2421 lines
78 KiB
Cython
2421 lines
78 KiB
Cython
"""
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_Timestamp is a c-defined subclass of datetime.datetime
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_Timestamp is PITA. Because we inherit from datetime, which has very specific
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construction requirements, we need to do object instantiation in python
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(see Timestamp class below). This will serve as a C extension type that
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shadows the python class, where we do any heavy lifting.
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"""
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import warnings
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cimport cython
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (
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int8_t,
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int64_t,
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ndarray,
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uint8_t,
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)
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cnp.import_array()
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from cpython.datetime cimport ( # alias bc `tzinfo` is a kwarg below
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PyDate_Check,
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PyDateTime_Check,
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PyDelta_Check,
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PyTZInfo_Check,
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datetime,
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import_datetime,
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time,
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tzinfo as tzinfo_type,
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)
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from cpython.object cimport (
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Py_EQ,
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Py_GE,
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Py_GT,
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Py_LE,
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Py_LT,
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Py_NE,
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PyObject_RichCompare,
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PyObject_RichCompareBool,
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)
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import_datetime()
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from pandas._libs.tslibs cimport ccalendar
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from pandas._libs.tslibs.base cimport ABCTimestamp
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from pandas.util._exceptions import find_stack_level
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from pandas._libs.tslibs.conversion cimport (
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_TSObject,
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convert_datetime_to_tsobject,
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convert_to_tsobject,
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maybe_localize_tso,
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)
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from pandas._libs.tslibs.dtypes cimport (
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npy_unit_to_abbrev,
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periods_per_day,
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periods_per_second,
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)
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from pandas._libs.tslibs.util cimport (
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is_array,
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is_datetime64_object,
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is_float_object,
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is_integer_object,
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is_timedelta64_object,
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)
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from pandas._libs.tslibs.fields import (
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RoundTo,
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get_date_name_field,
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get_start_end_field,
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round_nsint64,
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)
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from pandas._libs.tslibs.nattype cimport (
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NPY_NAT,
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c_NaT as NaT,
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)
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from pandas._libs.tslibs.np_datetime cimport (
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NPY_DATETIMEUNIT,
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NPY_FR_ns,
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cmp_dtstructs,
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cmp_scalar,
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convert_reso,
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get_conversion_factor,
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get_datetime64_unit,
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get_datetime64_value,
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get_unit_from_dtype,
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npy_datetimestruct,
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npy_datetimestruct_to_datetime,
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pandas_datetime_to_datetimestruct,
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pydatetime_to_dtstruct,
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)
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from pandas._libs.tslibs.np_datetime import (
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OutOfBoundsDatetime,
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OutOfBoundsTimedelta,
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)
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from pandas._libs.tslibs.offsets cimport (
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BaseOffset,
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is_offset_object,
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to_offset,
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)
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from pandas._libs.tslibs.timedeltas cimport (
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_Timedelta,
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delta_to_nanoseconds,
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ensure_td64ns,
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is_any_td_scalar,
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)
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from pandas._libs.tslibs.timedeltas import Timedelta
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from pandas._libs.tslibs.timezones cimport (
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get_timezone,
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is_utc,
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maybe_get_tz,
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treat_tz_as_pytz,
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tz_compare,
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utc_pytz as UTC,
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)
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from pandas._libs.tslibs.tzconversion cimport (
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tz_convert_from_utc_single,
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tz_localize_to_utc_single,
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)
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# ----------------------------------------------------------------------
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# Constants
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_zero_time = time(0, 0)
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_no_input = object()
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# ----------------------------------------------------------------------
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cdef inline _Timestamp create_timestamp_from_ts(
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int64_t value,
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npy_datetimestruct dts,
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tzinfo tz,
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BaseOffset freq,
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bint fold,
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NPY_DATETIMEUNIT reso=NPY_FR_ns,
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):
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""" convenience routine to construct a Timestamp from its parts """
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cdef:
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_Timestamp ts_base
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int64_t pass_year = dts.year
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# We pass year=1970/1972 here and set year below because with non-nanosecond
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# resolution we may have datetimes outside of the stdlib pydatetime
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# implementation bounds, which would raise.
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# NB: this means the C-API macro PyDateTime_GET_YEAR is unreliable.
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if 1 <= pass_year <= 9999:
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# we are in-bounds for pydatetime
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pass
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elif ccalendar.is_leapyear(dts.year):
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pass_year = 1972
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else:
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pass_year = 1970
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ts_base = _Timestamp.__new__(Timestamp, pass_year, dts.month,
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dts.day, dts.hour, dts.min,
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dts.sec, dts.us, tz, fold=fold)
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ts_base.value = value
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ts_base._freq = freq
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ts_base.year = dts.year
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ts_base.nanosecond = dts.ps // 1000
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ts_base._reso = reso
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return ts_base
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def _unpickle_timestamp(value, freq, tz, reso=NPY_FR_ns):
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# GH#41949 dont warn on unpickle if we have a freq
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ts = Timestamp._from_value_and_reso(value, reso, tz)
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ts._set_freq(freq)
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return ts
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# ----------------------------------------------------------------------
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def integer_op_not_supported(obj):
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# GH#22535 add/sub of integers and int-arrays is no longer allowed
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# Note we return rather than raise the exception so we can raise in
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# the caller; mypy finds this more palatable.
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cls = type(obj).__name__
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# GH#30886 using an fstring raises SystemError
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int_addsub_msg = (
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f"Addition/subtraction of integers and integer-arrays with {cls} is "
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"no longer supported. Instead of adding/subtracting `n`, "
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"use `n * obj.freq`"
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)
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return TypeError(int_addsub_msg)
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class MinMaxReso:
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"""
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We need to define min/max/resolution on both the Timestamp _instance_
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and Timestamp class. On an instance, these depend on the object's _reso.
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On the class, we default to the values we would get with nanosecond _reso.
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See also: timedeltas.MinMaxReso
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"""
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def __init__(self, name):
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self._name = name
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def __get__(self, obj, type=None):
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cls = Timestamp
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if self._name == "min":
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val = np.iinfo(np.int64).min + 1
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elif self._name == "max":
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val = np.iinfo(np.int64).max
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else:
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assert self._name == "resolution"
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val = 1
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cls = Timedelta
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if obj is None:
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# i.e. this is on the class, default to nanos
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return cls(val)
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elif self._name == "resolution":
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return Timedelta._from_value_and_reso(val, obj._reso)
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else:
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return Timestamp._from_value_and_reso(val, obj._reso, tz=None)
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def __set__(self, obj, value):
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raise AttributeError(f"{self._name} is not settable.")
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# ----------------------------------------------------------------------
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cdef class _Timestamp(ABCTimestamp):
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# higher than np.ndarray and np.matrix
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__array_priority__ = 100
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dayofweek = _Timestamp.day_of_week
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dayofyear = _Timestamp.day_of_year
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min = MinMaxReso("min")
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max = MinMaxReso("max")
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resolution = MinMaxReso("resolution") # GH#21336, GH#21365
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cpdef void _set_freq(self, freq):
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# set the ._freq attribute without going through the constructor,
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# which would issue a warning
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# Caller is responsible for validation
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self._freq = freq
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@property
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def freq(self):
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warnings.warn(
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"Timestamp.freq is deprecated and will be removed in a future version.",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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return self._freq
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# -----------------------------------------------------------------
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# Constructors
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@classmethod
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def _from_value_and_reso(cls, int64_t value, NPY_DATETIMEUNIT reso, tzinfo tz):
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cdef:
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npy_datetimestruct dts
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_TSObject obj = _TSObject()
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if value == NPY_NAT:
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return NaT
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if reso < NPY_DATETIMEUNIT.NPY_FR_s or reso > NPY_DATETIMEUNIT.NPY_FR_ns:
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raise NotImplementedError(
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"Only resolutions 's', 'ms', 'us', 'ns' are supported."
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)
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obj.value = value
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pandas_datetime_to_datetimestruct(value, reso, &obj.dts)
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maybe_localize_tso(obj, tz, reso)
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return create_timestamp_from_ts(
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value, obj.dts, tz=obj.tzinfo, freq=None, fold=obj.fold, reso=reso
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)
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@classmethod
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def _from_dt64(cls, dt64: np.datetime64):
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# construct a Timestamp from a np.datetime64 object, keeping the
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# resolution of the input.
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# This is herely mainly so we can incrementally implement non-nano
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# (e.g. only tznaive at first)
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cdef:
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npy_datetimestruct dts
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int64_t value
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NPY_DATETIMEUNIT reso
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reso = get_datetime64_unit(dt64)
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value = get_datetime64_value(dt64)
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return cls._from_value_and_reso(value, reso, None)
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# -----------------------------------------------------------------
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def __hash__(_Timestamp self):
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if self.nanosecond:
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return hash(self.value)
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if not (1 <= self.year <= 9999):
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# out of bounds for pydatetime
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return hash(self.value)
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if self.fold:
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return datetime.__hash__(self.replace(fold=0))
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return datetime.__hash__(self)
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def __richcmp__(_Timestamp self, object other, int op):
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cdef:
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_Timestamp ots
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int ndim
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if isinstance(other, _Timestamp):
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ots = other
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elif other is NaT:
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return op == Py_NE
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elif is_datetime64_object(other):
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ots = _Timestamp._from_dt64(other)
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elif PyDateTime_Check(other):
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if self.nanosecond == 0:
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val = self.to_pydatetime()
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return PyObject_RichCompareBool(val, other, op)
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try:
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ots = type(self)(other)
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except ValueError:
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return self._compare_outside_nanorange(other, op)
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elif is_array(other):
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# avoid recursion error GH#15183
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if other.dtype.kind == "M":
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if self.tz is None:
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return PyObject_RichCompare(self.asm8, other, op)
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elif op == Py_NE:
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return np.ones(other.shape, dtype=np.bool_)
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elif op == Py_EQ:
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return np.zeros(other.shape, dtype=np.bool_)
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raise TypeError(
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"Cannot compare tz-naive and tz-aware timestamps"
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)
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elif other.dtype.kind == "O":
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# Operate element-wise
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return np.array(
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[PyObject_RichCompare(self, x, op) for x in other],
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dtype=bool,
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)
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elif op == Py_NE:
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return np.ones(other.shape, dtype=np.bool_)
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elif op == Py_EQ:
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return np.zeros(other.shape, dtype=np.bool_)
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return NotImplemented
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elif PyDate_Check(other):
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# returning NotImplemented defers to the `date` implementation
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# which incorrectly drops tz and normalizes to midnight
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# before comparing
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# We follow the stdlib datetime behavior of never being equal
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warnings.warn(
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"Comparison of Timestamp with datetime.date is deprecated in "
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"order to match the standard library behavior. "
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"In a future version these will be considered non-comparable. "
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"Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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return NotImplemented
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else:
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return NotImplemented
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if not self._can_compare(ots):
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if op == Py_NE or op == Py_EQ:
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return NotImplemented
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raise TypeError(
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"Cannot compare tz-naive and tz-aware timestamps"
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)
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if self._reso == ots._reso:
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return cmp_scalar(self.value, ots.value, op)
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return self._compare_mismatched_resos(ots, op)
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# TODO: copied from Timedelta; try to de-duplicate
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cdef inline bint _compare_mismatched_resos(self, _Timestamp other, int op):
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# Can't just dispatch to numpy as they silently overflow and get it wrong
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cdef:
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npy_datetimestruct dts_self
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npy_datetimestruct dts_other
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# dispatch to the datetimestruct utils instead of writing new ones!
