import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline[/code]//anaconda/lib/python2.7/site-packages/matplotlib/Font_manager.py:273:UserWarning: Matplotlib is building the Font cache using fc-List. This maytake a moment. warnings.warn(‘Matplotlib is building the Font cache using fc-List. This may take a moment.’)```code from pandas import SerIEs,DataFrame[/code]#### Time SeirIEs Analysis **** > build-in package time datetime calendar```code from datetime import datetime[/code]```code Now = datetime.Now()[/code]```code Now[/code]datetime.datetime(2016, 2, 1, 11, 11, 8, 934671) > ** display time right Now**```code Now.year,Now.month,Now.day[/code](2016, 2, 1) datetime以毫秒形势存储��和⌚️，**datetime.datedelta**表示两个datetime对象之间的时间差```code delta = datetime(2011,1,7) - datetime(2008,6,24,8,15)[/code]显示的前一个是天数，后一个是秒钟 —- delta.days delta.seconds```code delta[/code]datetime.timedelta(926, 56700) ### 可以给datetime对象加上或者减去一个或者多个timedelta，会产生一个新对象```code from datetime import timedelta[/code]```code start = datetime(2011, 1, 7)[/code]```code start + timedelta(12)[/code]datetime.datetime(2011, 1, 19, 0, 0)```code start - timedelta(12) * 4[/code]datetime.datetime(2010, 11, 20, 0, 0) > 可见timedelta是以天为单位 ####datetime模块中的数据类型 —– - date | 以公历形式存储日历日期（年、月、日） - time | 将时间存储为时、分、秒、毫秒 -datetime | 存储时间和日期 - timedelta| 比阿诗两个datetime值之间的差（日， 秒， 毫秒） ## strtransformed to datetime use ** str ** or ** strftime(invoke a formed str) **,datetime object and pandas.Timestamp can be formulated to string```code stamp = datetime(2011, 1, 3)[/code]```code str(stamp)[/code]‘2011-01-03 00:00:00’```code stamp.strftime('%Y-%m-%d')[/code]‘2011-01-03’```code stamp.strftime('%Y-%m')[/code]‘2011-01’```code value = '2011-01-03'[/code]```code datetime.strptime(value, '%Y-%m-%d')[/code]datetime.datetime(2011, 1, 3, 0, 0)```code datestrs = ['7/6/2011','8/6/2011'][/code]```code [datetime.strptime(x, '%m/%d/%Y') for x in datestrs][/code][datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]datetime.striptime 是通过已知格式进行日期解析的最佳方式，但每次都要编写格式定义 -使用dateutil中的parser.parse来实现```code from dateutil.parser import parse[/code]```code parse('2011-01-03')[/code]datetime.datetime(2011, 1, 3, 0, 0) parse的解析能力很强，几乎可以解析一切格式```code parse('Jan 31,1997 10:45 PM')[/code]datetime.datetime(1997, 1, 31, 22, 45)```code parse('6/30/2011', dayfirst=True)[/code]datetime.datetime(2011, 6, 30, 0, 0)```code datestrs[/code][‘7/6/2011’, ‘8/6/2011’] # pd.to_datetime()```code pd.to_datetime(datestrs)[/code]DatetimeIndex([‘2011-07-06’, ‘2011-08-06’], dtype=’datetime64[ns]’, freq=None)```code dates = [datetime(2011, 1, 2),datetime(2011,1,5),datetime(2011,1,7), datetime(2011,1,8),datetime(2011,1,10),datetime(2011,1,12)][/code]```code ts = SerIEs(np.random.randn(6), index=dates)[/code]```code ts[/code]2011-01-02 0.573974 2011-01-05 -0.337112 2011-01-07 -1.650845 2011-01-080.450012 2011-01-10 -1.253801 2011-01-12 -0.402997 dtype: float64```code type(ts)[/code]pandas.core.serIEs.SerIEs```code ts.index[/code]DatetimeIndex([‘2011-01-02’, ‘2011-01-05’, ‘2011-01-07’, ‘2011-01-08’,‘2011-01-10’, ‘2011-01-12’], dtype=’datetime64[ns]’, freq=None)```code ts + ts[::2][/code]2011-01-02 1.147949 2011-01-05 NaN 2011-01-07 -3.301690 2011-01-08 NaN2011-01-10 -2.507602 2011-01-12 NaN dtype: float64```code ts[::2][/code]2011-01-02 0.573974 2011-01-07 -1.650845 2011-01-10 -1.253801 dtype: float64## 索引、选取、子集构造```code ts['1/10/2011'][/code]-1.2538008746706757 传入可以解释为日期的字符，就可以代替索引 ```code ts['20110110'][/code]-1.2538008746706757 ```code longer_ts=SerIEs(np.random.randn(1000),index=pd.date_range('20000101',periods=1000))[/code]```code longer_ts[/code]2000-01-01 -1.025498 2000-01-02 -0.913267 2000-01-03 0.240895 2000-01-04-1.475368 2000-01-05 -1.675558 2000-01-06 1.020005 2000-01-07 0.6380972000-01-08 0.503482 2000-01-09 -0.541771 2000-01-10 -1.107036 2000-01-110.797612 2000-01-12 1.691745 2000-01-13 1.889323 2000-01-14 -0.8521262000-01-15 -0.987578 2000-01-16 0.558084 2000-01-17 -0.842907 2000-01-181.932399 2000-01-19 -1.126650 2000-01-20 -0.529707 2000-01-21 0.1167562000-01-22 -0.012790 2000-01-23 0.501330 2000-01-24 0.346976 2000-01-25-0.880443 2000-01-26 -0.229017 2000-01-27 0.926648 2000-01-28 0.8944912000-01-29 -0.573260 2000-01-30 -1.712945 … 2002-08-28 -0.751376 2002-08-29-1.731035 2002-08-30 -0.150107 2002-08-31 -0.621332 2002-09-01 0.4493112002-09-02 0.873422 2002-09-03 1.496143 2002-09-04 -0.581023 2002-09-052.882920 2002-09-06 -0.347482 2002-09-07 0.165490 2002-09-08 -0.4756422002-09-09 0.191958 2002-09-10 0.801963 2002-09-11 -1.603021 2002-09-121.114401 2002-09-13 0.994800 2002-09-14 -0.974208 2002-09-15 2.0967472002-09-16 -0.252620 2002-09-17 -0.279536 2002-09-18 -0.059076 2002-09-19-0.497615 2002-09-20 -0.009895 2002-09-21 1.813504 2002-09-22 0.8638852002-09-23 1.330777 2002-09-24 -0.394473 2002-09-25 -1.163973 2002-09-26-0.986664 Freq: D, dtype: float64```code longer_ts['2002'][/code]2002-01-01 -1.249172 2002-01-02 -1.368829 2002-01-03 0.097135 2002-01-04-0.972259 2002-01-05 -0.640629 2002-01-06 0.619072 2002-01-07 1.6257692002-01-08 -0.893140 2002-01-09 0.113725 2002-01-10 0.446898 2002-01-11-0.382041 2002-01-12 -1.667311 2002-01-13 -0.307464 2002-01-14 0.6233832002-01-15 -0.211188 2002-01-16 -1.166355 2002-01-17 0.399710 2002-01-18-0.171451 2002-01-19 -1.591578 2002-01-20 -0.367654 2002-01-21 0.9857782002-01-22 0.125848 2002-01-23 1.366708 2002-01-24 0.449383 2002-01-250.211848 2002-01-26 -1.033201 2002-01-27 0.668416 2002-01-28 0.4026932002-01-29 -0.730690 2002-01-30 1.666659 … 2002-08-28 -0.751376 2002-08-29-1.731035 2002-08-30 -0.150107 2002-08-31 -0.621332 2002-09-01 0.4493112002-09-02 0.873422 2002-09-03 1.496143 2002-09-04 -0.581023 2002-09-052.882920 2002-09-06 -0.347482 2002-09-07 0.165490 2002-09-08 -0.4756422002-09-09 0.191958 2002-09-10 0.801963 2002-09-11 -1.603021 2002-09-121.114401 2002-09-13 0.994800 2002-09-14 -0.974208 2002-09-15 2.0967472002-09-16 -0.252620 2002-09-17 -0.279536 2002-09-18 -0.059076 2002-09-19-0.497615 2002-09-20 -0.009895 2002-09-21 1.813504 2002-09-22 0.8638852002-09-23 1.330777 2002-09-24 -0.394473 2002-09-25 -1.163973 2002-09-26-0.986664 Freq: D, dtype: float64```code longer_ts['2001/03'][/code]2001-03-01 -0.130463 2001-03-02 -1.245341 2001-03-03 1.035173 2001-03-041.115275 