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

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.

argstr, timedelta, list-like or Series

The data to be converted to timedelta.

unitstr, default ‘ns’

Denotes the unit of the arg. Possible values: (‘Y’, ‘M’, ‘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’).

boxbool, default True
  • If True returns a Timedelta/TimedeltaIndex of the results.

  • If False returns a numpy.timedelta64 or numpy.darray of values of dtype timedelta64[ns].

Deprecated since version 0.25.0: Use to_numpy() or Timedelta.to_timedelta64() instead to get an ndarray of values or numpy.timedelta64, respectively.

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.

timedelta64 or numpy.array of timedelta64

Output type returned if parsing succeeded.

See also


Cast argument to a specified dtype.


Convert argument to datetime.


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)

Returning an ndarray by using the ‘box’ keyword argument:

>>> pd.to_timedelta(np.arange(5), box=False)
array([0, 1, 2, 3, 4], dtype='timedelta64[ns]')
Scroll To Top