Resample TimedeltaIndex and normalize to frequency
For example, I have got this Series : 17:50:51.050929 5601 17:52:15.429169 5601 17:52:19.538702 5601 17:53:44.776350 5601 17:53:51.870372 5598 17:55:33.952417 5600 17:56:48.736539 5596 17:57:01.205767 5593 17:57:26.066097 5593 17:57:30.644398 5591 I want to resample it but I want that the index start to a rounded frequency. So in the case above, I want the first index 17:51:00 if I resample on Min frequency. However Pandas implements it like that : a.resample('1T', 'mean') Out: 17:50:51.050929 5601.000000 17:51:51.050929 5601.000000 17:52:51.050929 5601.000000 17:53:51.050929 5598.000000 17:54:51.050929 5600.000000 17:55:51.050929 5596.000000 17:56:51.050929 5592.333333 17:57:51.050929 NaN How can I have a TimedeltaIndex starting from a rounded index ? Such as Timestamp resampling
A quick way to do it is to normalise the index before resampling (using either floor, ceil, or round): a.index = a.index.floor(freq='1T') a = a.resample('1T').mean()
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