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test_datetime64.py
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# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import datetime, timedelta
from itertools import product, starmap
import operator
import warnings
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.conversion import localize_pydatetime
from pandas._libs.tslibs.offsets import shift_months
from pandas.compat.numpy import np_datetime64_compat
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import (
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
TimedeltaIndex,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.core.arrays import DatetimeArray, TimedeltaArray
from pandas.core.ops import roperator
from pandas.tests.arithmetic.common import (
assert_invalid_addsub_type,
assert_invalid_comparison,
get_upcast_box,
)
# ------------------------------------------------------------------
# Comparisons
class TestDatetime64ArrayLikeComparisons:
# Comparison tests for datetime64 vectors fully parametrized over
# DataFrame/Series/DatetimeIndex/DatetimeArray. Ideally all comparison
# tests will eventually end up here.
def test_compare_zerodim(self, tz_naive_fixture, box_with_array):
# Test comparison with zero-dimensional array is unboxed
tz = tz_naive_fixture
box = box_with_array
xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
dti = date_range("20130101", periods=3, tz=tz)
other = np.array(dti.to_numpy()[0])
dtarr = tm.box_expected(dti, box)
result = dtarr <= other
expected = np.array([True, False, False])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
@pytest.mark.parametrize(
"other",
[
"foo",
-1,
99,
4.0,
object(),
timedelta(days=2),
# GH#19800, GH#19301 datetime.date comparison raises to
# match DatetimeIndex/Timestamp. This also matches the behavior
# of stdlib datetime.datetime
datetime(2001, 1, 1).date(),
# GH#19301 None and NaN are *not* cast to NaT for comparisons
None,
np.nan,
],
)
def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array):
# GH#22074, GH#15966
tz = tz_naive_fixture
rng = date_range("1/1/2000", periods=10, tz=tz)
dtarr = tm.box_expected(rng, box_with_array)
assert_invalid_comparison(dtarr, other, box_with_array)
@pytest.mark.parametrize(
"other",
[
list(range(10)),
np.arange(10),
np.arange(10).astype(np.float32),
np.arange(10).astype(object),
pd.timedelta_range("1ns", periods=10).array,
np.array(pd.timedelta_range("1ns", periods=10)),
list(pd.timedelta_range("1ns", periods=10)),
pd.timedelta_range("1 Day", periods=10).astype(object),
pd.period_range("1971-01-01", freq="D", periods=10).array,
pd.period_range("1971-01-01", freq="D", periods=10).astype(object),
],
)
def test_dt64arr_cmp_arraylike_invalid(self, other, tz_naive_fixture):
# We don't parametrize this over box_with_array because listlike
# other plays poorly with assert_invalid_comparison reversed checks
tz = tz_naive_fixture
dta = date_range("1970-01-01", freq="ns", periods=10, tz=tz)._data
assert_invalid_comparison(dta, other, tm.to_array)
def test_dt64arr_cmp_mixed_invalid(self, tz_naive_fixture):
tz = tz_naive_fixture
dta = date_range("1970-01-01", freq="h", periods=5, tz=tz)._data
other = np.array([0, 1, 2, dta[3], pd.Timedelta(days=1)])
result = dta == other
expected = np.array([False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = dta != other
tm.assert_numpy_array_equal(result, ~expected)
msg = "Invalid comparison between|Cannot compare type|not supported between"
with pytest.raises(TypeError, match=msg):
dta < other
with pytest.raises(TypeError, match=msg):
dta > other
with pytest.raises(TypeError, match=msg):
dta <= other
with pytest.raises(TypeError, match=msg):
dta >= other
def test_dt64arr_nat_comparison(self, tz_naive_fixture, box_with_array):
# GH#22242, GH#22163 DataFrame considered NaT == ts incorrectly
tz = tz_naive_fixture
