pd.Series(np.arange(5, dtype="int64"))
tm.assert_equal(result, expected)
def test_tdi_mul_float_series(self, box_with_array):
box = box_with_array
xbox = pd.Series if box in [pd.Index, tm.to_array] else box
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
idx = tm.box_expected(idx, box)
rng5f = np.arange(5, dtype="float64")
expected = TimedeltaIndex(rng5f * (rng5f + 1.0))
expected = tm.box_expected(expected, xbox)
result = idx * Series(rng5f + 1.0)
tm.assert_equal(result, expected)
# TODO: Put Series/DataFrame in others?
@pytest.mark.parametrize(
"other",
[
np.arange(1, 11),
pd.Int64Index(range(1, 11)),
pd.UInt64Index(range(1, 11)),
pd.Float64Index(range(1, 11)),
pd.RangeIndex(1, 11),
],
ids=lambda x: type(x).__name__,
)
def test_tdi_rmul_arraylike(self, other, box_with_array):
box = box_with_array
xbox = get_upcast_box(box, other)
tdi = TimedeltaIndex(["1 Day"] * 10)
expected = timedelta_range("1 days", "10 days")
expected._data.freq = None
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, xbox)
result = other * tdi
tm.assert_equal(result, expected)
commute = tdi * other
tm.assert_equal(commute, expected)
# ------------------------------------------------------------------
# __div__, __rdiv__
def test_td64arr_div_nat_invalid(self, box_with_array):
# don't allow division by NaT (maybe could in the future)
rng = timedelta_range("1 days", "10 days", name="foo")
rng = tm.box_expected(rng, box_with_array)
with pytest.raises(TypeError, match="'?true_divide'? cannot use operands"):
rng / pd.NaT
with pytest.raises(TypeError, match="Cannot divide NaTType by"):
pd.NaT / rng
def test_td64arr_div_td64nat(self, box_with_array):
# GH#23829
rng = timedelta_range("1 days", "10 days")
rng = tm.box_expected(rng, box_with_array)
other = np.timedelta64("NaT")
expected = np.array([np.nan] * 10)
expected = tm.box_expected(expected, box_with_array)
result = rng / other
tm.assert_equal(result, expected)
result = other / rng
tm.assert_equal(result, expected)
def test_td64arr_div_int(self, box_with_array):
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
idx = tm.box_expected(idx, box_with_array)
result = idx / 1
tm.assert_equal(result, idx)
with pytest.raises(TypeError, match="Cannot divide"):
# GH#23829
1 / idx
def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array):
# GH#20088, GH#22163 ensure DataFrame returns correct dtype
rng = timedelta_range("1 days", "10 days", name="foo")
expected = pd.Float64Index((np.arange(10) + 1) * 12, name="foo")
rng = tm.box_expected(rng, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = rng / two_hours
tm.assert_equal(result, expected)
result = two_hours / rng
expected = 1 / expected
tm.assert_equal(result, expected)
def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array):
rng = TimedeltaIndex(["1 days", pd.NaT, "2 days"], name="foo")
expected = pd.Float64Index([12, np.nan, 24], name="foo")
rng = tm.box_expected(rng, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = rng / two_hours
tm.assert_equal(result, expected)
result = two_hours / rng
expected = 1 / expected
tm.assert_equal(result, expected)
def test_td64arr_div_td64_ndarray(self, box_with_array):
# GH#22631
rng = TimedeltaIndex(["1 days", pd.NaT, "2 days"])
expected = pd.Float64Index([12, np.nan, 24])
rng = tm.box_expected(rng, box_with_array)
expected = tm.box_expected(expected, box_with_array)
other = np.array([2, 4, 2], dtype="m8[h]")
result = rng / other
tm.assert_equal(result, expected)
result = rng / tm.box_expected(other, box_with_array)
tm.assert_equal(result, expected)
result = rng / other.astype(object)
tm.assert_equal(result, expected)
result = rng / list(other)
tm.assert_equal(result, expected)
# reversed op
expected = 1 / expected
result = other / rng
tm.assert_equal(result, expected)
result = tm.box_expected(other, box_with_array) / rng
tm.assert_equal(result, expected)
result = other.astype(object) / rng
tm.assert_equal(result, expected)
result = list(other) / rng
tm.assert_equal(result, expected)
