pandas时间序列操作方法pd.date_range()
通过pandas.date_range命令查看date_range函数帮助文档
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（相当于步进），取值为string或DateOffset，默认为’D’。（关于freq参数详解请移步https://blog.csdn.net/weixin_43343144/article/details/102823058）
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，再取左闭右开的结果，若closed='right’表示在返回的结果基础上，再取左开右闭的结果
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')
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原文链接：https://blog.csdn.net/kancy110/article/details/77131539