box = box_with_array
xbox = box if box is not pd.Index else np.ndarray
ts = pd.Timestamp.now(tz)
ser = pd.Series([ts, pd.NaT])
# FIXME: Can't transpose because that loses the tz dtype on
# the NaT column
obj = tm.box_expected(ser, box, transpose=False)
expected = pd.Series([True, False], dtype=np.bool_)
expected = tm.box_expected(expected, xbox, transpose=False)
result = obj == ts
tm.assert_equal(result, expected)
class TestDatetime64SeriesComparison:
# TODO: moved from tests.series.test_operators; needs cleanup
@pytest.mark.parametrize(
"pair",
[
(
[pd.Timestamp("2011-01-01"), NaT, pd.Timestamp("2011-01-03")],
[NaT, NaT, pd.Timestamp("2011-01-03")],
),
(
[pd.Timedelta("1 days"), NaT, pd.Timedelta("3 days")],
[NaT, NaT, pd.Timedelta("3 days")],
),
(
[pd.Period("2011-01", freq="M"), NaT, pd.Period("2011-03", freq="M")],
[NaT, NaT, pd.Period("2011-03", freq="M")],
),
],
)
@pytest.mark.parametrize("reverse", [True, False])
@pytest.mark.parametrize("dtype", [None, object])
def test_nat_comparisons(self, dtype, index_or_series, reverse, pair):
box = index_or_series
l, r = pair
if reverse:
# add lhs / rhs switched data
l, r = r, l
left = Series(l, dtype=dtype)
right = box(r, dtype=dtype)
# Series, Index
expected = Series([False, False, True])
tm.assert_series_equal(left == right, expected)
expected = Series([True, True, False])
tm.assert_series_equal(left != right, expected)
expected = Series([False, False, False])
tm.assert_series_equal(left < right, expected)
expected = Series([False, False, False])
tm.assert_series_equal(left > right, expected)
expected = Series([False, False, True])
tm.assert_series_equal(left >= right, expected)
expected = Series([False, False, True])
tm.assert_series_equal(left <= right, expected)
def test_comparison_invalid(self, tz_naive_fixture, box_with_array):
# GH#4968
# invalid date/int comparisons
tz = tz_naive_fixture
ser = Series(range(5))
ser2 = Series(pd.date_range("20010101", periods=5, tz=tz))
ser = tm.box_expected(ser, box_with_array)
ser2 = tm.box_expected(ser2, box_with_array)
assert_invalid_comparison(ser, ser2, box_with_array)
@pytest.mark.parametrize(
"data",
[
[Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")],
[Timedelta("1 days"), NaT, Timedelta("3 days")],
[Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")],
],
)
@pytest.mark.parametrize("dtype", [None, object])
def test_nat_comparisons_scalar(self, dtype, data, box_with_array):
if box_with_array is tm.to_array and dtype is object:
# dont bother testing ndarray comparison methods as this fails
# on older numpys (since they check object identity)
return
xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
left = Series(data, dtype=dtype)
left = tm.box_expected(left, box_with_array)
expected = [False, False, False]
expected = tm.box_expected(expected, xbox)
tm.assert_equal(left == NaT, expected)
tm.assert_equal(NaT == left, expected)
expected = [True, True, True]
expected = tm.box_expected(expected, xbox)
tm.assert_equal(left != NaT, expected)
tm.assert_equal(NaT != left, expected)
expected = [False, False, False]
expected = tm.box_expected(expected, xbox)
tm.assert_equal(left < NaT, expected)
tm.assert_equal(NaT > left, expected)
tm.assert_equal(left <= NaT, expected)
tm.assert_equal(NaT >= left, expected)
tm.assert_equal(left > NaT, expected)
tm.assert_equal(NaT < left, expected)
tm.assert_equal(left >= NaT, expected)
tm.assert_equal(NaT <= left, expected)
@pytest.mark.parametrize("val", [datetime(2000, 1, 4), datetime(2000, 1, 5)])
def test_series_comparison_scalars(self, val):
series = Series(date_range("1/1/2000", periods=10))
result = series > val
expected = Series([x > val for x in series])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"left,right", [("lt", "gt"), ("le", "ge"), ("eq", "eq"), ("ne", "ne")]
)
def test_timestamp_compare_series(self, left, right):
