pd.to_timedelta
将参数转换为timedelta,Timedelta在pandas中是一个表示两个datetime值之间的差(如日,秒和微妙)的类型,2个Datetime数据运算相减得出的结果就是一个Timedelta数据类型
pandas.to_timedelta(arg, unit=None, errors='raise')
参数:
- arg:str, timedelta, list-like or Series,要转换为timedelta的数据
- unit:str, optional,可选,表示数字arg的arg单位。默认为"ns",在版本1.1.0中更改:arg上下文字符串和 时不能指定
errors="raise"
- ‘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’
- errors:{‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’,
- 如果为“ raise”,则无效的解析将引发异常。
- 如果为“强制”,则将无效解析设置为NaT。
- 如果为“ ignore”,则无效的解析将返回输入。
返回:
timedelta64 or numpy.array of timedelta64
例子
将单个字符串解析为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')
解析字符串列表或数组
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)
通过指定unit关键字参数来转换数字
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) '''