2001-03-05 0.013602 2001-03-06 0.828075 2001-03-07 -0.8025642001-03-08 2.067711 2001-03-09 2.158392 2001-03-10 1.348256 2001-03-111.282607 2001-03-12 -1.088485 2001-03-13 -0.882978 2001-03-14 -0.0308722001-03-15 0.840561 2001-03-16 -0.061428 2001-03-17 0.170721 2001-03-180.895892 2001-03-19 -0.050714 2001-03-20 0.608656 2001-03-21 1.2221772001-03-22 0.889833 2001-03-23 -0.932351 2001-03-24 0.163275 2001-03-250.001171 2001-03-26 0.969950 2001-03-27 -0.118747 2001-03-28 -0.8404782001-03-29 -2.654215 2001-03-30 -0.351836 2001-03-31 -0.365322 Freq: D, dtype:float64```code ts['20110101':'20110201'][/code]2011-01-02 0.573974 2011-01-05 -0.337112 2011-01-07 -1.650845 2011-01-080.450012 2011-01-10 -1.253801 2011-01-12 -0.402997 dtype: float64```code ts.truncate(after='20110109')[/code]2011-01-02 0.573974 2011-01-05 -0.337112 2011-01-07 -1.650845 2011-01-080.450012 dtype: float64```code dates = pd.date_range('20000101', periods=100, freq='W-WED')[/code]```code dates[/code]DatetimeIndex([‘2000-01-05’, ‘2000-01-12’, ‘2000-01-19’, ‘2000-01-26’,‘2000-02-02’, ‘2000-02-09’, ‘2000-02-16’, ‘2000-02-23’, ‘2000-03-01’,‘2000-03-08’, ‘2000-03-15’, ‘2000-03-22’, ‘2000-03-29’, ‘2000-04-05’,‘2000-04-12’, ‘2000-04-19’, ‘2000-04-26’, ‘2000-05-03’, ‘2000-05-10’,‘2000-05-17’, ‘2000-05-24’, ‘2000-05-31’, ‘2000-06-07’, ‘2000-06-14’,‘2000-06-21’, ‘2000-06-28’, ‘2000-07-05’, ‘2000-07-12’, ‘2000-07-19’,‘2000-07-26’, ‘2000-08-02’, ‘2000-08-09’, ‘2000-08-16’, ‘2000-08-23’,‘2000-08-30’, ‘2000-09-06’, ‘2000-09-13’, ‘2000-09-20’, ‘2000-09-27’,‘2000-10-04’, ‘2000-10-11’, ‘2000-10-18’, ‘2000-10-25’, ‘2000-11-01’,‘2000-11-08’, ‘2000-11-15’, ‘2000-11-22’, ‘2000-11-29’, ‘2000-12-06’,‘2000-12-13’, ‘2000-12-20’, ‘2000-12-27’, ‘2001-01-03’, ‘2001-01-10’,‘2001-01-17’, ‘2001-01-24’, ‘2001-01-31’, ‘2001-02-07’, ‘2001-02-14’,‘2001-02-21’, ‘2001-02-28’, ‘2001-03-07’, ‘2001-03-14’, ‘2001-03-21’,‘2001-03-28’, ‘2001-04-04’, ‘2001-04-11’, ‘2001-04-18’, ‘2001-04-25’,‘2001-05-02’, ‘2001-05-09’, ‘2001-05-16’, ‘2001-05-23’, ‘2001-05-30’,‘2001-06-06’, ‘2001-06-13’, ‘2001-06-20’, ‘2001-06-27’, ‘2001-07-04’,‘2001-07-11’, ‘2001-07-18’, ‘2001-07-25’, ‘2001-08-01’, ‘2001-08-08’,‘2001-08-15’, ‘2001-08-22’, ‘2001-08-29’, ‘2001-09-05’, ‘2001-09-12’,‘2001-09-19’, ‘2001-09-26’, ‘2001-10-03’, ‘2001-10-10’, ‘2001-10-17’,‘2001-10-24’, ‘2001-10-31’, ‘2001-11-07’, ‘2001-11-14’, ‘2001-11-21’,‘2001-11-28’], dtype=’datetime64[ns]’, freq=’W-WED’)```code long_df = DataFrame(np.random.randn(100,4),index=dates,columns=['colorado','Texas','New York','Ohio'])[/code]```code long_df.ix['5-2001'][/code]| colorado | Texas | New York | Ohio ---|---|---|---|--- 2001-05-02 | 1.783070 | 1.090816 | -1.035363 | -0.089864 2001-05-09 | -1.290700 | 1.311863 | -0.596037 | 0.819694 2001-05-16 | 0.688693 | -0.249644 | -0.859212 | 0.879270 2001-05-23 | -1.602660 | 1.211236 | -1.028336 | 2.022514 2001-05-30 | -0.705427 | -0.189235 | -0.710712 | -2.397815```code dates = pd.DatetimeIndex(['1/1/2000','1/2/2000', '1/2/2000','1/2/2000', '1/3/2000'])[/code]```code dup_ts = SerIEs(np.arange(5), index=dates)[/code]```code dup_ts[/code] 2000-01-01 0 2000-01-02 