You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
314 lines
10 KiB
Python
314 lines
10 KiB
Python
from datetime import timedelta
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import Timedelta
|
|
import pandas._testing as tm
|
|
from pandas.core.arrays import (
|
|
DatetimeArray,
|
|
TimedeltaArray,
|
|
)
|
|
|
|
|
|
class TestNonNano:
|
|
@pytest.fixture(params=["s", "ms", "us"])
|
|
def unit(self, request):
|
|
return request.param
|
|
|
|
@pytest.fixture
|
|
def tda(self, unit):
|
|
arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
|
|
return TimedeltaArray._simple_new(arr, dtype=arr.dtype)
|
|
|
|
def test_non_nano(self, unit):
|
|
arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
|
|
tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype)
|
|
|
|
assert tda.dtype == arr.dtype
|
|
assert tda[0].unit == unit
|
|
|
|
def test_as_unit_raises(self, tda):
|
|
# GH#50616
|
|
with pytest.raises(ValueError, match="Supported units"):
|
|
tda.as_unit("D")
|
|
|
|
tdi = pd.Index(tda)
|
|
with pytest.raises(ValueError, match="Supported units"):
|
|
tdi.as_unit("D")
|
|
|
|
@pytest.mark.parametrize("field", TimedeltaArray._field_ops)
|
|
def test_fields(self, tda, field):
|
|
as_nano = tda._ndarray.astype("m8[ns]")
|
|
tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
|
|
|
|
result = getattr(tda, field)
|
|
expected = getattr(tda_nano, field)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_to_pytimedelta(self, tda):
|
|
as_nano = tda._ndarray.astype("m8[ns]")
|
|
tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
|
|
|
|
result = tda.to_pytimedelta()
|
|
expected = tda_nano.to_pytimedelta()
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_total_seconds(self, unit, tda):
|
|
as_nano = tda._ndarray.astype("m8[ns]")
|
|
tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
|
|
|
|
result = tda.total_seconds()
|
|
expected = tda_nano.total_seconds()
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_timedelta_array_total_seconds(self):
|
|
# GH34290
|
|
expected = Timedelta("2 min").total_seconds()
|
|
|
|
result = pd.array([Timedelta("2 min")]).total_seconds()[0]
|
|
assert result == expected
|
|
|
|
def test_total_seconds_nanoseconds(self):
|
|
# issue #48521
|
|
start_time = pd.Series(["2145-11-02 06:00:00"]).astype("datetime64[ns]")
|
|
end_time = pd.Series(["2145-11-02 07:06:00"]).astype("datetime64[ns]")
|
|
expected = (end_time - start_time).values / np.timedelta64(1, "s")
|
|
result = (end_time - start_time).dt.total_seconds().values
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize(
|
|
"nat", [np.datetime64("NaT", "ns"), np.datetime64("NaT", "us")]
|
|
)
|
|
def test_add_nat_datetimelike_scalar(self, nat, tda):
|
|
result = tda + nat
|
|
assert isinstance(result, DatetimeArray)
|
|
assert result._creso == tda._creso
|
|
assert result.isna().all()
|
|
|
|
result = nat + tda
|
|
assert isinstance(result, DatetimeArray)
|
|
assert result._creso == tda._creso
|
|
assert result.isna().all()
|
|
|
|
def test_add_pdnat(self, tda):
|
|
result = tda + pd.NaT
|
|
assert isinstance(result, TimedeltaArray)
|
|
assert result._creso == tda._creso
|
|
assert result.isna().all()
|
|
|
|
result = pd.NaT + tda
|
|
assert isinstance(result, TimedeltaArray)
|
|
assert result._creso == tda._creso
|
|
assert result.isna().all()
