from __future__ import annotations
|
|
from collections import abc
|
from datetime import datetime
|
from functools import partial
|
from itertools import islice
|
from typing import (
|
TYPE_CHECKING,
|
Callable,
|
Hashable,
|
List,
|
Tuple,
|
TypedDict,
|
Union,
|
cast,
|
overload,
|
)
|
import warnings
|
|
import numpy as np
|
|
from pandas._libs import (
|
lib,
|
tslib,
|
)
|
from pandas._libs.tslibs import (
|
OutOfBoundsDatetime,
|
Timedelta,
|
Timestamp,
|
astype_overflowsafe,
|
get_unit_from_dtype,
|
iNaT,
|
is_supported_unit,
|
nat_strings,
|
parsing,
|
timezones as libtimezones,
|
)
|
from pandas._libs.tslibs.conversion import precision_from_unit
|
from pandas._libs.tslibs.parsing import (
|
DateParseError,
|
guess_datetime_format,
|
)
|
from pandas._libs.tslibs.strptime import array_strptime
|
from pandas._typing import (
|
AnyArrayLike,
|
ArrayLike,
|
DateTimeErrorChoices,
|
npt,
|
)
|
from pandas.util._exceptions import find_stack_level
|
|
from pandas.core.dtypes.common import (
|
ensure_object,
|
is_datetime64_dtype,
|
is_datetime64tz_dtype,
|
is_float,
|
is_integer,
|
is_integer_dtype,
|
is_list_like,
|
is_numeric_dtype,
|
is_scalar,
|
)
|
from pandas.core.dtypes.generic import (
|
ABCDataFrame,
|
ABCSeries,
|
)
|
from pandas.core.dtypes.missing import notna
|
|
from pandas.arrays import (
|
DatetimeArray,
|
IntegerArray,
|
PandasArray,
|
)
|
from pandas.core import algorithms
|
from pandas.core.algorithms import unique
|
from pandas.core.arrays.base import ExtensionArray
|
from pandas.core.arrays.datetimes import (
|
maybe_convert_dtype,
|
objects_to_datetime64ns,
|
tz_to_dtype,
|
)
|
from pandas.core.construction import extract_array
|
from pandas.core.indexes.base import Index
|
from pandas.core.indexes.datetimes import DatetimeIndex
|
|
if TYPE_CHECKING:
|
from pandas._libs.tslibs.nattype import NaTType
|
from pandas._libs.tslibs.timedeltas import UnitChoices
|
|
from pandas import (
|
DataFrame,
|
Series,
|
)
|
|
# ---------------------------------------------------------------------
|
# types used in annotations
|
|
ArrayConvertible = Union[List, Tuple, AnyArrayLike]
|
Scalar = Union[float, str]
|
DatetimeScalar = Union[Scalar, datetime]
|
|
DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
|
|
DatetimeDictArg = Union[List[Scalar], Tuple[Scalar, ...], AnyArrayLike]
|
|
|
class YearMonthDayDict(TypedDict, total=True):
|
year: DatetimeDictArg
|
month: DatetimeDictArg
|
day: DatetimeDictArg
|
|
|
class FulldatetimeDict(YearMonthDayDict, total=False):
|
hour: DatetimeDictArg
|
hours: DatetimeDictArg
|
minute: DatetimeDictArg
|
minutes: DatetimeDictArg
|
second: DatetimeDictArg
|
seconds: DatetimeDictArg
|
ms: DatetimeDictArg
|
us: DatetimeDictArg
|
ns: DatetimeDictArg
|
|
|
DictConvertible = Union[FulldatetimeDict, "DataFrame"]
|
start_caching_at = 50
|
|
|
# ---------------------------------------------------------------------
|
|
|
def _guess_datetime_format_for_array(arr, dayfirst: bool | None = False) -> str | None:
|
# Try to guess the format based on the first non-NaN element, return None if can't
|
if (first_non_null := tslib.first_non_null(arr)) != -1:
|
if type(first_non_nan_element := arr[first_non_null]) is str:
|
# GH#32264 np.str_ object
|
guessed_format = guess_datetime_format(
|
first_non_nan_element, dayfirst=dayfirst
|
)
|
if guessed_format is not None:
|
return guessed_format
|
# If there are multiple non-null elements, warn about
|
# how parsing might not be consistent
|
if tslib.first_non_null(arr[first_non_null + 1 :]) != -1:
|
warnings.warn(
|
"Could not infer format, so each element will be parsed "
|
"individually, falling back to `dateutil`. To ensure parsing is "
|
"consistent and as-expected, please specify a format.",
|
UserWarning,
|
stacklevel=find_stack_level(),
|
)
|
return None
|
|
|
def should_cache(
|
arg: ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
|
) -> bool:
|
"""
|
Decides whether to do caching.
|
|
If the percent of unique elements among `check_count` elements less
|
than `unique_share * 100` then we can do caching.
|
|
Parameters
|
----------
|
arg: listlike, tuple, 1-d array, Series
|
unique_share: float, default=0.7, optional
|
0 < unique_share < 1
|
check_count: int, optional
|
0 <= check_count <= len(arg)
|
|
Returns
|
-------
|
do_caching: bool
|
|
Notes
|
-----
|
By default for a sequence of less than 50 items in size, we don't do
|
caching; for the number of elements less than 5000, we take ten percent of
|
all elements to check for a uniqueness share; if the sequence size is more
|
than 5000, then we check only the first 500 elements.
