issue_comments: 325773402
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/1534#issuecomment-325773402 | https://api.github.com/repos/pydata/xarray/issues/1534 | 325773402 | MDEyOklzc3VlQ29tbWVudDMyNTc3MzQwMg== | 23199378 | 2017-08-29T19:31:00Z | 2017-08-29T19:31:00Z | NONE | Hello Ryan, I have read a bit about dask. Am I missing the Pandas Panel analog in Dask? My data is in netcdf4, and the files can have as many as 17 variables or more. It's not clear how to get this easily into dask. In Pandas I think the entire netCDF file equates to a Panel. A single variable would be a DataFrame. Rather than wandering around in the weeds, I could use a hint here. Do I really need to open the netCDF4 file, then iterate over my variables and deal them into a series of dask data frames? That seems very un-pythonic. I tried this... presumably from here: http://www.unidata.ucar.edu/software/netcdf/docs/interoperability_hdf5.html I can open a netCDF4 file as "HDF5" using dask. Let's try a dask example (http://dask.pydata.org/en/latest/examples/dataframe-hdf5.html) with one of my netCDF files: df = dd.read_hdf('reallybignetCDF4file.nc',key='/c') # this does not work Thanks, Marinna |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
253407851 |