Python时间序列分析，,Pandas生成时间

文章由Byrx.net分享于2019-08-28 02:08:10评论（619）

Python时间序列分析，,Pandas生成时间

Pandas生成时间序列：

import pandas as pdimport numpy as np

时间序列

时间戳（timestamp）固定周期（period）时间间隔（interval）

技术分享图片

date_range

可以指定开始时间与周期H：小时D：天

M：月

# TIMES的几种书写方式 #2016 Jul 1; 7/1/2016; 1/7/2016 ;2016-07-01; 2016/07/01rng = pd.date_range(‘2016-07-01‘, periods = 10, freq = ‘3D‘)#不传freq则默认是Drng

　　结果：

DatetimeIndex([‘2016-07-01‘, ‘2016-07-04‘, ‘2016-07-07‘, ‘2016-07-10‘,               ‘2016-07-13‘, ‘2016-07-16‘, ‘2016-07-19‘, ‘2016-07-22‘,               ‘2016-07-25‘, ‘2016-07-28‘],              dtype=‘datetime64[ns]‘, freq=‘3D‘)

View Code

time=pd.Series(np.random.randn(20),           index=pd.date_range(dt.datetime(2016,1,1),periods=20))print(time)#结果：2016-01-01   -0.1293792016-01-02    0.1644802016-01-03   -0.6391172016-01-04   -0.4272242016-01-05    2.0551332016-01-06    1.1160752016-01-07    0.3574262016-01-08    0.2742492016-01-09    0.8344052016-01-10   -0.0054442016-01-11   -0.1344092016-01-12    0.2493182016-01-13   -0.2978422016-01-14   -0.1285142016-01-15    0.0636902016-01-16   -2.2460312016-01-17    0.3595522016-01-18    0.3830302016-01-19    0.4027172016-01-20   -0.694068Freq: D, dtype: float64

truncate过滤

time.truncate(before=‘2016-1-10‘)#1月10之前的都被过滤掉了

　　结果：

2016-01-10   -0.0054442016-01-11   -0.1344092016-01-12    0.2493182016-01-13   -0.2978422016-01-14   -0.1285142016-01-15    0.0636902016-01-16   -2.2460312016-01-17    0.3595522016-01-18    0.3830302016-01-19    0.4027172016-01-20   -0.694068Freq: D, dtype: float64

View Code

time.truncate(after=‘2016-1-10‘)#1月10之后的都被过滤掉了#结果：2016-01-01   -0.1293792016-01-02    0.1644802016-01-03   -0.6391172016-01-04   -0.4272242016-01-05    2.0551332016-01-06    1.1160752016-01-07    0.3574262016-01-08    0.2742492016-01-09    0.8344052016-01-10   -0.005444Freq: D, dtype: float64

print(time[‘2016-01-15‘])#0.063690487247print(time[‘2016-01-15‘:‘2016-01-20‘])结果：2016-01-15    0.0636902016-01-16   -2.2460312016-01-17    0.3595522016-01-18    0.3830302016-01-19    0.4027172016-01-20   -0.694068Freq: D, dtype: float64data=pd.date_range(‘2010-01-01‘,‘2011-01-01‘,freq=‘M‘)print(data)#结果：DatetimeIndex([‘2010-01-31‘, ‘2010-02-28‘, ‘2010-03-31‘, ‘2010-04-30‘,               ‘2010-05-31‘, ‘2010-06-30‘, ‘2010-07-31‘, ‘2010-08-31‘,               ‘2010-09-30‘, ‘2010-10-31‘, ‘2010-11-30‘, ‘2010-12-31‘],              dtype=‘datetime64[ns]‘, freq=‘M‘)

