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