test_nat.py

from datetime import datetime, timedelta

import numpy as np
import pytest
import pytz

from pandas._libs.tslibs import iNaT
import pandas.compat as compat

from pandas import (
    DatetimeIndex,
    Index,
    NaT,
    Period,
    Series,
    Timedelta,
    TimedeltaIndex,
    Timestamp,
    isna,
)
from pandas.core.arrays import PeriodArray
from pandas.util import testing as tm


@pytest.mark.parametrize(
    "nat,idx",
    [
        (Timestamp("NaT"), DatetimeIndex),
        (Timedelta("NaT"), TimedeltaIndex),
        (Period("NaT", freq="M"), PeriodArray),
    ],
)
def test_nat_fields(nat, idx):

    for field in idx._field_ops:
        # weekday is a property of DTI, but a method
        # on NaT/Timestamp for compat with datetime
        if field == "weekday":
            continue

        result = getattr(NaT, field)
        assert np.isnan(result)

        result = getattr(nat, field)
        assert np.isnan(result)

    for field in idx._bool_ops:

        result = getattr(NaT, field)
        assert result is False

        result = getattr(nat, field)
        assert result is False


def test_nat_vector_field_access():
    idx = DatetimeIndex(["1/1/2000", None, None, "1/4/2000"])

    for field in DatetimeIndex._field_ops:
        # weekday is a property of DTI, but a method
        # on NaT/Timestamp for compat with datetime
        if field == "weekday":
            continue

        result = getattr(idx, field)
        expected = Index([getattr(x, field) for x in idx])
        tm.assert_index_equal(result, expected)

    ser = Series(idx)

    for field in DatetimeIndex._field_ops:
        # weekday is a property of DTI, but a method
        # on NaT/Timestamp for compat with datetime
        if field == "weekday":
            continue

        result = getattr(ser.dt, field)
        expected = [getattr(x, field) for x in idx]
        tm.assert_series_equal(result, Series(expected))

    for field in DatetimeIndex._bool_ops:
        result = getattr(ser.dt, field)
        expected = [getattr(x, field) for x in idx]
        tm.assert_series_equal(result, Series(expected))


@pytest.mark.parametrize("klass", [Timestamp, Timedelta, Period])
@pytest.mark.parametrize("value", [None, np.nan, iNaT, float("nan"), NaT, "NaT", "nat"])
def test_identity(klass, value):
    assert klass(value) is NaT


@pytest.mark.parametrize("klass", [Timestamp, Timedelta, Period])
@pytest.mark.parametrize("value", ["", "nat", "NAT", None, np.nan])
def test_equality(klass, value):
    if klass is Period and value == "":
        pytest.skip("Period cannot parse empty string")

    assert klass(value).value == iNaT


@pytest.mark.parametrize("klass", [Timestamp, Timedelta])
@pytest.mark.parametrize("method", ["round", "floor", "ceil"])
@pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"])
def test_round_nat(klass, method, freq):
    # see gh-14940
    ts = klass("nat")

    round_method = getattr(ts, method)
    assert round_method(freq) is ts


@pytest.mark.parametrize(
    "method",
    [
        "astimezone",
        "combine",
        "ctime",
        "dst",
        "fromordinal",
        "fromtimestamp",
        "isocalendar",
        "strftime",
        "strptime",
        "time",
        "timestamp",
        "timetuple",
        "timetz",
        "toordinal",
        "tzname",
        "utcfromtimestamp",
        "utcnow",
        "utcoffset",
        "utctimetuple",
        "timestamp",
    ],
)
def test_nat_methods_raise(method):
    # see gh-9513, gh-17329
    msg = "NaTType does not support {method}".format(method=method)

    with pytest.raises(ValueError, match=msg):
        getattr(NaT, method)()


@pytest.mark.parametrize("method", ["weekday", "isoweekday"])
def test_nat_methods_nan(method):
    # see gh-9513, gh-17329
    assert np.isnan(getattr(NaT, method)())


@pytest.mark.parametrize(
    "method", ["date", "now", "replace", "today", "tz_convert", "tz_localize"]
)
def test_nat_methods_nat(method):
    # see gh-8254, gh-9513, gh-17329
    assert getattr(NaT, method)() is NaT


