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 working with timedeltas that have a finer granularity than microseconds, be aware that the additional precision will not be represented in BigQuery.
Examples:
Converting a Scalar to timedelta
>>> import bigframes.pandas as bpd >>> 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 00:00:01 1 0 days 00:00:02 2 0 days 00:00:03 dtype: 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 numeric arg. 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: