""" _Timestamp is a c-defined subclass of datetime.datetime _Timestamp is PITA. Because we inherit from datetime, which has very specific construction requirements, we need to do object instantiation in python (see Timestamp class below). This will serve as a C extension type that shadows the python class, where we do any heavy lifting. """ import warnings cimport cython import numpy as np cimport numpy as cnp from numpy cimport ( int8_t, int64_t, ndarray, uint8_t, ) cnp.import_array() from cpython.datetime cimport ( # alias bc `tzinfo` is a kwarg below PyDate_Check, PyDateTime_Check, PyDelta_Check, PyTZInfo_Check, datetime, import_datetime, time, tzinfo as tzinfo_type, ) from cpython.object cimport ( Py_EQ, Py_GE, Py_GT, Py_LE, Py_LT, Py_NE, PyObject_RichCompare, PyObject_RichCompareBool, ) import_datetime() from pandas._libs.tslibs cimport ccalendar from pandas._libs.tslibs.base cimport ABCTimestamp from pandas.util._exceptions import find_stack_level from pandas._libs.tslibs.conversion cimport ( _TSObject, convert_datetime_to_tsobject, convert_to_tsobject, maybe_localize_tso, ) from pandas._libs.tslibs.dtypes cimport ( npy_unit_to_abbrev, periods_per_day, periods_per_second, ) from pandas._libs.tslibs.util cimport ( is_array, is_datetime64_object, is_float_object, is_integer_object, is_timedelta64_object, ) from pandas._libs.tslibs.fields import ( RoundTo, get_date_name_field, get_start_end_field, round_nsint64, ) from pandas._libs.tslibs.nattype cimport ( NPY_NAT, c_NaT as NaT, ) from pandas._libs.tslibs.np_datetime cimport ( NPY_DATETIMEUNIT, NPY_FR_ns, cmp_dtstructs, cmp_scalar, convert_reso, get_conversion_factor, get_datetime64_unit, get_datetime64_value, get_unit_from_dtype, npy_datetimestruct, npy_datetimestruct_to_datetime, pandas_datetime_to_datetimestruct, pydatetime_to_dtstruct, ) from pandas._libs.tslibs.np_datetime import ( OutOfBoundsDatetime, OutOfBoundsTimedelta, ) from pandas._libs.tslibs.offsets cimport ( BaseOffset, is_offset_object, to_offset, ) from pandas._libs.tslibs.timedeltas cimport ( _Timedelta, delta_to_nanoseconds, ensure_td64ns, is_any_td_scalar, ) from pandas._libs.tslibs.timedeltas import Timedelta from pandas._libs.tslibs.timezones cimport ( get_timezone, is_utc, maybe_get_tz, treat_tz_as_pytz, tz_compare, utc_pytz as UTC, ) from pandas._libs.tslibs.tzconversion cimport ( tz_convert_from_utc_single, tz_localize_to_utc_single, ) # ---------------------------------------------------------------------- # Constants _zero_time = time(0, 0) _no_input = object() # ---------------------------------------------------------------------- cdef inline _Timestamp create_timestamp_from_ts( int64_t value, npy_datetimestruct dts, tzinfo tz, BaseOffset freq, bint fold, NPY_DATETIMEUNIT reso=NPY_FR_ns, ): """ convenience routine to construct a Timestamp from its parts """ cdef: _Timestamp ts_base int64_t pass_year = dts.year # We pass year=1970/1972 here and set year below because with non-nanosecond # resolution we may have datetimes outside of the stdlib pydatetime # implementation bounds, which would raise. # NB: this means the C-API macro PyDateTime_GET_YEAR is unreliable. if 1 <= pass_year <= 9999: # we are in-bounds for pydatetime pass elif ccalendar.is_leapyear(dts.year): pass_year = 1972 else: pass_year = 1970 ts_base = _Timestamp.