pandas中时间序列——date_range函数

来源：互联网发布：优化语文教学五环节编辑：程序博客网时间：2024/06/02 08:20

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，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 pdIn [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='datetime64[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='datetime64[ns]', freq='3D')

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