issues: 253407851

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
253407851 MDU6SXNzdWUyNTM0MDc4NTE= 1534 to_dataframe (pandas) usage question 23199378 closed 0     6 2017-08-28T18:02:56Z 2017-09-07T08:00:41Z 2017-09-07T08:00:41Z NONE      

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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