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pandas_datetime_to_datetimestruct(self.value, self._reso, &dts_self)
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pandas_datetime_to_datetimestruct(other.value, other._reso, &dts_other)
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return cmp_dtstructs(&dts_self, &dts_other, op)
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cdef bint _compare_outside_nanorange(_Timestamp self, datetime other,
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int op) except -1:
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cdef:
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datetime dtval = self.to_pydatetime(warn=False)
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if not self._can_compare(other):
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return NotImplemented
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if self.nanosecond == 0:
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return PyObject_RichCompareBool(dtval, other, op)
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# otherwise we have dtval < self
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if op == Py_NE:
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return True
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if op == Py_EQ:
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return False
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if op == Py_LE or op == Py_LT:
|
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return self.year <= other.year
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if op == Py_GE or op == Py_GT:
|
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return self.year >= other.year
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|
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cdef bint _can_compare(self, datetime other):
|
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if self.tzinfo is not None:
|
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return other.tzinfo is not None
|
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return other.tzinfo is None
|
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|
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@cython.overflowcheck(True)
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def __add__(self, other):
|
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cdef:
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int64_t nanos = 0
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|
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if is_any_td_scalar(other):
|
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if is_timedelta64_object(other):
|
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other_reso = get_datetime64_unit(other)
|
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if (
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other_reso == NPY_DATETIMEUNIT.NPY_FR_GENERIC
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):
|
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# TODO: deprecate allowing this? We only get here
|
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# with test_timedelta_add_timestamp_interval
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other = np.timedelta64(other.view("i8"), "ns")
|
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elif (
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other_reso == NPY_DATETIMEUNIT.NPY_FR_Y or other_reso == NPY_DATETIMEUNIT.NPY_FR_M
|
|
):
|
|
# TODO: deprecate allowing these? or handle more like the
|
|
# corresponding DateOffsets?
|
|
# TODO: no tests get here
|
|
other = ensure_td64ns(other)
|
|
|
|
if isinstance(other, _Timedelta):
|
|
# TODO: share this with __sub__, Timedelta.__add__
|
|
# We allow silent casting to the lower resolution if and only
|
|
# if it is lossless. See also Timestamp.__sub__
|
|
# and Timedelta.__add__
|
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try:
|
|
if self._reso < other._reso:
|
|
other = (<_Timedelta>other)._as_reso(self._reso, round_ok=False)
|
|
elif self._reso > other._reso:
|
|
self = (<_Timestamp>self)._as_reso(other._reso, round_ok=False)
|
|
except ValueError as err:
|
|
raise ValueError(
|
|
"Timestamp addition with mismatched resolutions is not "
|
|
"allowed when casting to the lower resolution would require "
|
|
"lossy rounding."
|
|
) from err
|
|
|
|
try:
|
|
nanos = delta_to_nanoseconds(
|
|
other, reso=self._reso, round_ok=False
|
|
)
|
|
except OutOfBoundsTimedelta:
|
|
raise
|
|
except ValueError as err:
|
|
raise ValueError(
|
|
"Addition between Timestamp and Timedelta with mismatched "
|
|
"resolutions is not allowed when casting to the lower "
|
|
"resolution would require lossy rounding."
|
|
) from err
|
|
|
|
try:
|
|
new_value = self.value + nanos
|
|
except OverflowError:
|
|
# Use Python ints
|
|
# Hit in test_tdi_add_overflow
|
|
new_value = int(self.value) + int(nanos)
|
|
|
|
try:
|
|
result = type(self)._from_value_and_reso(
|
|
new_value, reso=self._reso, tz=self.tzinfo
|
|
)
|
|
except OverflowError as err:
|
|
# TODO: don't hard-code nanosecond here
|
|
raise OutOfBoundsDatetime(
|
|
f"Out of bounds nanosecond timestamp: {new_value}"
|
|
) from err
|
|
|
|
if result is not NaT:
|
|
result._set_freq(self._freq) # avoid warning in constructor
|
|
return result
|
|
|
|
elif is_integer_object(other):
|
|
raise integer_op_not_supported(self)
|
|
|
|
elif is_array(other):
|
|
if other.dtype.kind in ['i', 'u']:
|
|
raise integer_op_not_supported(self)
|
|
if other.dtype.kind == "m":
|
|
if self.tz is None:
|
|
return self.asm8 + other
|
|
return np.asarray(
|
|
[self + other[n] for n in range(len(other))],
|
|
dtype=object,
|
|
)
|
|
|
|
elif not isinstance(self, _Timestamp):
|
|
# cython semantics, args have been switched and this is __radd__
|
|
# TODO(cython3): remove this it moved to __radd__
|
|
return other.__add__(self)
|
|
return NotImplemented
|
|
|
|
def __radd__(self, other):
|
|
# Have to duplicate checks to avoid infinite recursion due to NotImplemented
|
|
if is_any_td_scalar(other) or is_integer_object(other) or is_array(other):
|
|
return self.__add__(other)
|
|
return NotImplemented
|
|
|
|
def __sub__(self, other):
|
|
if other is NaT:
|
|
return NaT
|
|
|
|
elif is_any_td_scalar(other) or is_integer_object(other):
|
|
neg_other = -other
|
|
return self + neg_other
|
|
|
|
elif is_array(other):
|
|
if other.dtype.kind in ['i', 'u']:
|
|
raise integer_op_not_supported(self)
|
|
if other.dtype.kind == "m":
|
|
if self.tz is None:
|
|
return self.asm8 - other
|
|
return np.asarray(
|
|
[self - other[n] for n in range(len(other))],
|
|
dtype=object,
|
|
)
|
|
return NotImplemented
|
|
|
|
# coerce if necessary if we are a Timestamp-like
|
|
if (PyDateTime_Check(self)
|
|
and (PyDateTime_Check(other) or is_datetime64_object(other))):
|
|
# both_timestamps is to determine whether Timedelta(self - other)
|
|
# should raise the OOB error, or fall back returning a timedelta.
|
|
# TODO(cython3): clean out the bits that moved to __rsub__
|
|
both_timestamps = (isinstance(other, _Timestamp) and
|
|
isinstance(self, _Timestamp))
|
|
if isinstance(self, _Timestamp):
|
|
other = type(self)(other)
|
|
else:
|
|
self = type(other)(self)
|
|
|
|
if (self.tzinfo is None) ^ (other.tzinfo is None):
|
|
raise TypeError(
|
|
"Cannot subtract tz-naive and tz-aware datetime-like objects."
|
|
)
|
|
|
|
# We allow silent casting to the lower resolution if and only
|
|
# if it is lossless.
|
|
try:
|
|
if self._reso < other._reso:
|
|
other = (<_Timestamp>other)._as_reso(self._reso, round_ok=False)
|
|
elif self._reso > other._reso:
|
|
self = (<_Timestamp>self)._as_reso(other._reso, round_ok=False)
|
|
except ValueError as err:
|
|
raise ValueError(
|
|
"Timestamp subtraction with mismatched resolutions is not "
|
|
"allowed when casting to the lower resolution would require "
|
|
"lossy rounding."
|
|
) from err
|
|
|
|
# scalar Timestamp/datetime - Timestamp/datetime -> yields a
|
|
# Timedelta
|
|
try:
|
|
res_value = self.value - other.value
|
|
return Timedelta._from_value_and_reso(res_value, self._reso)
|
|
except (OverflowError, OutOfBoundsDatetime, OutOfBoundsTimedelta) as err:
|
|
if isinstance(other, _Timestamp):
|
|
if both_timestamps:
|
|
raise OutOfBoundsDatetime(
|
|
"Result is too large for pandas.Timedelta. Convert inputs "
|
|
"to datetime.datetime with 'Timestamp.to_pydatetime()' "
|
|
"before subtracting."
|
|
) from err
|
|
# We get here in stata tests, fall back to stdlib datetime
|
|
# method and return stdlib timedelta object
|
|
pass
|
|
elif is_datetime64_object(self):
|
|
# GH#28286 cython semantics for __rsub__, `other` is actually
|
|
# the Timestamp
|
|
# TODO(cython3): remove this, this moved to __rsub__
|
|
return type(other)(self) - other
|
|
|
|
return NotImplemented
|
|
|
|
def __rsub__(self, other):
|
|
if PyDateTime_Check(other):
|
|
try:
|
|
return type(self)(other) - self
|
|
except (OverflowError, OutOfBoundsDatetime) as err:
|
|
# We get here in stata tests, fall back to stdlib datetime
|
|
# method and return stdlib timedelta object
|
|
pass
|
|
elif is_datetime64_object(other):
|
|
return type(self)(other) - self
|
|
return NotImplemented
|
|
|
|
# -----------------------------------------------------------------
|
|
|
|
cdef int64_t _maybe_convert_value_to_local(self):
|
|
"""Convert UTC i8 value to local i8 value if tz exists"""
|
|
cdef:
|
|
int64_t val
|
|
tzinfo own_tz = self.tzinfo
|
|
npy_datetimestruct dts
|
|
|
|
if own_tz is not None and not is_utc(own_tz):
|
|
pydatetime_to_dtstruct(self, &dts)
|
|
val = npy_datetimestruct_to_datetime(self._reso, &dts) + self.nanosecond
|
|
else:
|
|
val = self.value
|
|
return val
|
|
|
|
@cython.boundscheck(False)
|
|
cdef bint _get_start_end_field(self, str field, freq):
|
|
cdef:
|
|
int64_t val
|
|
dict kwds
|
|
ndarray[uint8_t, cast=True] out
|
|
int month_kw
|
|
|
|
if freq:
|
|
kwds = freq.kwds
|
|
month_kw = kwds.get('startingMonth', kwds.get('month', 12))
|
|
freqstr = self._freqstr
|
|
else:
|
|
month_kw = 12
|
|
freqstr = None
|
|
|
|
val = self._maybe_convert_value_to_local()
|
|
|
|
out = get_start_end_field(np.array([val], dtype=np.int64),
|
|
field, freqstr, month_kw, self._reso)
|
|
return out[0]
|
|
|
|
cdef _warn_on_field_deprecation(self, freq, str field):
|
|
"""
|
|
Warn if the removal of .freq change the value of start/end properties.