1 2000-01-02 2 2000-01-02 3 2000-01-03 4 dtype: int64通过检查索引的** is_unique ** 属性，判断是不是唯一```code dup_ts.index.is_unique[/code]False 对这个时间序列进行索引，要么产生标量值，要么产生切片，具体要看所选的 > **时间点是否重复** none repeat(2000-1-3)```code dup_ts['1/3/2000'][/code]4 repeat (2000-1-2)```code dup_ts['1/2/2000'][/code]2000-01-02 1 2000-01-02 2 2000-01-02 3 dtype: int64 define whether it isreaptable or not```code dup_ts.index.is_unique[/code]False # 对具有非唯一时间戳的数据聚合 # > groupby(level=0) level=0意味着索引唯一一层！！！ —-```code grouped = dup_ts.groupby(level=0)[/code]```code grouped.mean(),grouped.count()[/code](2000-01-01 0 2000-01-02 2 2000-01-03 4 dtype: int64, 2000-01-01 1 2000-01-023 2000-01-03 1 dtype: int64) > 将时间序列转换成 **具有固定频率（每日）的时间序列** - resample```code ts.resample('D')[/code]2011-01-02 0.573974 2011-01-03 NaN 2011-01-04 NaN 2011-01-05 -0.3371122011-01-06 NaN 2011-01-07 -1.650845 2011-01-08 0.450012 2011-01-09 NaN2011-01-10 -1.253801 2011-01-11 NaN 2011-01-12 -0.402997 Freq: D, dtype:float64 生成日期范围 - pandas.date_range - 类型：DatetimeIndex```code index = pd.date_range('4/1/2012','6/1/2012')[/code]## base frequency - 基础频率通常以一个字符串表示，M每月，H每小时 - 对于每个基础频率都有一个偏移量与之对应 - dateoffset```code from pandas.tserIEs.offsets import Hour, Minute[/code]```code hour = Hour()[/code]```code hour[/code]> 传入一个整数即可定义偏移量的倍数：```code four_hours = Hour(4)[/code]```code four_hours[/code]```code pd.date_range('1/1/2000','1/3/2000 23:59',freq='4h')[/code]DatetimeIndex([‘2000-01-01 00:00:00’, ‘2000-01-01 04:00:00’, ‘2000-01-0108:00:00’, ‘2000-01-01 12:00:00’, ‘2000-01-01 16:00:00’, ‘2000-01-0120:00:00’, ‘2000-01-02 00:00:00’, ‘2000-01-02 04:00:00’, ‘2000-01-0208:00:00’, ‘2000-01-02 12:00:00’, ‘2000-01-02 16:00:00’, ‘2000-01-0220:00:00’, ‘2000-01-03 00:00:00’, ‘2000-01-03 04:00:00’, ‘2000-01-0308:00:00’, ‘2000-01-03 12:00:00’, ‘2000-01-03 16:00:00’, ‘2000-01-0320:00:00’], dtype=’datetime64[ns]’, freq=’4H’) 偏移量可以通过加法链接```code Hour(2) + Minute(30)[/code]```code pd.date_range('1/1/2000', periods=10, freq='1h30min')[/code]DatetimeIndex([‘2000-01-01 00:00:00’, ‘2000-01-01 01:30:00’, ‘2000-01-0103:00:00’, ‘2000-01-01 04:30:00’, ‘2000-01-01 06:00:00’, ‘2000-01-0107:30:00’, ‘2000-01-01 09:00:00’, ‘2000-01-01 10:30:00’, ‘2000-01-0112:00:00’, ‘2000-01-01 13:30:00’], dtype=’datetime64[ns]’, freq=’90T’) ###WOM(week of month)```code rng = pd.date_range('1/1/2012','9/1/2012',freq='WOM-3FRI')[/code]```code pd.date_range('1/1/2012','9/1/2012',freq='W-FRI')[/code]DatetimeIndex([‘2012-01-06’, ‘2012-01-13’, ‘2012-01-20’, ‘2012-01-27’,‘2012-02-03’, ‘2012-02-10’, ‘2012-02-17’, ‘2012-02-24’, ‘2012-03-02’,‘2012-03-09’, ‘2012-03-16’, ‘2012-03-23’, ‘2012-03-30’, ‘2012-04-06’,‘2012-04-13’, ‘2012-04-20’, ‘2012-04-27’, ‘2012-05-04’, ‘2012-05-11’,‘2012-05-18’, ‘2012-05-25’, ‘2012-06-01’, ‘2012-06-08’, ‘2012-06-15’,‘2012-06-22’, ‘2012-06-29’, ‘2012-07-06’, ‘2012-07-13’, ‘2012-07-20’,‘2012-07-27’, ‘2012-08-03’, ‘2012-08-10’, ‘2012-08-17’, ‘2012-08-24’,‘2012-08-31’], dtype=’datetime64[ns]’, freq=’W-FRI’) > 时间表别名10-4 P314 ###移动（超前和滞后）数据 - 移动（shifting）指的是沿着时间轴将数据迁移或者后移 - SerIEs &Dataframe都有一个shift方法单纯执行前移后移 - 保持索引不变```code ts = SerIEs(np.random.randn(4),index=pd.date_range('1/1/2000',periods=4,freq='M'))[/code]```code ts[/code]2000-01-31 -0.550830 2000-02-29 -1.297499 2000-03-31 1.178102 2000-04-301.359573 Freq: M, dtype: float64```code ts.shift(-2)[/code]2000-01-31 1.178102 2000-02-29 1.359573 2000-03-31 NaN 2000-04-30 NaN Freq: M,dtype: float64 shift ususally used to calculate the pct change of a serIEs```code ts / ts.shift(1) - 1[/code]2000-01-31 NaN 2000-02-29 1.355534 2000-03-31 -1.907979 2000-04-30 0.154037Freq: M, dtype: float64```code ts.pct_change()[/code]2000-01-31 NaN 2000-02-29 1.355534 2000-03-31 -1.907979 2000-04-30 0.154037Freq: M, dtype: float64```code ts.shift(2, freq='M')[/code]2000-03-31 -0.550830 2000-04-30 -1.297499 2000-05-31 1.178102 2000-06-301.359573 Freq: M, dtype: float64```code ts.shift(3, freq='D')[/code]2000-02-03 -0.550830 2000-03-03 -1.297499 2000-04-03 1.178102 2000-05-031.359573 dtype: float64```code type(ts)[/code]pandas.core.serIEs.SerIEs```code ts.shift()[/code]2000-01-31 NaN 2000-02-29 -0.550830 2000-03-31 -1.297499 2000-04-30 1.178102Freq: M, dtype: float64```code ts.shift(3)[/code]2000-01-31 NaN 2000-02-29 NaN 2000-03-31 NaN 2000-04-30 -0.55083 Freq: M,dtype: float64```code ts.shift(freq='D')[/code]2000-02-01 -0.550830 2000-03-01 -1.297499 2000-04-01 1.178102 2000-05-011.359573 Freq: MS, dtype: float64```code ts.shift(periods=2)[/code]2000-01-31 NaN 2000-02-29 NaN 2000-03-31 -0.550830 2000-04-30 -1.297499 Freq:M, dtype: float64 freq means move the index by the frequence```code from pandas.tserIEs.offsets import Day, MonthEnd[/code]如果增加的是⚓️点偏移量（比如MonthEnd)，第一次增量会讲原来的日期向前滚动到适合规则的下一个日期 -今天11月17号，MonthEnd就是这个月末11.31```code Now = datetime(2011, 11, 17)[/code]```code Now + 3*Day()[/code]Timestamp(‘2011-11-20 00:00:00’)```code Now + MonthEnd()[/code]Timestamp(‘2011-11-30 00:00:00’)```code Now + MonthEnd(2)[/code]Timestamp(‘2011-12-31 00:00:00’)```code offset = MonthEnd()[/code]```code offset.rollforward(Now)[/code]Timestamp(‘2011-11-30 00:00:00’)```code offset.rollback(Now)[/code]Timestamp(‘2011-10-31 00:00:00’) 巧妙的使用**groupby**和**⚓️点偏移量**```code ts = SerIEs(np.random.randn(20), index=pd.date_range('1/15/2000',periods=20,freq='4d'))[/code]```code ts.groupby(offset.rollforward).mean()[/code]2000-01-31 -0.223943 2000-02-29 -0.241283 2000-03-31 -0.080391 dtype: float64更方便快捷的方法应该是用 > resample```code ts.resample('M', how='mean')[/code]2000-01-31 -0.223943 2000-02-29 -0.241283 2000-03-31 -0.080391 Freq: M, dtype:float64 # import pytz —- pytz是一个世界时区的库，时区名```code import pytz[/code]```code pytz.common_timezones[-5:][/code][‘US/Eastern’, ‘US/Hawaii’, ‘US/Mountain’, ‘US/Pacific’, ‘UTC’]```code tz = pytz.timezone('US/Eastern')[/code]```code tz[/code]### 本地化和转换```code rng = pd.date_range('3/9/2012 9:30',periods=6, freq='D') ts = SerIEs(np.random.randn(len(rng)),index=rng)[/code]```code del index[/code]```code ts.index.tz[/code]add a time zone set of the ts - make it print```code pd.date_range('3/9/2000 9:30',periods=10, freq='D',tz='UTC')[/code]DatetimeIndex([‘2000-03-09 09:30:00+00:00’, ‘2000-03-10 09:30:00+00:00’,‘2000-03-11 09:30:00+00:00’, ‘2000-03-12 09:30:00+00:00’, ‘2000-03-1309:30:00+00:00’, ‘2000-03-14 