You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
760 lines
26 KiB
760 lines
26 KiB
"""
|
|
Tests for DatetimeArray
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
from datetime import timedelta
|
|
import operator
|
|
|
|
try:
|
|
from zoneinfo import ZoneInfo
|
|
except ImportError:
|
|
# Cannot assign to a type
|
|
ZoneInfo = None # type: ignore[misc, assignment]
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas._libs.tslibs import (
|
|
npy_unit_to_abbrev,
|
|
tz_compare,
|
|
)
|
|
|
|
from pandas.core.dtypes.dtypes import DatetimeTZDtype
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
from pandas.core.arrays import (
|
|
DatetimeArray,
|
|
TimedeltaArray,
|
|
)
|
|
|
|
|
|
class TestNonNano:
|
|
@pytest.fixture(params=["s", "ms", "us"])
|
|
def unit(self, request):
|
|
"""Fixture returning parametrized time units"""
|
|
return request.param
|
|
|
|
@pytest.fixture
|
|
def dtype(self, unit, tz_naive_fixture):
|
|
tz = tz_naive_fixture
|
|
if tz is None:
|
|
return np.dtype(f"datetime64[{unit}]")
|
|
else:
|
|
return DatetimeTZDtype(unit=unit, tz=tz)
|
|
|
|
@pytest.fixture
|
|
def dta_dti(self, unit, dtype):
|
|
tz = getattr(dtype, "tz", None)
|
|
|
|
dti = pd.date_range("2016-01-01", periods=55, freq="D", tz=tz)
|
|
if tz is None:
|
|
arr = np.asarray(dti).astype(f"M8[{unit}]")
|
|
else:
|
|
arr = np.asarray(dti.tz_convert("UTC").tz_localize(None)).astype(
|
|
f"M8[{unit}]"
|
|
)
|
|
|
|
dta = DatetimeArray._simple_new(arr, dtype=dtype)
|
|
return dta, dti
|
|
|
|
@pytest.fixture
|
|
def dta(self, dta_dti):
|
|
dta, dti = dta_dti
|
|
return dta
|
|
|
|
def test_non_nano(self, unit, dtype):
|
|
arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
|
|
dta = DatetimeArray._simple_new(arr, dtype=dtype)
|
|
|
|
assert dta.dtype == dtype
|
|
assert dta[0].unit == unit
|
|
assert tz_compare(dta.tz, dta[0].tz)
|
|
assert (dta[0] == dta[:1]).all()
|
|
|
|
@pytest.mark.parametrize(
|
|
"field", DatetimeArray._field_ops + DatetimeArray._bool_ops
|
|
)
|
|
def test_fields(self, unit, field, dtype, dta_dti):
|
|
dta, dti = dta_dti
|
|
|
|
assert (dti == dta).all()
|
|
|
|
res = getattr(dta, field)
|
|
expected = getattr(dti._data, field)
|
|
tm.assert_numpy_array_equal(res, expected)
|
|
|
|
def test_normalize(self, unit):
|
|
dti = pd.date_range("2016-01-01 06:00:00", periods=55, freq="D")
|
|
arr = np.asarray(dti).astype(f"M8[{unit}]")
|
|
|
|
dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)
|
|
|
|
assert not dta.is_normalized
|
|
|
|
# TODO: simplify once we can just .astype to other unit
|
|
exp = np.asarray(dti.normalize()).astype(f"M8[{unit}]")
|
|
expected = DatetimeArray._simple_new(exp, dtype=exp.dtype)
|
|
|
|
res = dta.normalize()
|
|
tm.assert_extension_array_equal(res, expected)
|
|
|
|
def test_simple_new_requires_match(self, unit):
|
|
arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]")
|
|
dtype = DatetimeTZDtype(unit, "UTC")
|
|
|
|
dta = DatetimeArray._simple_new(arr, dtype=dtype)
|
|
assert dta.dtype == dtype
|
|
|
|
wrong = DatetimeTZDtype("ns", "UTC")
|
|
with pytest.raises(AssertionError, match=""):
|
|
DatetimeArray._simple_new(arr, dtype=wrong)
|
|
|
|
def test_std_non_nano(self, unit):
|
|
dti = pd.date_range("2016-01-01", periods=55, freq="D")
|
|
arr = np.asarray(dti).astype(f"M8[{unit}]")
|
|
|
|
dta = DatetimeArray._simple_new(arr, dtype=arr.dtype)
