pandas.date_range函数

语法：pandas.date_range(start=None, end=None, periods=None, freq='D', tz=None, normalize=False, name=None, closed=None, **kwargs)

该函数主要用于生成一个固定频率的时间索引，在调用构造方法时，必须指定start、end、periods中的两个参数值，否则报错。

主要参数说明：

periods：固定时期，取值为整数或None

freq：日期偏移量，取值为string或DateOffset，默认为'D'

normalize：若参数为True表示将start、end参数值正则化到午夜时间戳

name：生成时间索引对象的名称，取值为string或None

closed：可以理解成在closed=None情况下返回的结果中，若closed=‘left’表示在返回的结果基础上，再取左开右闭的结果，若closed='right'表示在返回的结果基础上，再取做闭右开的结果

 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')