　　技术分享图片

#时间戳pd.Timestamp(‘2016-07-10‘)#Timestamp(‘2016-07-10 00:00:00‘)# 可以指定更多细节pd.Timestamp(‘2016-07-10 10‘)#Timestamp(‘2016-07-10 10:00:00‘)pd.Timestamp(‘2016-07-10 10:15‘)#Timestamp(‘2016-07-10 10:15:00‘)# How much detail can you add?t = pd.Timestamp(‘2016-07-10 10:15‘)# 时间区间pd.Period(‘2016-01‘)#Period(‘2016-01‘, ‘M‘)pd.Period(‘2016-01-01‘)#Period(‘2016-01-01‘, ‘D‘)# TIME OFFSETSpd.Timedelta(‘1 day‘)#Timedelta(‘1 days 00:00:00‘)pd.Period(‘2016-01-01 10:10‘) + pd.Timedelta(‘1 day‘)#Period(‘2016-01-02 10:10‘, ‘T‘)pd.Timestamp(‘2016-01-01 10:10‘) + pd.Timedelta(‘1 day‘)#Timestamp(‘2016-01-02 10:10:00‘)pd.Timestamp(‘2016-01-01 10:10‘) + pd.Timedelta(‘15 ns‘)#Timestamp(‘2016-01-01 10:10:00.000000015‘)p1 = pd.period_range(‘2016-01-01 10:10‘, freq = ‘25H‘, periods = 10)p2 = pd.period_range(‘2016-01-01 10:10‘, freq = ‘1D1H‘, periods = 10)p1p2结果：PeriodIndex([‘2016-01-01 10:00‘, ‘2016-01-02 11:00‘, ‘2016-01-03 12:00‘,             ‘2016-01-04 13:00‘, ‘2016-01-05 14:00‘, ‘2016-01-06 15:00‘,             ‘2016-01-07 16:00‘, ‘2016-01-08 17:00‘, ‘2016-01-09 18:00‘,             ‘2016-01-10 19:00‘],            dtype=‘period[25H]‘, freq=‘25H‘)PeriodIndex([‘2016-01-01 10:00‘, ‘2016-01-02 11:00‘, ‘2016-01-03 12:00‘,             ‘2016-01-04 13:00‘, ‘2016-01-05 14:00‘, ‘2016-01-06 15:00‘,             ‘2016-01-07 16:00‘, ‘2016-01-08 17:00‘, ‘2016-01-09 18:00‘,             ‘2016-01-10 19:00‘],            dtype=‘period[25H]‘, freq=‘25H‘)# 指定索引rng = pd.date_range(‘2016 Jul 1‘, periods = 10, freq = ‘D‘)rngpd.Series(range(len(rng)), index = rng)结果：2016-07-01    02016-07-02    12016-07-03    22016-07-04    32016-07-05    42016-07-06    52016-07-07    62016-07-08    72016-07-09    82016-07-10    9Freq: D, dtype: int32periods = [pd.Period(‘2016-01‘), pd.Period(‘2016-02‘), pd.Period(‘2016-03‘)]ts = pd.Series(np.random.randn(len(periods)), index = periods)ts结果：2016-01   -0.0158372016-02   -0.9234632016-03   -0.485212Freq: M, dtype: float64type(ts.index)#pandas.core.indexes.period.PeriodIndex# 时间戳和时间周期可以转换ts = pd.Series(range(10), pd.date_range(‘07-10-16 8:00‘, periods = 10, freq = ‘H‘))ts结果：2016-07-10 08:00:00    02016-07-10 09:00:00    12016-07-10 10:00:00    22016-07-10 11:00:00    32016-07-10 12:00:00    42016-07-10 13:00:00    52016-07-10 14:00:00    62016-07-10 15:00:00    72016-07-10 16:00:00    82016-07-10 17:00:00    9Freq: H, dtype: int32ts_period = ts.to_period()ts_period结果：2016-07-10 08:00    02016-07-10 09:00    12016-07-10 10:00    22016-07-10 11:00    32016-07-10 12:00    42016-07-10 13:00    52016-07-10 14:00    62016-07-10 15:00    72016-07-10 16:00    82016-07-10 17:00    9Freq: H, dtype: int32时间周期与时间戳的区别ts_period[‘2016-07-10 08:30‘:‘2016-07-10 11:45‘] #时间周期包含08:00结果：2016-07-10 08:00    02016-07-10 09:00    12016-07-10 10:00    22016-07-10 11:00    3Freq: H, dtype: int32ts[‘2016-07-10 08:30‘:‘2016-07-10 11:45‘] #时间戳不包含08:30#结果：2016-07-10 09:00:00    12016-07-10 10:00:00    22016-07-10 11:00:00    3Freq: H, dtype: int32

