I hate to be the "you're holding it wrong" guy but 90% of "Pandas bad!" posts I find are either outright misinformed or mischaracterizing one person's particular opinion as some kind of common truth. This one is both!

> That comes from the assumption that there is almost always a meaningful index (timestamps)

The index can be literally any unique row label or ID. It's idiosyncratic among "data frames" (SQL has no equivalent concept, and the R community has disowned theirs), but it's really not such a crazy thing to have row labels built into your data table. Excel supports this in several different ways (frozen columns, VLOOKUP) and users expect it in just about any table-oriented GUI tool.

> having to write index=False every single time you write to disk

If you're actually using the index as it's meant to be used, you'd see why this isn't the default setting.

> functions seemingly randomly returning dataframes with column data as the index

I assume you're talking about the behavior of .groupby() and .rolling()? It's never been random. Under-documented and hard to reason about group_keys= and related options, yes. But not random.

> appending the index to the Series numpy data leading to incredibly confusing bugs

I've been using Pandas professionally almost daily since 2015 and I have no idea what this means.