Pandas-时间（三）：pd.date_range()【创建日期范围】【返回DatetimeIndex类型】【生成方式、normalize、closed、频率、复合频率、频率转换、位移】

pd.date_range()-日期范围：生成日期范围

• 2种生成方式：①start + end； ②start/end + periods
• 默认频率：day
• 直接生成DatetimeIndex

pd.date_range(start=None, end=None, periods=None, freq=‘D’, tz=None, normalize=False, name=None, closed=None, **kwargs)

• start：开始时间
• end：结束时间
• periods：偏移量
• freq：频率，默认天，pd.date_range()默认频率为日历日，pd.bdate_range()默认频率为工作日
• tz：时区
• name：索引对象名称

一、生成方式

生成方式：

1. start + end；
2. start + periods
3. end + periods

import pandas as pd

# pd.date_range()-日期范围：生成日期范围
# 2种生成方式：①start + end； ②start/end + periods
# 默认频率：day
# 直接生成DatetimeIndex

rng1 = pd.date_range(start='1/1/2017', end='1/10/2017', normalize=True)
rng2 = pd.date_range(start='1/1/2017', periods=10)
rng3 = pd.date_range(end='1/30/2017 15:00:00', periods=10)  # 增加了时、分、秒
print("rng1 = {0}, type(rng1) = {1}".format(rng1, type(rng1)))
print('-' * 200)
print("rng2 = {0}, type(rng2) = {1}".format(rng2, type(rng2)))
print('-' * 200)
print("rng3 = {0}, type(rng3) = {1}".format(rng3, type(rng3)))
print('-' * 200)

打印结果：

rng1 = 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'), type(rng1) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
rng2 = 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'), type(rng2) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
rng3 = DatetimeIndex(['2017-01-21 15:00:00', '2017-01-22 15:00:00',
               '2017-01-23 15:00:00', '2017-01-24 15:00:00',
               '2017-01-25 15:00:00', '2017-01-26 15:00:00',
               '2017-01-27 15:00:00', '2017-01-28 15:00:00',
               '2017-01-29 15:00:00', '2017-01-30 15:00:00'],
              dtype='datetime64[ns]', freq='D'), type(rng3) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Process finished with exit code 0

二、normalize参数

normalize：时间参数值正则化到午夜时间戳（这里最后就直接变成0:00:00，并不是15:30:00）

import pandas as pd

# pd.date_range()-日期范围：生成日期范围
# 2种生成方式：①start + end； ②start/end + periods
# 默认频率：day

# normalize：时间参数值正则化到午夜时间戳（这里最后就直接变成0:00:00，并不是15:30:00）
rng4 = pd.date_range(start='1/1/2017 15:30', periods=3, name='hello world!', normalize=True)
print("rng4 = {0}, type(rng4) = {1}".format(rng4, type(rng4)))
print('-' * 200)

打印结果：

rng4 = DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', name='hello world!', freq='D'), type(rng4) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

三、closed参数

closed：默认为None的情况下，左闭右闭，left则左闭右开，right则左开右闭

import pandas as pd

# pd.date_range()-日期范围：生成日期范围
# 2种生成方式：①start + end； ②start/end + periods
# 默认频率：day

# closed：默认为None的情况下，左闭右闭，left则左闭右开，right则左开右闭
rng5 = pd.date_range('20170101', '20170104')  # 20170101也可读取
rng6 = pd.date_range('20170101', '20170104', closed='right')
rng7 = pd.date_range('20170101', '20170104', closed='left')
print("rng5 = {0}, type(rng5) = {1}".format(rng5, type(rng5)))
print("rng6 = {0}, type(rng6) = {1}".format(rng6, type(rng6)))
print("rng7 = {0}, type(rng7) = {1}".format(rng7, type(rng7)))
print('-' * 200)

打印结果：

rng5 = DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D'), type(rng5) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
rng6 = DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D'), type(rng6) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
rng7 = DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D'), type(rng7) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

四、pd.bdate_range()：默认频率为工作日

import pandas as pd

# pd.bdate_range()：生成日期范围【默认频率为工作日】
rng8 = pd.bdate_range('20170101', '20170105')
print("rng8 = {0}, type(rng8) = {1}".format(rng8, type(rng8)))
print('-' * 200)

打印结果：

rng8 = DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05'], dtype='datetime64[ns]', freq='B'), type(rng8) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

