# 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 OutOfBoundsDatetime, PerformanceWarning import pandas as pd from pandas import ( DataFrame, DatetimeIndex, NaT, Series, Timedelta, TimedeltaIndex, Timestamp, timedelta_range, ) import pandas._testing as tm from pandas.tests.arithmetic.common import ( assert_invalid_addsub_type, assert_invalid_comparison, get_upcast_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) @pytest.mark.parametrize( "td_scalar", [timedelta(days=1), Timedelta(days=1), Timedelta(days=1).to_timedelta64()], ) def test_compare_timedeltalike_scalar(self, box_with_array, td_scalar): # regression test for GH#5963 box = box_with_array xbox = box if box is not pd.Index else np.ndarray ser = pd.Series([timedelta(days=1), timedelta(days=2)]) ser = tm.box_expected(ser, box) actual = ser > td_scalar expected = pd.Series([False, True]) expected = tm.box_expected(expected, xbox) tm.assert_equal(actual, expected) @pytest.mark.parametrize("invalid", [345600000000000, "a"]) def test_td64_comparisons_invalid(self, box_with_array, invalid): # GH#13624 for str box = box_with_array rng = timedelta_range("1 days", periods=10) obj = tm.box_expected(rng, box) assert_invalid_comparison(obj, invalid, box) @pytest.mark.parametrize( "other", [ list(range(10)), np.arange(10), np.arange(10).astype(np.float32), np.arange(10).astype(object), pd.date_range("1970-01-01", periods=10, tz="UTC").array, np.array(pd.date_range("1970-01-01", periods=10)), list(pd.date_range("1970-01-01", periods=10)), pd.date_range("1970-01-01", 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_td64arr_cmp_arraylike_invalid(self, other): # We don't parametrize this over box_with_array because listlike # other plays poorly with assert_invalid_comparison reversed checks rng = timedelta_range("1 days", periods=10)._data assert_invalid_comparison(rng, other, tm.to_array) def test_td64arr_cmp_mixed_invalid(self): rng = timedelta_range("1 days", periods=5)._data other = np.array([0, 1, 2, rng[3], pd.Timestamp.now()]) result = rng == other expected = np.array([False, False, False, True, False]) tm.assert_numpy_array_equal(result, expected) result = rng != other tm.assert_numpy_array_equal(result, ~expected) msg = "Invalid comparison between|Cannot compare type|not supported between" with pytest.raises(TypeError, match=msg): rng < other with pytest.raises(TypeError, match=msg): rng > other with pytest.raises(TypeError, match=msg): rng <= other with pytest.raises(TypeError, match=msg): rng >= other class TestTimedelta64ArrayComparisons: # TODO: All of these need to be parametrized over box @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) 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"unsupported operand type\(s\) for -" with pytest.raises(TypeError, match=msg): td - dt msg = "(bad|unsupported) operand type for unary" 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(TypeError): 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) def test_tda_add_sub_index(self): # Check that TimedeltaArray defers to Index on arithmetic ops tdi = TimedeltaIndex(["1 days", pd.NaT, "2 days"]) tda = tdi.array dti = pd.date_range("1999-12-31", periods=3, freq="D") result = tda + dti expected = tdi + dti tm.assert_index_equal(result, expected) result = tda + tdi expected = tdi + tdi tm.assert_index_equal(result, expected) result = tda - tdi expected = tdi - tdi tm.assert_index_equal(result, expected) # ------------------------------------------------------------- # Binary operations TimedeltaIndex and timedelta-like def test_tdi_iadd_timedeltalike(self, two_hours): # 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 += two_hours tm.assert_index_equal(rng, expected) def test_tdi_isub_timedeltalike(self, two_hours): # 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 -= two_hours tm.assert_index_equal(rng, expected) # ------------------------------------------------------------- def test_tdi_ops_attributes(self): rng = timedelta_range("2 days", periods=5, freq="2D", name="x") result = rng + 1 * rng.freq exp = timedelta_range("4 days", periods=5, freq="2D", name="x") tm.assert_index_equal(result, exp) assert result.freq == "2D" result = rng - 2 * rng.freq exp = timedelta_range("-2 days", periods=5, freq="2D", name="x") tm.assert_index_equal(result, exp) assert result.freq == "2D" result = rng * 2 exp = timedelta_range("4 days", periods=5, freq="4D", name="x") tm.assert_index_equal(result, exp) assert result.freq == "4D" result = rng / 2 exp = timedelta_range("1 days", periods=5, freq="D", name="x") tm.assert_index_equal(result, exp) assert result.freq == "D" result = -rng exp = timedelta_range("-2 days", periods=5, freq="-2D", name="x") tm.assert_index_equal(result, exp) assert result.freq == "-2D" rng = pd.timedelta_range("-2 days", periods=5, freq="D", name="x") result = abs(rng) exp = TimedeltaIndex( ["2 days", "1 days", "0 days", "1 days", "2 days"], name="x" ) tm.assert_index_equal(result, exp) assert result.freq is None 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 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"])) msg = r"dtype datetime64\[ns\] cannot be converted to timedelta64\[ns\]" with pytest.raises(TypeError, match=msg): # Passing datetime64-dtype data to TimedeltaIndex is no longer # supported GH#29794 pd.to_timedelta(Series([pd.NaT])) sn = pd.to_timedelta(Series([pd.NaT], dtype="m8[ns]")) df1 = pd.DataFrame(["00:00:01"]).apply(pd.to_timedelta) df2 = pd.DataFrame(["00:00:02"]).apply(pd.to_timedelta) with pytest.raises(TypeError, match=msg): # Passing datetime64-dtype data to TimedeltaIndex is no longer # supported GH#29794 pd.DataFrame([pd.NaT]).apply(pd.to_timedelta) dfn = pd.DataFrame([pd.NaT.value]).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) # ------------------------------------------------------------- # 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) @pytest.mark.parametrize( "ts", [ Timestamp("2012-01-01"), Timestamp("2012-01-01").to_pydatetime(), Timestamp("2012-01-01").to_datetime64(), ], ) def test_td64arr_add_sub_datetimelike_scalar(self, ts, box_with_array): # GH#11925, GH#29558 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) # ------------------------------------------------------------------ # Invalid __add__/__sub__ operations # 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` assert_invalid_addsub_type(df, other) @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "H"]) @pytest.mark.parametrize("tdi_freq", [None, "H"]) def test_td64arr_sub_periodlike(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 # FIXME: don't leave commented-out # FIXME: this raises with period scalar but not with PeriodIndex? # with pytest.raises(TypeError): # pi - tdi # GH#13078 subtraction of Period scalar not supported with pytest.raises(TypeError): tdi - pi[0] with pytest.raises(TypeError): pi[0] - tdi @pytest.mark.parametrize( "other", [ # GH#12624 for str case "a", # GH#19123 1, 1.5, np.array(2), ], ) def test_td64arr_addsub_numeric_scalar_invalid(self, box_with_array, other): # vector-like others are tested in test_td64arr_add_sub_numeric_arr_invalid tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") tdarr = tm.box_expected(tdser, box_with_array) assert_invalid_addsub_type(tdarr, other) @pytest.mark.parametrize( "vec", [ np.array([1, 2, 3]), pd.Index([1, 2, 3]), Series([1, 2, 3]), DataFrame([[1, 2, 3]]), ], ids=lambda x: type(x).__name__, ) def test_td64arr_addsub_numeric_arr_invalid( self, box_with_array, vec, any_real_dtype ): tdser = pd.Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") tdarr = tm.box_expected(tdser, box_with_array) vector = vec.astype(any_real_dtype) assert_invalid_addsub_type(tdarr, vector) def test_td64arr_add_sub_int(self, box_with_array, one): # Variants of `one` for #19012, deprecated GH#22535 rng = timedelta_range("1 days 09:00:00", freq="H", periods=10) tdarr = tm.box_expected(rng, box_with_array) msg = "Addition/subtraction of integers" assert_invalid_addsub_type(tdarr, one, msg) # TOOD: get inplace ops into assert_invalid_addsub_type with pytest.raises(TypeError, match=msg): tdarr += one with pytest.raises(TypeError, match=msg): tdarr -= one def test_td64arr_add_sub_integer_array(self, box_with_array): # GH#19959, deprecated GH#22535 rng = timedelta_range("1 days 09:00:00", freq="H", periods=3) tdarr = tm.box_expected(rng, box_with_array) other = tm.box_expected([4, 3, 2], box_with_array) msg = "Addition/subtraction of integers and integer-arrays" assert_invalid_addsub_type(tdarr, other, msg) def test_td64arr_addsub_integer_array_no_freq(self, box_with_array): # GH#19959 tdi = TimedeltaIndex(["1 Day", "NaT", "3 Hours"]) tdarr = tm.box_expected(tdi, box_with_array) other = tm.box_expected([14, -1, 16], box_with_array) msg = "Addition/subtraction of integers" assert_invalid_addsub_type(tdarr, other, msg) # ------------------------------------------------------------------ # Operations with timedelta-like others # TODO: this was taken from tests.series.test_ops; de-duplicate 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_with_array): box = box_with_array 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_with_array): box = box_with_array 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_with_array): # GH#23320 special handling for timedelta64("NaT") box = box_with_array 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_with_array): # GH#18808 box = box_with_array 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_with_array): # only test adding/sub offsets as + is now numeric box = box_with_array 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_with_array): # only test adding/sub offsets as - is now numeric box = box_with_array 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_with_array): # GH#18849 box = box_with_array 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_with_array): # GH#18824, GH#19744 box = box_with_array xbox = box if box is not tm.to_array else pd.Index exname = names[2] if box is not tm.to_array else names[1] 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=exname ) tdi = tm.box_expected(tdi, box) expected = tm.box_expected(expected, xbox) # 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 exname = names[2] if box is not tm.to_array else names[1] 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=exname) 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=exname) 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 # ------------------------------------------------------------------ # Unsorted def test_td64arr_add_sub_object_array(self, box_with_array): tdi = pd.timedelta_range("1 day", periods=3, freq="D") tdarr = tm.box_expected(tdi, box_with_array) other = np.array( [pd.Timedelta(days=1), pd.offsets.Day(2), pd.Timestamp("2000-01-04")] ) warn = PerformanceWarning if box_with_array is not pd.DataFrame else None with tm.assert_produces_warning(warn): result = tdarr + other expected = pd.Index( [pd.Timedelta(days=2), pd.Timedelta(days=4), pd.Timestamp("2000-01-07")] ) expected = tm.box_expected(expected, box_with_array) tm.assert_equal(result, expected) with pytest.raises(TypeError): with tm.assert_produces_warning(warn): tdarr - other with tm.assert_produces_warning(warn): result = other - tdarr expected = pd.Index( [pd.Timedelta(0), pd.Timedelta(0), pd.Timestamp("2000-01-01")] ) expected = tm.box_expected(expected, box_with_array) tm.assert_equal(result, expected) 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="unsupported operand type"): 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 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( "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, any_real_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(any_real_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( "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, any_real_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(any_real_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 exname = names[2] if box is not tm.to_array else names[1] 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=exname, ) tdi = tm.box_expected(tdi, box) box = Series if (box is pd.Index or box is tm.to_array) 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 TestTimedelta64ArrayLikeArithmetic: # Arithmetic tests for timedelta64[ns] vectors fully parametrized over # DataFrame/Series/TimedeltaIndex/TimedeltaArray. Ideally all arithmetic # tests will eventually end up here. 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