pandas.to_timedelta¶

pandas.to_timedelta(arg, unit=None, 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.

Parameters

argstr, timedelta, list-like or Series

The data to be converted to timedelta.

unitstr, optional

Denotes the unit of the arg for numeric arg. Defaults to "ns".

Possible values:

‘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’

Changed in version 1.1.0: Must not be specified when arg context strings and errors="raise".

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

timedelta64 or numpy.array of timedelta64: Output type returned if parsing succeeded.

See also

DataFrame.astype: Cast argument to a specified dtype.
to_datetime: Convert argument to datetime.
convert_dtypes: Convert dtypes.

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 00:00:00.000015500')

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 00:00:00.000015500', NaT],
               dtype='timedelta64[ns]', freq=None)

Converting numbers by specifying the unit keyword argument:

>>> pd.to_timedelta(np.arange(5), unit='s')
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
                '0 days 00:00:03', '0 days 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_datetime pandas.date_range