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ai-content-maker/.venv/Lib/site-packages/pandas/_libs/tslibs/timestamps.pyx

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2024-05-03 04:18:51 +03:00
"""
_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
<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)