（三篇长文让你玩6Pandas）数据分析入门_PART2常用工具包_CH02数据分析工具：Pandas__Part02(时间序列全面分析)
2019-07-01 20:44
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''' 【课程2.8】 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 '''
'\n【课程2.8】 时间模块：datetime\n\ndatetime模块，主要掌握：datetime.date(), datetime.datetime(), datetime.timedelta()\n\n日期解析方法：parser.parse\n\n'
# datetime.date：date对象 import datetime # datetime.date.today 返回今日 # date(年，月，日) → 直接得到当时日期 # 输出格式为 datetime.date类 today=datetime.date.today() print(today,type(today)) t=datetime.date(2016,1,1) print(t,type(t))
2019-07-01 <class 'datetime.date'> 2016-01-01 <class 'datetime.date'>
# datetime.datetime：datetime对象!重点 # datetime.datetime.now()方法，输出当前时间 # datetime.datetime(年，月，日，时，分，秒)，至少输入年月日 # 输出格式为 datetime类 # 可通过str()转化为字符串 now=datetime.datetime.now() t1=datetime.datetime(2019,6,26) t2=datetime.datetime(2019,6,26,11,55,55) print(now,type(now)) print(str(now),type(str(now))) print(t1,type(t1)) print(t2,type(t2)) tdelta=t2-t1 print(tdelta,type(tdelta)) # 相减得到时间差 —— timedelta
2019-07-01 12:55:12.485330 <class 'datetime.datetime'> 2019-07-01 12:55:12.485330 <class 'str'> 2019-06-26 00:00:00 <class 'datetime.datetime'> 2019-06-26 11:55:55 <class 'datetime.datetime'> 11:55:55 <class 'datetime.timedelta'>
# datetime.timedelta：时间差 # 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” today=datetime.datetime.today() # datetime.datetime也有today()方法 yestoday=today-datetime.timedelta(1) print(today) print(yestoday)
2019-07-01 12:55:12.492313 2019-06-30 12:55:12.492313
# parser.parse：日期字符串转换
from dateutil.parser import parse
date='12-21-2017'
t=parse(date)
print(t,type(t))
# 直接将str转化成datetime.datetime
print(parse('2000-1-1'),'\n',
parse('5/1/2014'),'\n',
parse('5/1/2014', dayfirst = True),'\n', # 国际通用格式中，日在月之前，可以通过dayfirst来设置
parse('22/1/2014'),'\n',
parse('Jan 31, 1997 10:45 PM'))
# 各种格式可以解析，但无法支持中文
2017-12-21 00:00:00 <class 'datetime.datetime'> 2000-01-01 00:00:00 2014-05-01 00:00:00 2014-01-05 00:00:00 2014-01-22 00:00:00 1997-01-31 22:45:00
#作业answer
import datetime
#作业1
d1=datetime.datetime.now()
print('请输出当前时间:',d1)
d2=datetime.datetime(2017,5,1)
print('请输出时间:',d2)
d3=datetime.datetime(2000,12,1)
print('请输出时间:',d3)
请输出当前时间: 2019-07-01 12:55:12.506274 请输出时间: 2017-05-01 00:00:00 请输出时间: 2000-12-01 00:00:00
#作业2 d=datetime.datetime(2000,5,1) print(d+datetime.timedelta(1000))
2003-01-26 00:00:00
''' 【课程2.9】 Pandas时刻数据：Timestamp 时刻数据代表时间点，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 pandas.Timestamp() '''
'\n【课程2.9】 Pandas时刻数据：Timestamp\n\n时刻数据代表时间点，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\n\npandas.Timestamp()\n\n'
# pd.Timestamp() import numpy as np import pandas as pd date1=datetime.datetime(2016,12,1,12,12,12)# 创建一个datetime.datetime date2='2017 12 21'# 创建一个字符串 t1=pd.Timestamp(date1) t2=pd.Timestamp(date2) print(t1,type(t1)) print(t2,type(t2)) # 直接生成pandas的时刻数据 → 时间戳 # 数据类型为 pandas的Timestamp #注1 np.Timestamp和datetime.datetime都是时间戳只是所属模块不同，np.Timestamp更常用 #Timestamp(t)方法既可以识别时间格式字符串（常用方式都可）也可识别datetime
2016-12-01 12:12:12 <class 'pandas._libs.tslibs.timestamps.Timestamp'> 2017-12-21 00:00:00 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
# pd.to_datetime from datetime import datetime date1 = datetime(2016,12,1,12,45,30) date2 = '2017-12-21' t1 = pd.to_datetime(date1) t2 = pd.to_datetime(date2) print(t1,type(t1)) print(t2,type(t2)) # 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 lst_date=['2017-12-21', '2017-12-22', '2017-12-23'] t3=pd.to_datetime(lst_date) print(t3,type(t3)) # 多个时间数据，将会转换为pandas的DatetimeIndex
2016-12-01 12:45:30 <class 'pandas._libs.tslibs.timestamps.Timestamp'> 2017-12-21 00:00:00 <class 'pandas._libs.tslibs.timestamps.Timestamp'> DatetimeIndex(['2017-12-21', '2017-12-22', '2017-12-23'], dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
