716 lines
22 KiB
Cython
716 lines
22 KiB
Cython
import warnings
|
|
|
|
from pandas.util._exceptions import find_stack_level
|
|
|
|
cimport cython
|
|
|
|
from datetime import timezone
|
|
|
|
from cpython.datetime cimport (
|
|
PyDate_Check,
|
|
PyDateTime_Check,
|
|
datetime,
|
|
import_datetime,
|
|
timedelta,
|
|
tzinfo,
|
|
)
|
|
from cpython.object cimport PyObject
|
|
|
|
# import datetime C API
|
|
import_datetime()
|
|
|
|
|
|
cimport numpy as cnp
|
|
from numpy cimport (
|
|
int64_t,
|
|
ndarray,
|
|
)
|
|
|
|
import numpy as np
|
|
|
|
cnp.import_array()
|
|
|
|
from pandas._libs.tslibs.np_datetime cimport (
|
|
NPY_DATETIMEUNIT,
|
|
NPY_FR_ns,
|
|
check_dts_bounds,
|
|
npy_datetimestruct,
|
|
npy_datetimestruct_to_datetime,
|
|
pandas_datetime_to_datetimestruct,
|
|
pydate_to_dt64,
|
|
string_to_dts,
|
|
)
|
|
from pandas._libs.tslibs.strptime cimport parse_today_now
|
|
from pandas._libs.util cimport (
|
|
is_datetime64_object,
|
|
is_float_object,
|
|
is_integer_object,
|
|
)
|
|
|
|
from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
|
|
|
|
from pandas._libs.tslibs.conversion cimport (
|
|
_TSObject,
|
|
cast_from_unit,
|
|
convert_str_to_tsobject,
|
|
convert_timezone,
|
|
get_datetime64_nanos,
|
|
parse_pydatetime,
|
|
precision_from_unit,
|
|
)
|
|
from pandas._libs.tslibs.nattype cimport (
|
|
NPY_NAT,
|
|
c_NaT as NaT,
|
|
c_nat_strings as nat_strings,
|
|
)
|
|
from pandas._libs.tslibs.timestamps cimport _Timestamp
|
|
|
|
from pandas._libs.tslibs import (
|
|
Resolution,
|
|
get_resolution,
|
|
)
|
|
from pandas._libs.tslibs.timestamps import Timestamp
|
|
|
|
# Note: this is the only non-tslibs intra-pandas dependency here
|
|
|
|
from pandas._libs.missing cimport checknull_with_nat_and_na
|
|
from pandas._libs.tslibs.tzconversion cimport tz_localize_to_utc_single
|
|
|
|
|
|
def _test_parse_iso8601(ts: str):
|
|
"""
|
|
TESTING ONLY: Parse string into Timestamp using iso8601 parser. Used
|
|
only for testing, actual construction uses `convert_str_to_tsobject`
|
|
"""
|
|
cdef:
|
|
_TSObject obj
|
|
int out_local = 0, out_tzoffset = 0
|
|
NPY_DATETIMEUNIT out_bestunit
|
|
|
|
obj = _TSObject()
|
|
|
|
string_to_dts(ts, &obj.dts, &out_bestunit, &out_local, &out_tzoffset, True)
|
|
obj.value = npy_datetimestruct_to_datetime(NPY_FR_ns, &obj.dts)
|
|
check_dts_bounds(&obj.dts)
|
|
if out_local == 1:
|
|
obj.tzinfo = timezone(timedelta(minutes=out_tzoffset))
|
|
obj.value = tz_localize_to_utc_single(obj.value, obj.tzinfo)
|
|
return Timestamp(obj.value, tz=obj.tzinfo)
|
|
else:
|
|
return Timestamp(obj.value)
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def format_array_from_datetime(
|
|
ndarray values,
|
|
tzinfo tz=None,
|
|
str format=None,
|
|
na_rep: str | float = "NaT",
|
|
NPY_DATETIMEUNIT reso=NPY_FR_ns,
|
|
) -> np.ndarray:
|
|
"""
|
|
return a np object array of the string formatted values
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray[int64_t], arbitrary ndim
|
|
tz : tzinfo or None, default None
|
|
format : str or None, default None
|
|
a strftime capable string
|
|
na_rep : optional, default is None
|
|
a nat format
|
|
reso : NPY_DATETIMEUNIT, default NPY_FR_ns
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray[object]
