Interesting that the first "high" point is colored wrongly, since the moving average is only defined after. You could extrapolate here by repeating the first value, or use a symmetric / centered window.

Good points, and good suggestions for how to address. Thanks! I think with symmetric you'd run into the same problem, but on the right-hand-side for 7/31. Assuming that we're more interested in recent history than older history (esp if we imagine this as something that continues to update w/ new data), I'd rather the issue affect the oldest data rather than the newest. Your idea to apply the first moving average value to classify the first 6 data points is a good option. Essentially assuming that "ave of first 7 points is applicable to classify those points as 'high' or 'normal', which seems like a good assumption. Or, could calculate the moving average as the average of what is available (ie - average over only 2,3,4,5,6 days respectively for the first 6 data points). Upside: if trend was changing fast, it might capture that. Downside is this puts more weight on the earliest points and still leaves a question of what to do with the first point. Therefore, I think the repeat option above is preferable: simpler and likely less error-prone (unless trend changing fast). Alternatively could show those first 6 left-hand-side values in grey, instead of orange/blue, to designate a "undefined" sort of status and acknowledging that there isn't enough data for a 7-day moving average yet. I think in this case, where the data is static and I can see that it would work well, I would implement the "repeat" option that you suggested. If this was for a dashboard / diagnostic chart for somebody who works at the energy company or for myself, somebody who is going to look at the chart over and over with data for different customers / time periods and wants to prioritizing avoid misclassification, I'd lean towards the grey option. But, if it was a chart that the energy company was going to send to every customer as part of a report on their energy consumption, then I feel like there is an argument to be made for either: repeat (for simplicity) or grey (for avoiding misclassification). What do you think?

Alternatively, we could use the array-blur strategy instead of the rolling-window; array-blur is a fast approximation of a gaussian kernel; it does not lose any data point, and does not "vanish" at the ends of the series. Here's an (unpublished) notebook where I've applied this strategy https://observablehq.com/d/d1a59bb628f72118