# pd.to_datetime → 多个时间数据转换时间戳索引 date1 = [datetime(2015,6,1),datetime(2015,7,1),datetime(2015,8,1),datetime(2015,9,1),datetime(2015,10,1)] date2 = ['2017-2-1','2017-2-2','2017-2-3','2017-2-4','2017-2-5','2017-2-6'] print(date1) print(date2) t1 = pd.to_datetime(date2) t2 = pd.to_datetime(date2) print(t1) print(t2) # 多个时间数据转换为 DatetimeIndex date3 = ['2017-2-1','2017-2-2','2017-2-3','hello world!','2017-2-5','2017-2-6'] t3 = pd.to_datetime(date3, errors = 'ignore') print(t3,type(t3)) # 当一组时间序列中夹杂其他格式数据，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listofindex t4 = pd.to_datetime(date3, errors = 'coerce') print(t4,type(t4)) # errors = 'coerce'(强迫变成DatetimeIndex），缺失值返回NaT（Not a Time），结果为DatetimeIndex
[datetime.datetime(2015, 6, 1, 0, 0), datetime.datetime(2015, 7, 1, 0, 0), datetime.datetime(2015, 8, 1, 0, 0), datetime.datetime(2015, 9, 1, 0, 0), datetime.datetime(2015, 10, 1, 0, 0)] ['2017-2-1', '2017-2-2', '2017-2-3', '2017-2-4', '2017-2-5', '2017-2-6'] DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', '2017-02-04', '2017-02-05', '2017-02-06'], dtype='datetime64[ns]', freq=None) DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', '2017-02-04', '2017-02-05', '2017-02-06'], dtype='datetime64[ns]', freq=None) Index(['2017-2-1', '2017-2-2', '2017-2-3', 'hello world!', '2017-2-5', '2017-2-6'], dtype='object') <class 'pandas.core.indexes.base.Index'> DatetimeIndex(['2017-02-01', '2017-02-02', '2017-02-03', 'NaT', '2017-02-05', '2017-02-06'], dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
#作业answer
#作业1
import numpy as np
import pandas as pd
lst=[]
for i in range(1,32):
lst.append('2017-12-%i'%i)
print(lst)
d=pd.to_datetime(lst)
print(d)
print('月中日期为:\n',d[16])
['2017-12-1', '2017-12-2', '2017-12-3', '2017-12-4', '2017-12-5', '2017-12-6', '2017-12-7', '2017-12-8', '2017-12-9', '2017-12-10', '2017-12-11', '2017-12-12', '2017-12-13', '2017-12-14', '2017-12-15', '2017-12-16', '2017-12-17', '2017-12-18', '2017-12-19', '2017-12-20', '2017-12-21', '2017-12-22', '2017-12-23', '2017-12-24', '2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30', '2017-12-31'] DatetimeIndex(['2017-12-01', '2017-12-02', '2017-12-03', '2017-12-04', '2017-12-05', '2017-12-06', '2017-12-07', '2017-12-08', '2017-12-09', '2017-12-10', '2017-12-11', '2017-12-12', '2017-12-13', '2017-12-14', '2017-12-15', '2017-12-16', '2017-12-17', '2017-12-18', '2017-12-19', '2017-12-20', '2017-12-21', '2017-12-22', '2017-12-23', '2017-12-24', '2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30', '2017-12-31'], dtype='datetime64[ns]', freq=None) 月中日期为: 2017-12-17 00:00:00
f=open(r'C:\Users\HASEE\Desktop\pythontest\timetest','w')
stwrite='20150101,20150102,20150103,20150104,20150105,20150106,20150107,20150108'
f.write(stwrite)
f.close()
f=open(r'C:\Users\HASEE\Desktop\pythontest\timetest','r')
stread=f.read()
lst=stread.split(',')
print(lst)
print(pd.to_datetime(lst))
['20150101', '20150102', '20150103', '20150104', '20150105', '20150106', '20150107', '20150108'] DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04', '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08'], dtype='datetime64[ns]', freq=None)
''' 【课程2.10】 Pandas时间戳索引：DatetimeIndex 核心：pd.date_range() '''
'\n【课程2.10】 Pandas时间戳索引：DatetimeIndex\n\n核心：pd.date_range()\n\n'