# see gh-4982
# Make sure we can compare Timestamps on the right AND left hand side.
ser = pd.Series(pd.date_range("20010101", periods=10), name="dates")
s_nat = ser.copy(deep=True)
ser[0] = pd.Timestamp("nat")
ser[3] = pd.Timestamp("nat")
left_f = getattr(operator, left)
right_f = getattr(operator, right)
# No NaT
expected = left_f(ser, pd.Timestamp("20010109"))
result = right_f(pd.Timestamp("20010109"), ser)
tm.assert_series_equal(result, expected)
# NaT
expected = left_f(ser, pd.Timestamp("nat"))
result = right_f(pd.Timestamp("nat"), ser)
tm.assert_series_equal(result, expected)
# Compare to Timestamp with series containing NaT
expected = left_f(s_nat, pd.Timestamp("20010109"))
result = right_f(pd.Timestamp("20010109"), s_nat)
tm.assert_series_equal(result, expected)
# Compare to NaT with series containing NaT
expected = left_f(s_nat, pd.Timestamp("nat"))
result = right_f(pd.Timestamp("nat"), s_nat)
tm.assert_series_equal(result, expected)
def test_dt64arr_timestamp_equality(self, box_with_array):
# GH#11034
xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
ser = pd.Series([pd.Timestamp("2000-01-29 01:59:00"), "NaT"])
ser = tm.box_expected(ser, box_with_array)
result = ser != ser
expected = tm.box_expected([False, True], xbox)
tm.assert_equal(result, expected)
result = ser != ser[0]
expected = tm.box_expected([False, True], xbox)
tm.assert_equal(result, expected)
result = ser != ser[1]
expected = tm.box_expected([True, True], xbox)
tm.assert_equal(result, expected)
result = ser == ser
expected = tm.box_expected([True, False], xbox)
tm.assert_equal(result, expected)
result = ser == ser[0]
expected = tm.box_expected([True, False], xbox)
tm.assert_equal(result, expected)
result = ser == ser[1]
expected = tm.box_expected([False, False], xbox)
tm.assert_equal(result, expected)
class TestDatetimeIndexComparisons:
# TODO: moved from tests.indexes.test_base; parametrize and de-duplicate
@pytest.mark.parametrize(
"op",
[operator.eq, operator.ne, operator.gt, operator.lt, operator.ge, operator.le],
)
def test_comparators(self, op):
index = tm.makeDateIndex(100)
element = index[len(index) // 2]
element = Timestamp(element).to_datetime64()
arr = np.array(index)
arr_result = op(arr, element)
index_result = op(index, element)
assert isinstance(index_result, np.ndarray)
tm.assert_numpy_array_equal(arr_result, index_result)
@pytest.mark.parametrize(
"other",
[datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
)
def test_dti_cmp_datetimelike(self, other, tz_naive_fixture):
tz = tz_naive_fixture
dti = pd.date_range("2016-01-01", periods=2, tz=tz)
if tz is not None:
if isinstance(other, np.datetime64):
# no tzaware version available
return
other = localize_pydatetime(other, dti.tzinfo)
result = dti == other
expected = np.array([True, False])
tm.assert_numpy_array_equal(result, expected)
result = dti > other
expected = np.array([False, True])
tm.assert_numpy_array_equal(result, expected)
result = dti >= other
expected = np.array([True, True])
tm.assert_numpy_array_equal(result, expected)
result = dti < other
expected = np.array([False, False])
tm.assert_numpy_array_equal(result, expected)
result = dti <= other
expected = np.array([True, False])
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("dtype", [None, object])
def test_dti_cmp_nat(self, dtype, box_with_array):
if box_with_array is tm.to_array and dtype is object:
# dont bother testing ndarray comparison methods as this fails
# on older numpys (since they check object identity)
return
xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
left = pd.DatetimeIndex(
[pd.Timestamp("2011-01-01"), pd.NaT, pd.Timestamp("2011-01-03")]
)
right = pd.DatetimeIndex([pd.NaT, pd.NaT, pd.Timestamp("2011-01-03")])
left = tm.box_expected(left, box_with_array)
right = tm.box_expected(right, box_with_array)
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])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
result = lhs != rhs
expected = np.array([True, True, False])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(result, expected)
expected = np.array([False, False, False])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(lhs == pd.NaT, expected)
tm.assert_equal(pd.NaT == rhs, expected)
expected = np.array([True, True, True])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(lhs != pd.NaT, expected)
tm.assert_equal(pd.NaT != lhs, expected)
expected = np.array([False, False, False])
expected = tm.box_expected(expected, xbox)
tm.assert_equal(lhs < pd.NaT, expected)
tm.assert_equal(pd.NaT > lhs, expected)