|
All constants were chosen empirically by.
|
"""
|
do_caching = True
|
|
# default realization
|
if check_count is None:
|
# in this case, the gain from caching is negligible
|
if len(arg) <= start_caching_at:
|
return False
|
|
if len(arg) <= 5000:
|
check_count = len(arg) // 10
|
else:
|
check_count = 500
|
else:
|
assert (
|
0 <= check_count <= len(arg)
|
), "check_count must be in next bounds: [0; len(arg)]"
|
if check_count == 0:
|
return False
|
|
assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
|
|
try:
|
# We can't cache if the items are not hashable.
|
unique_elements = set(islice(arg, check_count))
|
except TypeError:
|
return False
|
if len(unique_elements) > check_count * unique_share:
|
do_caching = False
|
return do_caching
|
|
|
def _maybe_cache(
|
arg: ArrayConvertible,
|
format: str | None,
|
cache: bool,
|
convert_listlike: Callable,
|
) -> Series:
|
"""
|
Create a cache of unique dates from an array of dates
|
|
Parameters
|
----------
|
arg : listlike, tuple, 1-d array, Series
|
format : string
|
Strftime format to parse time
|
cache : bool
|
True attempts to create a cache of converted values
|
convert_listlike : function
|
Conversion function to apply on dates
|
|
Returns
|
-------
|
cache_array : Series
|
Cache of converted, unique dates. Can be empty
|
"""
|
from pandas import Series
|
|
cache_array = Series(dtype=object)
|
|
if cache:
|
# Perform a quicker unique check
|
if not should_cache(arg):
|
return cache_array
|
|
unique_dates = unique(arg)
|
if len(unique_dates) < len(arg):
|
cache_dates = convert_listlike(unique_dates, format)
|
# GH#45319
|
try:
|
cache_array = Series(cache_dates, index=unique_dates, copy=False)
|
except OutOfBoundsDatetime:
|
return cache_array
|
# GH#39882 and GH#35888 in case of None and NaT we get duplicates
|
if not cache_array.index.is_unique:
|
cache_array = cache_array[~cache_array.index.duplicated()]
|
return cache_array
|
|
|
def _box_as_indexlike(
|
dt_array: ArrayLike, utc: bool = False, name: Hashable = None
|
) -> Index:
|
"""
|
Properly boxes the ndarray of datetimes to DatetimeIndex
|
if it is possible or to generic Index instead
|
|
Parameters
|
----------
|
dt_array: 1-d array
|
Array of datetimes to be wrapped in an Index.
|
utc : bool
|
Whether to convert/localize timestamps to UTC.
|
name : string, default None
|
Name for a resulting index
|
|
Returns
|
-------
|
result : datetime of converted dates
|
- DatetimeIndex if convertible to sole datetime64 type
|
- general Index otherwise
|
"""
|
|
if is_datetime64_dtype(dt_array):
|
tz = "utc" if utc else None
|
return DatetimeIndex(dt_array, tz=tz, name=name)
|
return Index(dt_array, name=name, dtype=dt_array.dtype)
|
|
|
def _convert_and_box_cache(
|
arg: DatetimeScalarOrArrayConvertible,
|
cache_array: Series,
|
name: Hashable | None = None,
|
) -> Index:
|
"""
|
Convert array of dates with a cache and wrap the result in an Index.
|
|
Parameters
|
----------
|
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
|
cache_array : Series
|
Cache of converted, unique dates
|
name : string, default None
|
Name for a DatetimeIndex
|
|
Returns
|
-------
|
result : Index-like of converted dates
|
"""
|
from pandas import Series
|
|
result = Series(arg, dtype=cache_array.index.dtype).map(cache_array)
|
return _box_as_indexlike(result._values, utc=False, name=name)
|
|
|
def _return_parsed_timezone_results(
|
result: np.ndarray, timezones, utc: bool, name
|
) -> Index:
|
"""
|
Return results from array_strptime if a %z or %Z directive was passed.
|
|
Parameters
|
----------
|
result : ndarray[int64]
|
int64 date representations of the dates
|
timezones : ndarray
|
pytz timezone objects
|
utc : bool
|
Whether to convert/localize timestamps to UTC.
|
name : string, default None
|
Name for a DatetimeIndex
|
|
Returns
|
-------
|
tz_result : Index-like of parsed dates with timezone
|
"""
|
tz_results = np.empty(len(result), dtype=object)
|
for zone in unique(timezones):
|
mask = timezones == zone
|
dta = DatetimeArray(result[mask]).tz_localize(zone)
|
if utc:
|
if dta.tzinfo is None:
|
dta = dta.tz_localize("utc")
|
else:
|
dta = dta.tz_convert("utc")
|
tz_results[mask] = dta
|
|
return Index(tz_results, name=name)
|
|
|
def _convert_listlike_datetimes(
|
arg,
|
format: str | None,
|
name: Hashable = None,
|
utc: bool = False,
|
unit: str | None = None,
|
errors: DateTimeErrorChoices = "raise",
|
dayfirst: bool | None = None,
|
yearfirst: bool | None = None,
|
exact: bool = True,
|
):
|
"""
|
Helper function for to_datetime. Performs the conversions of 1D listlike
|
of dates
|
|
Parameters
|
----------
|
arg : list, tuple, ndarray, Series, Index
|
date to be parsed
|
name : object
|
None or string for the Index name
|
utc : bool
|
Whether to convert/localize timestamps to UTC.
