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) \'\'\'
时间特征处理
#时间特征处理
timedelta = pd.to_timedelta(df[\'Time\'], unit=\'s\')
df[\'Minute\'] = (timedelta.dt.components.minutes).astype(int)
df[\'Hour\'] = (timedelta.dt.components.hours).astype(int)