03. Pandas 2| 时间序列

时间序列

1.时间模块：datetime

datetime模块，主要掌握：datetime.date(), datetime.datetime(), datetime.timedelta()

日期解析方法：parser.parse

datetime.date：date对象

import datetime #也可以写成 from datetime import date
today = datetime.date.today()
print(today, type(today)) #2018-08-21 <class 'datetime.date'>
print(str(today), type(str(today)))#2018-08-21 <class 'str'>
t = datetime.date(2018, 12, 8)
print(t)#2018-12-08

datetime.date.today() 返回今日
输出格式为 date类

datetime.datetime：datetime对象

now = datetime.datetime.now()
print(now, type(now)) #2018-08-21 19:22:47.296548 <class 'datetime.datetime'>
print(str(now), type(str(now))) #2018-08-21 19:23:26.139769 <class 'str'>
t1 = datetime.datetime(2018, 8, 1)
t2 = datetime.datetime(2014, 9, 1, 12, 12, 12)
print(t1, t2) #2018-08-01 00:00:00  2014-09-01 12:12:12
print(t1 - t2) #1429 days, 11:47:48

datetime.datetime.now()方法，输出当前时间
输出格式为 datetime类
可通过str()转化为字符串

datetime.timedelta：时间差

today = datetime.datetime.today()
yestoday = today - datetime.timedelta(1) #日
print(today, yestoday) #2018-08-21 19:32:25.068595    2018-08-20 19:32:25.068595
print(today - datetime.timedelta(7)) #2018-08-14 19:32:25.068595

datetime.timedelta() 时间差主要用作时间的加减法，相当于可被识别的时间“差值”

parser.parse：日期字符串转换（parse() 转换为datetime类型）

from dateutil.parser import parse
date = '12-15-2018'
t = parse(date)
print(t, type(t))               #2018-12-15 00:00:00  <class 'datetime.datetime'>
print(parse('2009-1-2'),'
', #2009-01-02 00:00:00
      parse('5/3/2009'),'
', # 2009-05-03 00:00:00
      parse('5/3/2009',dayfirst = True),'
', # 2009-03-05 00:00:00 # 国际通用格式中，日在月之前，可以通过dayfirst来设置,如果是False就是 2009-05-03 00:00:00
      parse('22/1/2014'),'
',         # 2014-01-22 00:00:00
      parse('Jan 31, 1997 10:45 PM') # 1997-01-31 22:45:00
      )

2.Pandas时刻数据（时间点）

时刻数据代表时间点（可以是一年、一个月、一天、一分钟、一秒等），是pandas的数据类型，是将值与时间点相关联的最基本类型的时间序列数据

时间戳（timestamp），一个能表示一份数据在某个特定时间之前已经存在的、完整的、可验证的数据,通常是一个字符序列，唯一地标识某一刻的时间。

pandas.Timestamp()

pd.Timestamp( ) ---> 单个时间戳-创建方式

datetime.datetime(2016, 12, 2, 22, 15, 59) datetime类型 | ‘2018-12-7 12:07:47 ’ 字符串类型只能是单个时间数据

import numpy as np
import pandas as pd
date1 = datetime.datetime(2016,12,1,12,45,30)  #它是datetime类型
date2 = '2018-11-18' 　　　　　　　　　　　　　　　#‘20181118’、‘2/3/2018’、‘2018-11-18 12:08:13’等这些字符串都是可以识别的
t1 = pd.Timestamp(date1)
t2 = pd.Timestamp(date2)
print(t1, type(t1)) 　　　　　　　　　　#2016-12-01 12:45:30 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
print(t2)                                      #2018-11-18 00:00:00
print(pd.Timestamp('2017-12-09 15:09:21'))     #2017-12-09 15:09:21

　>>> print(date1, type(date1))
　2016-12-01 12:45:30 <class 'datetime.datetime'>

直接生成pandas的时刻数据 → 时间戳数据类型为 pandas的Timestamp

pd.to_datetime -- pd.to_datetime→多个时间数据转换时间戳索引

pd.to_datetime()：如果是单个时间数据，转换成pandas的时刻数据，数据类型为Timestamp；多个时间数据，将会转换为pandas的DatetimeIndex

datetime类型和Timestamp类型的区别；

Timestamp和DatetimeIndex的区别；

转换为pandas时刻数据的两个方法：直接Timestamp、to_datetime

from datetime import datetime
import pandas as pd
date1 = datetime(2018, 12, 2, 12, 24, 30)
date2 = '2017-07-21'
t1 = pd.to_datetime(date1)
t2 = pd.to_datetime(date2)
print(t1, type(t1)) #2018-12-02 12:24:30 <class 'pandas._libs.tslibs.timestamps.Timestamp'> 单个数据跟Timestamp没什么区别
print(t2, type(t2)) #2017-07-21 00:00:00 <class 'pandas._libs.tslibs.timestamps.Timestamp'>

lst_date = ['2017-12-9', '2017-10-19', '2018-9-9'] 　　　　　　　　　　　　　　　　　　　　　　　　#如果时间是个序列，多个数据，就有区别了
t3 = pd.to_datetime(lst_date)
print(t3, type(t3)) 
#DatetimeIndex(['2017-12-09', '2017-10-19', '2018-09-09'], dtype='datetime64[ns]', freq=None)  <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

pd.to_datetime( data, errors='ignore' | errors='coerce' )

>>> import numpy as np
>>> import pandas as pd
>>> from datetime import datetime   #如果不加这句话就要datetime.datetime
>>> date1 = [datetime(2018, 6, 1), datetime(2018, 7,1), datetime(2018,8,1)]     #datetime类型
>>> date2 = ['2017-2-1','2017-2-2','2017-2-3','2017-2-4','2017-2-5','2017-2-6']  #列表
>>> print(date1)
[datetime.datetime(2018, 6, 1, 0, 0), datetime.datetime(2018, 7, 1, 0, 0), datetime.datetime(2018, 8, 1, 0, 0)]
>>> print(date2)
['2017-2-1', '2017-2-2', '2017-2-3', '2017-2-4', '2017-2-5', '2017-2-6']