# pd.DatetimeIndex()即listoftimestamp #与TimeSeries时间序列 listoftimestamp为index的序列 # 直接生成时间戳索引，支持str、datetime.datetime # !!!单个时间戳为Timestamp，多个时间戳为DatetimeIndex rng=pd.DatetimeIndex(['12/1/2017','12/2/2017','12/3/2017','12/4/2017','12/5/2017']) print(rng,type(rng)) print(rng[0],type(rng[0])) # !!!以DatetimeIndex为index的Series，为TimeSries，时间序列 st=pd.Series(np.random.rand(len(rng)),index=rng) print(st,type(st)) print(st.index)
DatetimeIndex(['2017-12-01', '2017-12-02', '2017-12-03', '2017-12-04', '2017-12-05'], dtype='datetime64[ns]', freq=None) <class 'pandas.core.indexes.datetimes.DatetimeIndex'> 2017-12-01 00:00:00 <class 'pandas._libs.tslibs.timestamps.Timestamp'> 2017-12-01 0.745300 2017-12-02 0.876081 2017-12-03 0.142995 2017-12-04 0.938318 2017-12-05 0.043834 dtype: float64 <class 'pandas.core.series.Series'> DatetimeIndex(['2017-12-01', '2017-12-02', '2017-12-03', '2017-12-04', '2017-12-05'], dtype='datetime64[ns]', freq=None)
# pd.date_range()-日期范围：生成日期范围格式为DatetimeIndex 即listoftimestamp
# 2种生成方式：①start + end； ②start/end + periods
# 默认频率：day
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,type(rng1))
print(rng2)
print(rng3)
print('--------------')
# 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)
print(rng4)
print('-------')
# 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）
# name：索引对象名称
print(pd.date_range('20170101','20170104')) # 20170101也可读取
print(pd.date_range('20170101','20170104',closed = 'right'))
print(pd.date_range('20170101','20170104',closed = 'left'))
print('-------')
# closed：默认为None的情况下，左闭右闭，left则只左闭右开，right则只左开右闭
print(pd.bdate_range('20170101','20170107'))
# pd.bdate_range()默认频率为工作日
print(list(pd.date_range(start = '1/1/2017', periods = 10)))
# 直接转化为list，元素为Timestamp
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'>
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')
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')
--------------
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')
-------
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D')
-------
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05',
'2017-01-06'],
dtype='datetime64[ns]', freq='B')
[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'), 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'), Timestamp('2017-01-10 00:00:00', freq='D')]
# pd.date_range()-日期范围：频率(1)
print(pd.date_range('2017/1/1','2017/1/4')) # 默认freq = 'D'：每日历日
print(pd.date_range('2017/1/1','2017/1/4', freq = 'B')) # B：每工作日
print(pd.date_range('2017/1/1','2017/1/2', freq = 'H')) # H：每小时
print(pd.date_range('2017/1/1 12:00','2017/1/1 12:10', freq = 'T')) # T/MIN：每分
print(pd.date_range('2017/1/1 12:00:00','2017/1/1 12:00:10', freq = 'S')) # S：每秒
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）
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）
print(pd.date_range('20170101','20170201',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
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
#week of month-2mon
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D') DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='B') 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') 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') 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') 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') 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') DatetimeIndex(['2017-01-02', '2017-01-09', '2017-01-16', '2017-01-23', '2017-01-30'], dtype='datetime64[ns]', freq='W-MON') 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：频率(2)
print(pd.date_range('2017','2018', freq = 'M'))
print(pd.date_range('2017','2020', freq = 'Q-DEC'))
print(pd.date_range('2017','2020', freq = 'A-DEC'))
print('------')
# M：每月最后一个日历日
# 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
#quarter-month
