We will now examine several common operations that are performed on time-series data. These operations entail realigning data, changing the frequency of the samples and their values, and calculating aggregate results on continuously moving subsets of the data to determine the behavior of the values in the data as time changes. We will examine each of the following:
Shifting and lagging values to calculate percentage changes
Changing the frequency of the data in the time series
Up and down sampling of the intervals and values in the time series
Performing rolling-window calculations
A common operation on time-series data is to shift the values backward and forward in time. The pandas method for this is .shift(), which will shift values in Series or DataFrame a specified number of units of the index's frequency.
To demonstrate shifting, we will use the following Series. This Series has five values, is indexed by date starting at 2014-08-01, and uses...