|
unit : str
|
None or string of the frequency of the passed data
|
errors : str
|
error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
|
dayfirst : bool
|
dayfirst parsing behavior from to_datetime
|
yearfirst : bool
|
yearfirst parsing behavior from to_datetime
|
exact : bool, default True
|
exact format matching behavior from to_datetime
|
|
Returns
|
-------
|
Index-like of parsed dates
|
"""
|
if isinstance(arg, (list, tuple)):
|
arg = np.array(arg, dtype="O")
|
elif isinstance(arg, PandasArray):
|
arg = np.array(arg)
|
|
arg_dtype = getattr(arg, "dtype", None)
|
# these are shortcutable
|
tz = "utc" if utc else None
|
if is_datetime64tz_dtype(arg_dtype):
|
if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
|
return DatetimeIndex(arg, tz=tz, name=name)
|
if utc:
|
arg = arg.tz_convert(None).tz_localize("utc")
|
return arg
|
|
elif is_datetime64_dtype(arg_dtype):
|
arg_dtype = cast(np.dtype, arg_dtype)
|
if not is_supported_unit(get_unit_from_dtype(arg_dtype)):
|
# We go to closest supported reso, i.e. "s"
|
arg = astype_overflowsafe(
|
# TODO: looks like we incorrectly raise with errors=="ignore"
|
np.asarray(arg),
|
np.dtype("M8[s]"),
|
is_coerce=errors == "coerce",
|
)
|
|
if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
|
return DatetimeIndex(arg, tz=tz, name=name)
|
elif utc:
|
# DatetimeArray, DatetimeIndex
|
return arg.tz_localize("utc")
|
|
return arg
|
|
elif unit is not None:
|
if format is not None:
|
raise ValueError("cannot specify both format and unit")
|
return _to_datetime_with_unit(arg, unit, name, utc, errors)
|
elif getattr(arg, "ndim", 1) > 1:
|
raise TypeError(
|
"arg must be a string, datetime, list, tuple, 1-d array, or Series"
|
)
|
|
# warn if passing timedelta64, raise for PeriodDtype
|
# NB: this must come after unit transformation
|
try:
|
arg, _ = maybe_convert_dtype(arg, copy=False, tz=libtimezones.maybe_get_tz(tz))
|
except TypeError:
|
if errors == "coerce":
|
npvalues = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
|
return DatetimeIndex(npvalues, name=name)
|
elif errors == "ignore":
|
idx = Index(arg, name=name)
|
return idx
|
raise
|
|
arg = ensure_object(arg)
|
|
if format is None:
|
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
|
|
# `format` could be inferred, or user didn't ask for mixed-format parsing.
|
if format is not None and format != "mixed":
|
return _array_strptime_with_fallback(arg, name, utc, format, exact, errors)
|
|
result, tz_parsed = objects_to_datetime64ns(
|
arg,
|
dayfirst=dayfirst,
|
yearfirst=yearfirst,
|
utc=utc,
|
errors=errors,
|
allow_object=True,
|
)
|
|
if tz_parsed is not None:
|
# We can take a shortcut since the datetime64 numpy array
|
# is in UTC
|
dta = DatetimeArray(result, dtype=tz_to_dtype(tz_parsed))
|
return DatetimeIndex._simple_new(dta, name=name)
|
|
return _box_as_indexlike(result, utc=utc, name=name)
|
|
|
def _array_strptime_with_fallback(
|
arg,
|
name,
|
utc: bool,
|
fmt: str,
|
exact: bool,
|
errors: str,
|
) -> Index:
|
"""
|
Call array_strptime, with fallback behavior depending on 'errors'.
|
"""
|
result, timezones = array_strptime(arg, fmt, exact=exact, errors=errors, utc=utc)
|
if any(tz is not None for tz in timezones):
|
return _return_parsed_timezone_results(result, timezones, utc, name)
|
|
return _box_as_indexlike(result, utc=utc, name=name)
|
|
|
def _to_datetime_with_unit(arg, unit, name, utc: bool, errors: str) -> Index:
|
"""
|
to_datetime specalized to the case where a 'unit' is passed.