|
|
|
|
# we should match the nano-reso std, but floored to our reso.
|
|
res = dta.std()
|
|
assert res._creso == dta._creso
|
|
assert res == dti.std().floor(unit)
|
|
|
|
@pytest.mark.filterwarnings("ignore:Converting to PeriodArray.*:UserWarning")
|
|
def test_to_period(self, dta_dti):
|
|
dta, dti = dta_dti
|
|
result = dta.to_period("D")
|
|
expected = dti._data.to_period("D")
|
|
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_iter(self, dta):
|
|
res = next(iter(dta))
|
|
expected = dta[0]
|
|
|
|
assert type(res) is pd.Timestamp
|
|
assert res._value == expected._value
|
|
assert res._creso == expected._creso
|
|
assert res == expected
|
|
|
|
def test_astype_object(self, dta):
|
|
result = dta.astype(object)
|
|
assert all(x._creso == dta._creso for x in result)
|
|
assert all(x == y for x, y in zip(result, dta))
|
|
|
|
def test_to_pydatetime(self, dta_dti):
|
|
dta, dti = dta_dti
|
|
|
|
result = dta.to_pydatetime()
|
|
expected = dti.to_pydatetime()
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("meth", ["time", "timetz", "date"])
|
|
def test_time_date(self, dta_dti, meth):
|
|
dta, dti = dta_dti
|
|
|
|
result = getattr(dta, meth)
|
|
expected = getattr(dti, meth)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_format_native_types(self, unit, dtype, dta_dti):
|
|
# In this case we should get the same formatted values with our nano
|
|
# version dti._data as we do with the non-nano dta
|
|
dta, dti = dta_dti
|
|
|
|
res = dta._format_native_types()
|
|
exp = dti._data._format_native_types()
|
|
tm.assert_numpy_array_equal(res, exp)
|
|
|
|
def test_repr(self, dta_dti, unit):
|
|
dta, dti = dta_dti
|
|
|
|
assert repr(dta) == repr(dti._data).replace("[ns", f"[{unit}")
|
|
|
|
# TODO: tests with td64
|
|
def test_compare_mismatched_resolutions(self, comparison_op):
|
|
# comparison that numpy gets wrong bc of silent overflows
|
|
op = comparison_op
|
|
|
|
iinfo = np.iinfo(np.int64)
|
|
vals = np.array([iinfo.min, iinfo.min + 1, iinfo.max], dtype=np.int64)
|
|
|
|
# Construct so that arr2[1] < arr[1] < arr[2] < arr2[2]
|
|
arr = np.array(vals).view("M8[ns]")
|
|
arr2 = arr.view("M8[s]")
|
|
|
|
left = DatetimeArray._simple_new(arr, dtype=arr.dtype)
|
|
right = DatetimeArray._simple_new(arr2, dtype=arr2.dtype)
|
|
|
|
if comparison_op is operator.eq:
|
|
expected = np.array([False, False, False])
|
|
elif comparison_op is operator.ne:
|
|
expected = np.array([True, True, True])
|
|
elif comparison_op in [operator.lt, operator.le]:
|
|
expected = np.array([False, False, True])
|
|
else:
|
|
expected = np.array([False, True, False])
|
|
|
|
result = op(left, right)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = op(left[1], right)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
if op not in [operator.eq, operator.ne]:
|
|
# check that numpy still gets this wrong; if it is fixed we may be
|
|
# able to remove compare_mismatched_resolutions
|
|
np_res = op(left._ndarray, right._ndarray)
|
|
tm.assert_numpy_array_equal(np_res[1:], ~expected[1:])
|
|
|
|
def test_add_mismatched_reso_doesnt_downcast(self):
|
|
# https://github.com/pandas-dev/pandas/pull/48748#issuecomment-1260181008
|
|
td = pd.Timedelta(microseconds=1)
|
|
dti = pd.date_range("2016-01-01", periods=3) - td
|
|
dta = dti._data.as_unit("us")
|
|
|
|
res = dta + td.as_unit("us")