def test_tdarr_div_length_mismatch(self, box_with_array):
rng = TimedeltaIndex(["1 days", pd.NaT, "2 days"])
mismatched = [1, 2, 3, 4]
rng = tm.box_expected(rng, box_with_array)
for obj in [mismatched, mismatched[:2]]:
# one shorter, one longer
for other in [obj, np.array(obj), pd.Index(obj)]:
with pytest.raises(ValueError):
rng / other
with pytest.raises(ValueError):
other / rng
# ------------------------------------------------------------------
# __floordiv__, __rfloordiv__
def test_td64arr_floordiv_tdscalar(self, box_with_array, scalar_td):
# GH#18831
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
expected = Series([0, 0, np.nan])
td1 = tm.box_expected(td1, box_with_array, transpose=False)
expected = tm.box_expected(expected, box_with_array, transpose=False)
result = td1 // scalar_td
tm.assert_equal(result, expected)
def test_td64arr_rfloordiv_tdscalar(self, box_with_array, scalar_td):
# GH#18831
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
expected = Series([1, 1, np.nan])
td1 = tm.box_expected(td1, box_with_array, transpose=False)
expected = tm.box_expected(expected, box_with_array, transpose=False)
result = scalar_td // td1
tm.assert_equal(result, expected)
def test_td64arr_rfloordiv_tdscalar_explicit(self, box_with_array, scalar_td):
# GH#18831
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
expected = Series([1, 1, np.nan])
td1 = tm.box_expected(td1, box_with_array, transpose=False)
expected = tm.box_expected(expected, box_with_array, transpose=False)
# We can test __rfloordiv__ using this syntax,
# see `test_timedelta_rfloordiv`
result = td1.__rfloordiv__(scalar_td)
tm.assert_equal(result, expected)
def test_td64arr_floordiv_int(self, box_with_array):
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
idx = tm.box_expected(idx, box_with_array)
result = idx // 1
tm.assert_equal(result, idx)
pattern = "floor_divide cannot use operands|Cannot divide int by Timedelta*"
with pytest.raises(TypeError, match=pattern):
1 // idx
def test_td64arr_floordiv_tdlike_scalar(self, two_hours, box_with_array):
tdi = timedelta_range("1 days", "10 days", name="foo")
expected = pd.Int64Index((np.arange(10) + 1) * 12, name="foo")
tdi = tm.box_expected(tdi, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = tdi // two_hours
tm.assert_equal(result, expected)
# TODO: Is this redundant with test_td64arr_floordiv_tdlike_scalar?
@pytest.mark.parametrize(
"scalar_td",
[
timedelta(minutes=10, seconds=7),
Timedelta("10m7s"),
Timedelta("10m7s").to_timedelta64(),
],
ids=lambda x: type(x).__name__,
)
def test_td64arr_rfloordiv_tdlike_scalar(self, scalar_td, box_with_array):
# GH#19125
tdi = TimedeltaIndex(["00:05:03", "00:05:03", pd.NaT], freq=None)
expected = pd.Index([2.0, 2.0, np.nan])
tdi = tm.box_expected(tdi, box_with_array, transpose=False)
expected = tm.box_expected(expected, box_with_array, transpose=False)
res = tdi.__rfloordiv__(scalar_td)
tm.assert_equal(res, expected)
expected = pd.Index([0.0, 0.0, np.nan])
expected = tm.box_expected(expected, box_with_array, transpose=False)
res = tdi // (scalar_td)
tm.assert_equal(res, expected)
# ------------------------------------------------------------------
# mod, divmod
# TODO: operations with timedelta-like arrays, numeric arrays,
# reversed ops
def test_td64arr_mod_tdscalar(self, box_with_array, three_days):
tdi = timedelta_range("1 Day", "9 days")
tdarr = tm.box_expected(tdi, box_with_array)
expected = TimedeltaIndex(["1 Day", "2 Days", "0 Days"] * 3)
expected = tm.box_expected(expected, box_with_array)
result = tdarr % three_days
tm.assert_equal(result, expected)
if box_with_array is pd.DataFrame:
pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__")
result = divmod(tdarr, three_days)
tm.assert_equal(result[1], expected)
tm.assert_equal(result[0], tdarr // three_days)
def test_td64arr_mod_int(self, box_with_array):
tdi = timedelta_range("1 ns", "10 ns", periods=10)
tdarr = tm.box_expected(tdi, box_with_array)