>>> t1 = pd.to_datetime(date1)
>>> t2 = pd.to_datetime(date2)
>>> print(t1)
DatetimeIndex(['2018-06-01', '2018-07-01', '2018-08-01'], dtype='datetime64[ns]', freq=None)
>>> print(t2)
DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', '2017-02-04',
               '2017-02-05', '2017-02-06'],
              dtype='datetime64[ns]', freq=None)
>>> date3 = ['2017-9-1', '2018-11-10','Hello world!','2018-10-9', '2017-7-1']
>>> t3 = pd.to_datetime(date3, errors='ignore') #加上它就不会去解析它是否是时间序列了 ；当一组时间序列中夹杂其他格式数据时，可用errors参数返回。
　　　　　　　　　　　　　　　　　　　　　　　　#errors = 'ignore':不可解析时返回原始输入，这里就是直接生成一般数组
>>> print(t3, type(t3))
['2017-9-1' '2018-11-10' 'Hello world!' '2018-10-9' '2017-7-1'] <class 'numpy.ndarray'>
>>>
>>> t4 = pd.to_datetime(date3, errors='coerce') #会把不是时间序列的参数给去掉，当做缺失值，但它已经是时间序列了，DatetimeIndex类型        
　　　　　　　　　　　　　　　　　　　　　　　　# errors = 'coerce':不可扩展，缺失值返回NaT（Not a Time），结果认为DatetimeIndex
>>> print(t4, type(t4))
DatetimeIndex(['2017-09-01', '2018-11-10', 'NaT', '2018-10-09', '2017-07-01'], dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'>

3.Pandas时间戳索引

DatetimeIndex

核心：pd.date_range()

3.1 pd.DatetimeIndex() （时间戳索引）与TimeSeries时间序列

pd.DatatimeIndex（[多个时间序列]）

rng = pd.DatetimeIndex(['12/1/2018', '12/2/2018', '12/3/2018', '12/4/2018'])

pd.Series(np.random.rand(len(rng)),index = rng) #以DatetimeIndex为index的Series，为TimeSeries，时间序列。

>>> rng = pd.DatetimeIndex(['12/1/2018', '12/2/2018', '12/3/2018', '12/4/2018']) #DatetimeIndex这样一个直接把它变成DatetimeIndex类型的一个方法
>>> print(rng, type(rng))
DatetimeIndex(['2018-12-01', '2018-12-02', '2018-12-03', '2018-12-04'], dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
>>> print(rng[0], type(rng[0]))
2018-12-01 00:00:00 <class 'pandas._libs.tslibs.timestamps.Timestamp'>

>>> # 直接生成时间戳索引，支持str、datetime.datetime
... #rng[0] 单个时间戳为Timestamp，  rng[0:3] 多个时间戳为DatetimeIndex

>>> st = pd.Series(np.random.rand(len(rng)),index = rng) #以DatetimeIndex为index的Series，为TimeSeries，时间序列。
>>> print(st, type(st))
2018-12-01    0.063915
2018-12-02    0.726902
2018-12-03    0.135305
2018-12-04    0.237609
dtype: float64 <class 'pandas.core.series.Series'>
>>> print(st.index)
DatetimeIndex(['2018-12-01', '2018-12-02', '2018-12-03', '2018-12-04'], dtype='datetime64[ns]', freq=None)
>>>

3.2 pd.date_range()-日期范围：生成日期范围

date_range() 2种生成方式：①start + end； ②start/end + periods

pd.date_range('6/10/2018','10/5/2018') 、 pd.date_range('6/10/2018',periods=10) 、 pd.date_range(end='6/10/2018',periods=10)
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
# normalize 默认False，为True时就把时间给你变成00:00:00，但不会显示出来

#rng1 = pd.date_range('12/1/2018', '4/10/2017', normalize=True) #DatetimeIndex([], dtype='datetime64[ns]', freq='D')   <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
rng1 = pd.date_range('1/1/2017','1/10/2017', normalize=True)  #normalize=True就是把时间给你变成00:00:00，但不会显示出来
rng2 = pd.date_range(start='1/1/2018', periods=10)            #start=也可以不写的
rng3 = pd.date_range(end='1/30/2017 14:20:00', periods=10)

>>> print(rng1, type(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') <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
>>> print(rng2)
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
               '2018-01-09', '2018-01-10'],
              dtype='datetime64[ns]', freq='D')
>>> print(rng3)
DatetimeIndex(['2017-01-21 14:20:00', '2017-01-22 14:20:00',
               '2017-01-23 14:20:00', '2017-01-24 14:20:00',
               '2017-01-25 14:20:00', '2017-01-26 14:20:00',
               '2017-01-27 14:20:00', '2017-01-28 14:20:00',
               '2017-01-29 14:20:00', '2017-01-30 14:20:00'],
              dtype='datetime64[ns]', freq='D')

# 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

rng4 = pd.date_range(start='1/1/2017 15:30', periods=10, name='Hello world!', normalize=True) #它就会把15:30归为00:00，它不显示出来。name就是一个参数。
>>> print(rng4)
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]', name='Hello world!', freq='D')
>>>

>>> print(pd.date_range('20170101','20170104'))
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
>>> print(pd.date_range('20170101','20170104',closed='right'))
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
>>> print(pd.date_range('20170101','20170104',closed='left'))
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D')
>>>


>>> print(pd.date_range('20170101','20170107'))
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05',
               '2017-01-06'],
              dtype='datetime64[ns]', freq='B')

