pyspark.pandas.to_timedelta¶

pyspark.pandas.to_timedelta(arg, unit: Optional[str] = None, errors: str = 'raise')[source]¶

Convert argument to timedelta.

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’

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

rettimedelta64, TimedeltaIndex or Series of timedelta64 if parsing succeeded.

See also

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

Notes

If the precision is higher than nanoseconds, the precision of the duration is truncated to nanoseconds for string inputs.

Examples

Parsing a single string to a Timedelta:

>>> ps.to_timedelta('1 days 06:05:01.00003')
Timedelta('1 days 06:05:01.000030')
>>> ps.to_timedelta('15.5us')  
Timedelta('0 days 00:00:00.000015500')

Parsing a list or array of strings:

>>> ps.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:

>>> ps.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)
>>> ps.to_timedelta(np.arange(5), unit='d')  
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
               dtype='timedelta64[ns]', freq=None)

pyspark.pandas.date_range

pyspark.pandas.timedelta_range