|
|
"""
|
|
cdef:
|
|
int64_t val, ns, N = values.size
|
|
bint show_ms = False, show_us = False, show_ns = False
|
|
bint basic_format = False, basic_format_day = False
|
|
_Timestamp ts
|
|
object res
|
|
npy_datetimestruct dts
|
|
|
|
# Note that `result` (and thus `result_flat`) is C-order and
|
|
# `it` iterates C-order as well, so the iteration matches
|
|
# See discussion at
|
|
# github.com/pandas-dev/pandas/pull/46886#discussion_r860261305
|
|
ndarray result = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
|
|
object[::1] res_flat = result.ravel() # should NOT be a copy
|
|
cnp.flatiter it = cnp.PyArray_IterNew(values)
|
|
|
|
if tz is None:
|
|
# if we don't have a format nor tz, then choose
|
|
# a format based on precision
|
|
basic_format = format is None
|
|
if basic_format:
|
|
reso_obj = get_resolution(values, tz=tz, reso=reso)
|
|
show_ns = reso_obj == Resolution.RESO_NS
|
|
show_us = reso_obj == Resolution.RESO_US
|
|
show_ms = reso_obj == Resolution.RESO_MS
|
|
|
|
elif format == "%Y-%m-%d %H:%M:%S":
|
|
# Same format as default, but with hardcoded precision (s)
|
|
basic_format = True
|
|
show_ns = show_us = show_ms = False
|
|
|
|
elif format == "%Y-%m-%d %H:%M:%S.%f":
|
|
# Same format as default, but with hardcoded precision (us)
|
|
basic_format = show_us = True
|
|
show_ns = show_ms = False
|
|
|
|
elif format == "%Y-%m-%d":
|
|
# Default format for dates
|
|
basic_format_day = True
|
|
|
|
assert not (basic_format_day and basic_format)
|
|
|
|
for i in range(N):
|
|
# Analogous to: utc_val = values[i]
|
|
val = (<int64_t*>cnp.PyArray_ITER_DATA(it))[0]
|
|
|
|
if val == NPY_NAT:
|
|
res = na_rep
|
|
elif basic_format_day:
|
|
|
|
pandas_datetime_to_datetimestruct(val, reso, &dts)
|
|
res = f"{dts.year}-{dts.month:02d}-{dts.day:02d}"
|
|
|
|
elif basic_format:
|
|
|
|
pandas_datetime_to_datetimestruct(val, reso, &dts)
|
|
res = (f"{dts.year}-{dts.month:02d}-{dts.day:02d} "
|
|
f"{dts.hour:02d}:{dts.min:02d}:{dts.sec:02d}")
|
|
|
|
if show_ns:
|
|
ns = dts.ps // 1000
|
|
res += f".{ns + dts.us * 1000:09d}"
|
|
elif show_us:
|
|
res += f".{dts.us:06d}"
|
|
elif show_ms:
|
|
res += f".{dts.us // 1000:03d}"
|
|
|
|
else:
|
|
|
|
ts = Timestamp._from_value_and_reso(val, reso=reso, tz=tz)
|
|
if format is None:
|
|
# Use datetime.str, that returns ts.isoformat(sep=' ')
|
|
res = str(ts)
|
|
else:
|
|
|
|
# invalid format string
|
|
# requires dates > 1900
|
|
try:
|
|
# Note: dispatches to pydatetime
|
|
res = ts.strftime(format)
|
|
except ValueError:
|
|
# Use datetime.str, that returns ts.isoformat(sep=' ')
|
|
res = str(ts)
|
|
|
|
# Note: we can index result directly instead of using PyArray_MultiIter_DATA
|
|
# like we do for the other functions because result is known C-contiguous
|
|
# and is the first argument to PyArray_MultiIterNew2. The usual pattern
|
|
# does not seem to work with object dtype.
|
|
# See discussion at
|
|
# github.com/pandas-dev/pandas/pull/46886#discussion_r860261305
|
|
res_flat[i] = res
|
|
|
|
cnp.PyArray_ITER_NEXT(it)
|
|
|
|
return result
|
|
|
|
|
|
def array_with_unit_to_datetime(
|
|
ndarray[object] values,
|
|
str unit,
|
|
str errors="coerce"
|
|
):
|
|
"""
|
|
Convert the ndarray to datetime according to the time unit.