>>> print(list(pd.date_range(start='1/1/2017',periods=10)))#由多个时间戳组成的序列
[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 0
0:00:00', freq='D'), Timestamp('2017-01-06 00:00:00', freq='D'), Timestamp('2017-01-07 00:00:00', freq='D'), Timestamp('2017-01-08 00:00:00', freq='D'), Timestamp('2017-01-09 00:00:00', freq='D'), Tim
estamp('2017-01-10 00:00:00', freq='D')]
>>>

pd.date_range()-日期范围：freq 频率(1)

freq = 'B' 、‘H’、T、S、L、U、W-MON、

>>> print(pd.date_range('2017/1/1','2017/1/4'))  　　　　　　　　　　　　　　　　　　　#默认freq = 'D'：每日历日
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
>>> print(pd.date_range('2017/1/1','2017/1/4',freq='B'))  　　　　　　　　　　　　　　# B：每工作日
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='B') 
>>> print(pd.date_range('2017/1/1','2017/1/4',freq='H')) # H：每小时
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', '2017-01-02 01:00:00',
               '2017-01-02 02:00:00', '2017-01-02 03:00:00',
               '2017-01-02 04:00:00', '2017-01-02 05:00:00',
               '2017-01-02 06:00:00', '2017-01-02 07:00:00',
               '2017-01-02 08:00:00', '2017-01-02 09:00:00',
               '2017-01-02 10:00:00', '2017-01-02 11:00:00',
               '2017-01-02 12:00:00', '2017-01-02 13:00:00',
               '2017-01-02 14:00:00', '2017-01-02 15:00:00',
               '2017-01-02 16:00:00', '2017-01-02 17:00:00',
               '2017-01-02 18:00:00', '2017-01-02 19:00:00',
               '2017-01-02 20:00:00', '2017-01-02 21:00:00',
               '2017-01-02 22:00:00', '2017-01-02 23:00:00',
               '2017-01-03 00:00:00', '2017-01-03 01:00:00',
               '2017-01-03 02:00:00', '2017-01-03 03:00:00',
               '2017-01-03 04:00:00', '2017-01-03 05:00:00',
               '2017-01-03 06:00:00', '2017-01-03 07:00:00',
               '2017-01-03 08:00:00', '2017-01-03 09:00:00',
               '2017-01-03 10:00:00', '2017-01-03 11:00:00',
               '2017-01-03 12:00:00', '2017-01-03 13:00:00',
               '2017-01-03 14:00:00', '2017-01-03 15:00:00',
               '2017-01-03 16:00:00', '2017-01-03 17:00:00',
               '2017-01-03 18:00:00', '2017-01-03 19:00:00',
               '2017-01-03 20:00:00', '2017-01-03 21:00:00',
               '2017-01-03 22:00:00', '2017-01-03 23:00:00',
               '2017-01-04 00:00:00'],
              dtype='datetime64[ns]', freq='H')
>>> print(pd.date_range('2017/1/1 12:00','2017/1/1 12:10',freq='T')) # T/MIN：每分
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')
>>> print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10',freq='S'))  # S：每秒
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')
>>> print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10',freq='L')) # L：每毫秒（千分之一秒）
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')
>>> print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10',freq='U')) # U：每微秒（百万分之一秒）
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')
>>> print(pd.date_range('2017/1/1','2017/2/1',freq='W-MON'))  #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
DatetimeIndex(['2017-01-02', '2017-01-09', '2017-01-16', '2017-01-23',
               '2017-01-30'],
              dtype='datetime64[ns]', freq='W-MON')
>>> print(pd.date_range('2017/1/1','2017/5/1',freq='WOM-2MON')) # 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
DatetimeIndex(['2017-01-09', '2017-02-13', '2017-03-13', '2017-04-10'], dtype='datetime64[ns]', freq='WOM-2MON')
>>>

pd.date_range()-日期范围：freq 频率(2)

freq = 'M'、'Q-DEC'、‘A-DEC’、‘BM’、‘BQ-DEC’、‘BA-DEC’ 、'MS' 、‘QS-DEC’、‘AS-DEC’、‘BMS’、‘BQS-DEC’ 、‘BAS-DEC’

##########某个时刻的最后一个日历日
>>> print(pd.date_range('2017','2018',freq='M'))  # M：每月最后一个日历日
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')
>>> print(pd.date_range('2017','2020',freq='Q-DEC')) # 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 所以Q-月只有三种情况：1-4-7-10,2-5-8-11,3-6-9-12
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')
>>> print(pd.date_range('2017','2020',freq='A-DEC'))  # A-月：每年指定月份的最后一个日历日  # 月缩写：JAN/FEB/MAR/APR/MAY/JUN/JUL/AUG/SEP/OCT/NOV/DEC
DatetimeIndex(['2017-12-31', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='A-DEC')
>>>#################某个时刻的最后工作日
>>> print(pd.date_range('2017','2020',freq='BM'))   # BM：每月最后一个工作日
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',
               '2018-01-31', '2018-02-28', '2018-03-30', '2018-04-30',
               '2018-05-31', '2018-06-29', '2018-07-31', '2018-08-31',
               '2018-09-28', '2018-10-31', '2018-11-30', '2018-12-31',
               '2019-01-31', '2019-02-28', '2019-03-29', '2019-04-30',
               '2019-05-31', '2019-06-28', '2019-07-31', '2019-08-30',
               '2019-09-30', '2019-10-31', '2019-11-29', '2019-12-31'],
              dtype='datetime64[ns]', freq='BM')
>>> print(pd.date_range('2017','2020',freq='BQ-DEC')) # 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
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')
>>> print(pd.date_range('2017','2020',freq='BA-DEC')) # BA-月：每年指定月份的最后一个工作日
DatetimeIndex(['2017-12-29', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='BA-DEC')
>>>
################某个时刻的第一个日历日
>>> print(pd.date_range('2017','2018',freq='MS')) # M：每月第一个日历日
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')
>>> print(pd.date_range('2017','2020',freq='QS-DEC'))  # Q-月：指定月为季度末，每个季度末最后一月的第一个日历日
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')
>>> print(pd.date_range('2017','2020',freq='AS-DEC')) # A-月：每年指定月份的第一个日历日
DatetimeIndex(['2017-12-01', '2018-12-01', '2019-12-01'], dtype='datetime64[ns]', freq='AS-DEC')
>>>##############某个时刻的第一个日历日
>>> print(pd.date_range('2017','2018',freq='BMS'))  # BM：每月第一个工作日
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')
>>> print(pd.date_range('2017','2020',freq='BQS-DEC')) # BQ-月：指定月为季度末，每个季度末最后一月的第一个工作日
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')
>>> print(pd.date_range('2017','2020',freq='BAS-DEC')) # BA-月：每年指定月份的第一个工作日
DatetimeIndex(['2017-12-01', '2018-12-03', '2019-12-02'], dtype='datetime64[ns]', freq='BAS-DEC')
>>>