|
|
"""
|
|
cdef:
|
|
bint needs = False
|
|
|
|
if freq is not None:
|
|
kwds = freq.kwds
|
|
month_kw = kwds.get("startingMonth", kwds.get("month", 12))
|
|
freqstr = self._freqstr
|
|
if month_kw != 12:
|
|
needs = True
|
|
if freqstr.startswith("B"):
|
|
needs = True
|
|
|
|
if needs:
|
|
warnings.warn(
|
|
"Timestamp.freq is deprecated and will be removed in a future "
|
|
"version. When you have a freq, use "
|
|
f"freq.{field}(timestamp) instead.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
|
|
@property
|
|
def is_month_start(self) -> bool:
|
|
"""
|
|
Return True if date is first day of month.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_month_start
|
|
False
|
|
|
|
>>> ts = pd.Timestamp(2020, 1, 1)
|
|
>>> ts.is_month_start
|
|
True
|
|
"""
|
|
if self._freq is None:
|
|
# fast-path for non-business frequencies
|
|
return self.day == 1
|
|
self._warn_on_field_deprecation(self._freq, "is_month_start")
|
|
return self._get_start_end_field("is_month_start", self._freq)
|
|
|
|
@property
|
|
def is_month_end(self) -> bool:
|
|
"""
|
|
Return True if date is last day of month.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_month_end
|
|
False
|
|
|
|
>>> ts = pd.Timestamp(2020, 12, 31)
|
|
>>> ts.is_month_end
|
|
True
|
|
"""
|
|
if self._freq is None:
|
|
# fast-path for non-business frequencies
|
|
return self.day == self.days_in_month
|
|
self._warn_on_field_deprecation(self._freq, "is_month_end")
|
|
return self._get_start_end_field("is_month_end", self._freq)
|
|
|
|
@property
|
|
def is_quarter_start(self) -> bool:
|
|
"""
|
|
Return True if date is first day of the quarter.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_quarter_start
|
|
False
|
|
|
|
>>> ts = pd.Timestamp(2020, 4, 1)
|
|
>>> ts.is_quarter_start
|
|
True
|
|
"""
|
|
if self._freq is None:
|
|
# fast-path for non-business frequencies
|
|
return self.day == 1 and self.month % 3 == 1
|
|
self._warn_on_field_deprecation(self._freq, "is_quarter_start")
|
|
return self._get_start_end_field("is_quarter_start", self._freq)
|
|
|
|
@property
|
|
def is_quarter_end(self) -> bool:
|
|
"""
|
|
Return True if date is last day of the quarter.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_quarter_end
|
|
False
|
|
|
|
>>> ts = pd.Timestamp(2020, 3, 31)
|
|
>>> ts.is_quarter_end
|
|
True
|
|
"""
|
|
if self._freq is None:
|
|
# fast-path for non-business frequencies
|
|
return (self.month % 3) == 0 and self.day == self.days_in_month
|
|
self._warn_on_field_deprecation(self._freq, "is_quarter_end")
|
|
return self._get_start_end_field("is_quarter_end", self._freq)
|
|
|
|
@property
|
|
def is_year_start(self) -> bool:
|
|
"""
|
|
Return True if date is first day of the year.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_year_start
|
|
False
|
|
|
|
>>> ts = pd.Timestamp(2020, 1, 1)
|
|
>>> ts.is_year_start
|
|
True
|
|
"""
|
|
if self._freq is None:
|
|
# fast-path for non-business frequencies
|
|
return self.day == self.month == 1
|
|
self._warn_on_field_deprecation(self._freq, "is_year_start")
|
|
return self._get_start_end_field("is_year_start", self._freq)
|
|
|
|
@property
|
|
def is_year_end(self) -> bool:
|
|
"""
|
|
Return True if date is last day of the year.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_year_end
|
|
False
|
|
|
|
>>> ts = pd.Timestamp(2020, 12, 31)
|
|
>>> ts.is_year_end
|
|
True
|
|
"""
|
|
if self._freq is None:
|
|
# fast-path for non-business frequencies
|
|
return self.month == 12 and self.day == 31
|
|
self._warn_on_field_deprecation(self._freq, "is_year_end")
|
|
return self._get_start_end_field("is_year_end", self._freq)
|
|
|
|
@cython.boundscheck(False)
|
|
cdef _get_date_name_field(self, str field, object locale):
|
|
cdef:
|
|
int64_t val
|
|
object[::1] out
|
|
|
|
val = self._maybe_convert_value_to_local()
|
|
|
|
out = get_date_name_field(np.array([val], dtype=np.int64),
|
|
field, locale=locale, reso=self._reso)
|
|
return out[0]
|
|
|
|
def day_name(self, locale=None) -> str:
|
|
"""
|
|
Return the day name of the Timestamp with specified locale.
|
|
|
|
Parameters
|
|
----------
|
|
locale : str, default None (English locale)
|
|
Locale determining the language in which to return the day name.
|
|
|
|
Returns
|
|
-------
|
|
str
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> ts.day_name()
|
|
'Saturday'
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.day_name()
|
|
nan
|
|
"""
|
|
return self._get_date_name_field("day_name", locale)
|
|
|
|
def month_name(self, locale=None) -> str:
|
|
"""
|
|
Return the month name of the Timestamp with specified locale.
|
|
|
|
Parameters
|
|
----------
|
|
locale : str, default None (English locale)
|
|
Locale determining the language in which to return the month name.
|
|
|
|
Returns
|
|
-------
|
|
str
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> ts.month_name()
|
|
'March'
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.month_name()
|
|
nan
|
|
"""
|
|
return self._get_date_name_field("month_name", locale)
|
|
|
|
@property
|
|
def is_leap_year(self) -> bool:
|
|
"""
|
|
Return True if year is a leap year.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.is_leap_year
|
|
True
|
|
"""
|
|
return bool(ccalendar.is_leapyear(self.year))
|
|
|
|
@property
|
|
def day_of_week(self) -> int:
|
|
"""
|
|
Return day of the week.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.day_of_week
|
|
5
|
|
"""
|
|
return self.weekday()
|
|
|
|
@property
|
|
def day_of_year(self) -> int:
|
|
"""
|
|
Return the day of the year.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.day_of_year
|
|
74
|
|
"""
|
|
return ccalendar.get_day_of_year(self.year, self.month, self.day)
|
|
|
|
@property
|
|
def quarter(self) -> int:
|
|
"""
|
|
Return the quarter of the year.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.quarter
|
|
1
|
|
"""
|
|
return ((self.month - 1) // 3) + 1
|
|
|
|
@property
|
|
def week(self) -> int:
|
|
"""
|
|
Return the week number of the year.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.week
|
|
11
|
|
"""
|
|
return ccalendar.get_week_of_year(self.year, self.month, self.day)
|
|
|
|
@property
|
|
def days_in_month(self) -> int:
|
|
"""
|
|
Return the number of days in the month.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14)
|
|
>>> ts.days_in_month
|
|
31
|
|
"""
|
|
return ccalendar.get_days_in_month(self.year, self.month)
|
|
|
|
# -----------------------------------------------------------------
|
|
# Transformation Methods
|
|
|
|
def normalize(self) -> "Timestamp":
|
|
"""
|
|
Normalize Timestamp to midnight, preserving tz information.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14, 15, 30)
|
|
>>> ts.normalize()
|
|
Timestamp('2020-03-14 00:00:00')
|
|
"""
|
|
cdef:
|
|
local_val = self._maybe_convert_value_to_local()
|
|
int64_t normalized
|
|
int64_t ppd = periods_per_day(self._reso)
|
|
_Timestamp ts
|
|
|
|
normalized = normalize_i8_stamp(local_val, ppd)
|
|
ts = type(self)._from_value_and_reso(normalized, reso=self._reso, tz=None)
|
|
return ts.tz_localize(self.tzinfo)
|
|
|
|
# -----------------------------------------------------------------
|
|
# Pickle Methods
|
|
|
|
def __reduce_ex__(self, protocol):
|
|
# python 3.6 compat
|
|
# https://bugs.python.org/issue28730
|
|
# now __reduce_ex__ is defined and higher priority than __reduce__
|
|
return self.__reduce__()
|
|
|
|
def __setstate__(self, state):
|
|
self.value = state[0]
|
|
self._freq = state[1]
|
|
self.tzinfo = state[2]
|
|
|
|
if len(state) == 3:
|
|
# pre-non-nano pickle
|
|
# TODO: no tests get here 2022-05-10
|
|
reso = NPY_FR_ns
|
|
else:
|
|
reso = state[4]
|
|
self._reso = reso
|
|
|
|
def __reduce__(self):
|
|
object_state = self.value, self._freq, self.tzinfo, self._reso
|
|
return (_unpickle_timestamp, object_state)
|
|
|
|
# -----------------------------------------------------------------
|
|
# Rendering Methods
|
|
|
|
def isoformat(self, sep: str = "T", timespec: str = "auto") -> str:
|
|
"""
|
|
Return the time formatted according to ISO 8610.
|
|
|
|
The full format looks like 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn'.
|
|
By default, the fractional part is omitted if self.microsecond == 0
|
|
and self.nanosecond == 0.