def test_dti_cmp_nat_behaves_like_float_cmp_nan(self):
fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])
didx1 = pd.DatetimeIndex(
["2014-01-01", pd.NaT, "2014-03-01", pd.NaT, "2014-05-01", "2014-07-01"]
)
didx2 = pd.DatetimeIndex(
["2014-02-01", "2014-03-01", pd.NaT, pd.NaT, "2014-06-01", "2014-07-01"]
)
darr = np.array(
[
np_datetime64_compat("2014-02-01 00:00Z"),
np_datetime64_compat("2014-03-01 00:00Z"),
np_datetime64_compat("nat"),
np.datetime64("nat"),
np_datetime64_compat("2014-06-01 00:00Z"),
np_datetime64_compat("2014-07-01 00:00Z"),
]
)
cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, idx2 in cases:
result = idx1 < idx2
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx2 > idx1
expected = np.array([True, False, False, False, True, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= idx2
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx2 >= idx1
expected = np.array([True, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == idx2
expected = np.array([False, False, False, False, False, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != idx2
expected = np.array([True, True, True, True, True, False])
tm.assert_numpy_array_equal(result, expected)
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
result = idx1 < val
expected = np.array([False, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, True, True, True, True])
tm.assert_numpy_array_equal(result, expected)
# Check pd.NaT is handles as the same as np.nan
with tm.assert_produces_warning(None):
for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
result = idx1 < val
expected = np.array([True, False, False, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 > val
expected = np.array([False, False, False, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 <= val
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 >= val
expected = np.array([False, False, True, False, True, True])
tm.assert_numpy_array_equal(result, expected)
result = idx1 == val
expected = np.array([False, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = idx1 != val
expected = np.array([True, True, False, True, True, True])
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize(
"op",
[operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
)
def test_comparison_tzawareness_compat(self, op, box_df_fail):
# GH#18162
box = box_df_fail
dr = pd.date_range("2016-01-01", periods=6)
dz = dr.tz_localize("US/Pacific")
dr = tm.box_expected(dr, box)
dz = tm.box_expected(dz, box)
msg = "Cannot compare tz-naive and tz-aware"
with pytest.raises(TypeError, match=msg):
op(dr, dz)
# FIXME: DataFrame case fails to raise for == and !=, wrong
# message for inequalities
with pytest.raises(TypeError, match=msg):
op(dr, list(dz))
with pytest.raises(TypeError, match=msg):
op(dr, np.array(list(dz), dtype=object))
with pytest.raises(TypeError, match=msg):
op(dz, dr)
# FIXME: DataFrame case fails to raise for == and !=, wrong
# message for inequalities
with pytest.raises(TypeError, match=msg):
op(dz, list(dr))
with pytest.raises(TypeError, match=msg):
op(dz, np.array(list(dr), dtype=object))
# The aware==aware and naive==naive comparisons should *not* raise
assert np.all(dr == dr)
assert np.all(dr == list(dr))
assert np.all(list(dr) == dr)
assert np.all(np.array(list(dr), dtype=object) == dr)
assert np.all(dr == np.array(list(dr), dtype=object))
assert np.all(dz == dz)
assert np.all(dz == list(dz))
assert np.all(list(dz) == dz)
assert np.all(np.array(list(dz), dtype=object) == dz)
assert np.all(dz == np.array(list(dz), dtype=object))
@pytest.mark.parametrize(
"op",
[operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
)
def test_comparison_tzawareness_compat_scalars(self, op, box_with_array):
# GH#18162
dr = pd.date_range("2016-01-01", periods=6)
dz = dr.tz_localize("US/Pacific")
dr = tm.box_expected(dr, box_with_array)
dz = tm.box_expected(dz, box_with_array)
# Check comparisons against scalar Timestamps
ts = pd.Timestamp("2000-03-14 01:59")
ts_tz = pd.Timestamp("2000-03-14 01:59", tz="Europe/Amsterdam")
assert np.all(dr > ts)
msg = "Cannot compare tz-naive and tz-aware"
with pytest.raises(TypeError, match=msg):
op(dr, ts_tz)
assert np.all(dz > ts_tz)
with pytest.raises(TypeError, match=msg):
op(dz, ts)
# GH#12601: Check comparison against Timestamps and DatetimeIndex
with pytest.raises(TypeError, match=msg):
op(ts, dz)
@pytest.mark.parametrize(
"op",
[operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
)
@pytest.mark.parametrize(
"other",
[datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
)