五、DatetimeIndex类型转为list，元素为Timestamp类型

直接转化为list，元素为Timestamp类型

import pandas as pd

# pd.date_range()-日期范围：生成日期范围
rng9 = pd.date_range('20170101', '20170105')
print("rng9 = {0}, type(rng9) = {1}".format(rng9, type(rng9)))
print('-' * 150)

# 直接转化为list，元素为Timestamp
li = list(rng9)
print("li = ", li)

打印结果：

rng9 = DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05'],
              dtype='datetime64[ns]', freq='D'), type(rng9) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
------------------------------------------------------------------------------------------------------------------------------------------------------
li =  [Timestamp('2017-01-01 00:00:00', freq='D'), Timestamp('2017-01-02 00:00:00', freq='D'), Timestamp('2017-01-03 00:00:00', freq='D'), Timestamp('2017-01-04 00:00:00', freq='D'), Timestamp('2017-01-05 00:00:00', freq='D')]

Process finished with exit code 0

六、频率参数：freq

默认freq = ‘D’：每日历日

• B：每工作日
• H**：**每小时
• T/MIN：每分
• S：每秒
• L：每毫秒（千分之一秒）
• U：每微秒（百万分之一秒）
• W-MON：从指定星期几开始算起，每周
  • 星期几缩写：MON/TUE/WED/THU/FRI/SAT/SUN
• WOM-2MON：每月的第几个星期几开始算，这里是每月第二个星期一
• M：每月最后一个日历日
• MS：每月第一个日历日
• Q-月：指定月为季度末，每个季度末最后一月的最后一个日历日
  • 月缩写：JAN/FEB/MAR/APR/MAY/JUN/JUL/AUG/SEP/OCT/NOV/DEC
  • 所以Q-月只有三种情况：1-4-7-10,2-5-8-11,3-6-9-12
• QS-月：指定月为季度末，每个季度末最后一月的第一个日历日
• A-月：每年指定月份的最后一个日历日
• AS-月：每年指定月份的第一个日历日
• BM：每月最后一个工作日
• BMS：每月第一个工作日
• BQ-月：指定月为季度末，每个季度末最后一月的最后一个工作日
• BQS-月：指定月为季度末，每个季度末最后一月的第一个工作日
• BA-月：每年指定月份的最后一个工作日
• BAS-月：每年指定月份的第一个工作日

import numpy as np
import pandas as pd

# pd.date_range()-日期范围：频率参数

# 默认freq = 'D'：每日历日
print("pd.date_range('2017/1/1', '2017/1/4') = \n", pd.date_range('2017/1/1', '2017/1/4'))
print("-" * 100)
# B：每工作日
print("pd.date_range('2017/1/1', '2017/1/4', freq='B') = \n", pd.date_range('2017/1/1', '2017/1/4', freq='B'))
print("-" * 100)
# H：每小时
print("pd.date_range('2017/1/1', '2017/1/2', freq='H') = \n", pd.date_range('2017/1/1', '2017/1/2', freq='H'))
print("-" * 100)
# T/MIN：每分
print("pd.date_range('2017/1/1 12:00', '2017/1/1 12:10', freq='T') = \n", pd.date_range('2017/1/1 12:00', '2017/1/1 12:10', freq='T'))
print("-" * 100)
# S：每秒
print("pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='S') = \n", pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='S'))
print("-" * 100)
# L：每毫秒（千分之一秒）
print("pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='L') = \n", pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='L'))
print("-" * 100)
# U：每微秒（百万分之一秒）
print("pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='U') = \n", pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='U'))
print("-" * 200)

# W-MON：从指定星期几开始算起，每周
# 星期几缩写：MON/TUE/WED/THU/FRI/SAT/SUN
print("pd.date_range('2017/1/1', '2017/2/1', freq='W-MON') = \n", pd.date_range('2017/1/1', '2017/2/1', freq='W-MON'))
# WOM-2MON：每月的第几个星期几开始算，这里是每月第二个星期一
print("pd.date_range('2017/1/1', '2017/5/1', freq='WOM-2MON') = \n", pd.date_range('2017/1/1', '2017/5/1', freq='WOM-2MON'))
print("-" * 200)

