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)