|
|
# even though the result is an even number of days
|
|
# (so we _could_ downcast to unit="s"), we do not.
|
|
assert res.unit == "us"
|
|
|
|
@pytest.mark.parametrize(
|
|
"scalar",
|
|
[
|
|
timedelta(hours=2),
|
|
pd.Timedelta(hours=2),
|
|
np.timedelta64(2, "h"),
|
|
np.timedelta64(2 * 3600 * 1000, "ms"),
|
|
pd.offsets.Minute(120),
|
|
pd.offsets.Hour(2),
|
|
],
|
|
)
|
|
def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
|
|
dta, dti = dta_dti
|
|
|
|
td = pd.Timedelta(scalar)
|
|
exp_reso = max(dta._creso, td._creso)
|
|
exp_unit = npy_unit_to_abbrev(exp_reso)
|
|
|
|
expected = (dti + td)._data.as_unit(exp_unit)
|
|
result = dta + scalar
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
result = scalar + dta
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
expected = (dti - td)._data.as_unit(exp_unit)
|
|
result = dta - scalar
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_sub_datetimelike_scalar_mismatch(self):
|
|
dti = pd.date_range("2016-01-01", periods=3)
|
|
dta = dti._data.as_unit("us")
|
|
|
|
ts = dta[0].as_unit("s")
|
|
|
|
result = dta - ts
|
|
expected = (dti - dti[0])._data.as_unit("us")
|
|
assert result.dtype == "m8[us]"
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_sub_datetime64_reso_mismatch(self):
|
|
dti = pd.date_range("2016-01-01", periods=3)
|
|
left = dti._data.as_unit("s")
|
|
right = left.as_unit("ms")
|
|
|
|
result = left - right
|
|
exp_values = np.array([0, 0, 0], dtype="m8[ms]")
|
|
expected = TimedeltaArray._simple_new(
|
|
exp_values,
|
|
dtype=exp_values.dtype,
|
|
)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
result2 = right - left
|
|
tm.assert_extension_array_equal(result2, expected)
|
|
|
|
|
|
class TestDatetimeArrayComparisons:
|
|
# TODO: merge this into tests/arithmetic/test_datetime64 once it is
|
|
# sufficiently robust
|
|
|
|
def test_cmp_dt64_arraylike_tznaive(self, comparison_op):
|
|
# arbitrary tz-naive DatetimeIndex
|
|
op = comparison_op
|
|
|
|
dti = pd.date_range("2016-01-1", freq="MS", periods=9, tz=None)
|
|
arr = DatetimeArray(dti)
|
|
assert arr.freq == dti.freq
|
|
assert arr.tz == dti.tz
|
|
|
|
right = dti
|
|
|
|
expected = np.ones(len(arr), dtype=bool)
|
|
if comparison_op.__name__ in ["ne", "gt", "lt"]:
|
|
# for these the comparisons should be all-False
|
|
expected = ~expected
|
|
|
|
result = op(arr, arr)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
for other in [
|
|
right,
|
|
np.array(right),
|
|
list(right),
|
|
tuple(right),
|
|
right.astype(object),
|
|
]:
|
|
result = op(arr, other)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = op(other, arr)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
class TestDatetimeArray:
|
|
def test_astype_non_nano_tznaive(self):
|
|
dti = pd.date_range("2016-01-01", periods=3)
|
|
|
|
res = dti.astype("M8[s]")
|
|
assert res.dtype == "M8[s]"
|
|
|
|
dta = dti._data
|
|
res = dta.astype("M8[s]")
|
|
assert res.dtype == "M8[s]"
|
|
assert isinstance(res, pd.core.arrays.DatetimeArray) # used to be ndarray
|
|
|
|
def test_astype_non_nano_tzaware(self):
|
|
dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
|
|
|
|
res = dti.astype("M8[s, US/Pacific]")
|
|
assert res.dtype == "M8[s, US/Pacific]"
|
|
|
|
dta = dti._data
|
|
res = dta.astype("M8[s, US/Pacific]")
|
|
assert res.dtype == "M8[s, US/Pacific]"
|
|
|
|