pd.date_range()-日期范围：freq 复合频率

freq = '7D' 、‘2M’ 、‘2h30min’

>>> print(pd.date_range('2017/1/1','2017/2/1',freq='7D'))  # 7天
DatetimeIndex(['2017-01-01', '2017-01-08', '2017-01-15', '2017-01-22',
               '2017-01-29'],
              dtype='datetime64[ns]', freq='7D')
>>> print(pd.date_range('2017/1/1','2017/1/2',freq='2h30min')) # 2小时30分钟
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')
>>> print(pd.date_range('2017','2018',freq='2M'))  # 2月，每月最后一个日历日
DatetimeIndex(['2017-01-31', '2017-03-31', '2017-05-31', '2017-07-31',
               '2017-09-30', '2017-11-30'],
              dtype='datetime64[ns]', freq='2M')
>>>

asfreq：时期频率转换

ts.asfreq('4H', method='ffill')

>>> ts = pd.Series(np.random.rand(4),index=pd.date_range('20170101','20170104'))
>>> print(ts)
2017-01-01    0.516999
2017-01-02    0.882315
2017-01-03    0.775276
2017-01-04    0.440545
Freq: D, dtype: float64
>>>
>>> print(ts.asfreq('4H'))
2017-01-01 00:00:00    0.516999
2017-01-01 04:00:00         NaN
2017-01-01 08:00:00         NaN
2017-01-01 12:00:00         NaN
2017-01-01 16:00:00         NaN
2017-01-01 20:00:00         NaN
2017-01-02 00:00:00    0.882315
2017-01-02 04:00:00         NaN
2017-01-02 08:00:00         NaN
2017-01-02 12:00:00         NaN
2017-01-02 16:00:00         NaN
2017-01-02 20:00:00         NaN
2017-01-03 00:00:00    0.775276
2017-01-03 04:00:00         NaN
2017-01-03 08:00:00         NaN
2017-01-03 12:00:00         NaN
2017-01-03 16:00:00         NaN
2017-01-03 20:00:00         NaN
2017-01-04 00:00:00    0.440545
Freq: 4H, dtype: float64
>>> print(ts.asfreq('4H',method='ffill'))  #改变频率，这里是D改为4H；   method：插值模式，None不插值，ffill用之前的值填充，bfill用之后的值填充。
2017-01-01 00:00:00    0.516999
2017-01-01 04:00:00    0.516999
2017-01-01 08:00:00    0.516999
2017-01-01 12:00:00    0.516999
2017-01-01 16:00:00    0.516999
2017-01-01 20:00:00    0.516999
2017-01-02 00:00:00    0.882315
2017-01-02 04:00:00    0.882315
2017-01-02 08:00:00    0.882315
2017-01-02 12:00:00    0.882315
2017-01-02 16:00:00    0.882315
2017-01-02 20:00:00    0.882315
2017-01-03 00:00:00    0.775276
2017-01-03 04:00:00    0.775276
2017-01-03 08:00:00    0.775276
2017-01-03 12:00:00    0.775276
2017-01-03 16:00:00    0.775276
2017-01-03 20:00:00    0.775276
2017-01-04 00:00:00    0.440545
Freq: 4H, dtype: float64

>>> print(ts.asfreq('4H',method='bfill'))
2017-01-01 00:00:00    0.516999
2017-01-01 04:00:00    0.882315
2017-01-01 08:00:00    0.882315
2017-01-01 12:00:00    0.882315
2017-01-01 16:00:00    0.882315
2017-01-01 20:00:00    0.882315
2017-01-02 00:00:00    0.882315
2017-01-02 04:00:00    0.775276
2017-01-02 08:00:00    0.775276
2017-01-02 12:00:00    0.775276
2017-01-02 16:00:00    0.775276
2017-01-02 20:00:00    0.775276
2017-01-03 00:00:00    0.775276
2017-01-03 04:00:00    0.440545
2017-01-03 08:00:00    0.440545
2017-01-03 12:00:00    0.440545
2017-01-03 16:00:00    0.440545
2017-01-03 20:00:00    0.440545
2017-01-04 00:00:00    0.440545
Freq: 4H, dtype: float64

pd.date_range()-日期范围：超前/ 滞后数据 .shift( )

