将 Pandas 数据帧转换为时间序列
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Convert Pandas dataframe to time series
提问by greenafrican
I have a Pandas DataFrame:
我有一个Pandas数据帧：
Out[57]:
lastrun rate
0 2013-11-04 12:15:02 0
1 2013-11-04 13:14:50 4
2 2013-11-04 14:14:48 10
3 2013-11-04 16:14:59 16
I would like to convert that into an hourly time series and interpolate missing values (15:00) so that I end up with:
我想将其转换为每小时时间序列并插入缺失值（15:00），以便我最终得到：
2013-11-04 12:00:00 0
2013-11-04 13:00:00 4
2013-11-04 14:00:00 10
2013-11-04 15:00:00 13
2013-11-04 16:00:00 16
How do I convert / map the dataframe data to a time series in Pandas?
如何将数据帧数据转换/映射到 Pandas 中的时间序列？
回答by TomAugspurger
Assuming your 'lastrun' has datetime objects:
假设你的“lastrun”有日期时间对象：
In [22]: s = df.set_index('lastrun').resample('H')['rate']
In [23]: s
Out[23]:
lastrun
2013-11-04 12:00:00 0
2013-11-04 13:00:00 4
2013-11-04 14:00:00 10
2013-11-04 15:00:00 NaN
2013-11-04 16:00:00 16
Freq: H, dtype: float64
In [24]: s.interpolate()
Out[24]:
lastrun
2013-11-04 12:00:00 0
2013-11-04 13:00:00 4
2013-11-04 14:00:00 10
2013-11-04 15:00:00 13
2013-11-04 16:00:00 16
Freq: H, dtype: int64
That's if you want linear interpolation. There's a bunch more optionsin the upcoming .13 release!
那就是如果你想要线性插值。在即将发布的 .13 版本中有更多选项！
