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  • rabernat · 1 ✖

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  • to_dataframe (pandas) usage question · 1 ✖

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325447523 https://github.com/pydata/xarray/issues/1534#issuecomment-325447523 https://api.github.com/repos/pydata/xarray/issues/1534 MDEyOklzc3VlQ29tbWVudDMyNTQ0NzUyMw== rabernat 1197350 2017-08-28T19:03:09Z 2017-08-28T19:03:09Z MEMBER

Marinna,

You are correct. In the present release of Xarray, converting to a pandas dataframe loads all of the data eagerly into memory as a regular pandas object, giving up dask's parallel capabilities and potentially consuming lots of memory. With chunked Xarray data, It would be preferable instead to convert to a dask.dataframe, rather than a regular pandas dataframe, which would carry over some of the performance benefits.

This is a known issue: https://github.com/pydata/xarray/issues/1462

With a solution in the works: https://github.com/pydata/xarray/pull/1489

So hopefully a release of Xarray in the near future will have the feature you seek.

Alternatively, if you describe the filtering, masking, and other QA/QC that you need to do in more detail, we may be able to help you accomplish this entirely within Xarray.

Good luck! Ryan

On Mon, Aug 28, 2017 at 2:02 PM, Marinna Martini notifications@github.com wrote:

Apologies for what is probably a very newbie question:

If I convert such a large file to pandas using to_dataframe() to gain access to more pandas methods, will I lose the speed and dask capabillity that is so wonderful in xarray?

I have a very large netCDF file (3 GB with 3 Million data points of 1-2 Hz ADCP data) that needs to be reduced to hourly or 10 min averages. xarray is perfect for this. I am exploring resample and other methods. It is amazingly fast doing this:

ds = xr.open_dataset('hugefile.nc') ds_lp = ds.resample('H','time','mean')

And an offset of about half a day is introduced to the data. Probably user error or due to filtering. To figure this out, I am looking at using resample in pandas directly, or multindexing and reshaping using methods that are not inherited from pandas by xarray, then back to xarray using to_xarray. I will also need to be masking data (and other things pandas can do) during a QA/QC process. It appears that pandas can do masking and xarray does not inherit masking?

Am I understanding the relationship between xarray and pandas correctly?

Thanks, Marinna

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