# from non-nano to non-nano, preserving reso
|
|
res2 = res.astype("M8[s, UTC]")
|
|
assert res2.dtype == "M8[s, UTC]"
|
|
assert not tm.shares_memory(res2, res)
|
|
|
|
res3 = res.astype("M8[s, UTC]", copy=False)
|
|
assert res2.dtype == "M8[s, UTC]"
|
|
assert tm.shares_memory(res3, res)
|
|
|
|
def test_astype_to_same(self):
|
|
arr = DatetimeArray._from_sequence(
|
|
["2000"], dtype=DatetimeTZDtype(tz="US/Central")
|
|
)
|
|
result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
|
|
assert result is arr
|
|
|
|
@pytest.mark.parametrize("dtype", ["datetime64[ns]", "datetime64[ns, UTC]"])
|
|
@pytest.mark.parametrize(
|
|
"other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"]
|
|
)
|
|
def test_astype_copies(self, dtype, other):
|
|
# https://github.com/pandas-dev/pandas/pull/32490
|
|
ser = pd.Series([1, 2], dtype=dtype)
|
|
orig = ser.copy()
|
|
|
|
err = False
|
|
if (dtype == "datetime64[ns]") ^ (other == "datetime64[ns]"):
|
|
# deprecated in favor of tz_localize
|
|
err = True
|
|
|
|
if err:
|
|
if dtype == "datetime64[ns]":
|
|
msg = "Use obj.tz_localize instead or series.dt.tz_localize instead"
|
|
else:
|
|
msg = "from timezone-aware dtype to timezone-naive dtype"
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.astype(other)
|
|
else:
|
|
t = ser.astype(other)
|
|
t[:] = pd.NaT
|
|
tm.assert_series_equal(ser, orig)
|
|
|
|
@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
|
|
def test_astype_int(self, dtype):
|
|
arr = DatetimeArray._from_sequence([pd.Timestamp("2000"), pd.Timestamp("2001")])
|
|
|
|
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_astype_to_sparse_dt64(self):
|
|
# GH#50082
|
|
dti = pd.date_range("2016-01-01", periods=4)
|
|
dta = dti._data
|
|
result = dta.astype("Sparse[datetime64[ns]]")
|
|
|
|
assert result.dtype == "Sparse[datetime64[ns]]"
|
|
assert (result == dta).all()
|
|
|
|
def test_tz_setter_raises(self):
|
|
arr = DatetimeArray._from_sequence(
|
|
["2000"], dtype=DatetimeTZDtype(tz="US/Central")
|
|
)
|
|
with pytest.raises(AttributeError, match="tz_localize"):
|
|
arr.tz = "UTC"
|
|
|
|
def test_setitem_str_impute_tz(self, tz_naive_fixture):
|
|
# Like for getitem, if we are passed a naive-like string, we impute
|
|
# our own timezone.
|
|
tz = tz_naive_fixture
|
|
|
|
data = np.array([1, 2, 3], dtype="M8[ns]")
|
|
dtype = data.dtype if tz is None else DatetimeTZDtype(tz=tz)
|
|
arr = DatetimeArray(data, dtype=dtype)
|
|
expected = arr.copy()
|
|
|
|
ts = pd.Timestamp("2020-09-08 16:50").tz_localize(tz)
|
|
setter = str(ts.tz_localize(None))
|
|
|
|
# Setting a scalar tznaive string
|
|
expected[0] = ts
|
|
arr[0] = setter
|
|
tm.assert_equal(arr, expected)
|
|
|
|
# Setting a listlike of tznaive strings
|
|
expected[1] = ts
|
|
arr[:2] = [setter, setter]
|
|
tm.assert_equal(arr, expected)
|
|
|
|
def test_setitem_different_tz_raises(self):
|
|
# pre-2.0 we required exact tz match, in 2.0 we require only
|
|
# tzawareness-match
|
|
data = np.array([1, 2, 3], dtype="M8[ns]")
|
|
arr = DatetimeArray(data, copy=False, dtype=DatetimeTZDtype(tz="US/Central"))
|
|
with pytest.raises(TypeError, match="Cannot compare tz-naive and tz-aware"):
|
|
arr[0] = pd.Timestamp("2000")
|
|
|
|
ts = pd.Timestamp("2000", tz="US/Eastern")
|
|
arr[0] = ts
|
|
assert arr[0] == ts.tz_convert("US/Central")
|
|
|
|
def test_setitem_clears_freq(self):
|
|
