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pandas.to_timedelta(*args, **kwargs)
[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
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)
pandas.to_timedelta()
2017-01-12 04:50:40
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