Pandas可以提供与所有域的Time series数据一起使用的函数。它还使用NumPy datetime64和timedelta64 dtypes合并了其他Python库中的大量函数,例如scikits.timeseries。它提供了用于处理Time series数据的新函数。
Time series工具对于数据科学应用程序最有用,并且可以处理Python中使用的其他软件包。
import pandas as pd # Create the dates with frequency info = pd.date_range('5/4/2013', periods = 8, freq ='S') info
输出:
DatetimeIndex(['2013-05-04 00:00:00', '2013-05-04 00:00:01', '2013-05-04 00:00:02', '2013-05-04 00:00:03', '2013-05-04 00:00:04', '2013-05-04 00:00:05', '2013-05-04 00:00:06', '2013-05-04 00:00:07'], dtype='datetime64[ns]', freq='S')
info = pd.DataFrame({'year': [2014, 2012], 'month': [5, 7], 'day': [20, 17]}) pd.to_datetime(info) 0 2014-05-20 1 2012-07-17 dtype: datetime64[ns]
如果日期不符合时间戳,则可以传递errors ='ignore'。它将返回原始输入而不会引发任何异常。
如果您通过errors ='coerce',它将对NaT强制执行越界日期。
import pandas as pd pd.to_datetime('18000706', format='%Y%m%d', errors='ignore') datetime.datetime(1800, 7, 6, 0, 0) pd.to_datetime('18000706', format='%Y%m%d', errors='coerce')
输出:
Timestamp('1800-07-06 00:00:00')
import pandas as pd dmy = pd.date_range('2017-06-04', periods=5, freq='S') dmy
输出:
DatetimeIndex(['2017-06-04 00:00:00', '2017-06-04 00:00:01', '2017-06-04 00:00:02', '2017-06-04 00:00:03', '2017-06-04 00:00:04'], dtype='datetime64[ns]', freq='S')
import pandas as pd dmy = dmy.tz_localize('UTC') dmy
输出:
DatetimeIndex(['2017-06-04 00:00:00+00:00', '2017-06-04 00:00:01+00:00', '2017-06-04 00:00:02+00:00', '2017-06-04 00:00:03+00:00', '2017-06-04 00:00:04+00:00'], dtype='datetime64[ns, UTC]', freq='S')
import pandas as pd dmy = pd.date_range('2017-06-04', periods=5, freq='S') dmy
输出:
DatetimeIndex(['2017-06-04 00:00:00', '2017-06-04 00:00:01', '2017-06-04 00:00:02', '2017-06-04 00:00:03', '2017-06-04 00:00:04'], dtype='datetime64[ns]', freq='S')
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