a = DatetimeArray(pd.date_range("2000", periods=2, freq="D", tz="US/Central"))
|
|
a[0] = pd.Timestamp("2000", tz="US/Central")
|
|
assert a.freq is None
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.Timestamp("2021-01-01"),
|
|
pd.Timestamp("2021-01-01").to_datetime64(),
|
|
pd.Timestamp("2021-01-01").to_pydatetime(),
|
|
],
|
|
)
|
|
def test_setitem_objects(self, obj):
|
|
# make sure we accept datetime64 and datetime in addition to Timestamp
|
|
dti = pd.date_range("2000", periods=2, freq="D")
|
|
arr = dti._data
|
|
|
|
arr[0] = obj
|
|
assert arr[0] == obj
|
|
|
|
def test_repeat_preserves_tz(self):
|
|
dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
|
|
arr = DatetimeArray(dti)
|
|
|
|
repeated = arr.repeat([1, 1])
|
|
|
|
# preserves tz and values, but not freq
|
|
expected = DatetimeArray(arr.asi8, freq=None, dtype=arr.dtype)
|
|
tm.assert_equal(repeated, expected)
|
|
|
|
def test_value_counts_preserves_tz(self):
|
|
dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central")
|
|
arr = DatetimeArray(dti).repeat([4, 3])
|
|
|
|
result = arr.value_counts()
|
|
|
|
# Note: not tm.assert_index_equal, since `freq`s do not match
|
|
assert result.index.equals(dti)
|
|
|
|
arr[-2] = pd.NaT
|
|
result = arr.value_counts(dropna=False)
|
|
expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT], name="count")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("method", ["pad", "backfill"])
|
|
def test_fillna_preserves_tz(self, method):
|
|
dti = pd.date_range("2000-01-01", periods=5, freq="D", tz="US/Central")
|
|
arr = DatetimeArray(dti, copy=True)
|
|
arr[2] = pd.NaT
|
|
|
|
fill_val = dti[1] if method == "pad" else dti[3]
|
|
expected = DatetimeArray._from_sequence(
|
|
[dti[0], dti[1], fill_val, dti[3], dti[4]],
|
|
dtype=DatetimeTZDtype(tz="US/Central"),
|
|
)
|
|
|
|
result = arr._pad_or_backfill(method=method)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
# assert that arr and dti were not modified in-place
|
|
assert arr[2] is pd.NaT
|
|
assert dti[2] == pd.Timestamp("2000-01-03", tz="US/Central")
|
|
|
|
def test_fillna_2d(self):
|
|
dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
|
|
dta = dti._data.reshape(3, 2).copy()
|
|
dta[0, 1] = pd.NaT
|
|
dta[1, 0] = pd.NaT
|
|
|
|
res1 = dta._pad_or_backfill(method="pad")
|
|
expected1 = dta.copy()
|
|
expected1[1, 0] = dta[0, 0]
|
|
tm.assert_extension_array_equal(res1, expected1)
|
|
|
|
res2 = dta._pad_or_backfill(method="backfill")
|
|
expected2 = dta.copy()
|
|
expected2 = dta.copy()
|
|
expected2[1, 0] = dta[2, 0]
|
|
expected2[0, 1] = dta[1, 1]
|
|
tm.assert_extension_array_equal(res2, expected2)
|
|
|
|
# with different ordering for underlying ndarray; behavior should
|
|
# be unchanged
|
|
dta2 = dta._from_backing_data(dta._ndarray.copy(order="F"))
|
|
assert dta2._ndarray.flags["F_CONTIGUOUS"]
|
|
assert not dta2._ndarray.flags["C_CONTIGUOUS"]
|
|
tm.assert_extension_array_equal(dta, dta2)
|
|
|
|
res3 = dta2._pad_or_backfill(method="pad")
|
|
tm.assert_extension_array_equal(res3, expected1)
|
|
|
|
res4 = dta2._pad_or_backfill(method="backfill")
|
|
tm.assert_extension_array_equal(res4, expected2)
|
|
|
|
# test the DataFrame method while we're here
|
|
df = pd.DataFrame(dta)
|
|
res = df.ffill()
|
|
expected = pd.DataFrame(expected1)
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
