You havent seen anything until you need to put a 4.2gb gzipped csv into a pandas dataframe, which works without any issues I should note.
I really don’t think that’s a lot either. Nowadays we routinely process terabytes of data.
Yeah, it was just a simple example. Although using just pandas (without something like dask) for loading terabytes of data at once into a single dataframe may not be the best idea, even with enough memory.
Is 600 MB a lot for pandas? Of course, CSV isn’t really optimal but I would’ve sworn pandas happily works with gigabytes of data.
I guess it’s more of a critique of how bad CSV is for storing large data than pandas being inefficient
Is 600 MB a lot for pandas?
No, but it’s easy to make a program in Python that doesn’t like it.
It really depends on the machine that is running the code. Pandas will always have the entire thing loaded in memory, and while 600Mb is not a concern for our modern laptops running a single analysis at a time, it can get really messy if the person is not thinking about hardware limitations
Pandas supports lazy loading and can read files in chunks. Hell, even regular ole Python doesn’t need to read the whole file at once with
csv
What do you mean not optimal? This is quite literally the most popular format for any serious data handling and exchange. One byte per separator and newline is all you need. It is not compressed so allows you to stream as well. If you don’t need tree structure it is massively better than others
I think portability and easy parsing is the only advantage od CSV. It’s definitely good enough (maybe even the best) for small datasets but if you have a lot of data you need a compressed binary format, something like parquet.
600MB? What is this, 2004?
