Four hours is too long to spend fighting a chart, and the real problem here is not the data. It is the default assumption that time must be represented as a single, continuous line on the x-axis. The user's biannual periods, like May, Oct and Nov, Apr, simply do not align with calendar years, so any naive plotting attempt will produce gaps, overlaps, or a jumbled mess. The solution is not to force both series onto identical time stamps, but to treat each period as a categorical label, ordered by its start date, and then use a secondary axis for the yearly totals. That way, the chart respects the structure of the data instead of fighting it.
What this means for you is that you do not need a more powerful tool or a complex formula to solve this. You need to change how you think about the x-axis. If you map the biannual periods as categories, each one becomes a distinct point on the horizontal axis, evenly spaced regardless of the number of days in the period. The yearly totals, which are larger in scale, sit cleanly on a second y-axis. The visual comparison becomes immediate: you can see how each half-year expenditure trends against the annual total, without pretending the time intervals are equal. This is a practical fix that works in any standard spreadsheet application, and it takes minutes, not hours.
The frustration is understandable, but it also points to a broader lesson. Most spreadsheet pain comes from trying to make software do something that mirrors how we think the data *should* look, rather than how it is actually structured. The user has two different time granularities, and the answer is not to hide that fact but to make it explicit in the chart design. By using categorical labels and a secondary axis, you turn a confusing overlap into a clear comparison. That is the kind of small shift that turns a four-hour struggle into a five-minute task.
So, if you find yourself stuck on a similar alignment issue, stop trying to stretch or compress time. Label the periods, order them chronologically, and let the chart do the rest. Your data is not broken; the approach was. And once you see that, the solution is simple.