res = df.bfill()
|
|
expected = pd.DataFrame(expected2)
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
def test_array_interface_tz(self):
|
|
tz = "US/Central"
|
|
data = DatetimeArray(pd.date_range("2017", periods=2, tz=tz))
|
|
result = np.asarray(data)
|
|
|
|
expected = np.array(
|
|
[
|
|
pd.Timestamp("2017-01-01T00:00:00", tz=tz),
|
|
pd.Timestamp("2017-01-02T00:00:00", tz=tz),
|
|
],
|
|
dtype=object,
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = np.asarray(data, dtype=object)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = np.asarray(data, dtype="M8[ns]")
|
|
|
|
expected = np.array(
|
|
["2017-01-01T06:00:00", "2017-01-02T06:00:00"], dtype="M8[ns]"
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_array_interface(self):
|
|
data = DatetimeArray(pd.date_range("2017", periods=2))
|
|
expected = np.array(
|
|
["2017-01-01T00:00:00", "2017-01-02T00:00:00"], dtype="datetime64[ns]"
|
|
)
|
|
|
|
result = np.asarray(data)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = np.asarray(data, dtype=object)
|
|
expected = np.array(
|
|
[pd.Timestamp("2017-01-01T00:00:00"), pd.Timestamp("2017-01-02T00:00:00")],
|
|
dtype=object,
|
|
)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("index", [True, False])
|
|
def test_searchsorted_different_tz(self, index):
|
|
data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
|
|
arr = DatetimeArray(data, freq="D").tz_localize("Asia/Tokyo")
|
|
if index:
|
|
arr = pd.Index(arr)
|
|
|
|
expected = arr.searchsorted(arr[2])
|
|
result = arr.searchsorted(arr[2].tz_convert("UTC"))
|
|
assert result == expected
|
|
|
|
expected = arr.searchsorted(arr[2:6])
|
|
result = arr.searchsorted(arr[2:6].tz_convert("UTC"))
|
|
tm.assert_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("index", [True, False])
|
|
def test_searchsorted_tzawareness_compat(self, index):
|
|
data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
|
|
arr = DatetimeArray(data, freq="D")
|
|
if index:
|
|
arr = pd.Index(arr)
|
|
|
|
mismatch = arr.tz_localize("Asia/Tokyo")
|
|
|
|
msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr.searchsorted(mismatch[0])
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr.searchsorted(mismatch)
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
mismatch.searchsorted(arr[0])
|
|
with pytest.raises(TypeError, match=msg):
|
|
mismatch.searchsorted(arr)
|
|
|
|
@pytest.mark.parametrize(
|
|
"other",
|
|
[
|
|
1,
|
|
np.int64(1),
|
|
1.0,
|
|
np.timedelta64("NaT"),
|
|
pd.Timedelta(days=2),
|
|
"invalid",
|
|
np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
|
|
np.arange(10).view("timedelta64[ns]") * 24 * 3600 * 10**9,
|
|
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 = DatetimeArray(data, freq="D")
|
|
if index:
|
|
arr = pd.Index(arr)
|
|
|
|
msg = "|".join(
|
|
[
|
|
"searchsorted requires compatible dtype or scalar",
|
|
"value should be a 'Timestamp', 'NaT', or array of those. Got",
|
|
]
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr.searchsorted(other)
|
|
|
|
def test_shift_fill_value(self):
|
|
dti = pd.date_range("2016-01-01", periods=3)
|
|
|
|
dta = dti._data
|
|
expected = DatetimeArray(np.roll(dta._ndarray, 1))
|
|
|
|
fv = dta[-1]
|
|
for fill_value in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
|
|
result = dta.shift(1, fill_value=fill_value)
|
|
