datetimes.py

from __future__ import annotations

from collections import abc
from datetime import datetime
from functools import partial
import inspect
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 tslib
from pandas._libs.tslibs import (
    OutOfBoundsDatetime,
    Timedelta,
    Timestamp,
    iNaT,
    nat_strings,
    parsing,
    timezones,
)
from pandas._libs.tslibs.parsing import (
    DateParseError,
    format_is_iso,
    guess_datetime_format,
)
from pandas._libs.tslibs.strptime import array_strptime
from pandas._typing import (
    AnyArrayLike,
    ArrayLike,
    DateTimeErrorChoices,
    Timezone,
    npt,
)
from pandas.util._exceptions import find_stack_level

from pandas.core.dtypes.common import (
    ensure_object,
    is_datetime64_dtype,
    is_datetime64_ns_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,
)
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):
    # Try to guess the format based on the first non-NaN element
    non_nan_elements = notna(arr).nonzero()[0]
    if len(non_nan_elements):
        return guess_datetime_format(arr[non_nan_elements[0]], dayfirst=dayfirst)


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)
            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 | None = None, 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.
    tz : object
        None or '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).map(cache_array)
    return _box_as_indexlike(result._values, utc=None, name=name)


def _return_parsed_timezone_results(result: np.ndarray, timezones, tz, 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
    tz : object
        None or pytz timezone object
    name : string, default None
        Name for a DatetimeIndex

    Returns
    -------
    tz_result : Index-like of parsed dates with timezone
    """
    tz_results = np.array(
        [Timestamp(res).tz_localize(zone) for res, zone in zip(result, timezones)]
    )
    if tz is not None:
        # Convert to the same tz
        tz_results = np.array([tz_result.tz_convert(tz) for tz_result in tz_results])

    return Index(tz_results, name=name)


def _convert_listlike_datetimes(
    arg,
    format: str | None,
    name: Hashable = None,
    tz: Timezone | None = None,
    unit: str | None = None,
    errors: DateTimeErrorChoices = "raise",
    infer_datetime_format: bool = False,
    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
    tz : object
        None or 'utc'
    unit : str
        None or string of the frequency of the passed data
    errors : str
        error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
    infer_datetime_format : bool, default False
        inferring format behavior from to_datetime
    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")

    arg_dtype = getattr(arg, "dtype", None)
    # these are shortcutable
    if is_datetime64tz_dtype(arg_dtype):
        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
            return DatetimeIndex(arg, tz=tz, name=name)
        if tz == "utc":
            arg = arg.tz_convert(None).tz_localize(tz)
        return arg

    elif is_datetime64_ns_dtype(arg_dtype):
        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
            try:
                return DatetimeIndex(arg, tz=tz, name=name)
            except ValueError:
                pass
        elif tz:
            # DatetimeArray, DatetimeIndex
            return arg.tz_localize(tz)

        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, tz, 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
    orig_arg = arg
    try:
        arg, _ = maybe_convert_dtype(arg, copy=False, tz=timezones.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)
    require_iso8601 = False

    if infer_datetime_format and format is None:
        format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)

    if format is not None:
        # There is a special fast-path for iso8601 formatted
        # datetime strings, so in those cases don't use the inferred
        # format because this path makes process slower in this
        # special case
        format_is_iso8601 = format_is_iso(format)
        if format_is_iso8601:
            require_iso8601 = not infer_datetime_format
            format = None

    if format is not None:
        res = _to_datetime_with_format(
            arg, orig_arg, name, tz, format, exact, errors, infer_datetime_format
        )
        if res is not None:
            return res

    assert format is None or infer_datetime_format
    utc = tz == "utc"
    result, tz_parsed = objects_to_datetime64ns(
        arg,
        dayfirst=dayfirst,
        yearfirst=yearfirst,
        utc=utc,
        errors=errors,
        require_iso8601=require_iso8601,
        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)

    utc = tz == "utc"
    return _box_as_indexlike(result, utc=utc, name=name)


def _array_strptime_with_fallback(
    arg,
    name,
    tz,
    fmt: str,
    exact: bool,
    errors: str,
    infer_datetime_format: bool,
) -> Index | None:
    """
    Call array_strptime, with fallback behavior depending on 'errors'.
    """
    utc = tz == "utc"

    try:
        result, timezones = array_strptime(arg, fmt, exact=exact, errors=errors)
    except OutOfBoundsDatetime:
        if errors == "raise":
            raise
        elif errors == "coerce":
            result = np.empty(arg.shape, dtype="M8[ns]")
            iresult = result.view("i8")
            iresult.fill(iNaT)
        else:
            result = arg
    except ValueError:
        # if fmt was inferred, try falling back
        # to array_to_datetime - terminate here
        # for specified formats
        if not infer_datetime_format:
            if errors == "raise":
                raise
            elif errors == "coerce":
                result = np.empty(arg.shape, dtype="M8[ns]")
                iresult = result.view("i8")
                iresult.fill(iNaT)
            else:
                result = arg
        else:
            # Indicates to the caller to fallback to objects_to_datetime64ns
            return None
    else:
        if "%Z" in fmt or "%z" in fmt:
            return _return_parsed_timezone_results(result, timezones, tz, name)

    return _box_as_indexlike(result, utc=utc, name=name)


def _to_datetime_with_format(
    arg,
    orig_arg,
    name,
    tz,
    fmt: str,
    exact: bool,
    errors: str,
    infer_datetime_format: bool,
) -> Index | None:
    """
    Try parsing with the given format, returning None on failure.
    """
    result = None