ts.shift(1) 把昨天的数据移动 ts.shift(1, freq = 'D')对时间戳进行移动而不是数值了

>>> ts = pd.Series(np.random.rand(4),index=pd.date_range('20170101','20170104'))
>>> print(ts)
2017-01-01    0.421724
2017-01-02    0.102916
2017-01-03    0.411452
2017-01-04    0.626978
Freq: D, dtype: float64
>>> print(ts.shift(2)) # 正数：数值后移（滞后）；负数：数值前移（超前）
2017-01-01         NaN
2017-01-02         NaN
2017-01-03    0.421724
2017-01-04    0.102916
Freq: D, dtype: float64
>>> print(ts.shift(-2))
2017-01-01    0.411452
2017-01-02    0.626978
2017-01-03         NaN
2017-01-04         NaN
Freq: D, dtype: float64
>>>
>>> per = ts/ts.shift(1) - 1  #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;ts为今天的数据，ts.shift(1)为昨天的数据，ts/ts.shift(1)为百分比。再-1就是变化百分比了。
>>> print(per)
2017-01-01         NaN
2017-01-02   -0.755963
2017-01-03    2.997923
2017-01-04    0.523818
Freq: D, dtype: float64
>>>
>>> print(ts.shift(2,freq='D')) #加上freq参数：对时间戳进行位移，而不是对数值进行位移
2017-01-03    0.421724
2017-01-04    0.102916
2017-01-05    0.411452
2017-01-06    0.626978
Freq: D, dtype: float64
>>> print(ts.shift(2,freq='T'))
2017-01-01 00:02:00    0.421724
2017-01-02 00:02:00    0.102916
2017-01-03 00:02:00    0.411452
2017-01-04 00:02:00    0.626978
Freq: D, dtype: float64
>>>

4.Pandas时期：Period

pd.Period()

核心：pd.Period() ---->时间段、时间构造器；时间节面、时间戳、每个时期

pd.Period()参数：一个时间戳 + freq 参数 → freq 用于指明该 period 的长度，时间戳则说明该 period 在时间轴上的位置。

pd.Period('2017',freq = 'M') + 1

##pd.Period()创建时期 
>>> p = pd.Period('2017',freq = 'M') # 生成一个以2017-01开始，月为频率的时间构造器
>>> t = pd.DatetimeIndex(['2017-1-1'])
>>> print(p, type(p)) 
2017-01 <class 'pandas._libs.tslibs.period.Period'>
>>> print(t, type(t))
DatetimeIndex(['2017-01-01'], dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
>>>
>>> print(p + 1) # 通过加减整数，将周期整体移动
2017-02
>>> print(p - 2)
2016-11
>>> print(pd.Period('2012',freq = 'A-DEC') - 1)  #这里是按照 月、年 移动
2011
>>>

pd.period_range() 创建时期范围

Period('2011', freq = 'A-DEC')可以看成多个时间期的时间段中的游标

pd.Period('2017',freq = 'M') + 1   ；Period()和period_range()是两种不同的索引方式，一个为时间戳、另外一个为时期。
pd.period_range('1/1/2011', '1/1/2012', freq='M')  、pd.date_range('1/1/2011', '1/1/2012',freq='M')
period_range为PeriodIndex类型包含年月，没有日哦；           date_range为DatetimeIndex类型，包含年月日； 
Timestamp、DatetimeIndex都表示一个时间戳，是一个时间截面；Period是一个时期，是一个时间段！！但两者作为index时区别不大

##period_range()创建时期范围
>>> prng = pd.period_range('1/1/2011', '1/1/2012', freq='M') #只包含年、月
>>> rng = pd.date_range('1/1/2011', '1/1/2012',freq='M') #包含年、月、日 
>>> print(prng, type(prng))
PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06', #之前叫DatetimeIndex
             '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12',
             '2012-01'],
            dtype='period[M]', freq='M') <class 'pandas.core.indexes.period.PeriodIndex'>
>>> print(rng, type(rng))
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30',
               '2011-05-31', '2011-06-30', '2011-07-31', '2011-08-31',
               '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-31'],
              dtype='datetime64[ns]', freq='M') <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
>>>

>>> print(prng[0], type(prng[0])) #数据格式为PeriodIndex，单个数值为Period
2011-01 <class 'pandas._libs.tslibs.period.Period'>
>>>
>>> ts = pd.Series(np.random.rand(len(prng)),index=prng) #两者作为index时区别不大  
>>> ts2 = pd.Series(np.random.rand(len(rng)),index=rng)
>>> print(ts, type(ts))
2011-01    0.889509
2011-02    0.967148
2011-03    0.579234
2011-04    0.409504
2011-05    0.180216
2011-06    0.004549
2011-07    0.606768
2011-08    0.599321
2011-09    0.281182
2011-10    0.383243
2011-11    0.437894
2011-12    0.099335
2012-01    0.125945
Freq: M, dtype: float64 <class 'pandas.core.series.Series'>
>>> print(ts2, type(ts2))
2011-01-31    0.058635
2011-02-28    0.899287
2011-03-31    0.806039
2011-04-30    0.520745
2011-05-31    0.855713
2011-06-30    0.057417
2011-07-31    0.508203
2011-08-31    0.846018
2011-09-30    0.465259
2011-10-31    0.535451
2011-11-30    0.630897
2011-12-31    0.031109
Freq: M, dtype: float64 <class 'pandas.core.series.Series'>

>>> print(ts.index)  # 时间序列
PeriodIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05', '2011-06',
             '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12',
             '2012-01'],
            dtype='period[M]', freq='M')
>>> print(ts2.index)
DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30',
               '2011-05-31', '2011-06-30', '2011-07-31', '2011-08-31',
               '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-31'],
              dtype='datetime64[ns]', freq='M')
>>>
>>>

asfreq：频率转换

通过p.asfreq( freq, method=None, how=None)方法转换成别的频率

>>> p = pd.Period('2017','A-DEC')
>>> print(p)
2017
>>> print(p.asfreq('M',how = 'start')) #也可以写成how = 's'
2017-01
>>> print(p.asfreq('D',how = 'end')) #也可以写成how = 'e'
2017-12-31
>>>
>>> prng = pd.period_range('2017', '2018', freq='M')
>>> ts1 = pd.Series(np.random.rand(len(prng)),index=prng)
>>> print(ts1.head(), len(ts1))
2017-01    0.061827
2017-02    0.138509
2017-03    0.862916
2017-04    0.226967
2017-05    0.910585
Freq: M, dtype: float64 13
>>> ts2 = pd.Series(np.random.rand(len(prng)),index=prng.asfreq('D',how = 'start')) asfreq也可以转换为TimeSeries的index
>>> print(ts2.head(), len(ts2))
2017-01-01    0.476774
2017-02-01    0.625230
2017-03-01    0.281017
2017-04-01    0.165561
2017-05-01    0.429782
Freq: D, dtype: float64 13