tm.assert_datetime_array_equal(result, expected)
|
|
|
|
dta = dta.tz_localize("UTC")
|
|
expected = expected.tz_localize("UTC")
|
|
fv = dta[-1]
|
|
for fill_value in [fv, fv.to_pydatetime()]:
|
|
result = dta.shift(1, fill_value=fill_value)
|
|
tm.assert_datetime_array_equal(result, expected)
|
|
|
|
def test_shift_value_tzawareness_mismatch(self):
|
|
dti = pd.date_range("2016-01-01", periods=3)
|
|
|
|
dta = dti._data
|
|
|
|
fv = dta[-1].tz_localize("UTC")
|
|
for invalid in [fv, fv.to_pydatetime()]:
|
|
with pytest.raises(TypeError, match="Cannot compare"):
|
|
dta.shift(1, fill_value=invalid)
|
|
|
|
dta = dta.tz_localize("UTC")
|
|
fv = dta[-1].tz_localize(None)
|
|
for invalid in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
|
|
with pytest.raises(TypeError, match="Cannot compare"):
|
|
dta.shift(1, fill_value=invalid)
|
|
|
|
def test_shift_requires_tzmatch(self):
|
|
# pre-2.0 we required exact tz match, in 2.0 we require just
|
|
# matching tzawareness
|
|
dti = pd.date_range("2016-01-01", periods=3, tz="UTC")
|
|
dta = dti._data
|
|
|
|
fill_value = pd.Timestamp("2020-10-18 18:44", tz="US/Pacific")
|
|
|
|
result = dta.shift(1, fill_value=fill_value)
|
|
expected = dta.shift(1, fill_value=fill_value.tz_convert("UTC"))
|
|
tm.assert_equal(result, expected)
|
|
|
|
def test_tz_localize_t2d(self):
|
|
dti = pd.date_range("1994-05-12", periods=12, tz="US/Pacific")
|
|
dta = dti._data.reshape(3, 4)
|
|
result = dta.tz_localize(None)
|
|
|
|
expected = dta.ravel().tz_localize(None).reshape(dta.shape)
|
|
tm.assert_datetime_array_equal(result, expected)
|
|
|
|
roundtrip = expected.tz_localize("US/Pacific")
|
|
tm.assert_datetime_array_equal(roundtrip, dta)
|
|
|
|
easts = ["US/Eastern", "dateutil/US/Eastern"]
|
|
if ZoneInfo is not None:
|
|
try:
|
|
tz = ZoneInfo("US/Eastern")
|
|
except KeyError:
|
|
# no tzdata
|
|
pass
|
|
else:
|
|
# Argument 1 to "append" of "list" has incompatible type "ZoneInfo";
|
|
# expected "str"
|
|
easts.append(tz) # type: ignore[arg-type]
|
|
|
|
@pytest.mark.parametrize("tz", easts)
|
|
def test_iter_zoneinfo_fold(self, tz):
|
|
# GH#49684
|
|
utc_vals = np.array(
|
|
[1320552000, 1320555600, 1320559200, 1320562800], dtype=np.int64
|
|
)
|
|
utc_vals *= 1_000_000_000
|
|
|
|
dta = DatetimeArray(utc_vals).tz_localize("UTC").tz_convert(tz)
|
|
|
|
left = dta[2]
|
|
right = list(dta)[2]
|
|
assert str(left) == str(right)
|
|
# previously there was a bug where with non-pytz right would be
|
|
# Timestamp('2011-11-06 01:00:00-0400', tz='US/Eastern')
|
|
# while left would be
|
|
# Timestamp('2011-11-06 01:00:00-0500', tz='US/Eastern')
|
|
# The .value's would match (so they would compare as equal),
|
|
# but the folds would not
|
|
assert left.utcoffset() == right.utcoffset()
|
|
|
|
# The same bug in ints_to_pydatetime affected .astype, so we test
|
|
# that here.
|
|
right2 = dta.astype(object)[2]
|
|
assert str(left) == str(right2)
|
|
assert left.utcoffset() == right2.utcoffset()
|
|
|
|
|
|
def test_factorize_sort_without_freq():
|
|
dta = DatetimeArray._from_sequence([0, 2, 1])
|
|
|
|
msg = r"call pd.factorize\(obj, sort=True\) instead"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
dta.factorize(sort=True)
|
|
|
|
# Do TimedeltaArray while we're here
|
|
tda = dta - dta[0]
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
tda.factorize(sort=True)
|
|
|