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 >>>