# 所以Q-月只有三种情况：1-4-7-10,2-5-8-11,3-6-9-12
# A-月：每年指定月份的最后一个日历日
#annual-month
# 月缩写：JAN/FEB/MAR/APR/MAY/JUN/JUL/AUG/SEP/OCT/NOV/DEC
print(pd.date_range('2017','2018', freq = 'BM'))
print(pd.date_range('2017','2020', freq = 'BQ-DEC'))
print(pd.date_range('2017','2020', freq = 'BA-DEC'))
print('------')
# BM：每月最后一个工作日
# 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
# BA-月：每年指定月份的最后一个工作日
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') 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') DatetimeIndex(['2017-12-31', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='A-DEC') ------ 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') 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') DatetimeIndex(['2017-12-29', '2018-12-31', '2019-12-31'], dtype='datetime64[ns]', freq='BA-DEC') ------
# pd.date_range()-日期范围：复合频率
print(pd.date_range('2017/1/1','2017/2/1', freq = '7D')) # 7天
print(pd.date_range('2017/1/1','2017/1/2', freq = '2h30min')) # 2小时30分钟
print(pd.date_range('2017','2018', freq = '2M')) # 2月，每月最后一个日历日
DatetimeIndex(['2017-01-01', '2017-01-08', '2017-01-15', '2017-01-22', '2017-01-29'], dtype='datetime64[ns]', freq='7D') 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') DatetimeIndex(['2017-01-31', '2017-03-31', '2017-05-31', '2017-07-31', '2017-09-30', '2017-11-30'], dtype='datetime64[ns]', freq='2M')
# 针对TimeSeries的操作- asfreq：时期频率转换
ts=pd.Series(np.random.rand(4),
index=pd.date_range('20170101','20170104'))
print(ts)
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(front fill)用之前值填充
#bfill(behind fill)用之后值填充
2017-01-01 0.435837 2017-01-02 0.669417 2017-01-03 0.198081 2017-01-04 0.139088 Freq: D, dtype: float64 2017-01-01 00:00:00 0.435837 2017-01-01 04:00:00 0.435837 2017-01-01 08:00:00 0.435837 2017-01-01 12:00:00 0.435837 2017-01-01 16:00:00 0.435837 2017-01-01 20:00:00 0.435837 2017-01-02 00:00:00 0.669417 2017-01-02 04:00:00 0.669417 2017-01-02 08:00:00 0.669417 2017-01-02 12:00:00 0.669417 2017-01-02 16:00:00 0.669417 2017-01-02 20:00:00 0.669417 2017-01-03 00:00:00 0.198081 2017-01-03 04:00:00 0.198081 2017-01-03 08:00:00 0.198081 2017-01-03 12:00:00 0.198081 2017-01-03 16:00:00 0.198081 2017-01-03 20:00:00 0.198081 2017-01-04 00:00:00 0.139088 Freq: 4H, dtype: float64
# pd.date_range()-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)
print(ts.shift(2))
print(ts.shift(-2))
print('------')
# 正数：数值后移（滞后）；负数：数值前移（超前）
per = ts/ts.shift(1) - 1
print(per)
print('------')
# 计算变化百分比，这里计算：该时间戳与上一个时间戳相比（ts,shift(1)将昨天的数据移到今天的日期下)，变化百分比
print(ts.shift(2, freq = 'D'))#将所有时间戳+2D
print(ts.shift(2, freq = 'T'))#将所有时间戳+2min
# 加上freq参数：对时间戳进行位移！！！，而不是对数值进行位移
2017-01-01 0.696547 2017-01-02 0.529174 2017-01-03 0.404338 2017-01-04 0.741592 Freq: D, dtype: float64 2017-01-01 NaN 2017-01-02 NaN 2017-01-03 0.696547 2017-01-04 0.529174 Freq: D, dtype: float64 2017-01-01 0.404338 2017-01-02 0.741592 2017-01-03 NaN 2017-01-04 NaN Freq: D, dtype: float64 ------ 2017-01-01 NaN 2017-01-02 -0.240289 2017-01-03 -0.235907 2017-01-04 0.834088 Freq: D, dtype: float64 ------ 2017-01-03 0.696547 2017-01-04 0.529174 2017-01-05 0.404338 2017-01-06 0.741592 Freq: D, dtype: float64 2017-01-01 00:02:00 0.696547 2017-01-02 00:02:00 0.529174 2017-01-03 00:02:00 0.404338 2017-01-04 00:02:00 0.741592 Freq: D, dtype: float64
#总结2.10 #1pd.DatetimeIndex 即listoftimestamp #2TimeSeries 即index为DatetimeIndex的Series #3pd.date_range()即DatetimeIndex生成器 #4pd.date_range()常见freq用法 #对TimeSeries的操作 ts.asfreq（）以及ts.shift()