|
"""
|
arg = extract_array(arg, extract_numpy=True)
|
|
# GH#30050 pass an ndarray to tslib.array_with_unit_to_datetime
|
# because it expects an ndarray argument
|
if isinstance(arg, IntegerArray):
|
arr = arg.astype(f"datetime64[{unit}]")
|
tz_parsed = None
|
else:
|
arg = np.asarray(arg)
|
|
if arg.dtype.kind in ["i", "u"]:
|
# Note we can't do "f" here because that could induce unwanted
|
# rounding GH#14156, GH#20445
|
arr = arg.astype(f"datetime64[{unit}]", copy=False)
|
try:
|
arr = astype_overflowsafe(arr, np.dtype("M8[ns]"), copy=False)
|
except OutOfBoundsDatetime:
|
if errors == "raise":
|
raise
|
arg = arg.astype(object)
|
return _to_datetime_with_unit(arg, unit, name, utc, errors)
|
tz_parsed = None
|
|
elif arg.dtype.kind == "f":
|
mult, _ = precision_from_unit(unit)
|
|
mask = np.isnan(arg) | (arg == iNaT)
|
fvalues = (arg * mult).astype("f8", copy=False)
|
fvalues[mask] = 0
|
|
if (fvalues < Timestamp.min._value).any() or (
|
fvalues > Timestamp.max._value
|
).any():
|
if errors != "raise":
|
arg = arg.astype(object)
|
return _to_datetime_with_unit(arg, unit, name, utc, errors)
|
raise OutOfBoundsDatetime(f"cannot convert input with unit '{unit}'")
|
|
arr = fvalues.astype("M8[ns]", copy=False)
|
arr[mask] = np.datetime64("NaT", "ns")
|
|
tz_parsed = None
|
else:
|
arg = arg.astype(object, copy=False)
|
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
|
|
if errors == "ignore":
|
# Index constructor _may_ infer to DatetimeIndex
|
result = Index._with_infer(arr, name=name)
|
else:
|
result = DatetimeIndex(arr, name=name)
|
|
if not isinstance(result, DatetimeIndex):
|
return result
|
|
# GH#23758: We may still need to localize the result with tz
|
# GH#25546: Apply tz_parsed first (from arg), then tz (from caller)
|
# result will be naive but in UTC
|
result = result.tz_localize("UTC").tz_convert(tz_parsed)
|
|
if utc:
|
if result.tz is None:
|
result = result.tz_localize("utc")
|
else:
|
result = result.tz_convert("utc")
|
return result
|
|
|
def _adjust_to_origin(arg, origin, unit):
|
"""
|
Helper function for to_datetime.
|
Adjust input argument to the specified origin
|
|
Parameters
|
----------
|
arg : list, tuple, ndarray, Series, Index
|
date to be adjusted
|
origin : 'julian' or Timestamp
|
origin offset for the arg
|
unit : str
|
passed unit from to_datetime, must be 'D'
|
|
Returns
|
-------
|
ndarray or scalar of adjusted date(s)
|
"""
|
if origin == "julian":
|
original = arg
|
j0 = Timestamp(0).to_julian_date()
|
if unit != "D":
|
raise ValueError("unit must be 'D' for origin='julian'")
|
try:
|
arg = arg - j0
|
except TypeError as err:
|
raise ValueError(
|
"incompatible 'arg' type for given 'origin'='julian'"
|
) from err
|
|
# preemptively check this for a nice range
|
j_max = Timestamp.max.to_julian_date() - j0
|
j_min = Timestamp.min.to_julian_date() - j0
|
if np.any(arg > j_max) or np.any(arg < j_min):
|
raise OutOfBoundsDatetime(
|
f"{original} is Out of Bounds for origin='julian'"
|
)
|
else:
|
# arg must be numeric
|
if not (
|
(is_scalar(arg) and (is_integer(arg) or is_float(arg)))
|
or is_numeric_dtype(np.asarray(arg))
|
):
|
raise ValueError(
|
f"'{arg}' is not compatible with origin='{origin}'; "
|
"it must be numeric with a unit specified"
|
)
|
|
# we are going to offset back to unix / epoch time
|
try:
|
offset = Timestamp(origin, unit=unit)
|
except OutOfBoundsDatetime as err:
|
raise OutOfBoundsDatetime(f"origin {origin} is Out of Bounds") from err
|
except ValueError as err:
|
raise ValueError(
|
f"origin {origin} cannot be converted to a Timestamp"
|
) from err
|
|
if offset.tz is not None:
|
raise ValueError(f"origin offset {offset} must be tz-naive")
|
td_offset = offset - Timestamp(0)
|
|
# convert the offset to the unit of the arg
|
# this should be lossless in terms of precision
|
ioffset = td_offset // Timedelta(1, unit=unit)
|
|
# scalars & ndarray-like can handle the addition
|
if is_list_like(arg) and not isinstance(arg, (ABCSeries, Index, np.ndarray)):
|
arg = np.asarray(arg)
|
arg = arg + ioffset
|
return arg
|
|
|
@overload
|
def to_datetime(
|
arg: DatetimeScalar,
|
errors: DateTimeErrorChoices = ...,
|
dayfirst: bool = ...,
|
yearfirst: bool = ...,
|
utc: bool = ...,
|
format: str | None = ...,
|
exact: bool = ...,
|
unit: str | None = ...,
|
infer_datetime_format: bool = ...,
|
origin=...,
|
cache: bool = ...,
|
) -> Timestamp:
|
...
|
|
|
@overload
|
def to_datetime(
|
arg: Series | DictConvertible,
|
errors: DateTimeErrorChoices = ...,
|
dayfirst: bool = ...,
|
yearfirst: bool = ...,
|
utc: bool = ...,
|
format: str | None = ...,
|
exact: bool = ...,
|
unit: str | None = ...,
|
infer_datetime_format: bool = ...,
|
origin=...,
|
cache: bool = ...,
|
) -> Series:
|
...
|
|
|
@overload
|
def to_datetime(
|
arg: list | tuple | Index | ArrayLike,
|
errors: DateTimeErrorChoices = ...,
|
dayfirst: bool = ...,
|
yearfirst: bool = ...,
|
utc: bool = ...,
|
format: str | None = ...,
|
exact: bool = ...,
|
unit: str | None = ...,
|
infer_datetime_format: bool = ...,
|
origin=...,
|
cache: bool = ...,
|
) -> DatetimeIndex:
|
...