expected = TimedeltaIndex(["1 ns", "0 ns"] * 5)
expected = tm.box_expected(expected, box_with_array)
result = tdarr % 2
tm.assert_equal(result, expected)
with pytest.raises(TypeError):
2 % tdarr
if box_with_array is pd.DataFrame:
pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__")
result = divmod(tdarr, 2)
tm.assert_equal(result[1], expected)
tm.assert_equal(result[0], tdarr // 2)
def test_td64arr_rmod_tdscalar(self, box_with_array, three_days):
tdi = timedelta_range("1 Day", "9 days")
tdarr = tm.box_expected(tdi, box_with_array)
expected = ["0 Days", "1 Day", "0 Days"] + ["3 Days"] * 6
expected = TimedeltaIndex(expected)
expected = tm.box_expected(expected, box_with_array)
result = three_days % tdarr
tm.assert_equal(result, expected)
if box_with_array is pd.DataFrame:
pytest.xfail("DataFrame does not have __divmod__ or __rdivmod__")
result = divmod(three_days, tdarr)
tm.assert_equal(result[1], expected)
tm.assert_equal(result[0], three_days // tdarr)
# ------------------------------------------------------------------
# Operations with invalid others
def test_td64arr_mul_tdscalar_invalid(self, box_with_array, scalar_td):
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
td1 = tm.box_expected(td1, box_with_array)
# check that we are getting a TypeError
# with 'operate' (from core/ops.py) for the ops that are not
# defined
pattern = "operate|unsupported|cannot|not supported"
with pytest.raises(TypeError, match=pattern):
td1 * scalar_td
with pytest.raises(TypeError, match=pattern):
scalar_td * td1
def test_td64arr_mul_too_short_raises(self, box_with_array):
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
idx = tm.box_expected(idx, box_with_array)
with pytest.raises(TypeError):
idx * idx[:3]
with pytest.raises(ValueError):
idx * np.array([1, 2])
def test_td64arr_mul_td64arr_raises(self, box_with_array):
idx = TimedeltaIndex(np.arange(5, dtype="int64"))
idx = tm.box_expected(idx, box_with_array)
with pytest.raises(TypeError):
idx * idx
# ------------------------------------------------------------------
# Operations with numeric others
@pytest.mark.parametrize("one", [1, np.array(1), 1.0, np.array(1.0)])
def test_td64arr_mul_numeric_scalar(self, box_with_array, one):
# GH#4521
# divide/multiply by integers
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
expected = Series(["-59 Days", "-59 Days", "NaT"], dtype="timedelta64[ns]")
tdser = tm.box_expected(tdser, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = tdser * (-one)
tm.assert_equal(result, expected)
result = (-one) * tdser
tm.assert_equal(result, expected)
expected = Series(["118 Days", "118 Days", "NaT"], dtype="timedelta64[ns]")
expected = tm.box_expected(expected, box_with_array)
result = tdser * (2 * one)
tm.assert_equal(result, expected)
result = (2 * one) * tdser
tm.assert_equal(result, expected)
@pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)])
def test_td64arr_div_numeric_scalar(self, box_with_array, two):
# GH#4521
# divide/multiply by integers
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]")
tdser = tm.box_expected(tdser, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = tdser / two
tm.assert_equal(result, expected)
with pytest.raises(TypeError, match="Cannot divide"):
two / tdser
@pytest.mark.parametrize(
"dtype",
[
"int64",
"int32",
"int16",
"uint64",
"uint32",
"uint16",
"uint8",
"float64",
"float32",
"float16",
],
)
@pytest.mark.parametrize(
"vector",
[np.array([20, 30, 40]), pd.Index([20, 30, 40]), Series([20, 30, 40])],
ids=lambda x: type(x).__name__,
)
def test_td64arr_rmul_numeric_array(self, box_with_array, vector, dtype):
# GH#4521
# divide/multiply by integers
xbox = get_upcast_box(box_with_array, vector)
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
vector = vector.astype(dtype)
expected = Series(["1180 Days", "1770 Days", "NaT"], dtype="timedelta64[ns]")
tdser = tm.box_expected(tdser, box_with_array)
expected = tm.box_expected(expected, xbox)
result = tdser * vector
tm.assert_equal(result, expected)