#作业answer #answer 1 #生成TimeSeries就两步 #1生成dateindex（一般用date_range） #2生成TimeSeries #1生成dateindex di1=pd.date_range(start='2017-01',periods=5,freq='D') #生成TimeSeries ts1=pd.Series(data=np.random.rand(5),index=di1) print(ts1) di2=pd.date_range(start='2017-01-31',periods=5,freq='Q-Jan') ts2=pd.Series(data=np.random.rand(5),index=di2) print(ts2) di3=pd.date_range(start='2017-12-01',periods=4,freq='10t') tdf=pd.DataFrame(data=np.random.rand(4,4), columns=['value1','value2','value3','value4'], index=di3 ) print(tdf)
2017-01-01 0.324245 2017-01-02 0.115597 2017-01-03 0.407691 2017-01-04 0.996330 2017-01-05 0.495723 Freq: D, dtype: float64 2017-01-31 0.650247 2017-04-30 0.484225 2017-07-31 0.464591 2017-10-31 0.641705 2018-01-31 0.795587 Freq: Q-JAN, dtype: float64 value1 value2 value3 value4 2017-12-01 00:00:00 0.494249 0.925107 0.769139 0.950450 2017-12-01 00:10:00 0.170856 0.021507 0.119379 0.391953 2017-12-01 00:20:00 0.848345 0.312652 0.699692 0.931733 2017-12-01 00:30:00 0.559365 0.542697 0.125891 0.055697
di1=pd.date_range(start='2017 05 01 12',periods=5,freq='10min') ts1=pd.Series(data=np.random.rand(5),index=di1) print(ts1) ts2=ts1.asfreq(freq='5t',method='ffill') print(ts2)
2017-05-01 12:00:00 0.478802 2017-05-01 12:10:00 0.290849 2017-05-01 12:20:00 0.895803 2017-05-01 12:30:00 0.109338 2017-05-01 12:40:00 0.697874 Freq: 10T, dtype: float64 2017-05-01 12:00:00 0.478802 2017-05-01 12:05:00 0.478802 2017-05-01 12:10:00 0.290849 2017-05-01 12:15:00 0.290849 2017-05-01 12:20:00 0.895803 2017-05-01 12:25:00 0.895803 2017-05-01 12:30:00 0.109338 2017-05-01 12:35:00 0.109338 2017-05-01 12:40:00 0.697874 Freq: 5T, dtype: float64
''' 【课程2.11】 Pandas时期：Period 和TimeStamp不同 Period代表时期 TimeStamp代表时间点 核心：pd.Period() pd.period_range() '''
'\n【课程2.11】 Pandas时期：Period 和TimeStamp不同 \nPeriod代表时期 \nTimeStamp代表时间点\n\n\n核心：pd.Period()\n pd.period_range()\n\n'
# pd.Period()创建单个时期period 类比于pd.TimeStamp()
p = pd.Period('2017',freq='m')
print(p,type(p))
print(p+1)
print(p-2)
print(pd.Period('2012',freq='a')-1)
# 通过加减整数，按照 freq的单位，将单个period整体移动
2017-01 <class 'pandas._libs.tslibs.period.Period'> 2017-02 2016-11 2011
# pd.period_range()创建多个period listofperiods
prng=pd.period_range('20110101','20120101',freq='m')
print(prng,type(prng))
print(prng[0],type(prng[0]))
# 数据格式为PeriodIndex，单个数值为Period
ts = pd.Series(np.random.rand(len(prng)), index = prng)
print(ts,type(ts))
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') <class 'pandas.core.indexes.period.PeriodIndex'> 2011-01 <class 'pandas._libs.tslibs.period.Period'> 2011-01 0.270300 2011-02 0.528558 2011-03 0.626401 2011-04 0.812753 2011-05 0.353819 2011-06 0.150295 2011-07 0.461801 2011-08 0.533077 2011-09 0.054538 2011-10 0.833580 2011-11 0.894351 2011-12 0.383080 2012-01 0.181327 Freq: M, dtype: float64 <class 'pandas.core.series.Series'> 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')
# asfreq：频率转换
#TimeStamp和period freq的区别
#TimeStamp freq变的是采取区间，但是采取点单位一般不变
#eg freq=“M” 采取区间虽然是M，但是采取点还是每个M的最后1D
#除非 freq变得比D小 采取单位才会变
#eg freq='t' 采取单位变为分钟
#period变的是采取单位
#freq=‘M’则采取单位也是月
#但是period把freq变小不会增加数量 eg 2010-1 2010-2 freq='m'
#把freq='d'后不会有DateTimeSeries那样fill的方法 只能选start or end 挑一个月的一天
p = pd.Period('2017','A')
print(p)
print(p.asfreq('M', how = 'start')) # 也可写 how = 's'
print(p.asfreq('D', how = 'end')) # 也可写 how = 'e'
print('--------')
# 通过.asfreq(freq, method=None, how=None)方法转换成别的频率
prng = pd.period_range('2017','2018',freq = 'M')
ts1 = pd.Series(np.random.rand(len(prng)), index = prng)
ts2 = pd.Series(np.random.rand(len(prng)), index = prng.asfreq('D', how = 'start'))
print(ts1.head(),len(ts1))
print(ts2.head(),len(ts2))