|
|
|
|
If self.tzinfo is not None, the UTC offset is also attached, giving
|
|
giving a full format of 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn+HH:MM'.
|
|
|
|
Parameters
|
|
----------
|
|
sep : str, default 'T'
|
|
String used as the separator between the date and time.
|
|
|
|
timespec : str, default 'auto'
|
|
Specifies the number of additional terms of the time to include.
|
|
The valid values are 'auto', 'hours', 'minutes', 'seconds',
|
|
'milliseconds', 'microseconds', and 'nanoseconds'.
|
|
|
|
Returns
|
|
-------
|
|
str
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> ts.isoformat()
|
|
'2020-03-14T15:32:52.192548651'
|
|
>>> ts.isoformat(timespec='microseconds')
|
|
'2020-03-14T15:32:52.192548'
|
|
"""
|
|
base_ts = "microseconds" if timespec == "nanoseconds" else timespec
|
|
base = super(_Timestamp, self).isoformat(sep=sep, timespec=base_ts)
|
|
# We need to replace the fake year 1970 with our real year
|
|
base = f"{self.year}-" + base.split("-", 1)[1]
|
|
|
|
if self.nanosecond == 0 and timespec != "nanoseconds":
|
|
return base
|
|
|
|
if self.tzinfo is not None:
|
|
base1, base2 = base[:-6], base[-6:]
|
|
else:
|
|
base1, base2 = base, ""
|
|
|
|
if timespec == "nanoseconds" or (timespec == "auto" and self.nanosecond):
|
|
if self.microsecond:
|
|
base1 += f"{self.nanosecond:03d}"
|
|
else:
|
|
base1 += f".{self.nanosecond:09d}"
|
|
|
|
return base1 + base2
|
|
|
|
def __repr__(self) -> str:
|
|
stamp = self._repr_base
|
|
zone = None
|
|
|
|
try:
|
|
stamp += self.strftime('%z')
|
|
except ValueError:
|
|
year2000 = self.replace(year=2000)
|
|
stamp += year2000.strftime('%z')
|
|
|
|
if self.tzinfo:
|
|
zone = get_timezone(self.tzinfo)
|
|
try:
|
|
stamp += zone.strftime(' %%Z')
|
|
except AttributeError:
|
|
# e.g. tzlocal has no `strftime`
|
|
pass
|
|
|
|
tz = f", tz='{zone}'" if zone is not None else ""
|
|
freq = "" if self._freq is None else f", freq='{self._freqstr}'"
|
|
|
|
return f"Timestamp('{stamp}'{tz}{freq})"
|
|
|
|
@property
|
|
def _repr_base(self) -> str:
|
|
return f"{self._date_repr} {self._time_repr}"
|
|
|
|
@property
|
|
def _date_repr(self) -> str:
|
|
# Ideal here would be self.strftime("%Y-%m-%d"), but
|
|
# the datetime strftime() methods require year >= 1900 and is slower
|
|
return f'{self.year}-{self.month:02d}-{self.day:02d}'
|
|
|
|
@property
|
|
def _time_repr(self) -> str:
|
|
result = f'{self.hour:02d}:{self.minute:02d}:{self.second:02d}'
|
|
|
|
if self.nanosecond != 0:
|
|
result += f'.{self.nanosecond + 1000 * self.microsecond:09d}'
|
|
elif self.microsecond != 0:
|
|
result += f'.{self.microsecond:06d}'
|
|
|
|
return result
|
|
|
|
@property
|
|
def _short_repr(self) -> str:
|
|
# format a Timestamp with only _date_repr if possible
|
|
# otherwise _repr_base
|
|
if (self.hour == 0 and
|
|
self.minute == 0 and
|
|
self.second == 0 and
|
|
self.microsecond == 0 and
|
|
self.nanosecond == 0):
|
|
return self._date_repr
|
|
return self._repr_base
|
|
|
|
# -----------------------------------------------------------------
|
|
# Conversion Methods
|
|
|
|
@cython.cdivision(False)
|
|
cdef _Timestamp _as_reso(self, NPY_DATETIMEUNIT reso, bint round_ok=True):
|
|
cdef:
|
|
int64_t value, mult, div, mod
|
|
|
|
if reso == self._reso:
|
|
return self
|
|
|
|
value = convert_reso(self.value, self._reso, reso, round_ok=round_ok)
|
|
return type(self)._from_value_and_reso(value, reso=reso, tz=self.tzinfo)
|
|
|
|
def _as_unit(self, str unit, bint round_ok=True):
|
|
dtype = np.dtype(f"M8[{unit}]")
|
|
reso = get_unit_from_dtype(dtype)
|
|
try:
|
|
return self._as_reso(reso, round_ok=round_ok)
|
|
except OverflowError as err:
|
|
raise OutOfBoundsDatetime(
|
|
f"Cannot cast {self} to unit='{unit}' without overflow."
|
|
) from err
|
|
|
|
@property
|
|
def asm8(self) -> np.datetime64:
|
|
"""
|
|
Return numpy datetime64 format in nanoseconds.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(2020, 3, 14, 15)
|
|
>>> ts.asm8
|
|
numpy.datetime64('2020-03-14T15:00:00.000000000')
|
|
"""
|
|
return self.to_datetime64()
|
|
|
|
def timestamp(self):
|
|
"""
|
|
Return POSIX timestamp as float.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
|
|
>>> ts.timestamp()
|
|
1584199972.192548
|
|
"""
|
|
# GH 17329
|
|
# Note: Naive timestamps will not match datetime.stdlib
|
|
|
|
denom = periods_per_second(self._reso)
|
|
|
|
return round(self.value / denom, 6)
|
|
|
|
cpdef datetime to_pydatetime(_Timestamp self, bint warn=True):
|
|
"""
|
|
Convert a Timestamp object to a native Python datetime object.
|
|
|
|
If warn=True, issue a warning if nanoseconds is nonzero.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
|
|
>>> ts.to_pydatetime()
|
|
datetime.datetime(2020, 3, 14, 15, 32, 52, 192548)
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.to_pydatetime()
|
|
NaT
|
|
"""
|
|
if self.nanosecond != 0 and warn:
|
|
warnings.warn("Discarding nonzero nanoseconds in conversion.",
|
|
UserWarning, stacklevel=find_stack_level())
|
|
|
|
return datetime(self.year, self.month, self.day,
|
|
self.hour, self.minute, self.second,
|
|
self.microsecond, self.tzinfo, fold=self.fold)
|
|
|
|
cpdef to_datetime64(self):
|
|
"""
|
|
Return a numpy.datetime64 object with 'ns' precision.
|
|
"""
|
|
# TODO: find a way to construct dt64 directly from _reso
|
|
abbrev = npy_unit_to_abbrev(self._reso)
|
|
return np.datetime64(self.value, abbrev)
|
|
|
|
def to_numpy(self, dtype=None, copy=False) -> np.datetime64:
|
|
"""
|
|
Convert the Timestamp to a NumPy datetime64.
|
|
|
|
.. versionadded:: 0.25.0
|
|
|
|
This is an alias method for `Timestamp.to_datetime64()`. The dtype and
|
|
copy parameters are available here only for compatibility. Their values
|
|
will not affect the return value.
|
|
|
|
Returns
|
|
-------
|
|
numpy.datetime64
|
|
|
|
See Also
|
|
--------
|
|
DatetimeIndex.to_numpy : Similar method for DatetimeIndex.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> ts.to_numpy()
|
|
numpy.datetime64('2020-03-14T15:32:52.192548651')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.to_numpy()
|
|
numpy.datetime64('NaT')
|
|
"""
|
|
if dtype is not None or copy is not False:
|
|
raise ValueError(
|
|
"Timestamp.to_numpy dtype and copy arguments are ignored."
|
|
)
|
|
return self.to_datetime64()
|
|
|
|
def to_period(self, freq=None):
|
|
"""
|
|
Return an period of which this timestamp is an observation.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> # Year end frequency
|
|
>>> ts.to_period(freq='Y')
|
|
Period('2020', 'A-DEC')
|
|
|
|
>>> # Month end frequency
|
|
>>> ts.to_period(freq='M')
|
|
Period('2020-03', 'M')
|
|
|
|
>>> # Weekly frequency
|
|
>>> ts.to_period(freq='W')
|
|
Period('2020-03-09/2020-03-15', 'W-SUN')
|
|
|
|
>>> # Quarter end frequency
|
|
>>> ts.to_period(freq='Q')
|
|
Period('2020Q1', 'Q-DEC')
|
|
"""
|
|
from pandas import Period
|
|
|
|
if self.tz is not None:
|
|
# GH#21333
|
|
warnings.warn(
|
|
"Converting to Period representation will drop timezone information.",
|
|
UserWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
|
|
if freq is None:
|
|
freq = self._freq
|
|
warnings.warn(
|
|
"In a future version, calling 'Timestamp.to_period()' without "
|
|
"passing a 'freq' will raise an exception.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
|
|
return Period(self, freq=freq)
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
|
|
# Python front end to C extension type _Timestamp
|
|
# This serves as the box for datetime64
|
|
|
|
|
|
class Timestamp(_Timestamp):
|
|
"""
|
|
Pandas replacement for python datetime.datetime object.
|
|
|
|
Timestamp is the pandas equivalent of python's Datetime
|
|
and is interchangeable with it in most cases. It's the type used
|
|
for the entries that make up a DatetimeIndex, and other timeseries
|
|
oriented data structures in pandas.
|
|
|
|
Parameters
|
|
----------
|
|
ts_input : datetime-like, str, int, float
|
|
Value to be converted to Timestamp.
|
|
freq : str, DateOffset
|
|
Offset which Timestamp will have.
|
|
tz : str, pytz.timezone, dateutil.tz.tzfile or None
|
|
Time zone for time which Timestamp will have.
|
|
unit : str
|
|
Unit used for conversion if ts_input is of type int or float. The
|
|
valid values are 'D', 'h', 'm', 's', 'ms', 'us', and 'ns'. For
|
|
example, 's' means seconds and 'ms' means milliseconds.
|
|
year, month, day : int
|
|
hour, minute, second, microsecond : int, optional, default 0
|
|
nanosecond : int, optional, default 0
|
|
tzinfo : datetime.tzinfo, optional, default None
|
|
fold : {0, 1}, default None, keyword-only
|
|
Due to daylight saving time, one wall clock time can occur twice
|
|
when shifting from summer to winter time; fold describes whether the
|
|
datetime-like corresponds to the first (0) or the second time (1)
|
|
the wall clock hits the ambiguous time.