result = vector * tdser
tm.assert_equal(result, expected)
@pytest.mark.parametrize(
"dtype",
[
"int64",
"int32",
"int16",
"uint64",
"uint32",
"uint16",
"uint8",
"float64",
"float32",
"float16",
],
)
@pytest.mark.parametrize(
"vector",
[np.array([20, 30, 40]), pd.Index([20, 30, 40]), Series([20, 30, 40])],
ids=lambda x: type(x).__name__,
)
def test_td64arr_div_numeric_array(self, box_with_array, vector, dtype):
# GH#4521
# divide/multiply by integers
xbox = get_upcast_box(box_with_array, vector)
tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]")
vector = vector.astype(dtype)
expected = Series(["2.95D", "1D 23H 12m", "NaT"], dtype="timedelta64[ns]")
tdser = tm.box_expected(tdser, box_with_array)
expected = tm.box_expected(expected, xbox)
result = tdser / vector
tm.assert_equal(result, expected)
pattern = (
"true_divide cannot use operands|"
"cannot perform __div__|"
"cannot perform __truediv__|"
"unsupported operand|"
"Cannot divide"
)
with pytest.raises(TypeError, match=pattern):
vector / tdser
if not isinstance(vector, pd.Index):
# Index.__rdiv__ won't try to operate elementwise, just raises
result = tdser / vector.astype(object)
if box_with_array is pd.DataFrame:
expected = [tdser.iloc[0, n] / vector[n] for n in range(len(vector))]
else:
expected = [tdser[n] / vector[n] for n in range(len(tdser))]
expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
with pytest.raises(TypeError, match=pattern):
vector.astype(object) / tdser
@pytest.mark.parametrize(
"names",
[
(None, None, None),
("Egon", "Venkman", None),
("NCC1701D", "NCC1701D", "NCC1701D"),
],
)
def test_td64arr_mul_int_series(self, box_df_fail, names):
# GH#19042 test for correct name attachment
box = box_df_fail # broadcasts along wrong axis, but doesn't raise
tdi = TimedeltaIndex(
["0days", "1day", "2days", "3days", "4days"], name=names[0]
)
# TODO: Should we be parametrizing over types for `ser` too?
ser = Series([0, 1, 2, 3, 4], dtype=np.int64, name=names[1])
expected = Series(
["0days", "1day", "4days", "9days", "16days"],
dtype="timedelta64[ns]",
name=names[2],
)
tdi = tm.box_expected(tdi, box)
box = Series if (box is pd.Index and type(ser) is Series) else box
expected = tm.box_expected(expected, box)
result = ser * tdi
tm.assert_equal(result, expected)
# The direct operation tdi * ser still needs to be fixed.
result = ser.__rmul__(tdi)
tm.assert_equal(result, expected)
# TODO: Should we be parametrizing over types for `ser` too?
@pytest.mark.parametrize(
"names",
[
(None, None, None),
("Egon", "Venkman", None),
("NCC1701D", "NCC1701D", "NCC1701D"),
],
)
def test_float_series_rdiv_td64arr(self, box_with_array, names):
# GH#19042 test for correct name attachment
# TODO: the direct operation TimedeltaIndex / Series still
# needs to be fixed.
box = box_with_array
tdi = TimedeltaIndex(
["0days", "1day", "2days", "3days", "4days"], name=names[0]
)
ser = Series([1.5, 3, 4.5, 6, 7.5], dtype=np.float64, name=names[1])
xname = names[2] if box is not tm.to_array else names[1]
expected = Series(
[tdi[n] / ser[n] for n in range(len(ser))],
dtype="timedelta64[ns]",
name=xname,
)
xbox = box
if box in [pd.Index, tm.to_array] and type(ser) is Series:
xbox = Series
tdi = tm.box_expected(tdi, box)
expected = tm.box_expected(expected, xbox)
result = ser.__rdiv__(tdi)
if box is pd.DataFrame:
# TODO: Should we skip this case sooner or test something else?
assert result is NotImplemented
else:
tm.assert_equal(result, expected)
class TestTimedeltaArraylikeInvalidArithmeticOps:
def test_td64arr_pow_invalid(self, scalar_td, box_with_array):
td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
td1.iloc[2] = np.nan
td1 = tm.box_expected(td1, box_with_array)
# check that we are getting a TypeError
# with 'operate' (from core/ops.py) for the ops that are not
# defined
pattern = "operate|unsupported|cannot|not supported"
with pytest.raises(TypeError, match=pattern):
scalar_td ** td1
with pytest.raises(TypeError, match=pattern):
td1 ** scalar_td