|
|
|
|
This function converts an array of objects into a numpy array of
|
|
datetime64[ns]. It returns the converted array
|
|
and also returns the timezone offset
|
|
|
|
if errors:
|
|
- raise: return converted values or raise OutOfBoundsDatetime
|
|
if out of range on the conversion or
|
|
ValueError for other conversions (e.g. a string)
|
|
- ignore: return non-convertible values as the same unit
|
|
- coerce: NaT for non-convertibles
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
Date-like objects to convert.
|
|
unit : str
|
|
Time unit to use during conversion.
|
|
errors : str, default 'raise'
|
|
Error behavior when parsing.
|
|
|
|
Returns
|
|
-------
|
|
result : ndarray of m8 values
|
|
tz : parsed timezone offset or None
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, n=len(values)
|
|
int64_t mult
|
|
bint is_ignore = errors == "ignore"
|
|
bint is_coerce = errors == "coerce"
|
|
bint is_raise = errors == "raise"
|
|
ndarray[int64_t] iresult
|
|
tzinfo tz = None
|
|
float fval
|
|
|
|
assert is_ignore or is_coerce or is_raise
|
|
|
|
if unit == "ns":
|
|
result, tz = array_to_datetime(
|
|
values.astype(object, copy=False),
|
|
errors=errors,
|
|
)
|
|
return result, tz
|
|
|
|
mult, _ = precision_from_unit(unit)
|
|
|
|
result = np.empty(n, dtype="M8[ns]")
|
|
iresult = result.view("i8")
|
|
|
|
for i in range(n):
|
|
val = values[i]
|
|
|
|
try:
|
|
if checknull_with_nat_and_na(val):
|
|
iresult[i] = NPY_NAT
|
|
|
|
elif is_integer_object(val) or is_float_object(val):
|
|
|
|
if val != val or val == NPY_NAT:
|
|
iresult[i] = NPY_NAT
|
|
else:
|
|
iresult[i] = cast_from_unit(val, unit)
|
|
|
|
elif isinstance(val, str):
|
|
if len(val) == 0 or val in nat_strings:
|
|
iresult[i] = NPY_NAT
|
|
|
|
else:
|
|
|
|
try:
|
|
fval = float(val)
|
|
except ValueError:
|
|
raise ValueError(
|
|
f"non convertible value {val} with the unit '{unit}'"
|
|
)
|
|
warnings.warn(
|
|
"The behavior of 'to_datetime' with 'unit' when parsing "
|
|
"strings is deprecated. In a future version, strings will "
|
|
"be parsed as datetime strings, matching the behavior "
|
|
"without a 'unit'. To retain the old behavior, explicitly "
|
|
"cast ints or floats to numeric type before calling "
|
|
"to_datetime.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
|
|
iresult[i] = cast_from_unit(fval, unit)
|
|
|
|
else:
|
|
# TODO: makes more sense as TypeError, but that would be an
|
|
# API change.
|
|
raise ValueError(
|
|
f"unit='{unit}' not valid with non-numerical val='{val}'"
|
|
)
|
|
|
|
except (ValueError, OutOfBoundsDatetime, TypeError) as err:
|
|
if is_raise:
|
|
err.args = (f"{err}, at position {i}",)
|
|
raise
|
|
elif is_ignore:
|
|
# we have hit an exception
|
|
# and are in ignore mode
|
|
# redo as object
|
|
return _array_with_unit_to_datetime_object_fallback(values, unit)
|
|
else:
|
|
# is_coerce
|
|
iresult[i] = NPY_NAT
|
|
|
|
return result, tz
|
|
|
|
|
|
cdef _array_with_unit_to_datetime_object_fallback(ndarray[object] values, str unit):
|
|
cdef:
|
|
Py_ssize_t i, n = len(values)
|
|
ndarray[object] oresult
|
|
tzinfo tz = None
|
|
|
|
# TODO: fix subtle differences between this and no-unit code
|
|
oresult = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
|
|
for i in range(n):
|
|
val = values[i]
|
|
|
|
if checknull_with_nat_and_na(val):
|
|
oresult[i] = <object>NaT
|
|
elif is_integer_object(val) or is_float_object(val):
|
|
|
|
if val != val or val == NPY_NAT:
|
|
oresult[i] = <object>NaT
|
|
else:
|
|
try:
|
|
oresult[i] = Timestamp(val, unit=unit)
|
|
except OutOfBoundsDatetime:
|
|
oresult[i] = val
|
|
|
|
elif isinstance(val, str):
|
|
if len(val) == 0 or val in nat_strings:
|
|
oresult[i] = <object>NaT
|
|
|
|
else:
|
|
oresult[i] = val
|
|
|
|
return oresult, tz
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
def first_non_null(values: ndarray) -> int:
|
|
"""Find position of first non-null value, return -1 if there isn't one."""