# asfreq也可以转换TIMESeries的index
2017 2017-01 2017-12-31 -------- 2017-01 0.404408 2017-02 0.064027 2017-03 0.821275 2017-04 0.995918 2017-05 0.281146 Freq: M, dtype: float64 13 2017-01-01 0.920413 2017-02-01 0.320414 2017-03-01 0.847685 2017-04-01 0.261885 2017-05-01 0.145836 Freq: D, dtype: float64 13
# 时间戳与时期之间的转换：pd.to_period()、pd.to_timestamp()
rng = pd.date_range('2017/1/1', periods = 10, freq = 'M')
prng = pd.period_range('2017','2018', freq = 'M')
print(rng)
print(prng)
print('----------')
#既可以直接在 listoftimestamp 和 listofperiods 之间转换
print(rng.to_period(freq='a'))
#也可以直接对ts进行转换
# 每月最后一日，转化为每月
ts1 = pd.Series(np.random.rand(len(rng)), index = rng)
print(ts1)
print(ts1.to_period())
ts2 = pd.Series(np.random.rand(len(prng)), index = prng)
print(ts2)
print(ts2.to_timestamp(how='S'))
# 每月，转化为每月第一天
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'], dtype='datetime64[ns]', freq='M') PeriodIndex(['2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11', '2017-12', '2018-01'], dtype='period[M]', freq='M') ---------- PeriodIndex(['2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC') 2017-01-31 0.079345 2017-02-28 0.699806 2017-03-31 0.193150 2017-04-30 0.380251 2017-05-31 0.218401 2017-06-30 0.282242 2017-07-31 0.450215 2017-08-31 0.708842 2017-09-30 0.928040 2017-10-31 0.697656 Freq: M, dtype: float64 2017-01 0.079345 2017-02 0.699806 2017-03 0.193150 2017-04 0.380251 2017-05 0.218401 2017-06 0.282242 2017-07 0.450215 2017-08 0.708842 2017-09 0.928040 2017-10 0.697656 Freq: M, dtype: float64 2017-01 0.543350 2017-02 0.787547 2017-03 0.842127 2017-04 0.020524 2017-05 0.981086 2017-06 0.391997 2017-07 0.639751 2017-08 0.654858 2017-09 0.571412 2017-10 0.136572 2017-11 0.537329 2017-12 0.834277 2018-01 0.497881 Freq: M, dtype: float64 2017-01-01 0.543350 2017-02-01 0.787547 2017-03-01 0.842127 2017-04-01 0.020524 2017-05-01 0.981086 2017-06-01 0.391997 2017-07-01 0.639751 2017-08-01 0.654858 2017-09-01 0.571412 2017-10-01 0.136572 2017-11-01 0.537329 2017-12-01 0.834277 2018-01-01 0.497881 Freq: MS, dtype: float64
#作业answer p1=pd.period_range(start='2017-01',end='2017-05',freq='m') ts1=pd.Series(data=np.random.rand(5),index=p1) print(ts1) p2=pd.period_range(start='2017-01-1',periods=5,freq='2h') ts2=pd.Series(data=np.random.rand(5),index=p2) print(ts2)
2017-01 0.884941 2017-02 0.499193 2017-03 0.157167 2017-04 0.964902 2017-05 0.059249 Freq: M, dtype: float64 2017-01-01 00:00 0.865604 2017-01-01 02:00 0.262509 2017-01-01 04:00 0.087934 2017-01-01 06:00 0.834944 2017-01-01 08:00 0.915583 Freq: 2H, dtype: float64
'''
#总结2.11
1 p = pd.Period('2017',freq='m')
创建单个period
2 prng=pd.period_range('20110101','20120101',freq='m')
创建list of period
3p = pd.Period('2017','A')
print(p)
print(p.asfreq('M', how = 'start'))
更改period的频率
4pd.to_period()、pd.to_timestamp()
timestamp和period之间的转换
既可以直接在 listoftimestamp 和 listofperiods 之间转换
也可以直接对ts进行转换
'''
"\n#总结2.11\n1 p = pd.Period('2017',freq='m')\n创建单个period\n\n2 prng=pd.period_range('20110101','20120101',freq='m')\n创建list of period\n\n3p = pd.Period('2017','A')\nprint(p)\nprint(p.asfreq('M', how = 'start')) \n更改period的频率\n\n4pd.to_period()、pd.to_timestamp() \ntimestamp和period之间的转换\n既可以直接在 listoftimestamp 和 listofperiods 之间转换\n也可以直接对ts进行转换\n\n\n"
''' 【课程2.12】 时间序列 - 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 '''
'\n【课程2.12】 时间序列 - 索引及切片\n\nTimeSeries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\n\n同时TimeSeries通过时间序列有更便捷的方法做索引和切片\n \n'
#索引
from datetime import datetime
rng=pd.date_range('2017/1','2017/3')
ts=pd.Series(np.random.rand(len(rng)),index=rng)
print(ts.head())
#1 ts既支持基本下标位置索引
print(ts[0])
print(ts[:2])
print('-----')
#2 ts也支持 支持各种时间字符串，以及datetime.datetime
print(ts['2017/1/2'])
print(ts['20170103'])
print(ts['1/10/2017'])
print(ts[datetime(2017,1,20)])