# M：每月最后一个日历日
print("pd.date_range('2017', '2018', freq='M') = \n", pd.date_range('2017', '2018', freq='M'))
print("-" * 100)
# Q-月：指定月为季度末，每个季度末最后一月的最后一个日历日
# 月缩写：JAN/FEB/MAR/APR/MAY/JUN/JUL/AUG/SEP/OCT/NOV/DEC
# 所以Q-月只有三种情况：1-4-7-10,2-5-8-11,3-6-9-12
print("pd.date_range('2017', '2020', freq='Q-DEC') = \n", pd.date_range('2017', '2020', freq='Q-DEC'))
print("-" * 100)
# A-月：每年指定月份的最后一个日历日
print("pd.date_range('2017', '2020', freq='A-DEC') = \n", pd.date_range('2017', '2020', freq='A-DEC'))
print("-" * 200)

# BM：每月最后一个工作日
print("pd.date_range('2017', '2018', freq='BM') = \n", pd.date_range('2017', '2018', freq='BM'))
print("-" * 100)
# BQ-月：指定月为季度末，每个季度末最后一月的最后一个工作日
print("pd.date_range('2017', '2020', freq='BQ-DEC') = \n", pd.date_range('2017', '2020', freq='BQ-DEC'))
print("-" * 100)
# BA-月：每年指定月份的最后一个工作日
print("pd.date_range('2017', '2020', freq='BA-DEC') = \n", pd.date_range('2017', '2020', freq='BA-DEC'))
print("-" * 200)

# MS：每月第一个日历日
print("pd.date_range('2017', '2018', freq='MS') = \n", pd.date_range('2017', '2018', freq='MS'))
print("-" * 100)
# QS-月：指定月为季度末，每个季度末最后一月的第一个日历日
print("pd.date_range('2017', '2020', freq='QS-DEC') = \n", pd.date_range('2017', '2020', freq='QS-DEC'))
print("-" * 100)
# AS-月：每年指定月份的第一个日历日
print("pd.date_range('2017', '2020', freq='AS-DEC') = \n", pd.date_range('2017', '2020', freq='AS-DEC'))
print("-" * 200)

# BMS：每月第一个工作日
print("pd.date_range('2017', '2018', freq='BMS') = \n", pd.date_range('2017', '2018', freq='BMS'))
print("-" * 100)
# BQS-月：指定月为季度末，每个季度末最后一月的第一个工作日
print("pd.date_range('2017', '2020', freq='BQS-DEC') = \n", pd.date_range('2017', '2020', freq='BQS-DEC'))
print("-" * 100)
# BAS-月：每年指定月份的第一个工作日
print("pd.date_range('2017', '2020', freq='BAS-DEC') = \n", pd.date_range('2017', '2020', freq='BAS-DEC'))

