xarray
| How do I... | Solution |
|---|---|
| add a DataArray to my dataset as a new variable | my_dataset[varname] = my_dataArray or :pyDataset.assign (see also dictionary_like_methods) |
| add variables from other datasets to my dataset | :pyDataset.merge |
| add a new dimension and/or coordinate | :pyDataArray.expand_dims, :pyDataset.expand_dims |
| add a new coordinate variable | :pyDataArray.assign_coords |
| change a data variable to a coordinate variable | :pyDataset.set_coords |
| change the order of dimensions | :pyDataArray.transpose, :pyDataset.transpose |
| reshape dimensions | :pyDataArray.stack, :pyDataset.stack, :pyDataset.coarsen.construct, :pyDataArray.coarsen.construct |
| remove a variable from my object | :pyDataset.drop_vars, :pyDataArray.drop_vars |
| remove dimensions of length 1 or 0 | :pyDataArray.squeeze, :pyDataset.squeeze |
| remove all variables with a particular dimension | :pyDataset.drop_dims |
| convert non-dimension coordinates to data variables or remove them | :pyDataArray.reset_coords, :pyDataset.reset_coords |
| rename a variable, dimension or coordinate | :pyDataset.rename, :pyDataArray.rename, :pyDataset.rename_vars, :pyDataset.rename_dims, |
| convert a DataArray to Dataset or vice versa | :pyDataArray.to_dataset, :pyDataset.to_array, :pyDataset.to_stacked_array, :pyDataArray.to_unstacked_dataset |
| extract variables that have certain attributes | :pyDataset.filter_by_attrs |
| extract the underlying array (e.g. NumPy or Dask arrays) | :pyDataArray.data |
| convert to and extract the underlying NumPy array | :pyDataArray.values |
| convert to a pandas DataFrame | :pyDataset.to_dataframe |
| sort values | :pyDataset.sortby |
| find out if my xarray object is wrapping a Dask Array | :pydask.is_dask_collection |
| know how much memory my object requires | :pyDataArray.nbytes, :pyDataset.nbytes |
| Get axis number for a dimension | :pyDataArray.get_axis_num |
| convert a possibly irregularly sampled timeseries to a regularly sampled timeseries | :pyDataArray.resample, :pyDataset.resample (see resampling for more) |
| apply a function on all data variables in a Dataset | :pyDataset.map |
| write xarray objects with complex values to a netCDF file | :pyDataset.to_netcdf, :pyDataArray.to_netcdf specifying engine="h5netcdf", invalid_netcdf=True |
| make xarray objects look like other xarray objects | :py~xarray.ones_like, :py~xarray.zeros_like, :py~xarray.full_like, :pyDataset.reindex_like, :pyDataset.interp_like, :pyDataset.broadcast_like, :pyDataArray.reindex_like, :pyDataArray.interp_like, :pyDataArray.broadcast_like |
| Make sure my datasets have values at the same coordinate locations | xr.align(dataset_1, dataset_2, join="exact") |
| replace NaNs with other values | :pyDataset.fillna, :pyDataset.ffill, :pyDataset.bfill, :pyDataset.interpolate_na, :pyDataArray.fillna, :pyDataArray.ffill, :pyDataArray.bfill, :pyDataArray.interpolate_na |
| extract the year, month, day or similar from a DataArray of time values | obj.dt.month for example where obj is a :py~xarray.DataArray containing datetime64 or cftime values. See dt_accessor for more. |
| round off time values to a specified frequency | obj.dt.ceil, obj.dt.floor, obj.dt.round. See dt_accessor for more. |
make a mask that is True where an object contains any of the values in a array |
:pyDataset.isin, :pyDataArray.isin |
| Index using a boolean mask | :pyDataset.query, :pyDataArray.query, :pyDataset.where, :pyDataArray.where |
preserve attrs during (most) xarray operations |
xr.set_options(keep_attrs=True) |