Source code for pandas.core.tools.timedeltas

"""
timedelta support tools
"""
from __future__ import annotations

from datetime import timedelta
from typing import (
    TYPE_CHECKING,
    overload,
)

import numpy as np

from pandas._libs import lib
from pandas._libs.tslibs import (
    NaT,
    NaTType,
)
from pandas._libs.tslibs.timedeltas import (
    Timedelta,
    parse_timedelta_unit,
)

from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.generic import (
    ABCIndex,
    ABCSeries,
)

from pandas.core.arrays.timedeltas import sequence_to_td64ns

if TYPE_CHECKING:
    from pandas._libs.tslibs.timedeltas import UnitChoices
    from pandas._typing import (
        ArrayLike,
        DateTimeErrorChoices,
    )

    from pandas import (
        Index,
        Series,
        TimedeltaIndex,
    )


@overload
def to_timedelta(
    arg: str | float | timedelta,
    unit: UnitChoices | None = ...,
    errors: DateTimeErrorChoices = ...,
) -> Timedelta:
    ...


@overload
def to_timedelta(
    arg: Series,
    unit: UnitChoices | None = ...,
    errors: DateTimeErrorChoices = ...,
) -> Series:
    ...


@overload
def to_timedelta(
    arg: list | tuple | range | ArrayLike | Index,
    unit: UnitChoices | None = ...,
    errors: DateTimeErrorChoices = ...,
) -> TimedeltaIndex:
    ...


[docs]def to_timedelta( arg: str | int | float | timedelta | list | tuple | range | ArrayLike | Index | Series, unit: UnitChoices | None = None, errors: DateTimeErrorChoices = "raise", ) -> Timedelta | TimedeltaIndex | Series: """ Convert argument to timedelta. Timedeltas are absolute differences in times, expressed in difference units (e.g. days, hours, minutes, seconds). This method converts an argument from a recognized timedelta format / value into a Timedelta type. Parameters ---------- arg : str, timedelta, list-like or Series The data to be converted to timedelta. .. deprecated:: 1.2 Strings with units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version unit : str, optional Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``. Possible values: * 'W' * 'D' / 'days' / 'day' * 'hours' / 'hour' / 'hr' / 'h' * 'm' / 'minute' / 'min' / 'minutes' / 'T' * 'S' / 'seconds' / 'sec' / 'second' * 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' / 'L' * 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' / 'U' * 'ns' / 'nanoseconds' / 'nano' / 'nanos' / 'nanosecond' / 'N' .. versionchanged:: 1.1.0 Must not be specified when `arg` context strings and ``errors="raise"``. errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception. - If 'coerce', then invalid parsing will be set as NaT. - If 'ignore', then invalid parsing will return the input. Returns ------- timedelta If parsing succeeded. Return type depends on input: - list-like: TimedeltaIndex of timedelta64 dtype - Series: Series of timedelta64 dtype - scalar: Timedelta See Also -------- DataFrame.astype : Cast argument to a specified dtype. to_datetime : Convert argument to datetime. convert_dtypes : Convert dtypes. Notes ----- If the precision is higher than nanoseconds, the precision of the duration is truncated to nanoseconds for string inputs. Examples -------- Parsing a single string to a Timedelta: >>> pd.to_timedelta('1 days 06:05:01.00003') Timedelta('1 days 06:05:01.000030') >>> pd.to_timedelta('15.5us') Timedelta('0 days 00:00:00.000015500') Parsing a list or array of strings: >>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan']) TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT], dtype='timedelta64[ns]', freq=None) Converting numbers by specifying the `unit` keyword argument: >>> pd.to_timedelta(np.arange(5), unit='s') TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03', '0 days 00:00:04'], dtype='timedelta64[ns]', freq=None) >>> pd.to_timedelta(np.arange(5), unit='d') TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) """ if unit is not None: unit = parse_timedelta_unit(unit) if errors not in ("ignore", "raise", "coerce"): raise ValueError("errors must be one of 'ignore', 'raise', or 'coerce'.") if unit in {"Y", "y", "M"}: raise ValueError( "Units 'M', 'Y', and 'y' are no longer supported, as they do not " "represent unambiguous timedelta values durations." ) if arg is None: return arg elif isinstance(arg, ABCSeries): values = _convert_listlike(arg._values, unit=unit, errors=errors) return arg._constructor(values, index=arg.index, name=arg.name) elif isinstance(arg, ABCIndex): return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name) elif isinstance(arg, np.ndarray) and arg.ndim == 0: # extract array scalar and process below # error: Incompatible types in assignment (expression has type "object", # variable has type "Union[str, int, float, timedelta, List[Any], # Tuple[Any, ...], Union[Union[ExtensionArray, ndarray[Any, Any]], Index, # Series]]") [assignment] arg = lib.item_from_zerodim(arg) # type: ignore[assignment] elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1: return _convert_listlike(arg, unit=unit, errors=errors) elif getattr(arg, "ndim", 1) > 1: raise TypeError( "arg must be a string, timedelta, list, tuple, 1-d array, or Series" ) if isinstance(arg, str) and unit is not None: raise ValueError("unit must not be specified if the input is/contains a str") # ...so it must be a scalar value. Return scalar. return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors)
def _coerce_scalar_to_timedelta_type(r, unit="ns", errors="raise"): """Convert string 'r' to a timedelta object.""" result: Timedelta | NaTType try: result = Timedelta(r, unit) except ValueError: if errors == "raise": raise elif errors == "ignore": return r # coerce result = NaT return result def _convert_listlike(arg, unit=None, errors="raise", name=None): """Convert a list of objects to a timedelta index object.""" if isinstance(arg, (list, tuple)) or not hasattr(arg, "dtype"): # This is needed only to ensure that in the case where we end up # returning arg (errors == "ignore"), and where the input is a # generator, we return a useful list-like instead of a # used-up generator arg = np.array(list(arg), dtype=object) try: td64arr = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0] except ValueError: if errors == "ignore": return arg else: # This else-block accounts for the cases when errors='raise' # and errors='coerce'. If errors == 'raise', these errors # should be raised. If errors == 'coerce', we shouldn't # expect any errors to be raised, since all parsing errors # cause coercion to pd.NaT. However, if an error / bug is # introduced that causes an Exception to be raised, we would # like to surface it. raise from pandas import TimedeltaIndex value = TimedeltaIndex(td64arr, unit="ns", name=name) return value