issue_comments

As you wish — if there's a motivating example then that has more weight, and big issues should have ample supply of motivating examples. That said, if you have something ready to go, then happy to take a look at it.

The issue is that if you pass in names = ['a','b','c'] to pd.read_csv and there are more columns than names, it takes all the columns without a name and creates a multi-index. That was a bug in my code that I had more columns than names, didn't want a multi-index, and didn't make use of usecols.

This multi-index came from a small 12 MB file - 5000 rows and 40 variables. When I then did df.to_xarray() it filled up my RAM. If I ran the code I provided above, it worked.

Now that I've figured all this out, I don't think that any bugs exist in xarray or pandas, just my code. As usual :). But if the fact that I can fill ram with df.to_xarray() but not with the 3 lines shown above sounds like an issue you want to explore, I'm happy to provide an MWE on a new ticket and tag you there. Let me know...

Great! Post here / a new issue if something does come up!

@max-sixty Sorry for posting this here. This memory blow-up was a byproduct of another bug that it took me a few more hours to track down. This other bug is in Pandas, not xarray.

@mankoff Thanks for the issue, do you have a fuller reproduction? I'm happy to take a look at this.

Late reply, but if anyone else finds this issue, I was filling memory with: ds = df.to_xarray(), but if I build the dataset more manually, I have no memory issues:

python ds = xr.Dataset({df.columns[0]: xr.DataArray(data=df[df.columns[0]], dims=['index'], coords={'index':df.index})}) for c in df.columns[1:]: ds[c] = (('index'), df[c])

Does that sound like it will play well with GeoViews if I want widgets for the categorical vars?

Ok. Looks like the way forward is a netCDF file for each level of my categorical variables. Will give it a shot.

MetaCSV looks interesting but I haven't used it myself. My guess would be that it just wraps pandas/xarray for processing data, so I think it's unlikely to give a performance boost. It's more about a declarative way to specify how to load a CSV into pandas/xarray.

@shoyer Thank you. Does metacsv look likely to work to you? It has attracted almost no attention so I wonder if it will exhaust memory. I'm kind of surprised this path (csv -> xarray) isn't better fleshed out as I would have expected it to be very common, perhaps the most common esp. for "found data."

If you don't want the full Cartesian product, you need to ensure that the index only contains the variables you want to expand into a grid, e.g., time, lat and lon.

If the problem is only running out of memory (which is indeed likely with 1e9 rows), then you'll need to think about a more clever way to convert the data. One good option might be to groups over subsets of the data (using dask or another parallel processing library like spark or beam), and write a bunch of smaller netCDF which you then open with xarray's open_mfdataset(). It's probably most convenient to split over time, e.g., into files for each day or month.

This looks potentially helpful http://metacsv.readthedocs.io/en/latest/

PS: I started with Dask but haven't found a way to go from Dask to xarray.

Hi @jhamman The original data is literally just a flat csv file with ie: lat,lon,epoch,cat1,cat2,var1,var2,...,var50 with 1 billion rows.

I'm looking to xarray for GeoViews, which I think would benefit from having the data properly grouped/indexed by its categories

@brianmingus - any chance you can provide a reproducible example with some dummy data?