|
|
|
|
.. versionadded:: 1.1.0
|
|
|
|
Notes
|
|
-----
|
|
There are essentially three calling conventions for the constructor. The
|
|
primary form accepts four parameters. They can be passed by position or
|
|
keyword.
|
|
|
|
The other two forms mimic the parameters from ``datetime.datetime``. They
|
|
can be passed by either position or keyword, but not both mixed together.
|
|
|
|
Examples
|
|
--------
|
|
Using the primary calling convention:
|
|
|
|
This converts a datetime-like string
|
|
|
|
>>> pd.Timestamp('2017-01-01T12')
|
|
Timestamp('2017-01-01 12:00:00')
|
|
|
|
This converts a float representing a Unix epoch in units of seconds
|
|
|
|
>>> pd.Timestamp(1513393355.5, unit='s')
|
|
Timestamp('2017-12-16 03:02:35.500000')
|
|
|
|
This converts an int representing a Unix-epoch in units of seconds
|
|
and for a particular timezone
|
|
|
|
>>> pd.Timestamp(1513393355, unit='s', tz='US/Pacific')
|
|
Timestamp('2017-12-15 19:02:35-0800', tz='US/Pacific')
|
|
|
|
Using the other two forms that mimic the API for ``datetime.datetime``:
|
|
|
|
>>> pd.Timestamp(2017, 1, 1, 12)
|
|
Timestamp('2017-01-01 12:00:00')
|
|
|
|
>>> pd.Timestamp(year=2017, month=1, day=1, hour=12)
|
|
Timestamp('2017-01-01 12:00:00')
|
|
"""
|
|
|
|
@classmethod
|
|
def fromordinal(cls, ordinal, freq=None, tz=None):
|
|
"""
|
|
Construct a timestamp from a a proleptic Gregorian ordinal.
|
|
|
|
Parameters
|
|
----------
|
|
ordinal : int
|
|
Date corresponding to a proleptic Gregorian ordinal.
|
|
freq : str, DateOffset
|
|
Offset to apply to the Timestamp.
|
|
tz : str, pytz.timezone, dateutil.tz.tzfile or None
|
|
Time zone for the Timestamp.
|
|
|
|
Notes
|
|
-----
|
|
By definition there cannot be any tz info on the ordinal itself.
|
|
|
|
Examples
|
|
--------
|
|
>>> pd.Timestamp.fromordinal(737425)
|
|
Timestamp('2020-01-01 00:00:00')
|
|
"""
|
|
return cls(datetime.fromordinal(ordinal),
|
|
freq=freq, tz=tz)
|
|
|
|
@classmethod
|
|
def now(cls, tz=None):
|
|
"""
|
|
Return new Timestamp object representing current time local to tz.
|
|
|
|
Parameters
|
|
----------
|
|
tz : str or timezone object, default None
|
|
Timezone to localize to.
|
|
|
|
Examples
|
|
--------
|
|
>>> pd.Timestamp.now() # doctest: +SKIP
|
|
Timestamp('2020-11-16 22:06:16.378782')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.now()
|
|
NaT
|
|
"""
|
|
if isinstance(tz, str):
|
|
tz = maybe_get_tz(tz)
|
|
return cls(datetime.now(tz))
|
|
|
|
@classmethod
|
|
def today(cls, tz=None):
|
|
"""
|
|
Return the current time in the local timezone.
|
|
|
|
This differs from datetime.today() in that it can be localized to a
|
|
passed timezone.
|
|
|
|
Parameters
|
|
----------
|
|
tz : str or timezone object, default None
|
|
Timezone to localize to.
|
|
|
|
Examples
|
|
--------
|
|
>>> pd.Timestamp.today() # doctest: +SKIP
|
|
Timestamp('2020-11-16 22:37:39.969883')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.today()
|
|
NaT
|
|
"""
|
|
return cls.now(tz)
|
|
|
|
@classmethod
|
|
def utcnow(cls):
|
|
"""
|
|
Timestamp.utcnow()
|
|
|
|
Return a new Timestamp representing UTC day and time.
|
|
|
|
Examples
|
|
--------
|
|
>>> pd.Timestamp.utcnow() # doctest: +SKIP
|
|
Timestamp('2020-11-16 22:50:18.092888+0000', tz='UTC')
|
|
"""
|
|
return cls.now(UTC)
|
|
|
|
@classmethod
|
|
def utcfromtimestamp(cls, ts):
|
|
"""
|
|
Timestamp.utcfromtimestamp(ts)
|
|
|
|
Construct a naive UTC datetime from a POSIX timestamp.
|
|
|
|
Examples
|
|
--------
|
|
>>> pd.Timestamp.utcfromtimestamp(1584199972)
|
|
Timestamp('2020-03-14 15:32:52')
|
|
"""
|
|
# GH#22451
|
|
warnings.warn(
|
|
"The behavior of Timestamp.utcfromtimestamp is deprecated, in a "
|
|
"future version will return a timezone-aware Timestamp with UTC "
|
|
"timezone. To keep the old behavior, use "
|
|
"Timestamp.utcfromtimestamp(ts).tz_localize(None). "
|
|
"To get the future behavior, use Timestamp.fromtimestamp(ts, 'UTC')",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
return cls(datetime.utcfromtimestamp(ts))
|
|
|
|
@classmethod
|
|
def fromtimestamp(cls, ts, tz=None):
|
|
"""
|
|
Timestamp.fromtimestamp(ts)
|
|
|
|
Transform timestamp[, tz] to tz's local time from POSIX timestamp.
|
|
|
|
Examples
|
|
--------
|
|
>>> pd.Timestamp.fromtimestamp(1584199972)
|
|
Timestamp('2020-03-14 15:32:52')
|
|
|
|
Note that the output may change depending on your local time.
|
|
"""
|
|
tz = maybe_get_tz(tz)
|
|
return cls(datetime.fromtimestamp(ts, tz))
|
|
|
|
def strftime(self, format):
|
|
"""
|
|
Return a formatted string of the Timestamp.
|
|
|
|
Parameters
|
|
----------
|
|
format : str
|
|
Format string to convert Timestamp to string.
|
|
See strftime documentation for more information on the format string:
|
|
https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> ts.strftime('%Y-%m-%d %X')
|
|
'2020-03-14 15:32:52'
|
|
"""
|
|
return datetime.strftime(self, format)
|
|
|
|
# Issue 25016.
|
|
@classmethod
|
|
def strptime(cls, date_string, format):
|
|
"""
|
|
Timestamp.strptime(string, format)
|
|
|
|
Function is not implemented. Use pd.to_datetime().
|
|
"""
|
|
raise NotImplementedError(
|
|
"Timestamp.strptime() is not implemented. "
|
|
"Use to_datetime() to parse date strings."
|
|
)
|
|
|
|
@classmethod
|
|
def combine(cls, date, time):
|
|
"""
|
|
Timestamp.combine(date, time)
|
|
|
|
Combine date, time into datetime with same date and time fields.
|
|
|
|
Examples
|
|
--------
|
|
>>> from datetime import date, time
|
|
>>> pd.Timestamp.combine(date(2020, 3, 14), time(15, 30, 15))
|
|
Timestamp('2020-03-14 15:30:15')
|
|
"""
|
|
return cls(datetime.combine(date, time))
|
|
|
|
def __new__(
|
|
cls,
|
|
object ts_input=_no_input,
|
|
object freq=None,
|
|
tz=None,
|
|
unit=None,
|
|
year=None,
|
|
month=None,
|
|
day=None,
|
|
hour=None,
|
|
minute=None,
|
|
second=None,
|
|
microsecond=None,
|
|
nanosecond=None,
|
|
tzinfo_type tzinfo=None,
|
|
*,
|
|
fold=None,
|
|
):
|
|
# The parameter list folds together legacy parameter names (the first
|
|
# four) and positional and keyword parameter names from pydatetime.
|
|
#
|
|
# There are three calling forms:
|
|
#
|
|
# - In the legacy form, the first parameter, ts_input, is required
|
|
# and may be datetime-like, str, int, or float. The second
|
|
# parameter, offset, is optional and may be str or DateOffset.
|
|
#
|
|
# - ints in the first, second, and third arguments indicate
|
|
# pydatetime positional arguments. Only the first 8 arguments
|
|
# (standing in for year, month, day, hour, minute, second,
|
|
# microsecond, tzinfo) may be non-None. As a shortcut, we just
|
|
# check that the second argument is an int.
|
|
#
|
|
# - Nones for the first four (legacy) arguments indicate pydatetime
|
|
# keyword arguments. year, month, and day are required. As a
|
|
# shortcut, we just check that the first argument was not passed.
|
|
#
|
|
# Mixing pydatetime positional and keyword arguments is forbidden!
|
|
|
|
cdef:
|
|
_TSObject ts
|
|
tzinfo_type tzobj
|
|
|
|
_date_attributes = [year, month, day, hour, minute, second,
|
|
microsecond, nanosecond]
|
|
|
|
if tzinfo is not None:
|
|
# GH#17690 tzinfo must be a datetime.tzinfo object, ensured
|
|
# by the cython annotation.
|
|
if tz is not None:
|
|
if (is_integer_object(tz)
|
|
and is_integer_object(ts_input)
|
|
and is_integer_object(freq)
|
|
):
|
|
# GH#31929 e.g. Timestamp(2019, 3, 4, 5, 6, tzinfo=foo)
|
|
# TODO(GH#45307): this will still be fragile to
|
|
# mixed-and-matched positional/keyword arguments
|
|
ts_input = datetime(
|
|
ts_input,
|
|
freq,
|
|
tz,
|
|
unit or 0,
|
|
year or 0,
|
|
month or 0,
|
|
day or 0,
|
|
fold=fold or 0,
|
|
)
|
|
nanosecond = hour
|
|
tz = tzinfo
|
|
return cls(ts_input, nanosecond=nanosecond, tz=tz)
|
|
|
|
raise ValueError('Can provide at most one of tz, tzinfo')
|
|
|
|
# User passed tzinfo instead of tz; avoid silently ignoring
|
|
tz, tzinfo = tzinfo, None
|
|
|
|
# Allow fold only for unambiguous input
|
|
if fold is not None:
|
|
if fold not in [0, 1]:
|
|
raise ValueError(
|
|
"Valid values for the fold argument are None, 0, or 1."
