pandas.to_timedelta

pandas.to_timedelta(arg, unit='ns', box=True, errors='raise') [source]

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)