|
|
|
def to_datetime(
|
arg: DatetimeScalarOrArrayConvertible | DictConvertible,
|
errors: DateTimeErrorChoices = "raise",
|
dayfirst: bool = False,
|
yearfirst: bool = False,
|
utc: bool = False,
|
format: str | None = None,
|
exact: bool | lib.NoDefault = lib.no_default,
|
unit: str | None = None,
|
infer_datetime_format: lib.NoDefault | bool = lib.no_default,
|
origin: str = "unix",
|
cache: bool = True,
|
) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
|
"""
|
Convert argument to datetime.
|
|
This function converts a scalar, array-like, :class:`Series` or
|
:class:`DataFrame`/dict-like to a pandas datetime object.
|
|
Parameters
|
----------
|
arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
|
The object to convert to a datetime. If a :class:`DataFrame` is provided, the
|
method expects minimally the following columns: :const:`"year"`,
|
:const:`"month"`, :const:`"day"`.
|
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
|
- If :const:`'raise'`, then invalid parsing will raise an exception.
|
- If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`.
|
- If :const:`'ignore'`, then invalid parsing will return the input.
|
dayfirst : bool, default False
|
Specify a date parse order if `arg` is str or is list-like.
|
If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"`
|
is parsed as :const:`2012-11-10`.
|
|
.. warning::
|
|
``dayfirst=True`` is not strict, but will prefer to parse
|
with day first.
|
|
yearfirst : bool, default False
|
Specify a date parse order if `arg` is str or is list-like.
|
|
- If :const:`True` parses dates with the year first, e.g.
|
:const:`"10/11/12"` is parsed as :const:`2010-11-12`.
|
- If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is
|
preceded (same as :mod:`dateutil`).
|
|
.. warning::
|
|
``yearfirst=True`` is not strict, but will prefer to parse
|
with year first.
|
|
utc : bool, default False
|
Control timezone-related parsing, localization and conversion.
|
|
- If :const:`True`, the function *always* returns a timezone-aware
|
UTC-localized :class:`Timestamp`, :class:`Series` or
|
:class:`DatetimeIndex`. To do this, timezone-naive inputs are
|
*localized* as UTC, while timezone-aware inputs are *converted* to UTC.
|
|
- If :const:`False` (default), inputs will not be coerced to UTC.
|
Timezone-naive inputs will remain naive, while timezone-aware ones
|
will keep their time offsets. Limitations exist for mixed
|
offsets (typically, daylight savings), see :ref:`Examples
|
<to_datetime_tz_examples>` section for details.
|
|
See also: pandas general documentation about `timezone conversion and
|
localization
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
|
#time-zone-handling>`_.
|
|
format : str, default None
|
The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
|
`strftime documentation
|
<https://docs.python.org/3/library/datetime.html
|
#strftime-and-strptime-behavior>`_ for more information on choices, though
|
note that :const:`"%f"` will parse all the way up to nanoseconds.
|
You can also pass:
|
|
- "ISO8601", to parse any `ISO8601 <https://en.wikipedia.org/wiki/ISO_8601>`_
|
time string (not necessarily in exactly the same format);
|
- "mixed", to infer the format for each element individually. This is risky,
|
and you should probably use it along with `dayfirst`.
|
exact : bool, default True
|
Control how `format` is used:
|
|
- If :const:`True`, require an exact `format` match.
|
- If :const:`False`, allow the `format` to match anywhere in the target
|
string.
|
|
Cannot be used alongside ``format='ISO8601'`` or ``format='mixed'``.
|
unit : str, default 'ns'
|
The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
|
integer or float number. This will be based off the origin.
|
Example, with ``unit='ms'`` and ``origin='unix'``, this would calculate
|
the number of milliseconds to the unix epoch start.
|
infer_datetime_format : bool, default False
|
If :const:`True` and no `format` is given, attempt to infer the format
|
of the datetime strings based on the first non-NaN element,
|
and if it can be inferred, switch to a faster method of parsing them.
|
In some cases this can increase the parsing speed by ~5-10x.
|
|
.. deprecated:: 2.0.0
|
A strict version of this argument is now the default, passing it has
|
no effect.
|
|
origin : scalar, default 'unix'
|
Define the reference date. The numeric values would be parsed as number
|
of units (defined by `unit`) since this reference date.
|
|
- If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01.
|
- If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to
|
beginning of Julian Calendar. Julian day number :const:`0` is assigned
|
to the day starting at noon on January 1, 4713 BC.
|
- If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date
|
string), origin is set to Timestamp identified by origin.
|
- If a float or integer, origin is the millisecond difference
|
relative to 1970-01-01.
|
cache : bool, default True
|
If :const:`True`, use a cache of unique, converted dates to apply the
|
datetime conversion. May produce significant speed-up when parsing
|
duplicate date strings, especially ones with timezone offsets. The cache
|
is only used when there are at least 50 values. The presence of
|
out-of-bounds values will render the cache unusable and may slow down
|
parsing.
|
|
Returns
|
-------
|
datetime
|
If parsing succeeded.