# Bug in NumPy? https://github.com/numpy/numpy/issues/13841
# Raising in __eq__ will fallback to NumPy, which warns, fails,
# then re-raises the original exception. So we just need to ignore.
@pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
@pytest.mark.filterwarnings("ignore:Converting timezone-aware:FutureWarning")
def test_scalar_comparison_tzawareness(
self, op, other, tz_aware_fixture, box_with_array
):
tz = tz_aware_fixture
dti = pd.date_range("2016-01-01", periods=2, tz=tz)
dtarr = tm.box_expected(dti, box_with_array)
msg = "Cannot compare tz-naive and tz-aware"
with pytest.raises(TypeError, match=msg):
op(dtarr, other)
with pytest.raises(TypeError, match=msg):
op(other, dtarr)
@pytest.mark.parametrize(
"op",
[operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
)
def test_nat_comparison_tzawareness(self, op):
# GH#19276
# tzaware DatetimeIndex should not raise when compared to NaT
dti = pd.DatetimeIndex(
["2014-01-01", pd.NaT, "2014-03-01", pd.NaT, "2014-05-01", "2014-07-01"]
)
expected = np.array([op == operator.ne] * len(dti))
result = op(dti, pd.NaT)
tm.assert_numpy_array_equal(result, expected)
result = op(dti.tz_localize("US/Pacific"), pd.NaT)
tm.assert_numpy_array_equal(result, expected)
def test_dti_cmp_str(self, tz_naive_fixture):
# GH#22074
# regardless of tz, we expect these comparisons are valid
tz = tz_naive_fixture
rng = date_range("1/1/2000", periods=10, tz=tz)
other = "1/1/2000"
result = rng == other
expected = np.array([True] + [False] * 9)
tm.assert_numpy_array_equal(result, expected)
result = rng != other
expected = np.array([False] + [True] * 9)
tm.assert_numpy_array_equal(result, expected)
result = rng < other
expected = np.array([False] * 10)
tm.assert_numpy_array_equal(result, expected)
result = rng <= other
expected = np.array([True] + [False] * 9)
tm.assert_numpy_array_equal(result, expected)
result = rng > other
expected = np.array([False] + [True] * 9)
tm.assert_numpy_array_equal(result, expected)
result = rng >= other
expected = np.array([True] * 10)
tm.assert_numpy_array_equal(result, expected)
def test_dti_cmp_list(self):
rng = date_range("1/1/2000", periods=10)
result = rng == list(rng)
expected = rng == rng
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize(
"other",
[
pd.timedelta_range("1D", periods=10),
pd.timedelta_range("1D", periods=10).to_series(),
pd.timedelta_range("1D", periods=10).asi8.view("m8[ns]"),
],
ids=lambda x: type(x).__name__,
)
def test_dti_cmp_tdi_tzawareness(self, other):
# GH#22074
# reversion test that we _don't_ call _assert_tzawareness_compat
# when comparing against TimedeltaIndex
dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")
result = dti == other
expected = np.array([False] * 10)
tm.assert_numpy_array_equal(result, expected)
result = dti != other
expected = np.array([True] * 10)
tm.assert_numpy_array_equal(result, expected)
msg = "Invalid comparison between"
with pytest.raises(TypeError, match=msg):
dti < other
with pytest.raises(TypeError, match=msg):
dti <= other
with pytest.raises(TypeError, match=msg):
dti > other
with pytest.raises(TypeError, match=msg):
dti >= other
def test_dti_cmp_object_dtype(self):
# GH#22074
dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")
other = dti.astype("O")
result = dti == other
expected = np.array([True] * 10)
tm.assert_numpy_array_equal(result, expected)
other = dti.tz_localize(None)
msg = "Cannot compare tz-naive and tz-aware"
with pytest.raises(TypeError, match=msg):
# tzawareness failure
dti != other
other = np.array(list(dti[:5]) + [Timedelta(days=1)] * 5)
result = dti == other
expected = np.array([True] * 5 + [False] * 5)
tm.assert_numpy_array_equal(result, expected)
msg = "Cannot compare type"
with pytest.raises(TypeError, match=msg):
dti >= other
