# pandas.to_timedelta

`pandas.``to_timedelta`(arg, unit='ns', box=True, errors='raise')

  Convert argument to timedelta

  Parameters:

  arg : string, timedelta, list, tuple, 1-d array, or Series

  unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, which is an

  > integer/float number

  box : boolean, default True

  > - If True returns a Timedelta/TimedeltaIndex of the results
  > - if False returns a np.timedelta64 or ndarray of values of dtype timedelta64[ns]

  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: | ret : timedelta64/arrays of timedelta64 if parsing succeeded

  See also

  `pandas.DataFrame.astype`
    Cast argument to a specified dtype.
  `pandas.to_datetime`
    Convert argument to datetime.

  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 00:00:00.000015')

  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 00:00:00.000015', NaT],
                     dtype='timedelta64[ns]', freq=None)

  Converting numbers by specifying the unit keyword argument:

      >>> pd.to_timedelta(np.arange(5), unit='s')
      TimedeltaIndex(['00:00:00', '00:00:01', '00:00:02',
                      '00:00:03', '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)

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