|
Return type depends on input (types in parenthesis correspond to
|
fallback in case of unsuccessful timezone or out-of-range timestamp
|
parsing):
|
|
- scalar: :class:`Timestamp` (or :class:`datetime.datetime`)
|
- array-like: :class:`DatetimeIndex` (or :class:`Series` with
|
:class:`object` dtype containing :class:`datetime.datetime`)
|
- Series: :class:`Series` of :class:`datetime64` dtype (or
|
:class:`Series` of :class:`object` dtype containing
|
:class:`datetime.datetime`)
|
- DataFrame: :class:`Series` of :class:`datetime64` dtype (or
|
:class:`Series` of :class:`object` dtype containing
|
:class:`datetime.datetime`)
|
|
Raises
|
------
|
ParserError
|
When parsing a date from string fails.
|
ValueError
|
When another datetime conversion error happens. For example when one
|
of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or
|
when a Timezone-aware :class:`datetime.datetime` is found in an array-like
|
of mixed time offsets, and ``utc=False``.
|
|
See Also
|
--------
|
DataFrame.astype : Cast argument to a specified dtype.
|
to_timedelta : Convert argument to timedelta.
|
convert_dtypes : Convert dtypes.
|
|
Notes
|
-----
|
|
Many input types are supported, and lead to different output types:
|
|
- **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime`
|
module or :mod:`numpy`). They are converted to :class:`Timestamp` when
|
possible, otherwise they are converted to :class:`datetime.datetime`.
|
None/NaN/null scalars are converted to :const:`NaT`.
|
|
- **array-like** can contain int, float, str, datetime objects. They are
|
converted to :class:`DatetimeIndex` when possible, otherwise they are
|
converted to :class:`Index` with :class:`object` dtype, containing
|
:class:`datetime.datetime`. None/NaN/null entries are converted to
|
:const:`NaT` in both cases.
|
|
- **Series** are converted to :class:`Series` with :class:`datetime64`
|
dtype when possible, otherwise they are converted to :class:`Series` with
|
:class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null
|
entries are converted to :const:`NaT` in both cases.
|
|
- **DataFrame/dict-like** are converted to :class:`Series` with
|
:class:`datetime64` dtype. For each row a datetime is created from assembling
|
the various dataframe columns. Column keys can be common abbreviations
|
like [‘year’, ‘month’, ‘day’, ‘minute’, ‘second’, ‘ms’, ‘us’, ‘ns’]) or
|
plurals of the same.
|
|
The following causes are responsible for :class:`datetime.datetime` objects
|
being returned (possibly inside an :class:`Index` or a :class:`Series` with
|
:class:`object` dtype) instead of a proper pandas designated type
|
(:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series`
|
with :class:`datetime64` dtype):
|
|
- when any input element is before :const:`Timestamp.min` or after
|
:const:`Timestamp.max`, see `timestamp limitations
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
|
#timeseries-timestamp-limits>`_.
|
|
- when ``utc=False`` (default) and the input is an array-like or
|
:class:`Series` containing mixed naive/aware datetime, or aware with mixed
|
time offsets. Note that this happens in the (quite frequent) situation when
|
the timezone has a daylight savings policy. In that case you may wish to
|
use ``utc=True``.
|
|
Examples
|
--------
|
|
**Handling various input formats**
|
|
Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys
|
can be common abbreviations like ['year', 'month', 'day', 'minute', 'second',
|
'ms', 'us', 'ns']) or plurals of the same
|
|
>>> df = pd.DataFrame({'year': [2015, 2016],
|
... 'month': [2, 3],
|
... 'day': [4, 5]})
|
>>> pd.to_datetime(df)
|
0 2015-02-04
|
1 2016-03-05
|
dtype: datetime64[ns]
|
|
Using a unix epoch time
|
|
>>> pd.to_datetime(1490195805, unit='s')
|
Timestamp('2017-03-22 15:16:45')
|
>>> pd.to_datetime(1490195805433502912, unit='ns')
|
Timestamp('2017-03-22 15:16:45.433502912')
|
|
.. warning:: For float arg, precision rounding might happen. To prevent
|
unexpected behavior use a fixed-width exact type.
|
|
Using a non-unix epoch origin
|
|
>>> pd.to_datetime([1, 2, 3], unit='D',
|
... origin=pd.Timestamp('1960-01-01'))
|
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
|
dtype='datetime64[ns]', freq=None)
|
|
**Differences with strptime behavior**
|
|
:const:`"%f"` will parse all the way up to nanoseconds.
|
|
>>> pd.to_datetime('2018-10-26 12:00:00.0000000011',
|
... format='%Y-%m-%d %H:%M:%S.%f')
|
Timestamp('2018-10-26 12:00:00.000000001')
|
|
**Non-convertible date/times**
|
|
If a date does not meet the `timestamp limitations
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
|
#timeseries-timestamp-limits>`_, passing ``errors='ignore'``
|
will return the original input instead of raising any exception.
|
|
Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`,
|
in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`.