打印结果：

pd.date_range('2017/1/1', '2017/1/4') = 
 DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1', '2017/1/4', freq='B') = 
 DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='B')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1', '2017/1/2', freq='H') = 
 DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 01:00:00',
               '2017-01-01 02:00:00', '2017-01-01 03:00:00',
               '2017-01-01 04:00:00', '2017-01-01 05:00:00',
               '2017-01-01 06:00:00', '2017-01-01 07:00:00',
               '2017-01-01 08:00:00', '2017-01-01 09:00:00',
               '2017-01-01 10:00:00', '2017-01-01 11:00:00',
               '2017-01-01 12:00:00', '2017-01-01 13:00:00',
               '2017-01-01 14:00:00', '2017-01-01 15:00:00',
               '2017-01-01 16:00:00', '2017-01-01 17:00:00',
               '2017-01-01 18:00:00', '2017-01-01 19:00:00',
               '2017-01-01 20:00:00', '2017-01-01 21:00:00',
               '2017-01-01 22:00:00', '2017-01-01 23:00:00',
               '2017-01-02 00:00:00'],
              dtype='datetime64[ns]', freq='H')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1 12:00', '2017/1/1 12:10', freq='T') = 
 DatetimeIndex(['2017-01-01 12:00:00', '2017-01-01 12:01:00',
               '2017-01-01 12:02:00', '2017-01-01 12:03:00',
               '2017-01-01 12:04:00', '2017-01-01 12:05:00',
               '2017-01-01 12:06:00', '2017-01-01 12:07:00',
               '2017-01-01 12:08:00', '2017-01-01 12:09:00',
               '2017-01-01 12:10:00'],
              dtype='datetime64[ns]', freq='T')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='S') = 
 DatetimeIndex(['2017-01-01 12:00:00', '2017-01-01 12:00:01',
               '2017-01-01 12:00:02', '2017-01-01 12:00:03',
               '2017-01-01 12:00:04', '2017-01-01 12:00:05',
               '2017-01-01 12:00:06', '2017-01-01 12:00:07',
               '2017-01-01 12:00:08', '2017-01-01 12:00:09',
               '2017-01-01 12:00:10'],
              dtype='datetime64[ns]', freq='S')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='L') = 
 DatetimeIndex([       '2017-01-01 12:00:00', '2017-01-01 12:00:00.001000',
               '2017-01-01 12:00:00.002000', '2017-01-01 12:00:00.003000',
               '2017-01-01 12:00:00.004000', '2017-01-01 12:00:00.005000',
               '2017-01-01 12:00:00.006000', '2017-01-01 12:00:00.007000',
               '2017-01-01 12:00:00.008000', '2017-01-01 12:00:00.009000',
               ...
               '2017-01-01 12:00:09.991000', '2017-01-01 12:00:09.992000',
               '2017-01-01 12:00:09.993000', '2017-01-01 12:00:09.994000',
               '2017-01-01 12:00:09.995000', '2017-01-01 12:00:09.996000',
               '2017-01-01 12:00:09.997000', '2017-01-01 12:00:09.998000',
               '2017-01-01 12:00:09.999000',        '2017-01-01 12:00:10'],
              dtype='datetime64[ns]', length=10001, freq='L')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1 12:00:00', '2017/1/1 12:00:10', freq='U') = 
 DatetimeIndex([       '2017-01-01 12:00:00', '2017-01-01 12:00:00.000001',
               '2017-01-01 12:00:00.000002', '2017-01-01 12:00:00.000003',
               '2017-01-01 12:00:00.000004', '2017-01-01 12:00:00.000005',
               '2017-01-01 12:00:00.000006', '2017-01-01 12:00:00.000007',
               '2017-01-01 12:00:00.000008', '2017-01-01 12:00:00.000009',
               ...
               '2017-01-01 12:00:09.999991', '2017-01-01 12:00:09.999992',
               '2017-01-01 12:00:09.999993', '2017-01-01 12:00:09.999994',
               '2017-01-01 12:00:09.999995', '2017-01-01 12:00:09.999996',
               '2017-01-01 12:00:09.999997', '2017-01-01 12:00:09.999998',
               '2017-01-01 12:00:09.999999',        '2017-01-01 12:00:10'],
              dtype='datetime64[ns]', length=10000001, freq='U')
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1', '2017/2/1', freq='W-MON') = 
 DatetimeIndex(['2017-01-02', '2017-01-09', '2017-01-16', '2017-01-23', '2017-01-30'],
              dtype='datetime64[ns]', freq='W-MON')
pd.date_range('2017/1/1', '2017/5/1', freq='WOM-2MON') = 
 DatetimeIndex(['2017-01-09', '2017-02-13', '2017-03-13', '2017-04-10'], dtype='datetime64[ns]', freq='WOM-2MON')
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pd.date_range('2017', '2018', freq='M') = 
 DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',
               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
               '2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31'],
              dtype='datetime64[ns]', freq='M')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='Q-DEC') = 
 DatetimeIndex(['2017-03-31', '2017-06-30', '2017-09-30', '2017-12-31',
               '2018-03-31', '2018-06-30', '2018-09-30', '2018-12-31',
               '2019-03-31', '2019-06-30', '2019-09-30', '2019-12-31'],
              dtype='datetime64[ns]', freq='Q-DEC')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='A-DEC') = 
 DatetimeIndex(['2017-12-31', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='A-DEC')
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pd.date_range('2017', '2018', freq='BM') = 
 DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-28',
               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
               '2017-09-29', '2017-10-31', '2017-11-30', '2017-12-29'],
              dtype='datetime64[ns]', freq='BM')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='BQ-DEC') = 
 DatetimeIndex(['2017-03-31', '2017-06-30', '2017-09-29', '2017-12-29',
               '2018-03-30', '2018-06-29', '2018-09-28', '2018-12-31',
               '2019-03-29', '2019-06-28', '2019-09-30', '2019-12-31'],
              dtype='datetime64[ns]', freq='BQ-DEC')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='BA-DEC') = 
 DatetimeIndex(['2017-12-29', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='BA-DEC')
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pd.date_range('2017', '2018', freq='MS') = 
 DatetimeIndex(['2017-01-01', '2017-02-01', '2017-03-01', '2017-04-01',
               '2017-05-01', '2017-06-01', '2017-07-01', '2017-08-01',
               '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01',
               '2018-01-01'],
              dtype='datetime64[ns]', freq='MS')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='QS-DEC') = 
 DatetimeIndex(['2017-03-01', '2017-06-01', '2017-09-01', '2017-12-01',
               '2018-03-01', '2018-06-01', '2018-09-01', '2018-12-01',
               '2019-03-01', '2019-06-01', '2019-09-01', '2019-12-01'],
              dtype='datetime64[ns]', freq='QS-DEC')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='AS-DEC') = 
 DatetimeIndex(['2017-12-01', '2018-12-01', '2019-12-01'], dtype='datetime64[ns]', freq='AS-DEC')
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pd.date_range('2017', '2018', freq='BMS') = 
 DatetimeIndex(['2017-01-02', '2017-02-01', '2017-03-01', '2017-04-03',
               '2017-05-01', '2017-06-01', '2017-07-03', '2017-08-01',
               '2017-09-01', '2017-10-02', '2017-11-01', '2017-12-01',
               '2018-01-01'],
              dtype='datetime64[ns]', freq='BMS')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='BQS-DEC') = 
 DatetimeIndex(['2017-03-01', '2017-06-01', '2017-09-01', '2017-12-01',
               '2018-03-01', '2018-06-01', '2018-09-03', '2018-12-03',
               '2019-03-01', '2019-06-03', '2019-09-02', '2019-12-02'],
              dtype='datetime64[ns]', freq='BQS-DEC')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2020', freq='BAS-DEC') = 
 DatetimeIndex(['2017-12-01', '2018-12-03', '2019-12-02'], dtype='datetime64[ns]', freq='BAS-DEC')