时间戳与时期之间的转换：pd.to_period()、pd.to_timestamp()

ts.to_period() 转化为每月最后一日； ts.timestamp() 转化为每月第一日

rng.to_period() 将原来的DatetimeIndex转化为PeriodIndex； prng.to_timestamp() 将PeriodIndex转化为DatetimeIndex

>>> rng = pd.date_range('2017/1/1',periods = 10, freq = 'M')
>>> prng = pd.period_range('2017','2018',freq = 'M')
>>> ts1 = pd.Series(np.random.rand(len(rng)),index=rng)
>>> print(ts1.head())
2017-01-31    0.735182
2017-02-28    0.791190
2017-03-31    0.366768
2017-04-30    0.316335
2017-05-31    0.909333
Freq: M, dtype: float64
>>> print(ts1.to_period().head()) # 每月最后一日，转化为每月
2017-01    0.735182
2017-02    0.791190
2017-03    0.366768
2017-04    0.316335
2017-05    0.909333
Freq: M, dtype: float64
>>>
>>> ts1 = pd.Series(np.random.rand(len(prng)),index=prng)
>>> print(ts2.head())
2017-01-01    0.476774
2017-02-01    0.625230
2017-03-01    0.281017
2017-04-01    0.165561
2017-05-01    0.429782
Freq: D, dtype: float64
>>> print(ts2.to_timestamp().head()) #每月，转化为每月第一天
2017-01-01    0.476774
2017-02-01    0.625230
2017-03-01    0.281017
2017-04-01    0.165561
2017-05-01    0.429782
Freq: MS, dtype: float64
>>>

5.时间序列TimeSeries - 索引及切片

TimeSeries是Series的一个子类，所以Series索引及数据选取方面的方法基本一样

同时TimeSeries通过时间序列有更便捷的方法做索引和切片

pd.Series(np.random.rand(len(pd.period_range('1/1/2011', '1/1/2012'))),index=(pd.period_range('1/1/2011', '1/1/2012')))

pd.Series(np.random.rand(len(pd.date_range('2017/1','2017/3'))),index=(pd.date_range('2017/1','2017/3')))

索引 ts[0] ts[:2]下标位置索引 ts[ '2017/1/2' ]时间序列标签索引

>>> rng = pd.date_range('2017/1','2017/3')
>>> ts = pd.Series(np.random.rand(len(rng)),index=rng)
>>> print(ts.head())
2017-01-01    0.407246
2017-01-02    0.104561
2017-01-03    0.140087
2017-01-04    0.988668
2017-01-05    0.733602
Freq: D, dtype: float64
>>> print(ts[0])
0.40724601715639686
>>> print(ts[:2])   # 基本下标位置索引,末端取不到
2017-01-01    0.407246
2017-01-02    0.104561
Freq: D, dtype: float64
>>>
>>> print(ts['2017/1/2'])
0.10456068527347884
>>> print(ts['20170103'])
0.14008702206007018
>>> print(ts['1/10/2017'])
0.7621543091477885
>>> print(ts[datetime(2017,1,20)]) # 时间序列标签索引，支持各种时间字符串，以及datetime.datetime
0.8743928943800818
>>>

时间序列由于按照时间先后排序，故不用考虑顺序问题
索引方法同样适用于Dataframe

切片 ts['2017/1/5: 2017/1/10' ]按照index索引原理，末端包含哦

>>> rng = pd.date_range('2017/1','2017/3',freq = '12H')
>>> ts = pd.Series(np.random.rand(len(rng)), index = rng)
>>> print(ts['2017/1/5':'2017/1/10'])  # 和Series按照index索引原理一样 ，也是末端包含; 也可以加 ts.loc['2017/1/5':'2017/1/10']
2017-01-05 00:00:00    0.864954
2017-01-05 12:00:00    0.270408
2017-01-06 00:00:00    0.979987
2017-01-06 12:00:00    0.426279
2017-01-07 00:00:00    0.403995
2017-01-07 12:00:00    0.731792
2017-01-08 00:00:00    0.018432
2017-01-08 12:00:00    0.728155
2017-01-09 00:00:00    0.190817
2017-01-09 12:00:00    0.501240
2017-01-10 00:00:00    0.893398
2017-01-10 12:00:00    0.977586
Freq: 12H, dtype: float64
>>>
>>> print(ts['2017/2'].head()) # 传入月，直接得到一个切片; print(ts['1/2017'] 会把1月给你全部显示出来 可以直接切片.[::2]
2017-02-01 00:00:00    0.635405
2017-02-01 12:00:00    0.282502
2017-02-02 00:00:00    0.774583
2017-02-02 12:00:00    0.306548
2017-02-03 00:00:00    0.817818
Freq: 12H, dtype: float64
>>>