|
|
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
|
'13000101'
|
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
|
NaT
|
|
.. _to_datetime_tz_examples:
|
|
**Timezones and time offsets**
|
|
The default behaviour (``utc=False``) is as follows:
|
|
- Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`:
|
|
>>> pd.to_datetime(['2018-10-26 12:00:00', '2018-10-26 13:00:15'])
|
DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
|
dtype='datetime64[ns]', freq=None)
|
|
- Timezone-aware inputs *with constant time offset* are converted to
|
timezone-aware :class:`DatetimeIndex`:
|
|
>>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500'])
|
DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
|
dtype='datetime64[ns, UTC-05:00]', freq=None)
|
|
- However, timezone-aware inputs *with mixed time offsets* (for example
|
issued from a timezone with daylight savings, such as Europe/Paris)
|
are **not successfully converted** to a :class:`DatetimeIndex`. Instead a
|
simple :class:`Index` containing :class:`datetime.datetime` objects is
|
returned:
|
|
>>> pd.to_datetime(['2020-10-25 02:00 +0200', '2020-10-25 04:00 +0100'])
|
Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00],
|
dtype='object')
|
|
- A mix of timezone-aware and timezone-naive inputs is also converted to
|
a simple :class:`Index` containing :class:`datetime.datetime` objects:
|
|
>>> from datetime import datetime
|
>>> pd.to_datetime(["2020-01-01 01:00:00-01:00", datetime(2020, 1, 1, 3, 0)])
|
Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')
|
|
|
|
|
Setting ``utc=True`` solves most of the above issues:
|
|
- Timezone-naive inputs are *localized* as UTC
|
|
>>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
|
DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
|
dtype='datetime64[ns, UTC]', freq=None)
|
|
- Timezone-aware inputs are *converted* to UTC (the output represents the
|
exact same datetime, but viewed from the UTC time offset `+00:00`).
|
|
>>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
|
... utc=True)
|
DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
|
dtype='datetime64[ns, UTC]', freq=None)
|
|
- Inputs can contain both string or datetime, the above
|
rules still apply
|
|
>>> pd.to_datetime(['2018-10-26 12:00', datetime(2020, 1, 1, 18)], utc=True)
|
DatetimeIndex(['2018-10-26 12:00:00+00:00', '2020-01-01 18:00:00+00:00'],
|
dtype='datetime64[ns, UTC]', freq=None)
|
"""
|
if exact is not lib.no_default and format in {"mixed", "ISO8601"}:
|
raise ValueError("Cannot use 'exact' when 'format' is 'mixed' or 'ISO8601'")
|
if infer_datetime_format is not lib.no_default:
|
warnings.warn(
|
"The argument 'infer_datetime_format' is deprecated and will "
|
"be removed in a future version. "
|
"A strict version of it is now the default, see "
|
"https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
|
"You can safely remove this argument.",
|
stacklevel=find_stack_level(),
|
)
|
if arg is None:
|
return None
|
|
if origin != "unix":
|
arg = _adjust_to_origin(arg, origin, unit)
|
|
convert_listlike = partial(
|
_convert_listlike_datetimes,
|
utc=utc,
|
unit=unit,
|
dayfirst=dayfirst,
|
yearfirst=yearfirst,
|
errors=errors,
|
exact=exact,
|
)
|
# pylint: disable-next=used-before-assignment
|
result: Timestamp | NaTType | Series | Index
|
|
if isinstance(arg, Timestamp):
|
result = arg
|
if utc:
|
if arg.tz is not None:
|
result = arg.tz_convert("utc")
|
else:
|
result = arg.tz_localize("utc")
|
elif isinstance(arg, ABCSeries):
|
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
|
if not cache_array.empty:
|
result = arg.map(cache_array)
|
else:
|
values = convert_listlike(arg._values, format)
|
result = arg._constructor(values, index=arg.index, name=arg.name)
|
elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
|
result = _assemble_from_unit_mappings(arg, errors, utc)
|
elif isinstance(arg, Index):
|
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
|
if not cache_array.empty:
|
result = _convert_and_box_cache(arg, cache_array, name=arg.name)
|
else:
|
result = convert_listlike(arg, format, name=arg.name)
|
elif is_list_like(arg):
|
try:
|
# error: Argument 1 to "_maybe_cache" has incompatible type
|
# "Union[float, str, datetime, List[Any], Tuple[Any, ...], ExtensionArray,
|
# ndarray[Any, Any], Series]"; expected "Union[List[Any], Tuple[Any, ...],
|
# Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series], Series]"
|
argc = cast(
|
Union[list, tuple, ExtensionArray, np.ndarray, "Series", Index], arg
|
)
|
cache_array = _maybe_cache(argc, format, cache, convert_listlike)
|
except OutOfBoundsDatetime:
|
# caching attempts to create a DatetimeIndex, which may raise
|
# an OOB. If that's the desired behavior, then just reraise...
|
if errors == "raise":
|
raise
|
# ... otherwise, continue without the cache.
|
from pandas import Series
|
|
cache_array = Series([], dtype=object) # just an empty array
|
if not cache_array.empty:
|
result = _convert_and_box_cache(argc, cache_array)
|
else:
|
result = convert_listlike(argc, format)
|
else:
|
result = convert_listlike(np.array([arg]), format)[0]
|
if isinstance(arg, bool) and isinstance(result, np.bool_):
|
result = bool(result) # TODO: avoid this kludge.
