在不添加新日期的情况下重新采样盘中 pandas DataFrame
问题描述
我想在不添加新日期的情况下对一些日内数据进行下采样
I want to downsample some intraday data without adding in new days
df.resample('30Min')
将添加周末等，这是不可取的.反正有这个吗?
Will add weekends etc which is undesirable. Is there anyway around this?
解决方案
组合的 groupby/resample 可能会起作用:
A combined groupby/resample might work:
In [22]: dates = pd.date_range('01-Jan-2014','11-Jan-2014', freq='T')[0:-1]
...: dates = dates[dates.dayofweek < 5]
...: s = pd.TimeSeries(np.random.randn(dates.size), dates)
...:
In [23]: s.size
Out[23]: 11520
In [24]: s.groupby(lambda d: d.date()).resample('30min').size
Out[24]: 384
In [25]: s.groupby(lambda d: d.date()).resample('30min')
Out[25]:
2014-01-01 2014-01-01 00:00:00 0.202943
2014-01-01 00:30:00 -0.466010
2014-01-01 01:00:00 0.029175
2014-01-01 01:30:00 -0.064492
2014-01-01 02:00:00 -0.113348
2014-01-01 02:30:00 0.100408
2014-01-01 03:00:00 -0.036561
2014-01-01 03:30:00 -0.029578
2014-01-01 04:00:00 -0.047602
2014-01-01 04:30:00 -0.073846
2014-01-01 05:00:00 -0.410143
2014-01-01 05:30:00 0.143853
2014-01-01 06:00:00 -0.077783
2014-01-01 06:30:00 -0.122345
2014-01-01 07:00:00 0.153003
...
2014-01-10 2014-01-10 16:30:00 -0.107377
2014-01-10 17:00:00 -0.157420
2014-01-10 17:30:00 0.201802
2014-01-10 18:00:00 -0.189018
2014-01-10 18:30:00 -0.310503
2014-01-10 19:00:00 -0.086091
2014-01-10 19:30:00 -0.090800
2014-01-10 20:00:00 -0.263758
2014-01-10 20:30:00 -0.036789
2014-01-10 21:00:00 0.041957
2014-01-10 21:30:00 -0.192332
2014-01-10 22:00:00 -0.263690
2014-01-10 22:30:00 -0.395939
2014-01-10 23:00:00 -0.171149
2014-01-10 23:30:00 0.263057
Length: 384
In [26]: np.unique(_25.index.get_level_values(1).minute)
Out[26]: array([ 0, 30])
In [27]: np.unique(_25.index.get_level_values(1).dayofweek)
Out[27]: array([0, 1, 2, 3, 4])
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