    # shortcut formatting here
    if fmt == "%Y%m%d":
        # pass orig_arg as float-dtype may have been converted to
        # datetime64[ns]
        orig_arg = ensure_object(orig_arg)
        try:
            # may return None without raising
            result = _attempt_YYYYMMDD(orig_arg, errors=errors)
        except (ValueError, TypeError, OutOfBoundsDatetime) as err:
            raise ValueError(
                "cannot convert the input to '%Y%m%d' date format"
            ) from err
        if result is not None:
            utc = tz == "utc"
            return _box_as_indexlike(result, utc=utc, name=name)

    # fallback
    res = _array_strptime_with_fallback(
        arg, name, tz, fmt, exact, errors, infer_datetime_format
    )
    return res


def _to_datetime_with_unit(arg, unit, name, tz, 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)
        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 tz is not None:
        if result.tz is None:
            result = result.tz_localize(tz)
        else:
            result = result.tz_convert(tz)
    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)
        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 | None = ...,
    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 | None = ...,
    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 | None = ...,
    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 | None = None,
    format: str | None = None,
    exact: bool = True,
    unit: str | None = None,
    infer_datetime_format: bool = False,
    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. If a delimited date string cannot be parsed in
            accordance with the given `dayfirst` option, e.g.
            ``to_datetime(['31-12-2021'])``, then a warning will be shown.

    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 None
        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 the following differences:

        - :const:`"%f"` will parse all the way
          up to nanoseconds;

        - :const:`"%S"` without :const:`"%f"` will capture all the way
          up to nanoseconds if present as decimal places, and will also handle
          the case where the number of seconds is an integer.

    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.

    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.
    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, origin is set to Timestamp identified by
          origin.
    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.

        .. versionchanged:: 0.25.0
            changed default value from :const:`False` to :const:`True`.

    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]

    Passing ``infer_datetime_format=True`` can often-times speedup a parsing
    if its not an ISO8601 format exactly, but in a regular format.

    >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
    >>> s.head()
    0    3/11/2000
    1    3/12/2000
    2    3/13/2000
    3    3/11/2000
    4    3/12/2000
    dtype: object

    >>> %timeit pd.to_datetime(s, infer_datetime_format=True)  # doctest: +SKIP
    100 loops, best of 3: 10.4 ms per loop

    >>> %timeit pd.to_datetime(s, infer_datetime_format=False)  # doctest: +SKIP
    1 loop, best of 3: 471 ms per loop

    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')

    :const:`"%S"` without :const:`"%f"` will capture all the way
    up to nanoseconds if present as decimal places.

    >>> pd.to_datetime('2017-03-22 15:16:45.433502912',
    ...                format='%Y-%m-%d %H:%M:%S')
    Timestamp('2017-03-22 15:16:45.433502912')

    **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')
    datetime.datetime(1300, 1, 1, 0, 0)
    >>> 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', '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, pytz.FixedOffset(-300)]', 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 converted to
      a timezone-aware :class:`DatetimeIndex` if the offsets of the timezone-aware
      are constant:

    >>> from datetime import datetime
    >>> pd.to_datetime(["2020-01-01 01:00 -01:00", datetime(2020, 1, 1, 3, 0)])
    DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'],
                  dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None)

    |

    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 naive and aware, string or datetime, the above
      rules still apply

    >>> from datetime import timezone, timedelta
    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 12:00 -0530',
    ...                datetime(2020, 1, 1, 18),
    ...                datetime(2020, 1, 1, 18,
    ...                tzinfo=timezone(-timedelta(hours=1)))],
    ...                utc=True)
    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 17:30:00+00:00',
                   '2020-01-01 18:00:00+00:00', '2020-01-01 19:00:00+00:00'],
                  dtype='datetime64[ns, UTC]', freq=None)
    """
    if arg is None:
        return None

    if origin != "unix":
        arg = _adjust_to_origin(arg, origin, unit)

    tz = "utc" if utc else None
    convert_listlike = partial(
        _convert_listlike_datetimes,
        tz=tz,
        unit=unit,
        dayfirst=dayfirst,
        yearfirst=yearfirst,
        errors=errors,
        exact=exact,
        infer_datetime_format=infer_datetime_format,
    )
    # pylint: disable=used-before-assignment
    result: Timestamp | NaTType | Series | Index
    # pylint: enable=used-before-assignment

    if isinstance(arg, Timestamp):
        result = arg
        if tz is not None:
            if arg.tz is not None:
                result = arg.tz_convert(tz)
            else:
                result = arg.tz_localize(tz)
    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, tz)
    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, tz):
    """
    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
    tz : None or '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=tz)
    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


def to_time(
    arg,
    format=None,
    infer_time_format: bool = False,
    errors: DateTimeErrorChoices = "raise",
):
    # GH#34145
    warnings.warn(
        "`to_time` has been moved, should be imported from pandas.core.tools.times. "
        "This alias will be removed in a future version.",
        FutureWarning,
        stacklevel=find_stack_level(inspect.currentframe()),
    )
    from pandas.core.tools.times import to_time

    return to_time(arg, format, infer_time_format, errors)


__all__ = [
    "DateParseError",
    "should_cache",
    "to_datetime",
    "to_time",
]