|
|
cdef:
|
|
Py_ssize_t n = len(values)
|
|
Py_ssize_t i
|
|
for i in range(n):
|
|
val = values[i]
|
|
if checknull_with_nat_and_na(val):
|
|
continue
|
|
if (
|
|
isinstance(val, str)
|
|
and
|
|
(len(val) == 0 or val in nat_strings or val in ("now", "today"))
|
|
):
|
|
continue
|
|
return i
|
|
else:
|
|
return -1
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
cpdef array_to_datetime(
|
|
ndarray values, # object dtype, arbitrary ndim
|
|
str errors="raise",
|
|
bint dayfirst=False,
|
|
bint yearfirst=False,
|
|
bint utc=False,
|
|
):
|
|
"""
|
|
Converts a 1D array of date-like values to a numpy array of either:
|
|
1) datetime64[ns] data
|
|
2) datetime.datetime objects, if OutOfBoundsDatetime or TypeError
|
|
is encountered
|
|
|
|
Also returns a fixed-offset tzinfo object if an array of strings with the same
|
|
timezone offset is passed and utc=True is not passed. Otherwise, None
|
|
is returned
|
|
|
|
Handles datetime.date, datetime.datetime, np.datetime64 objects, numeric,
|
|
strings
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray of object
|
|
date-like objects to convert
|
|
errors : str, default 'raise'
|
|
error behavior when parsing
|
|
dayfirst : bool, default False
|
|
dayfirst parsing behavior when encountering datetime strings
|
|
yearfirst : bool, default False
|
|
yearfirst parsing behavior when encountering datetime strings
|
|
utc : bool, default False
|
|
indicator whether the dates should be UTC
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray
|
|
May be datetime64[ns] or object dtype
|
|
tzinfo or None
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, n = values.size
|
|
object val, tz
|
|
ndarray[int64_t] iresult
|
|
npy_datetimestruct dts
|
|
bint utc_convert = bool(utc)
|
|
bint seen_datetime_offset = False
|
|
bint is_raise = errors == "raise"
|
|
bint is_ignore = errors == "ignore"
|
|
bint is_coerce = errors == "coerce"
|
|
bint is_same_offsets
|
|
_TSObject _ts
|
|
float tz_offset
|
|
set out_tzoffset_vals = set()
|
|
tzinfo tz_out = None
|
|
bint found_tz = False, found_naive = False
|
|
cnp.broadcast mi
|
|
|
|
# specify error conditions
|
|
assert is_raise or is_ignore or is_coerce
|
|
|
|
result = np.empty((<object>values).shape, dtype="M8[ns]")
|
|
mi = cnp.PyArray_MultiIterNew2(result, values)
|
|
iresult = result.view("i8").ravel()
|
|
|
|
for i in range(n):
|
|
# Analogous to `val = values[i]`
|
|
val = <object>(<PyObject**>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
|
|
|
|
try:
|
|
if checknull_with_nat_and_na(val):
|
|
iresult[i] = NPY_NAT
|
|
|
|
elif PyDateTime_Check(val):
|
|
if val.tzinfo is not None:
|
|
found_tz = True
|
|
else:
|
|
found_naive = True
|
|
tz_out = convert_timezone(
|
|
val.tzinfo,
|
|
tz_out,
|
|
found_naive,
|
|
found_tz,
|
|
utc_convert,
|
|
)
|
|
iresult[i] = parse_pydatetime(val, &dts, utc_convert)
|
|
|
|
elif PyDate_Check(val):
|
|
iresult[i] = pydate_to_dt64(val, &dts)
|
|
check_dts_bounds(&dts)
|
|
|
|
elif is_datetime64_object(val):
|
|
iresult[i] = get_datetime64_nanos(val, NPY_FR_ns)
|
|
|
|
elif is_integer_object(val) or is_float_object(val):
|
|
# these must be ns unit by-definition
|
|
|
|
if val != val or val == NPY_NAT:
