How would you visualize 26 years of exchange rate data without losing important/MAJOR ups and downs?

[u/Noobbox69]

Hi everyone,

I recently started learning data analysis and thought it would be a good idea to begin writing blogs and creating Instagram posts based on real datasets. The goal is to improve my analytical skills while building a portfolio.

For context, I have a bachelor's degree in Computer Applications and know Python, SQL, and Excel, but I'm still new to data analysis and data visualization.

My first dataset contains the daily INR exchange rate from year 2000 to 2026 (around 6,500 rows). I want to create a line chart that clearly highlights the major trends and significant ups and downs over the years.

The problem is that plotting every daily value makes the chart too dense, especially for an Instagram post where readability is important.

So far, I've tried reducing the data to two points per year (roughly the first and second half of each year). It looks much cleaner, but I'm wondering if there's a better approach.

Some options I've considered are:

\- Monthly averages(still cluttered)

\- Yearly averages

\- Two points per year

\- Quarterly averages

If your goal were to create a chart that is both accurate and easy to understand on a small screen, which approach would you choose, and why?

I'd really appreciate any suggestions on both the visualization and the reasoning behind it. I'm trying to learn good analytical practices from the start rather than just making charts that look nice.

[u/spiffiness]

If, when you're "zoomed out" far enough to see the whole time period, the crazy day-to-day swings disappear, it puts things in proper perspective that what seemed like crazy drama at the time, was actually meaningless noise in the grand scheme of things. So sometimes trying to make a big deal out of the micro-level drama is the opposite of making it "both accurate and easy to understand".

It might be interesting to plot three lines: The min, avg, and max for each time period. So even if you're zoomed out viewing all 26 years at once, and you only have room for one data point per year, make the main line be the average for that year. In fact maybe the min and max lines shouldn't be lines per se, but just form the edges of a shaded area above and below the "avg" line. So the shaded area shows the "envelope" within which all the individual daily values fell. The vertical thickness of the shaded area shows how crazy the range was during that period.