bigframes.pandas.to_timedelta#

bigframes.pandas.to_timedelta(arg,unit:Literal['W','w','D','d','days','day','hours','hour','hr','h','m','minute','min','minutes','s','seconds','sec','second','ms','milliseconds','millisecond','milli','millis','us','microseconds','microsecond','µs','micro','micros']|None=None,*,session:Session|None=None)[source]#

Converts a scalar or Series to a timedelta object.

Note

BigQuery only supports precision up to microseconds (us). Therefore, when workingwith timedeltas that have a finer granularity than microseconds, be aware thatthe additional precision will not be represented in BigQuery.

Examples:

Converting a Scalar to timedelta

>>>importbigframes.pandasasbpd>>>scalar=2>>>bpd.to_timedelta(scalar,unit='s')Timedelta('0 days 00:00:02')

Converting a Series of integers to a Series of timedeltas

>>>int_series=bpd.Series([1,2,3])>>>bpd.to_timedelta(int_series,unit='s')0    0 days 00:00:011    0 days 00:00:022    0 days 00:00:03dtype: duration[us][pyarrow]

Parameters:

• arg (int,float,str,Series) – The object to convert to a dataframe

• unit (str,default 'us') –

  Denotes the unit of the arg for numericarg. Defaults to"us".

  Possible values:

  • ’W’

  • ’D’ / ‘days’ / ‘day’

  • ’hours’ / ‘hour’ / ‘hr’ / ‘h’ / ‘H’

  • ’m’ / ‘minute’ / ‘min’ / ‘minutes’

  • ’s’ / ‘seconds’ / ‘sec’ / ‘second’

  • ’ms’ / ‘milliseconds’ / ‘millisecond’ / ‘milli’ / ‘millis’

  • ’us’ / ‘microseconds’ / ‘microsecond’ / ‘micro’ / ‘micros’

Returns:

Return type depends on input- Series: Series of duration[us][pyarrow] dtype- scalar: timedelta

Return type:

Union[pandas.Timedelta,bigframes.pandas.Series]