|
|
iresult[i] = NPY_NAT
|
|
else:
|
|
# we now need to parse this as if unit='ns'
|
|
iresult[i] = cast_from_unit(val, "ns")
|
|
|
|
elif isinstance(val, str):
|
|
# string
|
|
if type(val) is not str:
|
|
# GH#32264 np.str_ object
|
|
val = str(val)
|
|
|
|
if parse_today_now(val, &iresult[i], utc):
|
|
# We can't _quite_ dispatch this to convert_str_to_tsobject
|
|
# bc there isn't a nice way to pass "utc"
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
continue
|
|
|
|
_ts = convert_str_to_tsobject(
|
|
val, None, unit="ns", dayfirst=dayfirst, yearfirst=yearfirst
|
|
)
|
|
_ts.ensure_reso(NPY_FR_ns, val)
|
|
|
|
iresult[i] = _ts.value
|
|
|
|
tz = _ts.tzinfo
|
|
if tz is not None:
|
|
# dateutil timezone objects cannot be hashed, so
|
|
# store the UTC offsets in seconds instead
|
|
nsecs = tz.utcoffset(None).total_seconds()
|
|
out_tzoffset_vals.add(nsecs)
|
|
# need to set seen_datetime_offset *after* the
|
|
# potentially-raising timezone(timedelta(...)) call,
|
|
# otherwise we can go down the is_same_offsets path
|
|
# bc len(out_tzoffset_vals) == 0
|
|
seen_datetime_offset = True
|
|
else:
|
|
# Add a marker for naive string, to track if we are
|
|
# parsing mixed naive and aware strings
|
|
out_tzoffset_vals.add("naive")
|
|
|
|
else:
|
|
raise TypeError(f"{type(val)} is not convertible to datetime")
|
|
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
|
|
except (TypeError, OverflowError, ValueError) as ex:
|
|
ex.args = (f"{ex}, at position {i}",)
|
|
if is_coerce:
|
|
iresult[i] = NPY_NAT
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
continue
|
|
elif is_raise:
|
|
raise
|
|
return values, None
|
|
|
|
if seen_datetime_offset and not utc_convert:
|
|
# GH#17697
|
|
# 1) If all the offsets are equal, return one offset for
|
|
# the parsed dates to (maybe) pass to DatetimeIndex
|
|
# 2) If the offsets are different, then force the parsing down the
|
|
# object path where an array of datetimes
|
|
# (with individual dateutil.tzoffsets) are returned
|
|
is_same_offsets = len(out_tzoffset_vals) == 1
|
|
if not is_same_offsets:
|
|
return _array_to_datetime_object(values, errors, dayfirst, yearfirst)
|
|
else:
|
|
tz_offset = out_tzoffset_vals.pop()
|
|
tz_out = timezone(timedelta(seconds=tz_offset))
|
|
return result, tz_out
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
cdef _array_to_datetime_object(
|
|
ndarray[object] values,
|
|
str errors,
|
|
bint dayfirst=False,
|
|
bint yearfirst=False,
|
|
):
|
|
"""
|
|
Fall back function for array_to_datetime
|
|
|
|
Attempts to parse datetime strings with dateutil to return an array
|
|
of datetime objects
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray[object]
|
|
date-like objects to convert
|
|
errors : str
|
|
error behavior when parsing
|
|
dayfirst : bool, default False
|
|
dayfirst parsing behavior when encountering datetime strings
|
|
yearfirst : bool, default False
|
|
yearfirst parsing behavior when encountering datetime strings
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray[object]
|
|
Literal[None]
|
|
"""
|
|
cdef:
|
|
Py_ssize_t i, n = values.size
|
|
object val
|
|
bint is_ignore = errors == "ignore"
|
|
bint is_coerce = errors == "coerce"