Process finished with exit code 0

七、复合频率

import pandas as pd

# 7天
print("pd.date_range('2017/1/1', '2017/2/1', freq='7D') = \n", pd.date_range('2017/1/1', '2017/2/1', freq='7D'))
print("-" * 100)
# 2小时30分钟
print("pd.date_range('2017/1/1', '2017/1/2', freq='2h30min') = \n", pd.date_range('2017/1/1', '2017/1/2', freq='2h30min'))
print("-" * 100)
# 2月，每月最后一个日历日
print("pd.date_range('2017', '2018', freq='2M') = \n", pd.date_range('2017', '2018', freq='2M'))
print("-" * 100)

打印结果：

pd.date_range('2017/1/1', '2017/2/1', freq='7D') = 
 DatetimeIndex(['2017-01-01', '2017-01-08', '2017-01-15', '2017-01-22',
               '2017-01-29'],
              dtype='datetime64[ns]', freq='7D')
----------------------------------------------------------------------------------------------------
pd.date_range('2017/1/1', '2017/1/2', freq='2h30min') = 
 DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 02:30:00',
               '2017-01-01 05:00:00', '2017-01-01 07:30:00',
               '2017-01-01 10:00:00', '2017-01-01 12:30:00',
               '2017-01-01 15:00:00', '2017-01-01 17:30:00',
               '2017-01-01 20:00:00', '2017-01-01 22:30:00'],
              dtype='datetime64[ns]', freq='150T')
----------------------------------------------------------------------------------------------------
pd.date_range('2017', '2018', freq='2M') = 
 DatetimeIndex(['2017-01-31', '2017-03-31', '2017-05-31', '2017-07-31',
               '2017-09-30', '2017-11-30'],
              dtype='datetime64[ns]', freq='2M')
----------------------------------------------------------------------------------------------------

Process finished with exit code 0

八、频率转换

import numpy as np
import pandas as pd

# 改变频率，这里是D改为4H
# method：插值模式，None不插值，ffill用之前值填充，bfill用之后值填充
ts = pd.Series(np.random.rand(4), index=pd.date_range('20170101', '20170104'))
print("ts = \n", ts)
print("-" * 100)
print("ts.asfreq('4H', method='ffill') = \n", ts.asfreq('4H', method='ffill'))

打印结果：

ts = 
 2017-01-01    0.695553
2017-01-02    0.036298
2017-01-03    0.316031
2017-01-04    0.757398
Freq: D, dtype: float64
----------------------------------------------------------------------------------------------------
ts.asfreq('4H', method='ffill') = 
 2017-01-01 00:00:00    0.695553
2017-01-01 04:00:00    0.695553
2017-01-01 08:00:00    0.695553
2017-01-01 12:00:00    0.695553
2017-01-01 16:00:00    0.695553
2017-01-01 20:00:00    0.695553
2017-01-02 00:00:00    0.036298
2017-01-02 04:00:00    0.036298
2017-01-02 08:00:00    0.036298
2017-01-02 12:00:00    0.036298
2017-01-02 16:00:00    0.036298
2017-01-02 20:00:00    0.036298
2017-01-03 00:00:00    0.316031
2017-01-03 04:00:00    0.316031
2017-01-03 08:00:00    0.316031
2017-01-03 12:00:00    0.316031
2017-01-03 16:00:00    0.316031
2017-01-03 20:00:00    0.316031
2017-01-04 00:00:00    0.757398
Freq: 4H, dtype: float64
----------------------------------------------------------------------------------------------------