|
|
)
|
|
|
|
if (ts_input is not _no_input and not (
|
|
PyDateTime_Check(ts_input) and
|
|
getattr(ts_input, 'tzinfo', None) is None)):
|
|
raise ValueError(
|
|
"Cannot pass fold with possibly unambiguous input: int, "
|
|
"float, numpy.datetime64, str, or timezone-aware "
|
|
"datetime-like. Pass naive datetime-like or build "
|
|
"Timestamp from components."
|
|
)
|
|
|
|
if tz is not None and PyTZInfo_Check(tz) and treat_tz_as_pytz(tz):
|
|
raise ValueError(
|
|
"pytz timezones do not support fold. Please use dateutil "
|
|
"timezones."
|
|
)
|
|
|
|
if hasattr(ts_input, 'fold'):
|
|
ts_input = ts_input.replace(fold=fold)
|
|
|
|
# GH 30543 if pd.Timestamp already passed, return it
|
|
# check that only ts_input is passed
|
|
# checking verbosely, because cython doesn't optimize
|
|
# list comprehensions (as of cython 0.29.x)
|
|
if (isinstance(ts_input, _Timestamp) and freq is None and
|
|
tz is None and unit is None and year is None and
|
|
month is None and day is None and hour is None and
|
|
minute is None and second is None and
|
|
microsecond is None and nanosecond is None and
|
|
tzinfo is None):
|
|
return ts_input
|
|
elif isinstance(ts_input, str):
|
|
# User passed a date string to parse.
|
|
# Check that the user didn't also pass a date attribute kwarg.
|
|
if any(arg is not None for arg in _date_attributes):
|
|
raise ValueError(
|
|
"Cannot pass a date attribute keyword "
|
|
"argument when passing a date string"
|
|
)
|
|
|
|
elif ts_input is _no_input:
|
|
# GH 31200
|
|
# When year, month or day is not given, we call the datetime
|
|
# constructor to make sure we get the same error message
|
|
# since Timestamp inherits datetime
|
|
datetime_kwargs = {
|
|
"hour": hour or 0,
|
|
"minute": minute or 0,
|
|
"second": second or 0,
|
|
"microsecond": microsecond or 0,
|
|
"fold": fold or 0
|
|
}
|
|
if year is not None:
|
|
datetime_kwargs["year"] = year
|
|
if month is not None:
|
|
datetime_kwargs["month"] = month
|
|
if day is not None:
|
|
datetime_kwargs["day"] = day
|
|
|
|
ts_input = datetime(**datetime_kwargs)
|
|
|
|
elif is_integer_object(freq):
|
|
# User passed positional arguments:
|
|
# Timestamp(year, month, day[, hour[, minute[, second[,
|
|
# microsecond[, nanosecond[, tzinfo]]]]]])
|
|
ts_input = datetime(ts_input, freq, tz, unit or 0,
|
|
year or 0, month or 0, day or 0, fold=fold or 0)
|
|
nanosecond = hour
|
|
tz = minute
|
|
freq = None
|
|
unit = None
|
|
|
|
if getattr(ts_input, 'tzinfo', None) is not None and tz is not None:
|
|
raise ValueError("Cannot pass a datetime or Timestamp with tzinfo with "
|
|
"the tz parameter. Use tz_convert instead.")
|
|
|
|
tzobj = maybe_get_tz(tz)
|
|
if tzobj is not None and is_datetime64_object(ts_input):
|
|
# GH#24559, GH#42288 In the future we will treat datetime64 as
|
|
# wall-time (consistent with DatetimeIndex)
|
|
warnings.warn(
|
|
"In a future version, when passing a np.datetime64 object and "
|
|
"a timezone to Timestamp, the datetime64 will be interpreted "
|
|
"as a wall time, not a UTC time. To interpret as a UTC time, "
|
|
"use `Timestamp(dt64).tz_localize('UTC').tz_convert(tz)`",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
# Once this deprecation is enforced, we can do
|
|
# return Timestamp(ts_input).tz_localize(tzobj)
|
|
ts = convert_to_tsobject(ts_input, tzobj, unit, 0, 0, nanosecond or 0)
|
|
|
|
if ts.value == NPY_NAT:
|
|
return NaT
|
|
|
|
if freq is None:
|
|
# GH 22311: Try to extract the frequency of a given Timestamp input
|
|
freq = getattr(ts_input, '_freq', None)
|
|
else:
|
|
warnings.warn(
|
|
"The 'freq' argument in Timestamp is deprecated and will be "
|
|
"removed in a future version.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
if not is_offset_object(freq):
|
|
freq = to_offset(freq)
|
|
|
|
return create_timestamp_from_ts(ts.value, ts.dts, ts.tzinfo, freq, ts.fold)
|
|
|
|
def _round(self, freq, mode, ambiguous='raise', nonexistent='raise'):
|
|
cdef:
|
|
int64_t nanos
|
|
|
|
to_offset(freq).nanos # raises on non-fixed freq
|
|
nanos = delta_to_nanoseconds(to_offset(freq), self._reso)
|
|
|
|
if self.tz is not None:
|
|
value = self.tz_localize(None).value
|
|
else:
|
|
value = self.value
|
|
|
|
value = np.array([value], dtype=np.int64)
|
|
|
|
# Will only ever contain 1 element for timestamp
|
|
r = round_nsint64(value, mode, nanos)[0]
|
|
result = Timestamp._from_value_and_reso(r, self._reso, None)
|
|
if self.tz is not None:
|
|
result = result.tz_localize(
|
|
self.tz, ambiguous=ambiguous, nonexistent=nonexistent
|
|
)
|
|
return result
|
|
|
|
def round(self, freq, ambiguous='raise', nonexistent='raise'):
|
|
"""
|
|
Round the Timestamp to the specified resolution.
|
|
|
|
Parameters
|
|
----------
|
|
freq : str
|
|
Frequency string indicating the rounding resolution.
|
|
ambiguous : bool or {'raise', 'NaT'}, default 'raise'
|
|
The behavior is as follows:
|
|
|
|
* bool contains flags to determine if time is dst or not (note
|
|
that this flag is only applicable for ambiguous fall dst dates).
|
|
* 'NaT' will return NaT for an ambiguous time.
|
|
* 'raise' will raise an AmbiguousTimeError for an ambiguous time.
|
|
|
|
nonexistent : {'raise', 'shift_forward', 'shift_backward, 'NaT', \
|
|
timedelta}, default 'raise'
|
|
A nonexistent time does not exist in a particular timezone
|
|
where clocks moved forward due to DST.
|
|
|
|
* 'shift_forward' will shift the nonexistent time forward to the
|
|
closest existing time.
|
|
* 'shift_backward' will shift the nonexistent time backward to the
|
|
closest existing time.
|
|
* 'NaT' will return NaT where there are nonexistent times.
|
|
* timedelta objects will shift nonexistent times by the timedelta.
|
|
* 'raise' will raise an NonExistentTimeError if there are
|
|
nonexistent times.
|
|
|
|
Returns
|
|
-------
|
|
a new Timestamp rounded to the given resolution of `freq`
|
|
|
|
Raises
|
|
------
|
|
ValueError if the freq cannot be converted
|
|
|
|
Notes
|
|
-----
|
|
If the Timestamp has a timezone, rounding will take place relative to the
|
|
local ("wall") time and re-localized to the same timezone. When rounding
|
|
near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
|
|
control the re-localization behavior.
|
|
|
|
Examples
|
|
--------
|
|
Create a timestamp object:
|
|
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
|
|
A timestamp can be rounded using multiple frequency units:
|
|
|
|
>>> ts.round(freq='H') # hour
|
|
Timestamp('2020-03-14 16:00:00')
|
|
|
|
>>> ts.round(freq='T') # minute
|
|
Timestamp('2020-03-14 15:33:00')
|
|
|
|
>>> ts.round(freq='S') # seconds
|
|
Timestamp('2020-03-14 15:32:52')
|
|
|
|
>>> ts.round(freq='L') # milliseconds
|
|
Timestamp('2020-03-14 15:32:52.193000')
|
|
|
|
``freq`` can also be a multiple of a single unit, like '5T' (i.e. 5 minutes):
|
|
|
|
>>> ts.round(freq='5T')
|
|
Timestamp('2020-03-14 15:35:00')
|
|
|
|
or a combination of multiple units, like '1H30T' (i.e. 1 hour and 30 minutes):
|
|
|
|
>>> ts.round(freq='1H30T')
|
|
Timestamp('2020-03-14 15:00:00')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.round()
|
|
NaT
|
|
|
|
When rounding near a daylight savings time transition, use ``ambiguous`` or
|
|
``nonexistent`` to control how the timestamp should be re-localized.
|
|
|
|
>>> ts_tz = pd.Timestamp("2021-10-31 01:30:00").tz_localize("Europe/Amsterdam")
|
|
|
|
>>> ts_tz.round("H", ambiguous=False)
|
|
Timestamp('2021-10-31 02:00:00+0100', tz='Europe/Amsterdam')
|
|
|
|
>>> ts_tz.round("H", ambiguous=True)
|
|
Timestamp('2021-10-31 02:00:00+0200', tz='Europe/Amsterdam')
|
|
"""
|
|
return self._round(
|
|
freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent
|
|
)
|
|
|
|
def floor(self, freq, ambiguous='raise', nonexistent='raise'):
|
|
"""
|
|
Return a new Timestamp floored to this resolution.
|
|
|
|
Parameters
|
|
----------
|
|
freq : str
|
|
Frequency string indicating the flooring resolution.
|
|
ambiguous : bool or {'raise', 'NaT'}, default 'raise'
|
|
The behavior is as follows:
|
|
|
|
* bool contains flags to determine if time is dst or not (note
|
|
that this flag is only applicable for ambiguous fall dst dates).
|
|
* 'NaT' will return NaT for an ambiguous time.
|
|
* 'raise' will raise an AmbiguousTimeError for an ambiguous time.
|
|
|
|
nonexistent : {'raise', 'shift_forward', 'shift_backward, 'NaT', \
|
|
timedelta}, default 'raise'
|
|
A nonexistent time does not exist in a particular timezone
|
|
where clocks moved forward due to DST.