数据重采样：

时间数据由一个频率转换到另一个频率降采样升采样

import pandas as pdimport numpy as nprng = pd.date_range(‘1/1/2011‘, periods=90, freq=‘D‘)#数据按天ts = pd.Series(np.random.randn(len(rng)), index=rng)ts.head()结果：2011-01-01   -1.0255622011-01-02    0.4108952011-01-03    0.6603112011-01-04    0.7102932011-01-05    0.444985Freq: D, dtype: float64ts.resample(‘M‘).sum()#数据降采样，降为月，指标是求和，也可以平均，自己指定结果：2011-01-31    2.5101022011-02-28    0.5832092011-03-31    2.749411Freq: M, dtype: float64ts.resample(‘3D‘).sum()#数据降采样，降为3天结果：2011-01-01    0.0456432011-01-04   -2.2552062011-01-07    0.5711422011-01-10    0.8350322011-01-13   -0.3967662011-01-16   -1.1562532011-01-19   -1.2868842011-01-22    2.8839522011-01-25    1.5669082011-01-28    1.4355632011-01-31    0.3115652011-02-03   -2.5412352011-02-06    0.3170752011-02-09    1.5988772011-02-12   -1.9505092011-02-15    2.9283122011-02-18   -0.7337152011-02-21    1.6748172011-02-24   -2.0788722011-02-27    2.1723202011-03-02   -2.0221042011-03-05   -0.0703562011-03-08    1.2766712011-03-11   -2.8351322011-03-14   -1.3841132011-03-17    1.5175652011-03-20   -0.5504062011-03-23    0.7734302011-03-26    2.2443192011-03-29    2.951082Freq: 3D, dtype: float64day3Ts = ts.resample(‘3D‘).mean()day3Ts结果：2011-01-01    0.0152142011-01-04   -0.7517352011-01-07    0.1903812011-01-10    0.2783442011-01-13   -0.1322552011-01-16   -0.3854182011-01-19   -0.4289612011-01-22    0.9613172011-01-25    0.5223032011-01-28    0.4785212011-01-31    0.1038552011-02-03   -0.8470782011-02-06    0.1056922011-02-09    0.5329592011-02-12   -0.6501702011-02-15    0.9761042011-02-18   -0.2445722011-02-21    0.5582722011-02-24   -0.6929572011-02-27    0.7241072011-03-02   -0.6740352011-03-05   -0.0234522011-03-08    0.4255572011-03-11   -0.9450442011-03-14   -0.4613712011-03-17    0.5058552011-03-20   -0.1834692011-03-23    0.2578102011-03-26    0.7481062011-03-29    0.983694Freq: 3D, dtype: float64print(day3Ts.resample(‘D‘).asfreq())#升采样，要进行插值结果：2011-01-01    0.0152142011-01-02         NaN2011-01-03         NaN2011-01-04   -0.7517352011-01-05         NaN2011-01-06         NaN2011-01-07    0.1903812011-01-08         NaN2011-01-09         NaN2011-01-10    0.2783442011-01-11         NaN2011-01-12         NaN2011-01-13   -0.1322552011-01-14         NaN2011-01-15         NaN2011-01-16   -0.3854182011-01-17         NaN2011-01-18         NaN2011-01-19   -0.4289612011-01-20         NaN2011-01-21         NaN2011-01-22    0.9613172011-01-23         NaN2011-01-24         NaN2011-01-25    0.5223032011-01-26         NaN2011-01-27         NaN2011-01-28    0.4785212011-01-29         NaN2011-01-30         NaN                ...   2011-02-28         NaN2011-03-01         NaN2011-03-02   -0.6740352011-03-03         NaN2011-03-04         NaN2011-03-05   -0.0234522011-03-06         NaN2011-03-07         NaN2011-03-08    0.4255572011-03-09         NaN2011-03-10         NaN2011-03-11   -0.9450442011-03-12         NaN2011-03-13         NaN2011-03-14   -0.4613712011-03-15         NaN2011-03-16         NaN2011-03-17    0.5058552011-03-18         NaN2011-03-19         NaN2011-03-20   -0.1834692011-03-21         NaN2011-03-22         NaN2011-03-23    0.2578102011-03-24         NaN2011-03-25         NaN2011-03-26    0.7481062011-03-27         NaN2011-03-28         NaN2011-03-29    0.983694Freq: D, Length: 88, dtype: float64

