Pandas 时间序列—date_range函数
函数原型:
pandas.date_range( start=None,
end=None,
periods=None,
freq='D',
tz=None,
normalize=False,
name=None,
closed=None,
**kwargs
)
参数:
start
:string 或 datetime-like,默认值是 None,表示日期的起点。end
:string 或 datetime-like,默认值是 None,表示日期的终点。periods
:integer 或 None,默认值是None,表示你要从这个函数产生多少个日期索引值;如果是None的话,那么start和end必须不能为None。freq
:string 或 DateOffset,默认值是 ’D’,表示以自然日为单位,这个参数用来指定计时单位,比如 ’5H’ 表示每隔5个小时计算一次。tz
:string 或 None,表示时区,例如:’Asia/Hong_Kong’。normalize
:bool,默认值为 False,如果为True的话,那么在产生时间索引值之前会先把start和end都转化为当日的午夜0点。name
:str,默认值为None,给返回的时间索引指定一个名字。closed
:string 或者 None,默认值为None,表示 start 和 end 这个区间端点是否包含在区间内,可以有三个值,left
表示左闭右开区间,righ
表示左开右闭区间,None
表示两边都是闭区间。
In [11]: import pandas as pd
In [12]: pd.date_range(start='20170101',end='20170110')
Out[12]:
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
'2017-01-09', '2017-01-10'],
dtype='datetime64[ns]', freq='D')
In [13]: pd.date_range(start='20170101',periods=10)
Out[13]:
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
'2017-01-09', '2017-01-10'],
dtype='datetime64[ns]', freq='D')
In [14]: pd.date_range(start='20170101',periods=10,freq='1D')
Out[14]:
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',
'2017-01-09', '2017-01-10'],
dtype='datetime64[ns]', freq='D')
In [15]: pd.date_range(start='20170101',end='20170110',freq='3D',name='dt')
Out[15]: DatetimeIndex(['2017-01-01', '2017-01-04', '2017-01-07', '2017-01-10'],
dtype='datetime64[ns]', name='dt', freq='3D')
In [16]: pd.date_range(start='2017-01-01 08:10:50',periods=10,freq='s',normaliz
...: e=True)
Out[16]:
DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 00:00:01',
'2017-01-01 00:00:02', '2017-01-01 00:00:03',
'2017-01-01 00:00:04', '2017-01-01 00:00:05',
'2017-01-01 00:00:06', '2017-01-01 00:00:07',
'2017-01-01 00:00:08', '2017-01-01 00:00:09'],
dtype='datetime64[ns]', freq='S')
In [17]: pd.date_range(start='2017-01-01 08:10:50',end='2017-01-02 09:20:40',fr
...: eq='s',normalize=True)
Out[17]:
DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 00:00:01',
'2017-01-01 00:00:02', '2017-01-01 00:00:03',
'2017-01-01 00:00:04', '2017-01-01 00:00:05',
'2017-01-01 00:00:06', '2017-01-01 00:00:07',
'2017-01-01 00:00:08', '2017-01-01 00:00:09',
...
'2017-01-01 23:59:51', '2017-01-01 23:59:52',
'2017-01-01 23:59:53', '2017-01-01 23:59:54',
'2017-01-01 23:59:55', '2017-01-01 23:59:56',
'2017-01-01 23:59:57', '2017-01-01 23:59:58',
'2017-01-01 23:59:59', '2017-01-02 00:00:00'],
dtype='datetime64[ns]', length=86401, freq='S')
In [18]: pd.date_range(start='2017-01-01 08:10:50',periods=15,freq='s',normaliz
...: e=False)
Out[18]:
DatetimeIndex(['2017-01-01 08:10:50', '2017-01-01 08:10:51',
'2017-01-01 08:10:52', '2017-01-01 08:10:53',
'2017-01-01 08:10:54', '2017-01-01 08:10:55',
'2017-01-01 08:10:56', '2017-01-01 08:10:57',
'2017-01-01 08:10:58', '2017-01-01 08:10:59',
'2017-01-01 08:11:00', '2017-01-01 08:11:01',
'2017-01-01 08:11:02', '2017-01-01 08:11:03',
'2017-01-01 08:11:04'],
dtype='datetime64[ns]', freq='S')
In [19]: pd.date_range(start='20170101',end='20170110',freq='3D',closed='left')
...:
Out[19]: DatetimeIndex(['2017-01-01', '2017-01-04', '2017-01-07'], dtype='dateti
me64[ns]', freq='3D')
In [20]: pd.date_range(start='20170101',end='20170110',freq='3D',closed='right'
...: )
Out[20]: DatetimeIndex(['2017-01-04', '2017-01-07', '2017-01-10'], dtype='dateti
me64[ns]', freq='3D')