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copied!<p>Perhaps the <code>base</code> parameter of df.resample() would help:</p> <pre><code>base : int, default 0 For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0 </code></pre> <p>Here's an example:</p> <pre><code>In [44]: df = pd.DataFrame(np.random.rand(28), ....: index=pd.DatetimeIndex(start='2012/9/1', periods=28, freq='H')) In [45]: df Out[45]: 0 2012-09-01 00:00:00 0.970273 2012-09-01 01:00:00 0.730171 2012-09-01 02:00:00 0.508588 2012-09-01 03:00:00 0.535351 2012-09-01 04:00:00 0.940255 2012-09-01 05:00:00 0.143483 2012-09-01 06:00:00 0.792659 2012-09-01 07:00:00 0.231413 2012-09-01 08:00:00 0.071676 2012-09-01 09:00:00 0.995202 2012-09-01 10:00:00 0.236551 2012-09-01 11:00:00 0.904853 2012-09-01 12:00:00 0.652873 2012-09-01 13:00:00 0.488400 2012-09-01 14:00:00 0.396647 2012-09-01 15:00:00 0.967261 2012-09-01 16:00:00 0.554188 2012-09-01 17:00:00 0.884086 2012-09-01 18:00:00 0.418577 2012-09-01 19:00:00 0.189584 2012-09-01 20:00:00 0.577041 2012-09-01 21:00:00 0.100332 2012-09-01 22:00:00 0.294672 2012-09-01 23:00:00 0.925425 2012-09-02 00:00:00 0.630807 2012-09-02 01:00:00 0.400261 2012-09-02 02:00:00 0.156469 2012-09-02 03:00:00 0.658608 </code></pre> <p> </p> <pre><code>In [46]: df.resample("24H", how=sum, label='left', closed='left', base=5) Out[46]: 0 2012-08-31 05:00:00 3.684638 2012-09-01 05:00:00 11.671068 In [47]: df.ix[:5].sum() Out[47]: 0 3.684638 In [48]: df.ix[5:].sum() Out[48]: 0 11.671068 </code></pre>

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