|
|
|
|
* 'shift_forward' will shift the nonexistent time forward to the
|
|
closest existing time.
|
|
* 'shift_backward' will shift the nonexistent time backward to the
|
|
closest existing time.
|
|
* 'NaT' will return NaT where there are nonexistent times.
|
|
* timedelta objects will shift nonexistent times by the timedelta.
|
|
* 'raise' will raise an NonExistentTimeError if there are
|
|
nonexistent times.
|
|
|
|
Raises
|
|
------
|
|
ValueError if the freq cannot be converted.
|
|
|
|
Notes
|
|
-----
|
|
If the Timestamp has a timezone, flooring will take place relative to the
|
|
local ("wall") time and re-localized to the same timezone. When flooring
|
|
near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
|
|
control the re-localization behavior.
|
|
|
|
Examples
|
|
--------
|
|
Create a timestamp object:
|
|
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
|
|
A timestamp can be floored using multiple frequency units:
|
|
|
|
>>> ts.floor(freq='H') # hour
|
|
Timestamp('2020-03-14 15:00:00')
|
|
|
|
>>> ts.floor(freq='T') # minute
|
|
Timestamp('2020-03-14 15:32:00')
|
|
|
|
>>> ts.floor(freq='S') # seconds
|
|
Timestamp('2020-03-14 15:32:52')
|
|
|
|
>>> ts.floor(freq='N') # nanoseconds
|
|
Timestamp('2020-03-14 15:32:52.192548651')
|
|
|
|
``freq`` can also be a multiple of a single unit, like '5T' (i.e. 5 minutes):
|
|
|
|
>>> ts.floor(freq='5T')
|
|
Timestamp('2020-03-14 15:30:00')
|
|
|
|
or a combination of multiple units, like '1H30T' (i.e. 1 hour and 30 minutes):
|
|
|
|
>>> ts.floor(freq='1H30T')
|
|
Timestamp('2020-03-14 15:00:00')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.floor()
|
|
NaT
|
|
|
|
When rounding near a daylight savings time transition, use ``ambiguous`` or
|
|
``nonexistent`` to control how the timestamp should be re-localized.
|
|
|
|
>>> ts_tz = pd.Timestamp("2021-10-31 03:30:00").tz_localize("Europe/Amsterdam")
|
|
|
|
>>> ts_tz.floor("2H", ambiguous=False)
|
|
Timestamp('2021-10-31 02:00:00+0100', tz='Europe/Amsterdam')
|
|
|
|
>>> ts_tz.floor("2H", ambiguous=True)
|
|
Timestamp('2021-10-31 02:00:00+0200', tz='Europe/Amsterdam')
|
|
"""
|
|
return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent)
|
|
|
|
def ceil(self, freq, ambiguous='raise', nonexistent='raise'):
|
|
"""
|
|
Return a new Timestamp ceiled to this resolution.
|
|
|
|
Parameters
|
|
----------
|
|
freq : str
|
|
Frequency string indicating the ceiling resolution.
|
|
ambiguous : bool or {'raise', 'NaT'}, default 'raise'
|
|
The behavior is as follows:
|
|
|
|
* bool contains flags to determine if time is dst or not (note
|
|
that this flag is only applicable for ambiguous fall dst dates).
|
|
* 'NaT' will return NaT for an ambiguous time.
|
|
* 'raise' will raise an AmbiguousTimeError for an ambiguous time.
|
|
|
|
nonexistent : {'raise', 'shift_forward', 'shift_backward, 'NaT', \
|
|
timedelta}, default 'raise'
|
|
A nonexistent time does not exist in a particular timezone
|
|
where clocks moved forward due to DST.
|
|
|
|
* 'shift_forward' will shift the nonexistent time forward to the
|
|
closest existing time.
|
|
* 'shift_backward' will shift the nonexistent time backward to the
|
|
closest existing time.
|
|
* 'NaT' will return NaT where there are nonexistent times.
|
|
* timedelta objects will shift nonexistent times by the timedelta.
|
|
* 'raise' will raise an NonExistentTimeError if there are
|
|
nonexistent times.
|
|
|
|
Raises
|
|
------
|
|
ValueError if the freq cannot be converted.
|
|
|
|
Notes
|
|
-----
|
|
If the Timestamp has a timezone, ceiling will take place relative to the
|
|
local ("wall") time and re-localized to the same timezone. When ceiling
|
|
near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
|
|
control the re-localization behavior.
|
|
|
|
Examples
|
|
--------
|
|
Create a timestamp object:
|
|
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
|
|
A timestamp can be ceiled using multiple frequency units:
|
|
|
|
>>> ts.ceil(freq='H') # hour
|
|
Timestamp('2020-03-14 16:00:00')
|
|
|
|
>>> ts.ceil(freq='T') # minute
|
|
Timestamp('2020-03-14 15:33:00')
|
|
|
|
>>> ts.ceil(freq='S') # seconds
|
|
Timestamp('2020-03-14 15:32:53')
|
|
|
|
>>> ts.ceil(freq='U') # microseconds
|
|
Timestamp('2020-03-14 15:32:52.192549')
|
|
|
|
``freq`` can also be a multiple of a single unit, like '5T' (i.e. 5 minutes):
|
|
|
|
>>> ts.ceil(freq='5T')
|
|
Timestamp('2020-03-14 15:35:00')
|
|
|
|
or a combination of multiple units, like '1H30T' (i.e. 1 hour and 30 minutes):
|
|
|
|
>>> ts.ceil(freq='1H30T')
|
|
Timestamp('2020-03-14 16:30:00')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.ceil()
|
|
NaT
|
|
|
|
When rounding near a daylight savings time transition, use ``ambiguous`` or
|
|
``nonexistent`` to control how the timestamp should be re-localized.
|
|
|
|
>>> ts_tz = pd.Timestamp("2021-10-31 01:30:00").tz_localize("Europe/Amsterdam")
|
|
|
|
>>> ts_tz.ceil("H", ambiguous=False)
|
|
Timestamp('2021-10-31 02:00:00+0100', tz='Europe/Amsterdam')
|
|
|
|
>>> ts_tz.ceil("H", ambiguous=True)
|
|
Timestamp('2021-10-31 02:00:00+0200', tz='Europe/Amsterdam')
|
|
"""
|
|
return self._round(freq, RoundTo.PLUS_INFTY, ambiguous, nonexistent)
|
|
|
|
@property
|
|
def tz(self):
|
|
"""
|
|
Alias for tzinfo.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp(1584226800, unit='s', tz='Europe/Stockholm')
|
|
>>> ts.tz
|
|
<DstTzInfo 'Europe/Stockholm' CET+1:00:00 STD>
|
|
"""
|
|
return self.tzinfo
|
|
|
|
@tz.setter
|
|
def tz(self, value):
|
|
# GH 3746: Prevent localizing or converting the index by setting tz
|
|
raise AttributeError(
|
|
"Cannot directly set timezone. "
|
|
"Use tz_localize() or tz_convert() as appropriate"
|
|
)
|
|
|
|
@property
|
|
def _freqstr(self):
|
|
return getattr(self._freq, "freqstr", self._freq)
|
|
|
|
@property
|
|
def freqstr(self):
|
|
"""
|
|
Return the total number of days in the month.
|
|
"""
|
|
warnings.warn(
|
|
"Timestamp.freqstr is deprecated and will be removed in a future version.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
return self._freqstr
|
|
|
|
def tz_localize(self, tz, ambiguous='raise', nonexistent='raise'):
|
|
"""
|
|
Localize the Timestamp to a timezone.
|
|
|
|
Convert naive Timestamp to local time zone or remove
|
|
timezone from timezone-aware Timestamp.
|
|
|
|
Parameters
|
|
----------
|
|
tz : str, pytz.timezone, dateutil.tz.tzfile or None
|
|
Time zone for time which Timestamp will be converted to.
|
|
None will remove timezone holding local time.
|
|
|
|
ambiguous : bool, 'NaT', default 'raise'
|
|
When clocks moved backward due to DST, ambiguous times may arise.
|
|
For example in Central European Time (UTC+01), when going from
|
|
03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
|
|
00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
|
|
`ambiguous` parameter dictates how ambiguous times should be
|
|
handled.
|
|
|
|
The behavior is as follows:
|
|
|
|
* bool contains flags to determine if time is dst or not (note
|
|
that this flag is only applicable for ambiguous fall dst dates).
|
|
* 'NaT' will return NaT for an ambiguous time.
|
|
* 'raise' will raise an AmbiguousTimeError for an ambiguous time.
|
|
|
|
nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, \
|
|
default 'raise'
|
|
A nonexistent time does not exist in a particular timezone
|
|
where clocks moved forward due to DST.
|
|
|
|
The behavior is as follows:
|
|
|
|
* 'shift_forward' will shift the nonexistent time forward to the
|
|
closest existing time.
|
|
* 'shift_backward' will shift the nonexistent time backward to the
|
|
closest existing time.
|
|
* 'NaT' will return NaT where there are nonexistent times.
|
|
* timedelta objects will shift nonexistent times by the timedelta.
|
|
* 'raise' will raise an NonExistentTimeError if there are
|
|
nonexistent times.
|
|
|
|
Returns
|
|
-------
|
|
localized : Timestamp
|
|
|
|
Raises
|
|
------
|
|
TypeError
|
|
If the Timestamp is tz-aware and tz is not None.
|
|
|
|
Examples
|
|
--------
|
|
Create a naive timestamp object:
|
|
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
|
|
>>> ts
|
|
Timestamp('2020-03-14 15:32:52.192548651')
|
|
|
|
Add 'Europe/Stockholm' as timezone:
|
|
|
|
>>> ts.tz_localize(tz='Europe/Stockholm')
|
|
Timestamp('2020-03-14 15:32:52.192548651+0100', tz='Europe/Stockholm')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.tz_localize()
|
|
NaT
|
|
"""
|
|
if ambiguous == 'infer':
|
|
raise ValueError('Cannot infer offset with only one time.')