09:30:00+00:00’, ‘2000-03-15 09:30:00+00:00’,‘2000-03-16 09:30:00+00:00’, ‘2000-03-17 09:30:00+00:00’, ‘2000-03-1809:30:00+00:00’], dtype=’datetime64[ns, UTC]’, freq=’D’) > The +00:00 means -time zone use *tz_localize* to localize the time zone```code ts_utc = ts.tz_localize('UTC') ts_utc[/code]2012-03-09 09:30:00+00:00 -0.258702 2012-03-10 09:30:00+00:00 -1.0190562012-03-11 09:30:00+00:00 1.044139 2012-03-12 09:30:00+00:00 0.8266842012-03-13 09:30:00+00:00 0.998759 2012-03-14 09:30:00+00:00 -0.839695 Freq:D, dtype: float64 just have a try of crtl+v```code ts_utc.index[/code]DatetimeIndex([‘2012-03-09 09:30:00+00:00’, ‘2012-03-10 09:30:00+00:00’,‘2012-03-11 09:30:00+00:00’, ‘2012-03-12 09:30:00+00:00’, ‘2012-03-1309:30:00+00:00’, ‘2012-03-14 09:30:00+00:00’], dtype=’datetime64[ns, UTC]’,freq=’D’) convert localized time zone to another one use: > *tz_convert*```code ts_utc.tz_convert('US/Eastern')[/code]2012-03-09 04:30:00-05:00 -0.258702 2012-03-10 04:30:00-05:00 -1.0190562012-03-11 05:30:00-04:00 1.044139 2012-03-12 05:30:00-04:00 0.8266842012-03-13 05:30:00-04:00 0.998759 2012-03-14 05:30:00-04:00 -0.839695 Freq:D, dtype: float64 *tz_localize* & *tz_convert* are also instance methods on*DatetimeIndex*```code ts.index.tz_localize('Asia/Shanghai')[/code]DatetimeIndex([‘2012-03-09 09:30:00+08:00’, ‘2012-03-10 09:30:00+08:00’,‘2012-03-11 09:30:00+08:00’, ‘2012-03-12 09:30:00+08:00’, ‘2012-03-1309:30:00+08:00’, ‘2012-03-14 09:30:00+08:00’], dtype=’datetime64[ns,Asia/Shanghai]’, freq=’D’) # operations with Time Zone - awrae TimestampObjects Localized from naive to time zone-aware and converted from one timezone to another```code stamp = pd.Timestamp('2011-03-12 4:00') stamp_utc = stamp.tz_localize('utc')[/code]```code stamp_utc.tz_convert('US/Eastern')[/code]Timestamp(‘2011-03-11 23:00:00-0500’, tz=’US/Eastern’) >Time zone-awareTimestamp objects internally store a UTC timestamp calue as nano-seconed sincethr UNIX epoch(January 1,1970) - this UTC value is invariant between time zoneconversions```code stamp_utc.value[/code]1299902400000000000```code stamp = pd.Timestamp('2012-03-12 01:30', tz='US/Eastern')[/code]```code stamp[/code]Timestamp(‘2012-03-12 01:30:00-0400’, tz=’US/Eastern’)```code stamp + Hour()[/code]Timestamp(‘2012-03-12 02:30:00-0400’, tz=’US/Eastern’) # operations betweendifferent time zones```code rng = pd.date_range('3/7/2012 9:30',periods=10, freq='B')[/code]```code ts = SerIEs(np.random.randn(len(rng)), index=rng)[/code]```code ts[/code]2012-03-07 09:30:00 0.315600 2012-03-08 09:30:00 0.616440 2012-03-09 09:30:00-1.633940 2012-03-12 09:30:00 0.260501 2012-03-13 09:30:00 -0.3946202012-03-14 09:30:00 -0.554103 2012-03-15 09:30:00 2.441851 2012-03-16 09:30:00-3.473308 2012-03-19 09:30:00 -0.339365 2012-03-20 09:30:00 0.335510 Freq: B,dtype: float64```code ts1 = ts[:7].tz_localize('Europe/London')[/code]```code ts2 = ts1[2:].tz_convert('Europe/Moscow')[/code]```code result = ts1 + ts2[/code]>different time zone can be added up together freely```code result.index[/code]DatetimeIndex([‘2012-03-07 09:30:00+00:00’, ‘2012-03-08 