# ------------------------------------------------------------------
# Arithmetic
class TestDatetime64Arithmetic:
# This class is intended for "finished" tests that are fully parametrized
# over DataFrame/Series/Index/DatetimeArray
# -------------------------------------------------------------
# Addition/Subtraction of timedelta-like
def test_dt64arr_add_timedeltalike_scalar(
self, tz_naive_fixture, two_hours, box_with_array
):
# GH#22005, GH#22163 check DataFrame doesn't raise TypeError
tz = tz_naive_fixture
rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
expected = pd.date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz)
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)
def test_dt64arr_iadd_timedeltalike_scalar(
self, tz_naive_fixture, two_hours, box_with_array
):
tz = tz_naive_fixture
rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
expected = pd.date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz)
rng = tm.box_expected(rng, box_with_array)
expected = tm.box_expected(expected, box_with_array)
rng += two_hours
tm.assert_equal(rng, expected)
def test_dt64arr_sub_timedeltalike_scalar(
self, tz_naive_fixture, two_hours, box_with_array
):
tz = tz_naive_fixture
rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
expected = pd.date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz)
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)
def test_dt64arr_isub_timedeltalike_scalar(
self, tz_naive_fixture, two_hours, box_with_array
):
tz = tz_naive_fixture
rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
expected = pd.date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz)
rng = tm.box_expected(rng, box_with_array)
expected = tm.box_expected(expected, box_with_array)
rng -= two_hours
tm.assert_equal(rng, expected)
# TODO: redundant with test_dt64arr_add_timedeltalike_scalar
def test_dt64arr_add_td64_scalar(self, box_with_array):
# scalar timedeltas/np.timedelta64 objects
# operate with np.timedelta64 correctly
ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
expected = Series(
[Timestamp("20130101 9:01:01"), Timestamp("20130101 9:02:01")]
)
dtarr = tm.box_expected(ser, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = dtarr + np.timedelta64(1, "s")
tm.assert_equal(result, expected)
result = np.timedelta64(1, "s") + dtarr
tm.assert_equal(result, expected)
expected = Series(
[Timestamp("20130101 9:01:00.005"), Timestamp("20130101 9:02:00.005")]
)
expected = tm.box_expected(expected, box_with_array)
result = dtarr + np.timedelta64(5, "ms")
tm.assert_equal(result, expected)
result = np.timedelta64(5, "ms") + dtarr
tm.assert_equal(result, expected)
def test_dt64arr_add_sub_td64_nat(self, box_with_array, tz_naive_fixture):
# GH#23320 special handling for timedelta64("NaT")
tz = tz_naive_fixture
dti = pd.date_range("1994-04-01", periods=9, tz=tz, freq="QS")
other = np.timedelta64("NaT")
expected = pd.DatetimeIndex(["NaT"] * 9, tz=tz)
# FIXME: fails with transpose=True due to tz-aware DataFrame
# transpose bug
obj = tm.box_expected(dti, box_with_array, transpose=False)
expected = tm.box_expected(expected, box_with_array, transpose=False)
result = obj + other
tm.assert_equal(result, expected)
result = other + obj
tm.assert_equal(result, expected)
result = obj - other
tm.assert_equal(result, expected)
msg = "cannot subtract"
with pytest.raises(TypeError, match=msg):
other - obj
def test_dt64arr_add_sub_td64ndarray(self, tz_naive_fixture, box_with_array):
tz = tz_naive_fixture
dti = pd.date_range("2016-01-01", periods=3, tz=tz)
tdi = pd.TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
tdarr = tdi.values
expected = pd.date_range("2015-12-31", periods=3, tz=tz)
dtarr = tm.box_expected(dti, box_with_array)
expected = tm.box_expected(expected, box_with_array)
result = dtarr + tdarr
tm.assert_equal(result, expected)
result = tdarr + dtarr