Process finished with exit code 0

九、超前/滞后数据（对数值进行位移）

import numpy as np
import pandas as pd

# 日期范围：超前/滞后数据

ts = pd.Series(np.random.rand(4), index=pd.date_range('20170101', '20170104'))
print("ts = \n", ts)
print("-" * 100)

# 正数：数值后移（滞后）
ts1 = ts.shift(2)
# 负数：数值前移（超前）
ts2 = ts.shift(-2)
print("ts1 = ts.shift(2) = \n", ts1)
print("-" * 100)
print("ts2 = ts.shift(-2) = \n", ts2)
print("-" * 200)

# 计算变化百分比，这里计算：该时间戳与上一个时间戳相比，变化百分比
print("ts = \n", ts)
print("-" * 50)
ts3 = ts.shift(1)
print("ts3 = ts.shift(1) = \n", ts3)
print("-" * 100)
print("ts / ts3 = \n", ts / ts3)
print("-" * 50)
per = ts / ts3 - 1
print("per = ts / ts3 - 1 = \n", per)
print("-" * 200)

打印结果：

ts = 
 2017-01-01    0.727595
2017-01-02    0.979893
2017-01-03    0.489282
2017-01-04    0.540695
Freq: D, dtype: float64
----------------------------------------------------------------------------------------------------
ts1 = ts.shift(2) = 
 2017-01-01         NaN
2017-01-02         NaN
2017-01-03    0.727595
2017-01-04    0.979893
Freq: D, dtype: float64
----------------------------------------------------------------------------------------------------
ts2 = ts.shift(-2) = 
 2017-01-01    0.489282
2017-01-02    0.540695
2017-01-03         NaN
2017-01-04         NaN
Freq: D, dtype: float64
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
ts = 
 2017-01-01    0.727595
2017-01-02    0.979893
2017-01-03    0.489282
2017-01-04    0.540695
Freq: D, dtype: float64
--------------------------------------------------
ts3 = ts.shift(1) = 
 2017-01-01         NaN
2017-01-02    0.727595
2017-01-03    0.979893
2017-01-04    0.489282
Freq: D, dtype: float64
----------------------------------------------------------------------------------------------------
ts / ts3 = 
 2017-01-01         NaN
2017-01-02    1.346756
2017-01-03    0.499322
2017-01-04    1.105080
Freq: D, dtype: float64
--------------------------------------------------
per = ts / ts3 - 1 = 
 2017-01-01         NaN
2017-01-02    0.346756
2017-01-03   -0.500678
2017-01-04    0.105080
Freq: D, dtype: float64
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

十、对时间戳进行位移（不是对数值进行位移）

import numpy as np
import pandas as pd

# 日期范围：超前/滞后数据

ts = pd.Series(np.random.rand(4), index=pd.date_range('20170101', '20170104'))
print("ts = \n", ts)
print("-" * 100)

# 加上freq参数：对时间戳进行位移，而不是对数值进行位移
print("ts.shift(2, freq='D') = \n", ts.shift(2, freq='D'))
print("-" * 50)
print("ts.shift(2, freq='T') = \n", ts.shift(2, freq='T'))
print("-" * 200)

打印结果：

ts = 
 2017-01-01    0.038605
2017-01-02    0.074905
2017-01-03    0.689343
2017-01-04    0.400337
Freq: D, dtype: float64
----------------------------------------------------------------------------------------------------
ts.shift(2, freq='D') = 
 2017-01-03    0.038605
2017-01-04    0.074905
2017-01-05    0.689343
2017-01-06    0.400337
Freq: D, dtype: float64
--------------------------------------------------
ts.shift(2, freq='T') = 
 2017-01-01 00:02:00    0.038605
2017-01-02 00:02:00    0.074905
2017-01-03 00:02:00    0.689343
2017-01-04 00:02:00    0.400337
Freq: D, dtype: float64
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Process finished with exit code 0