重复索引的时间序列

ts.is_unique 如果values值唯一，但index值不唯一，同样也会返回True；

>>> dates = pd.DatetimeIndex(['1/1/2015','1/2/2015','1/3/2015','1/4/2015','1/1/2015','1/2/2015'])
>>> ts = pd.Series(np.random.rand(6), index = dates)
>>> print(ts)
2015-01-01    0.943037
2015-01-02    0.426762
2015-01-03    0.838297
2015-01-04    0.963703
2015-01-01    0.080439
2015-01-02    0.997752
dtype: float64
>>> print(ts.is_unique,ts.index.is_unique)  # index有重复，values没有重复的；  is_unique是检查 → values唯一，index不唯一就返回True。
True False
>>> print(ts['20150101'],type(ts['20150101'])) # index有重复的将返回多个值
2015-01-01    0.943037
2015-01-01    0.080439
dtype: float64 <class 'pandas.core.series.Series'>
>>> print(ts['20150104'],type(ts['20150104']))
2015-01-04    0.963703
dtype: float64 <class 'pandas.core.series.Series'>
>>> print(ts.groupby(level = 0).mean())  # 通过groupby做分组，重复的值这里用平均值处理
2015-01-01    0.511738
2015-01-02    0.712257
2015-01-03    0.838297
2015-01-04    0.963703
dtype: float64
>>>

6.时间序列 - 重采样

从一个频率转化为另外一个频率，而且会有数据的聚合

将时间序列从一个频率转换为另一个频率的过程，且会有数据的结合

降采样：高频数据 → 低频数据，eg.以天为频率的数据转为以月为频率的数据
升采样：低频数据 → 高频数据，eg.以年为频率的数据转为以月为频率的数据

重采样：.resample()

创建一个以天为频率的TimeSeries，重采样为按2天为频率

ts.resample('2D').sum() / .mean() /.max() / .min() / .median() / .first() / .last() / .ohlc()

>>> rng = pd.date_range('20170101', periods = 12)
>>> ts = pd.Series(np.arange(12), index = rng)
>>> print(ts)
2017-01-01     0
2017-01-02     1
2017-01-03     2
2017-01-04     3
2017-01-05     4
2017-01-06     5
2017-01-07     6
2017-01-08     7
2017-01-09     8
2017-01-10     9
2017-01-11    10
2017-01-12    11
Freq: D, dtype: int32
>>> ts_re = ts.resample('5D')  #按照5天做一个重采样  ts.resample('5D')：  得到一个重采样构建器，频率改为5天  freq：重采样频率 → ts.resample('5D')
>>> ts_re2 = ts.resample('5D').sum() #做聚合，加个sum()  ts.resample('5D').sum():得到一个新的聚合后的Series，聚合方式为求和   .sum()：聚合方法
>>> print(ts_re, type(ts_re))  #得到的是一个构建器，并不是一个值
DatetimeIndexResampler [freq=<5 * Days>, axis=0, closed=left, label=left, convention=start, base=0] <class 'pandas.core.resample.DatetimeIndexResampler'>
>>> print(ts_re2, type(ts_re2))
2017-01-01    10
2017-01-06    35
2017-01-11    21
dtype: int32 <class 'pandas.core.series.Series'>
>>> print(ts.resample('5D').mean(),'→ 求平均值
')
2017-01-01     2.0
2017-01-06     7.0
2017-01-11    10.5
dtype: float64 → 求平均值

>>> print(ts.resample('5D').max(),'→ 求最大值
')
2017-01-01     4
2017-01-06     9
2017-01-11    11
dtype: int32 → 求最大值

>>> print(ts.resample('5D').min(),'→ 求最小值
')
2017-01-01     0
2017-01-06     5
2017-01-11    10
dtype: int32 → 求最小值

>>> print(ts.resample('5D').median(),'→ 求中值
')
2017-01-01     2.0
2017-01-06     7.0
2017-01-11    10.5
dtype: float64 → 求中值

>>> print(ts.resample('5D').first(),'→ 返回第一个值
')
2017-01-01     0
2017-01-06     5
2017-01-11    10
dtype: int32 → 返回第一个值

>>> print(ts.resample('5D').last(),'→ 返回最后一个值
')
2017-01-01     4
2017-01-06     9
2017-01-11    11
dtype: int32 → 返回最后一个值

>>> print(ts.resample('5D').ohlc(),'→ OHLC重采样
')  # OHLC:金融领域的时间序列聚合方式 → open开盘、high最大值、low最小值、close收盘
            open  high  low  close
2017-01-01     0     4    0      4
2017-01-06     5     9    5      9
2017-01-11    10    11   10     11 → OHLC重采样

降采样

ts.resample('5D', closed = 'left').sum() , #closed='left'为默认值也可以不写； left指定间隔左边为结束 → [1,2,3,4,5],[6,7,8,9,10],[11,12]

ts.resample('5D', closed = 'right').sum(),  #closed='right' right指定间隔右边为结束 → [1],[2,3,4,5,6],[7,8,9,10,11],[12]

>>> rng = pd.date_range('20170101', periods = 12)
>>> ts = pd.Series(np.arange(1,13), index = rng)
>>> print(ts)
2017-01-01     1
2017-01-02     2
2017-01-03     3
2017-01-04     4
2017-01-05     5
2017-01-06     6
2017-01-07     7
2017-01-08     8
2017-01-09     9
2017-01-10    10
2017-01-11    11
2017-01-12    12
Freq: D, dtype: int32
>>> print(ts.resample('5D').sum(),'→ 默认
') # 详解：这里values为0-11，按照5D重采样 → [1,2,3,4,5],[6,7,8,9,10],[11,12]
2017-01-01    15
2017-01-06    40
2017-01-11    23
dtype: int32 → 默认
# closed：各时间段哪一端是闭合（即包含）的，默认 左闭右闭 
>>> print(ts.resample('5D', closed = 'left').sum(),'→ left
') # left指定间隔左边为结束 → [1,2,3,4,5],[6,7,8,9,10],[11,12]
2017-01-01    15
2017-01-06    40
2017-01-11    23
dtype: int32 → left

>>> print(ts.resample('5D', closed = 'right').sum(),'→ right
') # right指定间隔右边为结束 → [1],[2,3,4,5,6],[7,8,9,10,11],[12]
2016-12-27     1
2017-01-01    20
2017-01-06    45
2017-01-11    12
dtype: int32 → right