tm.assert_equal(result, expected)
expected = pd.date_range("2016-01-02", periods=3, tz=tz)
expected = tm.box_expected(expected, box_with_array)
result = dtarr - tdarr
tm.assert_equal(result, expected)
msg = "cannot subtract|(bad|unsupported) operand type for unary"
with pytest.raises(TypeError, match=msg):
tdarr - dtarr
# -----------------------------------------------------------------
# Subtraction of datetime-like scalars
@pytest.mark.parametrize(
"ts",
[
pd.Timestamp("2013-01-01"),
pd.Timestamp("2013-01-01").to_pydatetime(),
pd.Timestamp("2013-01-01").to_datetime64(),
],
)
def test_dt64arr_sub_dtscalar(self, box_with_array, ts):
# GH#8554, GH#22163 DataFrame op should _not_ return dt64 dtype
idx = pd.date_range("2013-01-01", periods=3)
idx = tm.box_expected(idx, box_with_array)
expected = pd.TimedeltaIndex(["0 Days", "1 Day", "2 Days"])
expected = tm.box_expected(expected, box_with_array)
result = idx - ts
tm.assert_equal(result, expected)
def test_dt64arr_sub_datetime64_not_ns(self, box_with_array):
# GH#7996, GH#22163 ensure non-nano datetime64 is converted to nano
# for DataFrame operation
dt64 = np.datetime64("2013-01-01")
assert dt64.dtype == "datetime64[D]"
dti = pd.date_range("20130101", periods=3)
dtarr = tm.box_expected(dti, box_with_array)
expected = pd.TimedeltaIndex(["0 Days", "1 Day", "2 Days"])
expected = tm.box_expected(expected, box_with_array)
result = dtarr - dt64
tm.assert_equal(result, expected)
result = dt64 - dtarr
tm.assert_equal(result, -expected)
def test_dt64arr_sub_timestamp(self, box_with_array):
ser = pd.date_range("2014-03-17", periods=2, freq="D", tz="US/Eastern")
ts = ser[0]
ser = tm.box_expected(ser, box_with_array)
delta_series = pd.Series([np.timedelta64(0, "D"), np.timedelta64(1, "D")])
expected = tm.box_expected(delta_series, box_with_array)
tm.assert_equal(ser - ts, expected)
tm.assert_equal(ts - ser, -expected)
def test_dt64arr_sub_NaT(self, box_with_array):
# GH#18808
dti = pd.DatetimeIndex([pd.NaT, pd.Timestamp("19900315")])
ser = tm.box_expected(dti, box_with_array)
result = ser - pd.NaT
expected = pd.Series([pd.NaT, pd.NaT], dtype="timedelta64[ns]")
expected = tm.box_expected(expected, box_with_array)
tm.assert_equal(result, expected)
dti_tz = dti.tz_localize("Asia/Tokyo")
ser_tz = tm.box_expected(dti_tz, box_with_array)
result = ser_tz - pd.NaT
expected = pd.Series([pd.NaT, pd.NaT], dtype="timedelta64[ns]")
expected = tm.box_expected(expected, box_with_array)
tm.assert_equal(result, expected)
# -------------------------------------------------------------
# Subtraction of datetime-like array-like
def test_dt64arr_sub_dt64object_array(self, box_with_array, tz_naive_fixture):
dti = pd.date_range("2016-01-01", periods=3, tz=tz_naive_fixture)
expected = dti - dti
obj = tm.box_expected(dti, box_with_array)
expected = tm.box_expected(expected, box_with_array)
warn = None
if box_with_array is not pd.DataFrame or tz_naive_fixture is None:
warn = PerformanceWarning
with tm.assert_produces_warning(warn):
result = obj - obj.astype(object)
tm.assert_equal(result, expected)
def test_dt64arr_naive_sub_dt64ndarray(self, box_with_array):
dti = pd.date_range("2016-01-01", periods=3, tz=None)
dt64vals = dti.values
dtarr = tm.box_expected(dti, box_with_array)
expected = dtarr - dtarr
result = dtarr - dt64vals
tm.assert_equal(result, expected)
result = dt64vals - dtarr
tm.assert_equal(result, expected)
def test_dt64arr_aware_sub_dt64ndarray_raises(
self, tz_aware_fixture, box_with_array
):
tz = tz_aware_fixture
dti = pd.date_range("2016-01-01", periods=3, tz=tz)
dt64vals = dti.values
dtarr = tm.box_expected(dti, box_with_array)
msg = "subtraction must have the same timezones or"
with pytest.raises(TypeError, match=msg):
dtarr - dt64vals
with pytest.raises(TypeError, match=msg):
dt64vals - dtarr