print('-----')
# 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
2017-01-01 0.659568 2017-01-02 0.151966 2017-01-03 0.288654 2017-01-04 0.583045 2017-01-05 0.769449 Freq: D, dtype: float64 0.65956785928946 2017-01-01 0.659568 2017-01-02 0.151966 Freq: D, dtype: float64 ----- 0.15196600248594228 0.2886538761520884 0.0023193408918270597 0.20779042212900634 -----
# 切片
rng = pd.date_range('2017/1','2017/3',freq = '12H')
ts = pd.Series(np.random.rand(len(rng)), index = rng)
# 支持各种时间字符串时间切片，和Series按照数字index索引原理一样，也是末端包含
print(ts['2017/1/5':'2017/1/10'])
print('-----')
# 传入月，直接得到一个切片
print(ts['2017/2'].head())
2017-01-05 00:00:00 0.318971 2017-01-05 12:00:00 0.188438 2017-01-06 00:00:00 0.187238 2017-01-06 12:00:00 0.980087 2017-01-07 00:00:00 0.161376 2017-01-07 12:00:00 0.215822 2017-01-08 00:00:00 0.935772 2017-01-08 12:00:00 0.880521 2017-01-09 00:00:00 0.168200 2017-01-09 12:00:00 0.276908 2017-01-10 00:00:00 0.795692 2017-01-10 12:00:00 0.096272 Freq: 12H, dtype: float64 ----- 2017-02-01 00:00:00 0.536472 2017-02-01 12:00:00 0.063015 2017-02-02 00:00:00 0.159657 2017-02-02 12:00:00 0.614637 2017-02-03 00:00:00 0.984499 Freq: 12H, dtype: float64
# 重复索引的时间序列
# index有重复，is_unique检查 → values唯一，index不唯一
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)
print(ts.is_unique,ts.index.is_unique)
print('-----')
print(ts['20150101'],type(ts['20150101']))
print(ts['20150104'],type(ts['20150104']))
print('-----')
# index有重复的将返回多个值,类型为Series
# 通过groupby做分组，重复的值这里用平均值处理
print(ts.groupby(level=0).mean)
2015-01-01 0.510849 2015-01-02 0.791319 2015-01-03 0.552804 2015-01-04 0.678201 2015-01-01 0.571113 2015-01-02 0.584924 dtype: float64 True False ----- 2015-01-01 0.510849 2015-01-01 0.571113 dtype: float64 <class 'pandas.core.series.Series'> 2015-01-04 0.678201 dtype: float64 <class 'pandas.core.series.Series'> ----- <bound method GroupBy.mean of <pandas.core.groupby.generic.SeriesGroupBy object at 0x0000024CEA8505C0>>
#作业answer
di=pd.date_range(start='2017/12/1',end='2017/12/5 12:00:00',freq='12H')
ts=pd.DataFrame(data=np.random.rand(10,3),index=di,columns=['value1','value2','value3'])
print(ts)
print('---------------')
#1
print(ts.iloc[0:3])
print('---------------')
#2
print(ts.loc['2017-12-4 12:00:00'])
print('---------------')
#3
print(ts.loc['2017-12-4' : '2017-12-5'])
value1 value2 value3 2017-12-01 00:00:00 0.134941 0.767928 0.157234 2017-12-01 12:00:00 0.869153 0.842300 0.358291 2017-12-02 00:00:00 0.728543 0.706397 0.737571 2017-12-02 12:00:00 0.433480 0.464147 0.987012 2017-12-03 00:00:00 0.196458 0.474911 0.709055 2017-12-03 12:00:00 0.076818 0.907870 0.886325 2017-12-04 00:00:00 0.784369 0.985650 0.652622 2017-12-04 12:00:00 0.058532 0.784861 0.318300 2017-12-05 00:00:00 0.943825 0.025305 0.848250 2017-12-05 12:00:00 0.636666 0.767742 0.773654 --------------- value1 value2 value3 2017-12-01 00:00:00 0.134941 0.767928 0.157234 2017-12-01 12:00:00 0.869153 0.842300 0.358291 2017-12-02 00:00:00 0.728543 0.706397 0.737571 --------------- value1 0.058532 value2 0.784861 value3 0.318300 Name: 2017-12-04 12:00:00, dtype: float64 --------------- value1 value2 value3 2017-12-04 00:00:00 0.784369 0.985650 0.652622 2017-12-04 12:00:00 0.058532 0.784861 0.318300 2017-12-05 00:00:00 0.943825 0.025305 0.848250 2017-12-05 12:00:00 0.636666 0.767742 0.773654
''' #总结2.12 1 ts的索引 2 ts的切片 3 ts判断是否重复（data index） '''
'\n#总结2.12\n1 ts的索引\n2 ts的切片\n3 ts判断是否重复（data index）\n'
# 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
rng = pd.date_range('20170101', periods = 12)
ts = pd.Series(np.arange(12), index = rng)
print(ts)
print('-----')
ts_re = ts.resample('5D')