|
|
|
|
# TODO: 2022-07-11 this is the only test that gets to DTA.tz_convert
|
|
# or tz_localize with non-nano; implement tests specific to that.
|
|
def test_add_datetimelike_scalar(self, tda, tz_naive_fixture):
|
|
ts = pd.Timestamp("2016-01-01", tz=tz_naive_fixture).as_unit("ns")
|
|
|
|
expected = tda.as_unit("ns") + ts
|
|
res = tda + ts
|
|
tm.assert_extension_array_equal(res, expected)
|
|
res = ts + tda
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
ts += Timedelta(1) # case where we can't cast losslessly
|
|
|
|
exp_values = tda._ndarray + ts.asm8
|
|
expected = (
|
|
DatetimeArray._simple_new(exp_values, dtype=exp_values.dtype)
|
|
.tz_localize("UTC")
|
|
.tz_convert(ts.tz)
|
|
)
|
|
|
|
result = tda + ts
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
result = ts + tda
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_mul_scalar(self, tda):
|
|
other = 2
|
|
result = tda * other
|
|
expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
assert result._creso == tda._creso
|
|
|
|
def test_mul_listlike(self, tda):
|
|
other = np.arange(len(tda))
|
|
result = tda * other
|
|
expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
assert result._creso == tda._creso
|
|
|
|
def test_mul_listlike_object(self, tda):
|
|
other = np.arange(len(tda))
|
|
result = tda * other.astype(object)
|
|
expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
assert result._creso == tda._creso
|
|
|
|
def test_div_numeric_scalar(self, tda):
|
|
other = 2
|
|
result = tda / other
|
|
expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
assert result._creso == tda._creso
|
|
|
|
def test_div_td_scalar(self, tda):
|
|
other = timedelta(seconds=1)
|
|
result = tda / other
|
|
expected = tda._ndarray / np.timedelta64(1, "s")
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_div_numeric_array(self, tda):
|
|
other = np.arange(len(tda))
|
|
result = tda / other
|
|
expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
assert result._creso == tda._creso
|
|
|
|
def test_div_td_array(self, tda):
|
|
other = tda._ndarray + tda._ndarray[-1]
|
|
result = tda / other
|
|
expected = tda._ndarray / other
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_add_timedeltaarraylike(self, tda):
|
|
tda_nano = tda.astype("m8[ns]")
|
|
|
|
expected = tda_nano * 2
|
|
res = tda_nano + tda
|
|
tm.assert_extension_array_equal(res, expected)
|
|
res = tda + tda_nano
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
expected = tda_nano * 0
|
|
res = tda - tda_nano
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
res = tda_nano - tda
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
|
|
class TestTimedeltaArray:
|
|
@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
|
|
def test_astype_int(self, dtype):
|
|
arr = TimedeltaArray._from_sequence(
|
|
[Timedelta("1h"), Timedelta("2h")], dtype="m8[ns]"
|
|
)
|
|
|
|
if np.dtype(dtype) != np.int64:
|
|
with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
|
|
arr.astype(dtype)
|
|
return
|
|
|
|
result = arr.astype(dtype)
|
|
expected = arr._ndarray.view("i8")
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_setitem_clears_freq(self):
|
|
a = pd.timedelta_range("1h", periods=2, freq="h")._data
|
|
a[0] = Timedelta("1h")
|
|
assert a.freq is None
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
Timedelta(seconds=1),
|
|
Timedelta(seconds=1).to_timedelta64(),
|
|
Timedelta(seconds=1).to_pytimedelta(),
|
|
],
|
|
)
|
|
def test_setitem_objects(self, obj):
|
|
# make sure we accept timedelta64 and timedelta in addition to Timedelta
|
|
tdi = pd.timedelta_range("2 Days", periods=4, freq="h")
|
|
arr = tdi._data
|
|
|
|
arr[0] = obj
|
|
assert arr[0] == Timedelta(seconds=1)
|
|
|
|
@pytest.mark.parametrize(
|
|
"other",
|
|
[
|
|
1,
|
|
np.int64(1),
|
|
1.0,
|
|
np.datetime64("NaT"),
|
|
pd.Timestamp("2021-01-01"),
|
|
"invalid",
|
|
np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
|
|
(np.arange(10) * 24 * 3600 * 10**9).view("datetime64[ns]"),
|
|
pd.Timestamp("2021-01-01").to_period("D"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("index", [True, False])
|
|
def test_searchsorted_invalid_types(self, other, index):
|
|
data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
|
|
arr = pd.TimedeltaIndex(data, freq="D")._data
|
|
if index:
|
|
arr = pd.Index(arr)
|
|
|
|
msg = "|".join(
|
|
[
|
|
"searchsorted requires compatible dtype or scalar",
|
|
"value should be a 'Timedelta', 'NaT', or array of those. Got",
|
|
]
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr.searchsorted(other)
|
|
|
|
|
|
class TestUnaryOps:
|
|
def test_abs(self):
|
|
vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
|
|
arr = TimedeltaArray._from_sequence(vals)
|
|
|
|
evals = np.array([3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
|
|
expected = TimedeltaArray._from_sequence(evals)
|
|
|
|
result = abs(arr)
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
result2 = np.abs(arr)
|
|
tm.assert_timedelta_array_equal(result2, expected)
|
|
|
|
def test_pos(self):
|
|
vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
|
|
arr = TimedeltaArray._from_sequence(vals)
|
|
|
|
result = +arr
|
|
tm.assert_timedelta_array_equal(result, arr)
|
|
assert not tm.shares_memory(result, arr)
|
|
|
|
result2 = np.positive(arr)
|
|
tm.assert_timedelta_array_equal(result2, arr)
|
|
assert not tm.shares_memory(result2, arr)
|
|
|
|
def test_neg(self):
|
|
vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
|
|
arr = TimedeltaArray._from_sequence(vals)
|
|
|
|
evals = np.array([3600 * 10**9, "NaT", -7200 * 10**9], dtype="m8[ns]")
|
|
expected = TimedeltaArray._from_sequence(evals)
|
|
|
|
result = -arr
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
result2 = np.negative(arr)
|
|
tm.assert_timedelta_array_equal(result2, expected)
|
|
|
|
def test_neg_freq(self):
|
|
tdi = pd.timedelta_range("2 Days", periods=4, freq="h")
|
|
arr = tdi._data
|
|
|
|
expected = -tdi._data
|
|
|
|
result = -arr
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
result2 = np.negative(arr)
|
|
tm.assert_timedelta_array_equal(result2, expected)
|