|
|
|
|
nonexistent_options = ('raise', 'NaT', 'shift_forward', 'shift_backward')
|
|
if nonexistent not in nonexistent_options and not PyDelta_Check(nonexistent):
|
|
raise ValueError(
|
|
"The nonexistent argument must be one of 'raise', "
|
|
"'NaT', 'shift_forward', 'shift_backward' or a timedelta object"
|
|
)
|
|
|
|
if self.tzinfo is None:
|
|
# tz naive, localize
|
|
tz = maybe_get_tz(tz)
|
|
if not isinstance(ambiguous, str):
|
|
ambiguous = [ambiguous]
|
|
value = tz_localize_to_utc_single(self.value, tz,
|
|
ambiguous=ambiguous,
|
|
nonexistent=nonexistent,
|
|
reso=self._reso)
|
|
elif tz is None:
|
|
# reset tz
|
|
value = tz_convert_from_utc_single(self.value, self.tz, reso=self._reso)
|
|
|
|
else:
|
|
raise TypeError(
|
|
"Cannot localize tz-aware Timestamp, use tz_convert for conversions"
|
|
)
|
|
|
|
out = type(self)._from_value_and_reso(value, self._reso, tz=tz)
|
|
if out is not NaT:
|
|
out._set_freq(self._freq) # avoid warning in constructor
|
|
return out
|
|
|
|
def tz_convert(self, tz):
|
|
"""
|
|
Convert timezone-aware Timestamp to another time zone.
|
|
|
|
Parameters
|
|
----------
|
|
tz : str, pytz.timezone, dateutil.tz.tzfile or None
|
|
Time zone for time which Timestamp will be converted to.
|
|
None will remove timezone holding UTC time.
|
|
|
|
Returns
|
|
-------
|
|
converted : Timestamp
|
|
|
|
Raises
|
|
------
|
|
TypeError
|
|
If Timestamp is tz-naive.
|
|
|
|
Examples
|
|
--------
|
|
Create a timestamp object with UTC timezone:
|
|
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651', tz='UTC')
|
|
>>> ts
|
|
Timestamp('2020-03-14 15:32:52.192548651+0000', tz='UTC')
|
|
|
|
Change to Tokyo timezone:
|
|
|
|
>>> ts.tz_convert(tz='Asia/Tokyo')
|
|
Timestamp('2020-03-15 00:32:52.192548651+0900', tz='Asia/Tokyo')
|
|
|
|
Can also use ``astimezone``:
|
|
|
|
>>> ts.astimezone(tz='Asia/Tokyo')
|
|
Timestamp('2020-03-15 00:32:52.192548651+0900', tz='Asia/Tokyo')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.tz_convert(tz='Asia/Tokyo')
|
|
NaT
|
|
"""
|
|
if self.tzinfo is None:
|
|
# tz naive, use tz_localize
|
|
raise TypeError(
|
|
"Cannot convert tz-naive Timestamp, use tz_localize to localize"
|
|
)
|
|
else:
|
|
# Same UTC timestamp, different time zone
|
|
tz = maybe_get_tz(tz)
|
|
out = type(self)._from_value_and_reso(self.value, reso=self._reso, tz=tz)
|
|
if out is not NaT:
|
|
out._set_freq(self._freq) # avoid warning in constructor
|
|
return out
|
|
|
|
astimezone = tz_convert
|
|
|
|
def replace(
|
|
self,
|
|
year=None,
|
|
month=None,
|
|
day=None,
|
|
hour=None,
|
|
minute=None,
|
|
second=None,
|
|
microsecond=None,
|
|
nanosecond=None,
|
|
tzinfo=object,
|
|
fold=None,
|
|
):
|
|
"""
|
|
Implements datetime.replace, handles nanoseconds.
|
|
|
|
Parameters
|
|
----------
|
|
year : int, optional
|
|
month : int, optional
|
|
day : int, optional
|
|
hour : int, optional
|
|
minute : int, optional
|
|
second : int, optional
|
|
microsecond : int, optional
|
|
nanosecond : int, optional
|
|
tzinfo : tz-convertible, optional
|
|
fold : int, optional
|
|
|
|
Returns
|
|
-------
|
|
Timestamp with fields replaced
|
|
|
|
Examples
|
|
--------
|
|
Create a timestamp object:
|
|
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651', tz='UTC')
|
|
>>> ts
|
|
Timestamp('2020-03-14 15:32:52.192548651+0000', tz='UTC')
|
|
|
|
Replace year and the hour:
|
|
|
|
>>> ts.replace(year=1999, hour=10)
|
|
Timestamp('1999-03-14 10:32:52.192548651+0000', tz='UTC')
|
|
|
|
Replace timezone (not a conversion):
|
|
|
|
>>> import pytz
|
|
>>> ts.replace(tzinfo=pytz.timezone('US/Pacific'))
|
|
Timestamp('2020-03-14 15:32:52.192548651-0700', tz='US/Pacific')
|
|
|
|
Analogous for ``pd.NaT``:
|
|
|
|
>>> pd.NaT.replace(tzinfo=pytz.timezone('US/Pacific'))
|
|
NaT
|
|
"""
|
|
|
|
cdef:
|
|
npy_datetimestruct dts
|
|
int64_t value
|
|
object k, v
|
|
datetime ts_input
|
|
tzinfo_type tzobj
|
|
|
|
# set to naive if needed
|
|
tzobj = self.tzinfo
|
|
value = self.value
|
|
|
|
# GH 37610. Preserve fold when replacing.
|
|
if fold is None:
|
|
fold = self.fold
|
|
|
|
if tzobj is not None:
|
|
value = tz_convert_from_utc_single(value, tzobj, reso=self._reso)
|
|
|
|
# setup components
|
|
pandas_datetime_to_datetimestruct(value, self._reso, &dts)
|
|
dts.ps = self.nanosecond * 1000
|
|
|
|
# replace
|
|
def validate(k, v):
|
|
""" validate integers """
|
|
if not is_integer_object(v):
|
|
raise ValueError(
|
|
f"value must be an integer, received {type(v)} for {k}"
|
|
)
|
|
return v
|
|
|
|
if year is not None:
|
|
dts.year = validate('year', year)
|
|
if month is not None:
|
|
dts.month = validate('month', month)
|
|
if day is not None:
|
|
dts.day = validate('day', day)
|
|
if hour is not None:
|
|
dts.hour = validate('hour', hour)
|
|
if minute is not None:
|
|
dts.min = validate('minute', minute)
|
|
if second is not None:
|
|
dts.sec = validate('second', second)
|
|
if microsecond is not None:
|
|
dts.us = validate('microsecond', microsecond)
|
|
if nanosecond is not None:
|
|
dts.ps = validate('nanosecond', nanosecond) * 1000
|
|
if tzinfo is not object:
|
|
tzobj = tzinfo
|
|
|
|
# reconstruct & check bounds
|
|
if tzobj is not None and treat_tz_as_pytz(tzobj):
|
|
# replacing across a DST boundary may induce a new tzinfo object
|
|
# see GH#18319
|
|
ts_input = tzobj.localize(datetime(dts.year, dts.month, dts.day,
|
|
dts.hour, dts.min, dts.sec,
|
|
dts.us),
|
|
is_dst=not bool(fold))
|
|
tzobj = ts_input.tzinfo
|
|
else:
|
|
kwargs = {'year': dts.year, 'month': dts.month, 'day': dts.day,
|
|
'hour': dts.hour, 'minute': dts.min, 'second': dts.sec,
|
|
'microsecond': dts.us, 'tzinfo': tzobj,
|
|
'fold': fold}
|
|
ts_input = datetime(**kwargs)
|
|
|
|
ts = convert_datetime_to_tsobject(
|
|
ts_input, tzobj, nanos=dts.ps // 1000, reso=self._reso
|
|
)
|
|
return create_timestamp_from_ts(
|
|
ts.value, dts, tzobj, self._freq, fold, reso=self._reso
|
|
)
|
|
|
|
def to_julian_date(self) -> np.float64:
|
|
"""
|
|
Convert TimeStamp to a Julian Date.
|
|
|
|
0 Julian date is noon January 1, 4713 BC.
|
|
|
|
Examples
|
|
--------
|
|
>>> ts = pd.Timestamp('2020-03-14T15:32:52')
|
|
>>> ts.to_julian_date()
|
|
2458923.147824074
|
|
"""
|
|
year = self.year
|
|
month = self.month
|
|
day = self.day
|
|
if month <= 2:
|
|
year -= 1
|
|
month += 12
|
|
return (day +
|
|
np.fix((153 * month - 457) / 5) +
|
|
365 * year +
|
|
np.floor(year / 4) -
|
|
np.floor(year / 100) +
|
|
np.floor(year / 400) +
|
|
1721118.5 +
|
|
(self.hour +
|
|
self.minute / 60.0 +
|
|
self.second / 3600.0 +
|
|
self.microsecond / 3600.0 / 1e+6 +
|
|
self.nanosecond / 3600.0 / 1e+9
|
|
) / 24.0)
|
|
|
|
def isoweekday(self):
|
|
"""
|
|
Return the day of the week represented by the date.
|
|
|
|
Monday == 1 ... Sunday == 7.
|
|
"""
|
|
# same as super().isoweekday(), but that breaks because of how
|
|
# we have overriden year, see note in create_timestamp_from_ts
|
|
return self.weekday() + 1
|
|
|
|
def weekday(self):
|
|
"""
|
|
Return the day of the week represented by the date.
|
|
|
|
Monday == 0 ... Sunday == 6.
|
|
"""
|
|
# same as super().weekday(), but that breaks because of how
|
|
# we have overriden year, see note in create_timestamp_from_ts
|
|
return ccalendar.dayofweek(self.year, self.month, self.day)
|
|
|
|
|
|
# Aliases
|
|
Timestamp.weekofyear = Timestamp.week
|
|
Timestamp.daysinmonth = Timestamp.days_in_month
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
# Scalar analogues to functions in vectorized.pyx
|
|
|
|
|
|
@cython.cdivision(False)
|
|
cdef inline int64_t normalize_i8_stamp(int64_t local_val, int64_t ppd) nogil:
|
|
"""
|
|
Round the localized nanosecond timestamp down to the previous midnight.
|
|
|
|
Parameters
|
|
----------
|
|
local_val : int64_t
|
|
ppd : int64_t
|
|
Periods per day in the Timestamp's resolution.
|
|
|
|
Returns
|
|
-------
|
|
int64_t
|
|
"""
|
|
return local_val - (local_val % ppd)
|