插值方法：

ffill 空值取前面的值bfill 空值取后面的值interpolate 线性取值

day3Ts.resample(‘D‘).ffill(1)结果：2011-01-01    0.0152142011-01-02    0.0152142011-01-03         NaN2011-01-04   -0.7517352011-01-05   -0.7517352011-01-06         NaN2011-01-07    0.1903812011-01-08    0.1903812011-01-09         NaN2011-01-10    0.2783442011-01-11    0.2783442011-01-12         NaN2011-01-13   -0.1322552011-01-14   -0.1322552011-01-15         NaN2011-01-16   -0.3854182011-01-17   -0.3854182011-01-18         NaN2011-01-19   -0.4289612011-01-20   -0.4289612011-01-21         NaN2011-01-22    0.9613172011-01-23    0.9613172011-01-24         NaN2011-01-25    0.5223032011-01-26    0.5223032011-01-27         NaN2011-01-28    0.4785212011-01-29    0.4785212011-01-30         NaN                ...   2011-02-28    0.7241072011-03-01         NaN2011-03-02   -0.6740352011-03-03   -0.6740352011-03-04         NaN2011-03-05   -0.0234522011-03-06   -0.0234522011-03-07         NaN2011-03-08    0.4255572011-03-09    0.4255572011-03-10         NaN2011-03-11   -0.9450442011-03-12   -0.9450442011-03-13         NaN2011-03-14   -0.4613712011-03-15   -0.4613712011-03-16         NaN2011-03-17    0.5058552011-03-18    0.5058552011-03-19         NaN2011-03-20   -0.1834692011-03-21   -0.1834692011-03-22         NaN2011-03-23    0.2578102011-03-24    0.2578102011-03-25         NaN2011-03-26    0.7481062011-03-27    0.7481062011-03-28         NaN2011-03-29    0.983694Freq: D, Length: 88, dtype: float64day3Ts.resample(‘D‘).bfill(1)结果：2011-01-01    0.0152142011-01-02         NaN2011-01-03   -0.7517352011-01-04   -0.7517352011-01-05         NaN2011-01-06    0.1903812011-01-07    0.1903812011-01-08         NaN2011-01-09    0.2783442011-01-10    0.2783442011-01-11         NaN2011-01-12   -0.1322552011-01-13   -0.1322552011-01-14         NaN2011-01-15   -0.3854182011-01-16   -0.3854182011-01-17         NaN2011-01-18   -0.4289612011-01-19   -0.4289612011-01-20         NaN2011-01-21    0.9613172011-01-22    0.9613172011-01-23         NaN2011-01-24    0.5223032011-01-25    0.5223032011-01-26         NaN2011-01-27    0.4785212011-01-28    0.4785212011-01-29         NaN2011-01-30    0.103855                ...   2011-02-28         NaN2011-03-01   -0.6740352011-03-02   -0.6740352011-03-03         NaN2011-03-04   -0.0234522011-03-05   -0.0234522011-03-06         NaN2011-03-07    0.4255572011-03-08    0.4255572011-03-09         NaN2011-03-10   -0.9450442011-03-11   -0.9450442011-03-12         NaN2011-03-13   -0.4613712011-03-14   -0.4613712011-03-15         NaN2011-03-16    0.5058552011-03-17    0.5058552011-03-18         NaN2011-03-19   -0.1834692011-03-20   -0.1834692011-03-21         NaN2011-03-22    0.2578102011-03-23    0.2578102011-03-24         NaN2011-03-25    0.7481062011-03-26    0.7481062011-03-27         NaN2011-03-28    0.9836942011-03-29    0.983694Freq: D, Length: 88, dtype: float64day3Ts.resample(‘D‘).interpolate(‘linear‘)#线性拟合填充结果：2011-01-01    0.0152142011-01-02   -0.2404352011-01-03   -0.4960852011-01-04   -0.7517352011-01-05   -0.4376972011-01-06   -0.1236582011-01-07    0.1903812011-01-08    0.2197022011-01-09    0.2490232011-01-10    0.2783442011-01-11    0.1414782011-01-12    0.0046112011-01-13   -0.1322552011-01-14   -0.2166432011-01-15   -0.3010302011-01-16   -0.3854182011-01-17   -0.3999322011-01-18   -0.4144472011-01-19   -0.4289612011-01-20    0.0344652011-01-21    0.4978912011-01-22    0.9613172011-01-23    0.8149792011-01-24    0.6686412011-01-25    0.5223032011-01-26    0.5077092011-01-27    0.4931152011-01-28    0.4785212011-01-29    0.3536322011-01-30    0.228744                ...   2011-02-28    0.2580602011-03-01   -0.2079882011-03-02   -0.6740352011-03-03   -0.4571742011-03-04   -0.2403132011-03-05   -0.0234522011-03-06    0.1262182011-03-07    0.2758872011-03-08    0.4255572011-03-09   -0.0313102011-03-10   -0.4881772011-03-11   -0.9450442011-03-12   -0.7838202011-03-13   -0.6225952011-03-14   -0.4613712011-03-15   -0.1389622011-03-16    0.1834462011-03-17    0.5058552011-03-18    0.2760802011-03-19    0.0463062011-03-20   -0.1834692011-03-21   -0.0363762011-03-22    0.1107172011-03-23    0.2578102011-03-24    0.4212422011-03-25    0.5846742011-03-26    0.7481062011-03-27    0.8266362011-03-28    0.9051652011-03-29    0.983694Freq: D, Length: 88, dtype: float64

