braaannigan
Joined 4 years ago
Joined 4 years ago
I've shared my experience in this blog post: https://www.rhosignal.com/posts/advanced-data-analysis/
tldr: the exploratory analysis was nice, the text summaries are oft wrong, the choice of ML models was poor IMO and it gains a lot from expert guidance
When I say physical models here by the way I'm referring to physically-based mathematical models as well as numerical models.
It seems that the authors have done a good job in developing their model. My issue is with Nature deciding to publish it. If this paper was not published in Nature it would receive little attention within or without climate science - in fact many such statistical models are published each year without much comment. However, Nature have published a paper that I think many ocean scientists would feel draws dramatic conclusions from a weak basis but will now inevitably draw much more attention than more insightful papers.
MIT professor Carl Wunsch accused Nature in 2010 of near-tabloid science with a tendency towards sensational papers built on weak foundations. However, I've felt that Nature's choice of publications on climate in recent years has been high quality. This paper feels like a big step-down from that standard.
"Reading the full CSV without datetime parsing is in line in terms of speed though." This sentence was a bit ambiguous, but is important: if you read this file in pandas with engine='pyarrow' but don't convert the date/time column to a pandas datetime dtype you get the same ~100 ms read time as calling PyArrow directly. So basically the entire time is spent converting the strings to dates. If this datetime thing isn't an issue for you then you can just use the engine='pyarrow' argument to read CSVs with Pandas.
In my own tests with various datesets Polars has always been much faster than duckdb/pyarrow. For this relatively small dataset it's about 2x faster, which is about the smallest margin I've found. Polars is also much easier to write, as its query optimization is so effective - you don't need to know all the tricks that Pandas requires (and is still 3x-10x faster than Pandas even when you apply all the tricks).
I've started making videos to address the need for more guidance in Polars - see this new on reading CSVs: https://www.youtube.com/watch?v=nGritAo-71o