09:30:00+00:00’,‘2012-03-09 09:30:00+00:00’, ‘2012-03-12 09:30:00+00:00’, ‘2012-03-1309:30:00+00:00’, ‘2012-03-14 09:30:00+00:00’, ‘2012-03-15 09:30:00+00:00’],dtype=’datetime64[ns, UTC]’, freq=’B’) ## Periods and Periods Arithmetic >Periods - time spans - days, months,quarters,years```code p = pd.Period(2007, freq='A-DEC')[/code]```code p[/code]Period(‘2007’, ‘A-DEC’) ## Time SerIEs Plotting```code close_px_call = pd.read_csv('/Users/Houbowei/Desktop/SRP/books/pydata-book-master/pydata-book-master/ch09/stock_px.csv', parse_dates=True,index_col=0)[/code]```code close_px = close_px_call[['AAPL','MSFT','XOM']][/code]```code close_px = close_px.resample('B',fill_method='ffill')[/code]```code close_px[/code]| AAPL | MSFT | XOM ---|---|---|--- 2003-01-02 | 7.40 | 21.11 | 29.22 2003-01-03 | 7.45 | 21.14 | 29.24 2003-01-06 | 7.45 | 21.52 | 29.96 2003-01-07 | 7.43 | 21.93 | 28.95 2003-01-08 | 7.28 | 21.31 | 28.83 2003-01-09 | 7.34 | 21.93 | 29.44 2003-01-10 | 7.36 | 21.97 | 29.03 2003-01-13 | 7.32 | 22.16 | 28.91 2003-01-14 | 7.30 | 22.39 | 29.17 2003-01-15 | 7.22 | 22.11 | 28.77 2003-01-16 | 7.31 | 21.75 | 28.90 2003-01-17 | 7.05 | 20.22 | 28.60 2003-01-20 | 7.05 | 20.22 | 28.60 2003-01-21 | 7.01 | 20.17 | 27.94 2003-01-22 | 6.94 | 20.04 | 27.58 2003-01-23 | 7.09 | 20.54 | 27.52 2003-01-24 | 6.90 | 19.59 | 26.93 2003-01-27 | 7.07 | 19.32 | 26.21 2003-01-28 | 7.29 | 19.18 | 26.90 2003-01-29 | 7.47 | 19.61 | 27.88 2003-01-30 | 7.16 | 18.95 | 27.37 2003-01-31 | 7.18 | 18.65 | 28.13 2003-02-03 | 7.33 | 19.08 | 28.52 2003-02-04 | 7.30 | 18.59 | 28.52 2003-02-05 | 7.22 | 18.45 | 28.11 2003-02-06 | 7.22 | 18.63 | 27.87 2003-02-07 | 7.07 | 18.30 | 27.66 2003-02-10 | 7.18 | 18.62 | 27.87 2003-02-11 | 7.18 | 18.25 | 27.67 2003-02-12 | 7.20 | 18.25 | 27.12 … | … | … | … 2011-09-05 | 374.05 | 25.80 | 72.14 2011-09-06 | 379.74 | 25.51 | 71.15 2011-09-07 | 383.93 | 26.00 | 73.65 2011-09-08 | 384.14 | 26.22 | 72.82 2011-09-09 | 377.48 | 25.74 | 71.01 2011-09-12 | 379.94 | 25.89 | 71.84 2011-09-13 | 384.62 | 26.04 | 71.65 2011-09-14 | 389.30 | 26.50 | 72.64 2011-09-15 | 392.96 | 26.99 | 74.01 2011-09-16 | 400.50 | 27.12 | 74.55 2011-09-19 | 411.63 | 27.21 | 73.70 2011-09-20 | 413.45 | 26.98 | 74.01 2011-09-21 | 412.14 | 25.99 | 71.97 2011-09-22 | 401.82 | 25.06 | 69.24 2011-09-23 | 404.30 | 25.06 | 69.31 2011-09-26 | 403.17 | 25.44 | 71.72 2011-09-27 | 399.26 | 25.67 | 72.91 2011-09-28 | 397.01 | 25.58 | 72.07 2011-09-29 | 390.57 | 25.45 | 73.88 2011-09-30 | 381.32 | 24.89 | 72.63 2011-10-03 | 374.60 | 24.53 | 71.15 2011-10-04 | 372.50 | 25.34 | 72.83 2011-10-05 | 378.25 | 25.89 | 73.95 2011-10-06 | 377.37 | 26.34 | 73.89 2011-10-07 | 369.80 | 26.25 | 73.56 2011-10-10 | 388.81 | 26.94 | 76.28 2011-10-11 | 400.29 | 27.00 | 76.27 2011-10-12 | 402.19 | 26.96 | 77.16 2011-10-13 | 408.43 | 27.18 | 76.37 2011-10-14 | 422.00 | 27.27 | 78.11 2292 rows × 3 columns```code close_px.resample?