ts_re2 = ts.resample('5D').sum()
#如果不给聚合方法只能得到一个生成器
print(ts_re, type(ts_re))
print('-----')
print(ts_re2, type(ts_re2))
print('-----')
# ts.resample('5D')：得到一个重采样构建器，频率改为5天
# ts.resample('5D').sum():得到一个新的聚合后的Series，聚合方式为求和
# freq：重采样频率 → ts.resample('5D')
# .sum()：聚合方法
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 ----- DatetimeIndexResampler [freq=<5 * Days>, axis=0, closed=left, label=left, convention=start, base=0] <class 'pandas.core.resample.DatetimeIndexResampler'> ----- 2017-01-01 10 2017-01-06 35 2017-01-11 21 Freq: 5D, dtype: int32 <class 'pandas.core.series.Series'> -----
# 降采样 多项降为少项 时间点变大
rng=pd.date_range('20170101',periods=12)
ts=pd.Series(np.arange(1,13),index=rng)
print(ts)
print('---------')
print(ts.resample('5D').sum(),'→ 默认\n')
print(ts.resample('5D', closed = 'left').sum(),'→ left\n')
print(ts.resample('5D', closed = 'right').sum(),'→ right\n')
print('-----')
# closed：各时间段哪一端是闭合（即包含）的，默认 左闭右闭
# 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]
#resample（）降采样时必须要按照指定频率补全，如果无值也要补全
# left指定间隔左闭右开 → [1,2,3,4,5,6),[6,7,8,9,10,11),[11,12,(后面为补充的空值)13,14,15,16)
# right指定间隔左开右闭 → （27,28,29,30,31,（前面为补充的上个月空值）1],(1,2,3,4,5,6],(6,7,8,9,10,11],(11,12,13,14,15,16]
print(ts.resample('5D', label = 'left').sum(),'→ leftlabel\n')
print(ts.resample('5D', label = 'right').sum(),'→ rightlabel\n')
# label：聚合值的index，默认为取左
# 值采样认为默认（这里closed默认）
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 --------- 2017-01-01 15 2017-01-06 40 2017-01-11 23 Freq: 5D, dtype: int32 → 默认 2017-01-01 15 2017-01-06 40 2017-01-11 23 Freq: 5D, dtype: int32 → left 2016-12-27 1 2017-01-01 20 2017-01-06 45 2017-01-11 12 Freq: 5D, dtype: int32 → right ----- 2017-01-01 15 2017-01-06 40 2017-01-11 23 Freq: 5D, dtype: int32 → leftlabel 2017-01-06 15 2017-01-11 40 2017-01-16 23 Freq: 5D, dtype: int32 → rightlabel
# 升采样及插值
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)
print('---------')
print(ts.resample('15T').asfreq())
print(ts.resample('15T').ffill())
print(ts.resample('15T').bfill())
# 低频转高频，主要是如何插值
# .asfreq()：不做填充，返回Nan
# .ffill()：向上填充
# .bfill()：向下填充
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 --------- 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 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 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
#作业answer
di=pd.date_range(start='20170101',end='20170110')
ts1=pd.Series(data=np.random.rand(10),index=di)
print(ts1)
print('---------')
ts2=ts1.resample('3D',label='right',closed='right').mean()
print(ts2)
print('---------')
ts3=ts1.resample('12H').ffill()
print(ts3)
2017-01-01 0.528825 2017-01-02 0.595066 2017-01-03 0.721156 2017-01-04 0.637974 2017-01-05 0.449776 2017-01-06 0.430048 2017-01-07 0.256290 2017-01-08 0.493641 2017-01-09 0.272481 2017-01-10 0.878957 Freq: D, dtype: float64 --------- 2017-01-01 0.528825 2017-01-04 0.651399 2017-01-07 0.378704 2017-01-10 0.548360 Freq: 3D, dtype: float64 --------- 2017-01-01 00:00:00 0.528825 2017-01-01 12:00:00 0.528825 2017-01-02 00:00:00 0.595066 2017-01-02 12:00:00 0.595066 2017-01-03 00:00:00 0.721156 2017-01-03 12:00:00 0.721156 2017-01-04 00:00:00 0.637974 2017-01-04 12:00:00 0.637974 2017-01-05 00:00:00 0.449776 2017-01-05 12:00:00 0.449776 2017-01-06 00:00:00 0.430048 2017-01-06 12:00:00 0.430048 2017-01-07 00:00:00 0.256290 2017-01-07 12:00:00 0.256290 2017-01-08 00:00:00 0.493641 2017-01-08 12:00:00 0.493641 2017-01-09 00:00:00 0.272481 2017-01-09 12:00:00 0.272481 2017-01-10 00:00:00 0.878957 Freq: 12H, dtype: float64
''' 总结 1 ts降采样 2 ts升采样 '''
'\n总结\n1 ts降采样\n2 ts升采样\n'
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