@pytest.mark.parametrize(
    "get_nat", [lambda x: NaT, lambda x: Timedelta(x), lambda x: Timestamp(x)]
)
def test_nat_iso_format(get_nat):
    # see gh-12300
    assert get_nat("NaT").isoformat() == "NaT"


@pytest.mark.parametrize(
    "klass,expected",
    [
        (Timestamp, ["freqstr", "normalize", "to_julian_date", "to_period", "tz"]),
        (
            Timedelta,
            [
                "components",
                "delta",
                "is_populated",
                "resolution_string",
                "to_pytimedelta",
                "to_timedelta64",
                "view",
            ],
        ),
    ],
)
def test_missing_public_nat_methods(klass, expected):
    # see gh-17327
    #
    # NaT should have *most* of the Timestamp and Timedelta methods.
    # Here, we check which public methods NaT does not have. We
    # ignore any missing private methods.
    nat_names = dir(NaT)
    klass_names = dir(klass)

    missing = [x for x in klass_names if x not in nat_names and not x.startswith("_")]
    missing.sort()

    assert missing == expected


def _get_overlap_public_nat_methods(klass, as_tuple=False):
    """
    Get overlapping public methods between NaT and another class.

    Parameters
    ----------
    klass : type
        The class to compare with NaT
    as_tuple : bool, default False
        Whether to return a list of tuples of the form (klass, method).

    Returns
    -------
    overlap : list
    """
    nat_names = dir(NaT)
    klass_names = dir(klass)

    overlap = [
        x
        for x in nat_names
        if x in klass_names and not x.startswith("_") and callable(getattr(klass, x))
    ]

    # Timestamp takes precedence over Timedelta in terms of overlap.
    if klass is Timedelta:
        ts_names = dir(Timestamp)
        overlap = [x for x in overlap if x not in ts_names]

    if as_tuple:
        overlap = [(klass, method) for method in overlap]

    overlap.sort()
    return overlap


@pytest.mark.parametrize(
    "klass,expected",
    [
        (
            Timestamp,
            [
                "astimezone",
                "ceil",
                "combine",
                "ctime",
                "date",
                "day_name",
                "dst",
                "floor",
                "fromisoformat",
                "fromordinal",
                "fromtimestamp",
                "isocalendar",
                "isoformat",
                "isoweekday",
                "month_name",
                "now",
                "replace",
                "round",
                "strftime",
                "strptime",
                "time",
                "timestamp",
                "timetuple",
                "timetz",
                "to_datetime64",
                "to_numpy",
                "to_pydatetime",
                "today",
                "toordinal",
                "tz_convert",
                "tz_localize",
                "tzname",
                "utcfromtimestamp",
                "utcnow",
                "utcoffset",
                "utctimetuple",
                "weekday",
            ],
        ),
        (Timedelta, ["total_seconds"]),
    ],
)
def test_overlap_public_nat_methods(klass, expected):
    # see gh-17327
    #
    # NaT should have *most* of the Timestamp and Timedelta methods.
    # In case when Timestamp, Timedelta, and NaT are overlap, the overlap
    # is considered to be with Timestamp and NaT, not Timedelta.

    # "fromisoformat" was introduced in 3.7
    if klass is Timestamp and not compat.PY37:
        expected.remove("fromisoformat")

    assert _get_overlap_public_nat_methods(klass) == expected


@pytest.mark.parametrize(
    "compare",
    (
        _get_overlap_public_nat_methods(Timestamp, True)
        + _get_overlap_public_nat_methods(Timedelta, True)
    ),
)
def test_nat_doc_strings(compare):
    # see gh-17327
    #
    # The docstrings for overlapping methods should match.
    klass, method = compare
    klass_doc = getattr(klass, method).__doc__

    nat_doc = getattr(NaT, method).__doc__
    assert klass_doc == nat_doc


_ops = {
    "left_plus_right": lambda a, b: a + b,
    "right_plus_left": lambda a, b: b + a,
    "left_minus_right": lambda a, b: a - b,
    "right_minus_left": lambda a, b: b - a,
    "left_times_right": lambda a, b: a * b,
    "right_times_left": lambda a, b: b * a,
    "left_div_right": lambda a, b: a / b,
    "right_div_left": lambda a, b: b / a,
}