__new__(Timestamp, pass_year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz, fold=fold) ts_base.value = value ts_base._freq = freq ts_base.year = dts.year ts_base.nanosecond = dts.ps // 1000 ts_base._reso = reso return ts_base def _unpickle_timestamp(value, freq, tz, reso=NPY_FR_ns): # GH#41949 dont warn on unpickle if we have a freq ts = Timestamp._from_value_and_reso(value, reso, tz) ts._set_freq(freq) return ts # ---------------------------------------------------------------------- def integer_op_not_supported(obj): # GH#22535 add/sub of integers and int-arrays is no longer allowed # Note we return rather than raise the exception so we can raise in # the caller; mypy finds this more palatable. cls = type(obj).__name__ # GH#30886 using an fstring raises SystemError int_addsub_msg = ( f"Addition/subtraction of integers and integer-arrays with {cls} is " "no longer supported. Instead of adding/subtracting `n`, " "use `n * obj.freq`" ) return TypeError(int_addsub_msg) class MinMaxReso: """ We need to define min/max/resolution on both the Timestamp _instance_ and Timestamp class. On an instance, these depend on the object's _reso. On the class, we default to the values we would get with nanosecond _reso. See also: timedeltas.MinMaxReso """ def __init__(self, name): self._name = name def __get__(self, obj, type=None): cls = Timestamp if self._name == "min": val = np.iinfo(np.int64).min + 1 elif self._name == "max": val = np.iinfo(np.int64).max else: assert self._name == "resolution" val = 1 cls = Timedelta if obj is None: # i.e. this is on the class, default to nanos return cls(val) elif self._name == "resolution": return Timedelta._from_value_and_reso(val, obj._reso) else: return Timestamp._from_value_and_reso(val, obj._reso, tz=None) def __set__(self, obj, value): raise AttributeError(f"{self._name} is not settable.") # ---------------------------------------------------------------------- cdef class _Timestamp(ABCTimestamp): # higher than np.ndarray and np.matrix __array_priority__ = 100 dayofweek = _Timestamp.day_of_week dayofyear = _Timestamp.day_of_year min = MinMaxReso("min") max = MinMaxReso("max") resolution = MinMaxReso("resolution") # GH#21336, GH#21365 cpdef void _set_freq(self, freq): # set the ._freq attribute without going through the constructor, # which would issue a warning # Caller is responsible for validation self._freq = freq @property def freq(self): warnings.warn( "Timestamp.freq is deprecated and will be removed in a future version.", FutureWarning, stacklevel=find_stack_level(), ) return self._freq # ----------------------------------------------------------------- # Constructors @classmethod def _from_value_and_reso(cls, int64_t value, NPY_DATETIMEUNIT reso, tzinfo tz): cdef: npy_datetimestruct dts _TSObject obj = _TSObject() if value == NPY_NAT: return NaT if reso < NPY_DATETIMEUNIT.NPY_FR_s or reso > NPY_DATETIMEUNIT.NPY_FR_ns: raise NotImplementedError( "Only resolutions 's', 'ms', 'us', 'ns' are supported." ) obj.value = value pandas_datetime_to_datetimestruct(value, reso, &obj.dts) maybe_localize_tso(obj, tz, reso) return create_timestamp_from_ts( value, obj.dts, tz=obj.tzinfo, freq=None, fold=obj.fold, reso=reso ) @classmethod def _from_dt64(cls, dt64: np.datetime64): # construct a Timestamp from a np.datetime64 object, keeping the # resolution of the input. # This is herely mainly so we can incrementally implement non-nano # (e.g. only tznaive at first) cdef: npy_datetimestruct dts int64_t value NPY_DATETIMEUNIT reso reso = get_datetime64_unit(dt64) value = get_datetime64_value(dt64) return cls._from_value_and_reso(value, reso, None) # ----------------------------------------------------------------- def __hash__(_Timestamp self): if self.nanosecond: return hash(self.value) if not (1 <= self.year <= 9999): # out of bounds for pydatetime return hash(self.value) if self.fold: return datetime.__hash__(self.replace(fold=0)) return datetime.