乱七八糟

>>> dta = [44,43,18,18,45,36,87,43,45,17,17,47,42,42,43,47,17,17,45,44,44,47,46,16,16,64,42,41,40,45,15,16,47,42,42,44,41,18,16,44,44,42,44]>>> dta = np.array(dta, dtype = np.float)>>> dtaarray([ 44.,  43.,  18.,  18.,  45.,  36.,  87.,  43.,  45.,  17.,  17.,        47.,  42.,  42.,  43.,  47.,  17.,  17.,  45.,  44.,  44.,  47.,        46.,  16.,  16.,  64.,  42.,  41.,  40.,  45.,  15.,  16.,  47.,        42.,  42.,  44.,  41.,  18.,  16.,  44.,  44.,  42.,  44.])>>> dta = pd.Series(dta)>>> dta0     44.01     43.02     18.03     18.04     45.05     36.06     87.07     43.08     45.09     17.010    17.011    47.012    42.013    42.014    43.015    47.016    17.017    17.018    45.019    44.020    44.021    47.022    46.023    16.024    16.025    64.026    42.027    41.028    40.029    45.030    15.031    16.032    47.033    42.034    42.035    44.036    41.037    18.038    16.039    44.040    44.041    42.042    44.0dtype: float64>>> rng = pd.date_range('6/1/2017', '7/13/2017', freq = 'D')>>> rngDatetimeIndex(['2017-06-01', '2017-06-02', '2017-06-03', '2017-06-04',               '2017-06-05', '2017-06-06', '2017-06-07', '2017-06-08',               '2017-06-09', '2017-06-10', '2017-06-11', '2017-06-12',               '2017-06-13', '2017-06-14', '2017-06-15', '2017-06-16',               '2017-06-17', '2017-06-18', '2017-06-19', '2017-06-20',               '2017-06-21', '2017-06-22', '2017-06-23', '2017-06-24',               '2017-06-25', '2017-06-26', '2017-06-27', '2017-06-28',               '2017-06-29', '2017-06-30', '2017-07-01', '2017-07-02',               '2017-07-03', '2017-07-04', '2017-07-05', '2017-07-06',               '2017-07-07', '2017-07-08', '2017-07-09', '2017-07-10',               '2017-07-11', '2017-07-12', '2017-07-13'],              dtype='datetime64[ns]', freq='D')>>> rng[0]Timestamp('2017-06-01 00:00:00', freq='D')>>> rng.map(lambda t: t.strftime('%Y-%m-%d'))array([u'2017-06-01', u'2017-06-02', u'2017-06-03', u'2017-06-04',       u'2017-06-05', u'2017-06-06', u'2017-06-07', u'2017-06-08',       u'2017-06-09', u'2017-06-10', u'2017-06-11', u'2017-06-12',       u'2017-06-13', u'2017-06-14', u'2017-06-15', u'2017-06-16',       u'2017-06-17', u'2017-06-18', u'2017-06-19', u'2017-06-20',       u'2017-06-21', u'2017-06-22', u'2017-06-23', u'2017-06-24',       u'2017-06-25', u'2017-06-26', u'2017-06-27', u'2017-06-28',       u'2017-06-29', u'2017-06-30', u'2017-07-01', u'2017-07-02',       u'2017-07-03', u'2017-07-04', u'2017-07-05', u'2017-07-06',       u'2017-07-07', u'2017-07-08', u'2017-07-09', u'2017-07-10',       u'2017-07-11', u'2017-07-12', u'2017-07-13'],       dtype='<U10')>>> rng[0]Timestamp('2017-06-01 00:00:00', freq='D')>>> dta.index = pd.Index(rng)>>> dta2017-06-01    44.02017-06-02    43.02017-06-03    18.02017-06-04    18.02017-06-05    45.02017-06-06    36.02017-06-07    87.02017-06-08    43.02017-06-09    45.02017-06-10    17.02017-06-11    17.02017-06-12    47.02017-06-13    42.02017-06-14    42.02017-06-15    43.02017-06-16    47.02017-06-17    17.02017-06-18    17.02017-06-19    45.02017-06-20    44.02017-06-21    44.02017-06-22    47.02017-06-23    46.02017-06-24    16.02017-06-25    16.02017-06-26    64.02017-06-27    42.02017-06-28    41.02017-06-29    40.02017-06-30    45.02017-07-01    15.02017-07-02    16.02017-07-03    47.02017-07-04    42.02017-07-05    42.02017-07-06    44.02017-07-07    41.02017-07-08    18.02017-07-09    16.02017-07-10    44.02017-07-11    44.02017-07-12    42.02017-07-13    44.0Freq: D, dtype: float64>>> dta.plot(figsize = (12, 8))<matplotlib.axes._subplots.AxesSubplot object at 0x05ACC610>>>> plt.show()>>> diff1 = dta.diff(1)>>> sm.stats.durbin_watson(dta)0.23328335832083957>>> fig = plt.figure(figsize = (12, 8))>>> ax1 = fig.add_subplot(211)>>> fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(),lags = 40, ax = ax1)>>> fig.show()

import statsmodels.api as sm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dta = [44,43,18,18,45,36,87,43,45,17,17,47,42,42,43,47,17,17,45,44,44,47,46,16,16,64,42,41,40,45,15,16,47,42,42,44,41,18,16,44,44,42,44]
dta = np.array(dta, dtype = np.float)
dta = pd.Series(dta)
rng = pd.date_range('6/1/2017', '7/13/2017', freq = 'D')
#rng.map(lambda t: t.strftime('%Y-%m-%d'))
dta.index = pd.Index(rng)
dta.plot(figsize = (12, 8))
plt.show()
diff1 = dta.diff(1)
sm.stats.durbin_watson(dta)
fig = plt.figure(figsize = (12, 8))
ax1 = fig.add_subplot(211)
fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(),lags = 40, ax = ax1)
ax2 = fig.add_subplot(212)
fig = sm.graphics.tsa.plot_pacf(dta, lags = 40, ax = ax2)
fig.show()