@pytest.mark.parametrize("op_name", list(_ops.keys()))
@pytest.mark.parametrize(
    "value,val_type",
    [
        (2, "scalar"),
        (1.5, "scalar"),
        (np.nan, "scalar"),
        (timedelta(3600), "timedelta"),
        (Timedelta("5s"), "timedelta"),
        (datetime(2014, 1, 1), "timestamp"),
        (Timestamp("2014-01-01"), "timestamp"),
        (Timestamp("2014-01-01", tz="UTC"), "timestamp"),
        (Timestamp("2014-01-01", tz="US/Eastern"), "timestamp"),
        (pytz.timezone("Asia/Tokyo").localize(datetime(2014, 1, 1)), "timestamp"),
    ],
)
def test_nat_arithmetic_scalar(op_name, value, val_type):
    # see gh-6873
    invalid_ops = {
        "scalar": {"right_div_left"},
        "timedelta": {"left_times_right", "right_times_left"},
        "timestamp": {
            "left_times_right",
            "right_times_left",
            "left_div_right",
            "right_div_left",
        },
    }

    op = _ops[op_name]

    if op_name in invalid_ops.get(val_type, set()):
        if (
            val_type == "timedelta"
            and "times" in op_name
            and isinstance(value, Timedelta)
        ):
            msg = "Cannot multiply"
        else:
            msg = "unsupported operand type"

        with pytest.raises(TypeError, match=msg):
            op(NaT, value)
    else:
        if val_type == "timedelta" and "div" in op_name:
            expected = np.nan
        else:
            expected = NaT

        assert op(NaT, value) is expected


@pytest.mark.parametrize(
    "val,expected", [(np.nan, NaT), (NaT, np.nan), (np.timedelta64("NaT"), np.nan)]
)
def test_nat_rfloordiv_timedelta(val, expected):
    # see gh-#18846
    #
    # See also test_timedelta.TestTimedeltaArithmetic.test_floordiv
    td = Timedelta(hours=3, minutes=4)
    assert td // val is expected


@pytest.mark.parametrize(
    "op_name",
    ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"],
)
@pytest.mark.parametrize(
    "value",
    [
        DatetimeIndex(["2011-01-01", "2011-01-02"], name="x"),
        DatetimeIndex(["2011-01-01", "2011-01-02"], name="x"),
        TimedeltaIndex(["1 day", "2 day"], name="x"),
    ],
)
def test_nat_arithmetic_index(op_name, value):
    # see gh-11718
    exp_name = "x"
    exp_data = [NaT] * 2

    if isinstance(value, DatetimeIndex) and "plus" in op_name:
        expected = DatetimeIndex(exp_data, name=exp_name, tz=value.tz)
    else:
        expected = TimedeltaIndex(exp_data, name=exp_name)

    tm.assert_index_equal(_ops[op_name](NaT, value), expected)


@pytest.mark.parametrize(
    "op_name",
    ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"],
)
@pytest.mark.parametrize("box", [TimedeltaIndex, Series])
def test_nat_arithmetic_td64_vector(op_name, box):
    # see gh-19124
    vec = box(["1 day", "2 day"], dtype="timedelta64[ns]")
    box_nat = box([NaT, NaT], dtype="timedelta64[ns]")
    tm.assert_equal(_ops[op_name](vec, NaT), box_nat)


def test_nat_pinned_docstrings():
    # see gh-17327
    assert NaT.ctime.__doc__ == datetime.ctime.__doc__


def test_to_numpy_alias():
    # GH 24653: alias .to_numpy() for scalars
    expected = NaT.to_datetime64()
    result = NaT.to_numpy()

    assert isna(expected) and isna(result)


@pytest.mark.parametrize("other", [Timedelta(0), Timestamp(0)])
def test_nat_comparisons(compare_operators_no_eq_ne, other):
    # GH 26039
    assert getattr(NaT, compare_operators_no_eq_ne)(other) is False
    assert getattr(other, compare_operators_no_eq_ne)(NaT) is False