Pandas滑动窗口：

滑动窗口就是能够根据指定的单位长度来框住时间序列，从而计算框内的统计指标。相当于一个长度指定的滑块在刻度尺上面滑动，每滑动一个单位即可反馈滑块内的数据。

滑动窗口可以使数据更加平稳，浮动范围会比较小，具有代表性，单独拿出一个数据可能或多或少会离群，有差异或者错误，使用滑动窗口会更规范一些。

%matplotlib inline import matplotlib.pylabimport numpy as npimport pandas as pddf = pd.Series(np.random.randn(600), index = pd.date_range(‘7/1/2016‘, freq = ‘D‘, periods = 600))df.head()结果：2016-07-01   -0.1921402016-07-02    0.3579532016-07-03   -0.2018472016-07-04   -0.3722302016-07-05    1.414753Freq: D, dtype: float64r = df.rolling(window = 10)r#Rolling [window=10,center=False,axis=0]#r.max, r.median, r.std, r.skew倾斜度, r.sum, r.varprint(r.mean())结果：2016-07-01         NaN2016-07-02         NaN2016-07-03         NaN2016-07-04         NaN2016-07-05         NaN2016-07-06         NaN2016-07-07         NaN2016-07-08         NaN2016-07-09         NaN2016-07-10    0.3001332016-07-11    0.2847802016-07-12    0.2528312016-07-13    0.2206992016-07-14    0.1671372016-07-15    0.0185932016-07-16   -0.0614142016-07-17   -0.1345932016-07-18   -0.1533332016-07-19   -0.2189282016-07-20   -0.1694262016-07-21   -0.2197472016-07-22   -0.1812662016-07-23   -0.1736742016-07-24   -0.1306292016-07-25   -0.1667302016-07-26   -0.2330442016-07-27   -0.2566422016-07-28   -0.2807382016-07-29   -0.2898932016-07-30   -0.379625                ...   2018-01-22   -0.2114672018-01-23    0.0349962018-01-24   -0.1059102018-01-25   -0.1457742018-01-26   -0.0893202018-01-27   -0.1643702018-01-28   -0.1108922018-01-29   -0.2057862018-01-30   -0.1011622018-01-31   -0.0347602018-02-01    0.2293332018-02-02    0.0437412018-02-03    0.0528372018-02-04    0.0577462018-02-05   -0.0714012018-02-06   -0.0111532018-02-07   -0.0457372018-02-08   -0.0219832018-02-09   -0.1967152018-02-10   -0.0637212018-02-11   -0.2894522018-02-12   -0.0509462018-02-13   -0.0470142018-02-14    0.0487542018-02-15    0.1439492018-02-16    0.4248232018-02-17    0.3618782018-02-18    0.3632352018-02-19    0.5174362018-02-20    0.368020Freq: D, Length: 600, dtype: float64import matplotlib.pyplot as plt%matplotlib inlineplt.figure(figsize=(15, 5))df.plot(style=‘r--‘)df.rolling(window=10).mean().plot(style=‘b‘)#<matplotlib.axes._subplots.AxesSubplot at 0x249627fb6d8>

　　结果：

技术分享图片