[/code]```code close_px['AAPL'].plot()[/code]```code close_px.ix['2009'].plot()[/code]```code close_px['AAPL'].ix['01-2011':'03-2011'].plot()[/code]```code apple_q = close_px['AAPL'].resample('Q-DEC', fill_method='ffill')[/code]```code apple_q.ix['2009':].plot()[/code]```code close_px.AAPL.plot()[/code]```code close_px.plot()[/code]```code apple_std250 = pd.rolling_std(close_px.AAPL, 250)[/code]```code apple_std250.describe()[/code]count 2043.000000 mean 20.604571 std 12.606813 min 1.335707 25% 9.121461 50%22.231490 75% 32.411445 max 39.327273 name: AAPL, dtype: float64```code apple_std250.plot()[/code]```code close_px.describe()[/code]| AAPL | MSFT | XOM ---|---|---|--- count | 2292.000000 | 2292.000000 | 2292.000000 mean | 125.339895 | 23.953010 | 59.568473 std | 107.218553 | 3.267322 | 16.731836 min | 6.560000 | 14.330000 | 26.210000 25% | 37.122500 | 21.690000 | 49.517500 50% | 91.365000 | 24.000000 | 62.980000 75% | 185.535000 | 26.280000 | 72.540000 max | 422.000000 | 34.070000 | 87.480000```code close_px_call.describe()[/code] | AAPL | MSFT | XOM | SPX ---|---|---|---|--- count | 2214.000000 | 2214.000000 | 2214.000000 | 2214.000000 mean | 125.516147 | 23.945452 | 59.558744 | 1183.773311 std | 107.394693 | 3.255198 | 16.725025 | 180.983466 min | 6.560000 | 14.330000 | 26.210000 | 676.530000 25% | 37.135000 | 21.700000 | 49.492500 | 1077.060000 50% | 91.455000 | 24.000000 | 62.970000 | 1189.260000 75% | 185.605000 | 26.280000 | 72.510000 | 1306.057500 max | 422.000000 | 34.070000 | 87.480000 | 1565.150000```code spx = close_px_call.SPX.pct_change()[/code]```code spx[/code]```code 2003-01-02 NaN 2003-01-03 -0.000484 2003-01-06 0.022474 2003-01-07 -0.006545 2003-01-08 -0.014086 2003-01-09 0.019386 2003-01-10 0.000000 2003-01-13 -0.001412 2003-01-14 0.005830 2003-01-15 -0.014426 2003-01-16 -0.003942 2003-01-17 -0.014017 2003-01-21 -0.015702 2003-01-22 -0.010432 2003-01-23 0.010224 2003-01-24 -0.029233 2003-01-27 -0.016160 2003-01-28 0.013050 2003-01-29 0.006779 2003-01-30 -0.022849 2003-01-31 0.013130 2003-02-03 0.005399 2003-02-04 -0.014088 2003-02-05 -0.005435 2003-02-06 -0.006449 2003-02-07 -0.010094 2003-02-10 0.007569 2003-02-11 -0.008098 2003-02-12 -0.012687 2003-02-13 -0.001600 ... 2011-09-02 -0.025282 2011-09-06 -0.007436 2011-09-07 0.028646 2011-09-08 -0.010612 2011-09-09 -0.026705 2011-09-12 0.006966 2011-09-13 0.009120 2011-09-14 0.013480 2011-09-15 0.017187 2011-09-16 0.005707 2011-09-19 -0.009803 2011-09-20 -0.001661 2011-09-21 -0.029390 2011-09-22 -0.031883 2011-09-23 0.006082 2011-09-26 0.023336 2011-09-27 0.010688 2011-09-28 -0.020691 2011-09-29 0.008114 2011-09-30 -0.024974 2011-10-03 -0.028451 2011-10-04 0.022488 2011-10-05 0.017866 2011-10-06 0.018304 2011-10-07 -0.008163 2011-10-10 0.034125 2011-10-11 0.000544 2011-10-12 0.009795 2011-10-13 -0.002974 2011-10-14 0.017380 name: SPX, dtype: float64 returns = close_px.pct_change()[/code]```code corr = pd.rolling_corr(returns.AAPL, spx, 125 , min_periods=100)[/code]```code corr.plot()[/code]```code <matplotlib.axes._subplots.Axessubplot at 0x10bf49450> corr = pd.rolling_corr(returns, spx, 125, min_periods=100).plot()[/code] [/code]```code以上是内存溢出为你收集整理的《利用python做数据分析》第十章：时间序列分析全部内容，希望文章能够帮你解决《利用python做数据分析》第十章：时间序列分析所遇到的程序开发问题。
如果觉得内存溢出网站内容还不错，欢迎将内存溢出网站推荐给程序员好友。
欢迎分享，转载请注明来源：内存溢出
微信扫一扫
支付宝扫一扫
评论列表（0条）