时间序列数据的采样和画图 时间序列数据的采样和画图引入相关库时间序列数据的采样时间序列数据的画图
时间序列数据的采样和画图 引入相关库 import numpy as npimport pandas as pdfrom pandas import Series,DataFrame 时间序列数据的采样
使用date_range创建一个datetime对象，从2016-01-01开始，以天为间隔，一共366天
t_range=pd.date_range('2016-01-01','2016-12-31')t_range DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04', '2016-01-05', '2016-01-06', '2016-01-07', '2016-01-08', '2016-01-09', '2016-01-10', ... '2016-12-22', '2016-12-23', '2016-12-24', '2016-12-25', '2016-12-26', '2016-12-27', '2016-12-28', '2016-12-29', '2016-12-30', '2016-12-31'], dtype='datetime64[ns]', length=366, freq='D')以该datetime对象作为index，创建一个Series，长度也为366
s1=Series(np.random.randn(len(t_range)),index=t_range)s1 2016-01-01 1.4064612016-01-02 0.4479312016-01-03 1.6993942016-01-04 0.7887572016-01-05 -0.232023 ... 2016-12-27 -0.2471212016-12-28 0.6732012016-12-29 0.0915582016-12-30 -1.1637632016-12-31 0.482879Freq: D, Length: 366, dtype: float64 s1 2016-01-01 1.4064612016-01-02 0.4479312016-01-03 1.6993942016-01-04 0.7887572016-01-05 -0.232023 ... 2016-12-27 -0.2471212016-12-28 0.6732012016-12-29 0.0915582016-12-30 -1.1637632016-12-31 0.482879Freq: D, Length: 366, dtype: float64 s1['2016-01'] 2016-01-01 1.4064612016-01-02 0.4479312016-01-03 1.6993942016-01-04 0.7887572016-01-05 -0.2320232016-01-06 0.3489052016-01-07 -0.2369802016-01-08 0.1618072016-01-09 0.2186372016-01-10 1.6724602016-01-11 0.9832142016-01-12 0.0441902016-01-13 -0.6200902016-01-14 0.1013042016-01-15 1.8019192016-01-16 1.6243662016-01-17 0.6720932016-01-18 1.4757542016-01-19 0.5829162016-01-20 -0.2845062016-01-21 -1.6349102016-01-22 0.0005362016-01-23 -2.2545852016-01-24 0.2617072016-01-25 -0.3207442016-01-26 -0.1732442016-01-27 0.5441202016-01-28 0.0598522016-01-29 0.4792512016-01-30 0.8576162016-01-31 -0.815376Freq: D, dtype: float64 s1['2016-01'].mean() 0.3116365177410832把该Series采样，变成十二个月的数据，可以通过mean来算取每个月的评估值，一共十二个月，得到采样Series的大小为12，采样方法可以使用resample方法为按月进行采样。
s1_month=s1.resample('M').mean()s1_month 2016-01-31 0.3116372016-02-29 -0.0556872016-03-31 0.1523052016-04-30 0.0583712016-05-31 -0.1024822016-06-30 -0.1649702016-07-31 -0.2985552016-08-31 0.0410872016-09-30 0.2166052016-10-31 0.1375002016-11-30 -0.2698112016-12-31 -0.038291Freq: M, dtype: float64 s1_month.index DatetimeIndex(['2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30', '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31', '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31'], dtype='datetime64[ns]', freq='M')把resample方法改成按小时采样，由于原始数据一天只有一个数据点，所以变成按小时采样需要填充数据填满24个小时，ffill填充方法为使用一天的这个数据点往前填充，填充24小时
s1.resample('H').ffill() 2016-01-01 00:00:00 1.4064612016-01-01 01:00:00 1.4064612016-01-01 02:00:00 1.4064612016-01-01 03:00:00 1.4064612016-01-01 04:00:00 1.406461 ... 2016-12-30 20:00:00 -1.1637632016-12-30 21:00:00 -1.1637632016-12-30 22:00:00 -1.1637632016-12-30 23:00:00 -1.1637632016-12-31 00:00:00 0.482879Freq: H, Length: 8761, dtype: float64bfill方法的填充数据来自下一天即2016-01-02的数据
s1.resample('H').bfill() 2016-01-01 00:00:00 1.4064612016-01-01 01:00:00 0.4479312016-01-01 02:00:00 0.4479312016-01-01 03:00:00 0.4479312016-01-01 04:00:00 0.447931 ... 2016-12-30 20:00:00 0.4828792016-12-30 21:00:00 0.4828792016-12-30 22:00:00 0.4828792016-12-30 23:00:00 0.4828792016-12-31 00:00:00 0.482879Freq: H, Length: 8761, dtype: float64 时间序列数据的画图使用date_range再创建一个datetime，从2016-01-01到2016-12-31以每小时为单位，长度为8761
t_range=pd.date_range('2016-01-01','2016-12-31',freq='H')t_range DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00', '2016-01-01 02:00:00', '2016-01-01 03:00:00', '2016-01-01 04:00:00', '2016-01-01 05:00:00', '2016-01-01 06:00:00', '2016-01-01 07:00:00', '2016-01-01 08:00:00', '2016-01-01 09:00:00', ... '2016-12-30 15:00:00', '2016-12-30 16:00:00', '2016-12-30 17:00:00', '2016-12-30 18:00:00', '2016-12-30 19:00:00', '2016-12-30 20:00:00', '2016-12-30 21:00:00', '2016-12-30 22:00:00', '2016-12-30 23:00:00', '2016-12-31 00:00:00'], dtype='datetime64[ns]', length=8761, freq='H')创建一个空的DataFrame，以datetime作为index
stock_df=DataFrame(index=t_range)stock_df.head() 2016-01-01 00:00:002016-01-01 01:00:002016-01-01 02:00:002016-01-01 03:00:002016-01-01 04:00:00创建一个columns，作为阿里巴巴的股票，从数值范围为80-160，长度为8761
stock_df['BABA']=np.random.randint(80,160,size=len(t_range))stock_df.head() BABA2016-01-01 00:00:00842016-01-01 01:00:00802016-01-01 02:00:001112016-01-01 03:00:001592016-01-01 04:00:0094创建一个腾讯的股票，数值范围为30-50
stock_df['TENCENT']=np.random.randint(30,50,size=len(t_range))stock_df.head() BABATENCENT2016-01-01 00:00:0084332016-01-01 01:00:0080422016-01-01 02:00:00111482016-01-01 03:00:00159392016-01-01 04:00:009449对这个两列的DataFrame画图，创建一个matplotlib里面的图，这个图蓝线表示阿里，黄线表示腾讯，但是由于点太多比较拥挤
stock_df.plot() <matplotlib.axes._subplots.AxesSubplot at 0x19e61fe4dc8>如果上述方法显示不出图片的解决方法
import matplotlib.pyplot as pltplt.show()对DataFrame重新采样，变成每周一个点
weekly_df=DataFrame()对阿里巴巴的值按每周重新采样
weekly_df['BABA']=stock_df['BABA'].resample('W').mean()对腾讯的值按每周重新采样
weekly_df['TENCENT']=stock_df['TENCENT'].resample('W').mean() weekly_df.head() BABATENCENT2016-01-03117.70833339.9166672016-01-10120.47619039.7678572016-01-17122.42857139.6369052016-01-24116.70238139.2976192016-01-31120.70833339.839286展示曲线，可以清楚的看到两条线
weekly_df.plot() <matplotlib.axes._subplots.AxesSubplot at 0x19e629e5bc8>上面方法不能显示的解决办法
plt.show()