__hash__(self) def __richcmp__(_Timestamp self, object other, int op): cdef: _Timestamp ots int ndim if isinstance(other, _Timestamp): ots = other elif other is NaT: return op == Py_NE elif is_datetime64_object(other): ots = _Timestamp._from_dt64(other) elif PyDateTime_Check(other): if self.nanosecond == 0: val = self.to_pydatetime() return PyObject_RichCompareBool(val, other, op) try: ots = type(self)(other) except ValueError: return self._compare_outside_nanorange(other, op) elif is_array(other): # avoid recursion error GH#15183 if other.dtype.kind == "M": if self.tz is None: return PyObject_RichCompare(self.asm8, other, op) elif op == Py_NE: return np.ones(other.shape, dtype=np.bool_) elif op == Py_EQ: return np.zeros(other.shape, dtype=np.bool_) raise TypeError( "Cannot compare tz-naive and tz-aware timestamps" ) elif other.dtype.kind == "O": # Operate element-wise return np.array( [PyObject_RichCompare(self, x, op) for x in other], dtype=bool, ) elif op == Py_NE: return np.ones(other.shape, dtype=np.bool_) elif op == Py_EQ: return np.zeros(other.shape, dtype=np.bool_) return NotImplemented elif PyDate_Check(other): # returning NotImplemented defers to the `date` implementation # which incorrectly drops tz and normalizes to midnight # before comparing # We follow the stdlib datetime behavior of never being equal warnings.warn( "Comparison of Timestamp with datetime.date is deprecated in " "order to match the standard library behavior. " "In a future version these will be considered non-comparable. " "Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.", FutureWarning, stacklevel=find_stack_level(), ) return NotImplemented else: return NotImplemented if not self._can_compare(ots): if op == Py_NE or op == Py_EQ: return NotImplemented raise TypeError( "Cannot compare tz-naive and tz-aware timestamps" ) if self._reso == ots._reso: return cmp_scalar(self.value, ots.value, op) return self._compare_mismatched_resos(ots, op) # TODO: copied from Timedelta; try to de-duplicate cdef inline bint _compare_mismatched_resos(self, _Timestamp other, int op): # Can't just dispatch to numpy as they silently overflow and get it wrong cdef: npy_datetimestruct dts_self npy_datetimestruct dts_other # dispatch to the datetimestruct utils instead of writing new ones! pandas_datetime_to_datetimestruct(self.value, self._reso, &dts_self) pandas_datetime_to_datetimestruct(other.value, other._reso, &dts_other) return cmp_dtstructs(&dts_self, &dts_other, op) cdef bint _compare_outside_nanorange(_Timestamp self, datetime other, int op) except -1: cdef: datetime dtval = self.to_pydatetime(warn=False) if not self._can_compare(other): return NotImplemented if self.nanosecond == 0: return PyObject_RichCompareBool(dtval, other, op) # otherwise we have dtval < self if op == Py_NE: return True if op == Py_EQ: return False if op == Py_LE or op == Py_LT: return self.year <= other.year if op == Py_GE or op == Py_GT: return self.year >= other.year cdef bint _can_compare(self, datetime other): if self.tzinfo is not None: return other.tzinfo is not None return other.tzinfo is None @cython.overflowcheck(True) def __add__(self, other): cdef: int64_t nanos = 0 if is_any_td_scalar(other): if is_timedelta64_object(other): other_reso = get_datetime64_unit(other) if ( other_reso == NPY_DATETIMEUNIT.NPY_FR_GENERIC ): # TODO: deprecate allowing this? We only get here # with test_timedelta_add_timestamp_interval other = np.timedelta64(other.view("i8"), "ns") elif ( 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__ 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 """ 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)