为什么我可以正确绘制熊猫系列？

Question

我有一个要绘制的熊猫数据框

obs=pd.read_csv(obsfiles[0], skiprows=1)
obs.columns = ['date', 'time', 'gw', 'temp', 'volts', 'pulses']
obs['date'] = pd.to_datetime(obs['date'])
obs=obs[(obs['date'] >= '2016-01-01')]
obs = obs.set_index('date')
obs['gw'].plot()

为什么从5月5日开始绘制数据？我没有07之前的数据。我不确定出了什么问题...

In [7]: obs
Out[7]:
                 time      gw   temp  volts  pulses
date
2016-07-09   05:32:23  262.41  20.87  17.52       0
2016-07-09   17:32:23  241.59  20.90  17.84       0
2016-08-09   05:32:23  254.45  20.84  17.33       0
2016-08-09   17:32:23  230.87  20.90  17.68       0
2016-09-09   05:32:23  251.39  20.80  17.22       0
2016-09-09   17:32:23  224.44  20.90  17.62       0
2016-10-09   05:32:23  252.92  20.77  17.12       0
2016-10-09   17:32:23  242.81  20.84  17.27       0
2016-11-09   05:32:23  254.76  20.77  16.92       0
2016-11-09   17:32:23  228.42  20.87  17.42       0
2016-12-09   05:32:23  252.61  20.77  16.97       0
2016-12-09   17:32:23  233.63  20.84  17.29       0
2016-09-13   05:32:23  256.29  20.77  16.83       0
2016-09-13   17:32:23  235.16  20.84  17.21       0
2016-09-14   05:32:23  257.82  20.74  16.78       0
2016-09-14   17:32:23  240.67  20.80  17.08       0
2016-09-15   05:32:23  260.57  20.70  16.65       0
2016-09-15   17:32:23  236.38  20.80  17.10       0
2016-09-16   05:32:23  259.66  20.70  16.64       0
2016-09-16   17:32:23  234.55  20.80  17.07       0

来自评论

谢谢！

In [16]: obs=pd.read_csv(obsfiles[0], skiprows=1)
    ...: obs.columns = ['date', 'time', 'gw', 'temp', 'volts', 'pulses']
    ...: #obs['date']=parse_date_time(obs['date'])
    ...: obs['date'] = pd.to_datetime(obs['date'])
    ...: obs=obs[(obs['date'] >= '2016-01-01')]
    ...: obs = obs.set_index('date')
    ...: obs.sort_index()
Out[16]:
                 time      gw   temp  volts  pulses
date
2016-01-10   05:32:23  265.78  20.60  15.93       0
2016-01-10   17:32:23  238.53  20.67  16.28       0
2016-01-11   05:32:23  257.21  19.90  11.45       0
2016-01-11   17:32:23  234.55  20.13  12.91       0
2016-01-12   17:32:23  226.89  19.20   6.72       0
2016-01-12   05:32:23  237.00  18.93   0.15       0
2016-02-10   05:32:23  263.33  20.60  15.90       0
2016-02-10   17:32:23  241.59  20.67  16.21       0
2016-02-11   05:32:23  259.04  19.87  11.23       0
2016-02-11   17:32:23  235.16  20.10  12.78       0
2016-02-12   05:32:23  251.08  16.82   0.37       0
2016-02-12   17:32:23  229.04  19.06   6.05       0