|
|
# error: Incompatible return value type (got "Union[Timestamp, NaTType,
|
# Series, Index]", expected "Union[DatetimeIndex, Series, float, str,
|
# NaTType, None]")
|
return result # type: ignore[return-value]
|
|
|
# mappings for assembling units
|
_unit_map = {
|
"year": "year",
|
"years": "year",
|
"month": "month",
|
"months": "month",
|
"day": "day",
|
"days": "day",
|
"hour": "h",
|
"hours": "h",
|
"minute": "m",
|
"minutes": "m",
|
"second": "s",
|
"seconds": "s",
|
"ms": "ms",
|
"millisecond": "ms",
|
"milliseconds": "ms",
|
"us": "us",
|
"microsecond": "us",
|
"microseconds": "us",
|
"ns": "ns",
|
"nanosecond": "ns",
|
"nanoseconds": "ns",
|
}
|
|
|
def _assemble_from_unit_mappings(arg, errors: DateTimeErrorChoices, utc: bool):
|
"""
|
assemble the unit specified fields from the arg (DataFrame)
|
Return a Series for actual parsing
|
|
Parameters
|
----------
|
arg : DataFrame
|
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
|
|
- If :const:`'raise'`, then invalid parsing will raise an exception
|
- If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`
|
- If :const:`'ignore'`, then invalid parsing will return the input
|
utc : bool
|
Whether to convert/localize timestamps to UTC.
|
|
Returns
|
-------
|
Series
|
"""
|
from pandas import (
|
DataFrame,
|
to_numeric,
|
to_timedelta,
|
)
|
|
arg = DataFrame(arg)
|
if not arg.columns.is_unique:
|
raise ValueError("cannot assemble with duplicate keys")
|
|
# replace passed unit with _unit_map
|
def f(value):
|
if value in _unit_map:
|
return _unit_map[value]
|
|
# m is case significant
|
if value.lower() in _unit_map:
|
return _unit_map[value.lower()]
|
|
return value
|
|
unit = {k: f(k) for k in arg.keys()}
|
unit_rev = {v: k for k, v in unit.items()}
|
|
# we require at least Ymd
|
required = ["year", "month", "day"]
|
req = sorted(set(required) - set(unit_rev.keys()))
|
if len(req):
|
_required = ",".join(req)
|
raise ValueError(
|
"to assemble mappings requires at least that "
|
f"[year, month, day] be specified: [{_required}] is missing"
|
)
|
|
# keys we don't recognize
|
excess = sorted(set(unit_rev.keys()) - set(_unit_map.values()))
|
if len(excess):
|
_excess = ",".join(excess)
|
raise ValueError(
|
f"extra keys have been passed to the datetime assemblage: [{_excess}]"
|
)
|
|
def coerce(values):
|
# we allow coercion to if errors allows
|
values = to_numeric(values, errors=errors)
|
|
# prevent overflow in case of int8 or int16
|
if is_integer_dtype(values):
|
values = values.astype("int64", copy=False)
|
return values
|
|
values = (
|
coerce(arg[unit_rev["year"]]) * 10000
|
+ coerce(arg[unit_rev["month"]]) * 100
|
+ coerce(arg[unit_rev["day"]])
|
)
|
try:
|
values = to_datetime(values, format="%Y%m%d", errors=errors, utc=utc)
|
except (TypeError, ValueError) as err:
|
raise ValueError(f"cannot assemble the datetimes: {err}") from err
|
|
units: list[UnitChoices] = ["h", "m", "s", "ms", "us", "ns"]
|
for u in units:
|
value = unit_rev.get(u)
|
if value is not None and value in arg:
|
try:
|
values += to_timedelta(coerce(arg[value]), unit=u, errors=errors)
|
except (TypeError, ValueError) as err:
|
raise ValueError(
|
f"cannot assemble the datetimes [{value}]: {err}"
|
) from err
|
return values
|
|
|
def _attempt_YYYYMMDD(arg: npt.NDArray[np.object_], errors: str) -> np.ndarray | None:
|
"""
|
try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
|
arg is a passed in as an object dtype, but could really be ints/strings
|
with nan-like/or floats (e.g. with nan)
|
|
Parameters
|
----------
|
arg : np.ndarray[object]
|
errors : {'raise','ignore','coerce'}
|
"""
|
|
def calc(carg):
|
# calculate the actual result
|
carg = carg.astype(object, copy=False)
|
parsed = parsing.try_parse_year_month_day(
|
carg / 10000, carg / 100 % 100, carg % 100
|
)
|
return tslib.array_to_datetime(parsed, errors=errors)[0]
|
|
def calc_with_mask(carg, mask):
|
result = np.empty(carg.shape, dtype="M8[ns]")
|
iresult = result.view("i8")
|
iresult[~mask] = iNaT
|
|
masked_result = calc(carg[mask].astype(np.float64).astype(np.int64))
|
result[mask] = masked_result.astype("M8[ns]")
|
return result
|
|
# try intlike / strings that are ints
|
try:
|
return calc(arg.astype(np.int64))
|
except (ValueError, OverflowError, TypeError):
|
pass
|
|
# a float with actual np.nan
|
try:
|
carg = arg.astype(np.float64)
|
return calc_with_mask(carg, notna(carg))
|
except (ValueError, OverflowError, TypeError):
|
pass
|
|
# string with NaN-like
|
try:
|
# error: Argument 2 to "isin" has incompatible type "List[Any]"; expected
|
# "Union[Union[ExtensionArray, ndarray], Index, Series]"
|
mask = ~algorithms.isin(arg, list(nat_strings)) # type: ignore[arg-type]
|
return calc_with_mask(arg, mask)
|
except (ValueError, OverflowError, TypeError):
|
pass
|
|
return None
|
|
|
__all__ = [
|
"DateParseError",
|
"should_cache",
|
"to_datetime",
|
]
|
