Resampling, grouping, and aggregation by time

We have now covered many of the components of time series and the great convenience offered by pandas to work with time-stamped data. As we mentioned in the last section, most of the time you will think of a time series as a time-based index and one or more columns of data. Now, let's take that structure as a starting point and then move on to introduce some advanced capabilities in pandas.

Using the resample method

Suppose you were given 6,000 readings of a sensor dataset, and the sample rate was 10 Hz or 10 times per second. We can make a simulated series like this as follows. We can start as we did in the last section, creating a sequence of timestamps. Using an end time of 9:59.9 and a frequency of 100 ms (milliseconds) generates the correct number of points (6,000) on the correct interval (10 per second = 100 ms):

sensor_times = ((pd.date_range('00:00:00', '00:09:59.9', freq = '100ms'))...