|
|
bint is_raise = errors == "raise"
|
|
ndarray oresult_nd
|
|
ndarray[object] oresult
|
|
npy_datetimestruct dts
|
|
cnp.broadcast mi
|
|
_TSObject tsobj
|
|
|
|
assert is_raise or is_ignore or is_coerce
|
|
|
|
oresult_nd = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
|
|
mi = cnp.PyArray_MultiIterNew2(oresult_nd, values)
|
|
oresult = oresult_nd.ravel()
|
|
|
|
# We return an object array and only attempt to parse:
|
|
# 1) NaT or NaT-like values
|
|
# 2) datetime strings, which we return as datetime.datetime
|
|
# 3) special strings - "now" & "today"
|
|
for i in range(n):
|
|
# Analogous to: val = values[i]
|
|
val = <object>(<PyObject**>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
|
|
|
|
if checknull_with_nat_and_na(val) or PyDateTime_Check(val):
|
|
# GH 25978. No need to parse NaT-like or datetime-like vals
|
|
oresult[i] = val
|
|
elif isinstance(val, str):
|
|
if type(val) is not str:
|
|
# GH#32264 np.str_ objects
|
|
val = str(val)
|
|
|
|
if len(val) == 0 or val in nat_strings:
|
|
oresult[i] = "NaT"
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
continue
|
|
|
|
try:
|
|
tsobj = convert_str_to_tsobject(
|
|
val, None, unit="ns", dayfirst=dayfirst, yearfirst=yearfirst
|
|
)
|
|
tsobj.ensure_reso(NPY_FR_ns, val)
|
|
|
|
dts = tsobj.dts
|
|
oresult[i] = datetime(
|
|
dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us,
|
|
tzinfo=tsobj.tzinfo,
|
|
fold=tsobj.fold,
|
|
)
|
|
|
|
except (ValueError, OverflowError) as ex:
|
|
ex.args = (f"{ex}, at position {i}", )
|
|
if is_coerce:
|
|
oresult[i] = <object>NaT
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
continue
|
|
if is_raise:
|
|
raise
|
|
return values, None
|
|
else:
|
|
if is_raise:
|
|
raise
|
|
return values, None
|
|
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
|
|
return oresult_nd, None
|
|
|
|
|
|
def array_to_datetime_with_tz(ndarray values, tzinfo tz):
|
|
"""
|
|
Vectorized analogue to pd.Timestamp(value, tz=tz)
|
|
|
|
values has object-dtype, unrestricted ndim.
|
|
|
|
Major differences between this and array_to_datetime with utc=True
|
|
- np.datetime64 objects are treated as _wall_ times.
|
|
- tznaive datetimes are treated as _wall_ times.
|
|
"""
|
|
cdef:
|
|
ndarray result = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_INT64, 0)
|
|
cnp.broadcast mi = cnp.PyArray_MultiIterNew2(result, values)
|
|
Py_ssize_t i, n = values.size
|
|
object item
|
|
int64_t ival
|
|
datetime ts
|
|
|
|
for i in range(n):
|
|
# Analogous to `item = values[i]`
|
|
item = <object>(<PyObject**>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
|
|
|
|
if checknull_with_nat_and_na(item):
|
|
# this catches pd.NA which would raise in the Timestamp constructor
|
|
ival = NPY_NAT
|
|
|
|
else:
|
|
ts = Timestamp(item)
|
|
if ts is NaT:
|
|
ival = NPY_NAT
|
|
else:
|
|
if ts.tz is not None:
|
|
ts = ts.tz_convert(tz)
|
|
else:
|
|
# datetime64, tznaive pydatetime, int, float
|
|
ts = ts.tz_localize(tz)
|
|
ts = ts.as_unit("ns")
|
|
ival = ts._value
|
|
|
|
# Analogous to: result[i] = ival
|
|
(<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 0))[0] = ival
|
|
|
|
cnp.PyArray_MultiIter_NEXT(mi)
|
|
|
|
return result
|