>>> print(ts.resample('5D', label = 'left').sum(),'→ leftlabel
')  # label：聚合值的index，默认为分组之后的取左 # 值采样认为默认（这里closed默认）
2017-01-01    15
2017-01-06    40
2017-01-11    23
dtype: int32 → leftlabel

>>> print(ts.resample('5D', label = 'right').sum(),'→ rightlabel
')  #index标签取重采样之后的那个2017-01-06，left是默认的取2017-01-01
2017-01-06    15
2017-01-11    40
2017-01-16    23
dtype: int32 → rightlabel

>>>

升采样及插值

ts.resample('15T').asfreq() 低频转高频， .asfreq()：不做填充，返回Nan；   .ffill()：向上填充 ；  .bfill()：向下填充

>>> rng = pd.date_range('2017/1/1 0:0:0', periods = 5, freq = 'H')
>>> ts = pd.DataFrame(np.arange(15).reshape(5,3),
...                   index = rng,
...                   columns = ['a','b','c'])
>>> print(ts)
                      a   b   c
2017-01-01 00:00:00   0   1   2
2017-01-01 01:00:00   3   4   5
2017-01-01 02:00:00   6   7   8
2017-01-01 03:00:00   9  10  11
2017-01-01 04:00:00  12  13  14
>>> print(ts.resample('15T').asfreq())  # 低频转高频，主要是如何插值 # .asfreq()：不做填充，返回Nan
                        a     b     c
2017-01-01 00:00:00   0.0   1.0   2.0
2017-01-01 00:15:00   NaN   NaN   NaN
2017-01-01 00:30:00   NaN   NaN   NaN
2017-01-01 00:45:00   NaN   NaN   NaN
2017-01-01 01:00:00   3.0   4.0   5.0
2017-01-01 01:15:00   NaN   NaN   NaN
2017-01-01 01:30:00   NaN   NaN   NaN
2017-01-01 01:45:00   NaN   NaN   NaN
2017-01-01 02:00:00   6.0   7.0   8.0
2017-01-01 02:15:00   NaN   NaN   NaN
2017-01-01 02:30:00   NaN   NaN   NaN
2017-01-01 02:45:00   NaN   NaN   NaN
2017-01-01 03:00:00   9.0  10.0  11.0
2017-01-01 03:15:00   NaN   NaN   NaN
2017-01-01 03:30:00   NaN   NaN   NaN
2017-01-01 03:45:00   NaN   NaN   NaN
2017-01-01 04:00:00  12.0  13.0  14.0
>>> print(ts.resample('15T').ffill())  # .ffill()：向上填充
                      a   b   c
2017-01-01 00:00:00   0   1   2
2017-01-01 00:15:00   0   1   2
2017-01-01 00:30:00   0   1   2
2017-01-01 00:45:00   0   1   2
2017-01-01 01:00:00   3   4   5
2017-01-01 01:15:00   3   4   5
2017-01-01 01:30:00   3   4   5
2017-01-01 01:45:00   3   4   5
2017-01-01 02:00:00   6   7   8
2017-01-01 02:15:00   6   7   8
2017-01-01 02:30:00   6   7   8
2017-01-01 02:45:00   6   7   8
2017-01-01 03:00:00   9  10  11
2017-01-01 03:15:00   9  10  11
2017-01-01 03:30:00   9  10  11
2017-01-01 03:45:00   9  10  11
2017-01-01 04:00:00  12  13  14
>>> print(ts.resample('15T').bfill()) # .bfill()：向下填充
                      a   b   c
2017-01-01 00:00:00   0   1   2
2017-01-01 00:15:00   3   4   5
2017-01-01 00:30:00   3   4   5
2017-01-01 00:45:00   3   4   5
2017-01-01 01:00:00   3   4   5
2017-01-01 01:15:00   6   7   8
2017-01-01 01:30:00   6   7   8
2017-01-01 01:45:00   6   7   8
2017-01-01 02:00:00   6   7   8
2017-01-01 02:15:00   9  10  11
2017-01-01 02:30:00   9  10  11
2017-01-01 02:45:00   9  10  11
2017-01-01 03:00:00   9  10  11
2017-01-01 03:15:00  12  13  14
2017-01-01 03:30:00  12  13  14
2017-01-01 03:45:00  12  13  14
2017-01-01 04:00:00  12  13  14
>>>

时期重采样 - Period

>>> prng = pd.period_range('2016','2017',freq = 'M')
>>> ts = pd.Series(np.arange(len(prng)), index = prng)
>>> print(ts)
2016-01     0
2016-02     1
2016-03     2
2016-04     3
2016-05     4
2016-06     5
2016-07     6
2016-08     7
2016-09     8
2016-10     9
2016-11    10
2016-12    11
2017-01    12
Freq: M, dtype: int32 
>>> print(ts.resample('3M').sum()) #降采样
2016-01-31     0
2016-04-30     6
2016-07-31    15
2016-10-31    24
2017-01-31    33
Freq: 3M, dtype: int32
>>> print(ts.resample('15D').ffill())  # 升采样
2016-01-01     0
2016-01-16     0
2016-01-31     0
2016-02-15     1
2016-03-01     2
2016-03-16     2
2016-03-31     2
2016-04-15     3
2016-04-30     3
2016-05-15     4
2016-05-30     4
2016-06-14     5
2016-06-29     5
2016-07-14     6
2016-07-29     6
2016-08-13     7
2016-08-28     7
2016-09-12     8
2016-09-27     8
2016-10-12     9
2016-10-27     9
2016-11-11    10
2016-11-26    10
2016-12-11    11
2016-12-26    11
2017-01-10    12
2017-01-25    12
Freq: 15D, dtype: int32
>>>

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原文地址：https://www.cnblogs.com/shengyang17/p/9514557.html