| add a DataArray to my dataset as a new variable |
my_dataset[varname] = my_dataArray or :py:meth:`Dataset.assign` (see also :ref:`dictionary_like_methods`) |
| add variables from other datasets to my dataset |
:py:meth:`Dataset.merge` |
| add a new dimension and/or coordinate |
:py:meth:`DataArray.expand_dims`, :py:meth:`Dataset.expand_dims` |
| add a new coordinate variable |
:py:meth:`DataArray.assign_coords` |
| change a data variable to a coordinate variable |
:py:meth:`Dataset.set_coords` |
| change the order of dimensions |
:py:meth:`DataArray.transpose`, :py:meth:`Dataset.transpose` |
| remove a variable from my object |
:py:meth:`Dataset.drop_vars`, :py:meth:`DataArray.drop_vars` |
| remove dimensions of length 1 or 0 |
:py:meth:`DataArray.squeeze`, :py:meth:`Dataset.squeeze` |
| remove all variables with a particular dimension |
:py:meth:`Dataset.drop_dims` |
| convert non-dimension coordinates to data variables or remove them |
:py:meth:`DataArray.reset_coords`, :py:meth:`Dataset.reset_coords` |
| rename a variable, dimension or coordinate |
:py:meth:`Dataset.rename`, :py:meth:`DataArray.rename`, :py:meth:`Dataset.rename_vars`, :py:meth:`Dataset.rename_dims`, |
| convert a DataArray to Dataset or vice versa |
:py:meth:`DataArray.to_dataset`, :py:meth:`Dataset.to_array` |
| extract the underlying array (e.g. numpy or Dask arrays) |
:py:attr:`DataArray.data` |
| convert to and extract the underlying numpy array |
:py:attr:`DataArray.values` |
| find out if my xarray object is wrapping a Dask Array |
:py:func:`dask.is_dask_collection` |
| know how much memory my object requires |
:py:attr:`DataArray.nbytes`, :py:attr:`Dataset.nbytes` |
| convert a possibly irregularly sampled timeseries to a regularly sampled timeseries |
:py:meth:`DataArray.resample`, :py:meth:`Dataset.resample` (see :ref:`resampling` for more) |
| apply a function on all data variables in a Dataset |
:py:meth:`Dataset.map` |
| write xarray objects with complex values to a netCDF file |
:py:func:`Dataset.to_netcdf`, :py:func:`DataArray.to_netcdf` specifying engine="h5netcdf", invalid_netcdf=True |
| make xarray objects look like other xarray objects |
:py:func:`~xarray.ones_like`, :py:func:`~xarray.zeros_like`, :py:func:`~xarray.full_like`, :py:meth:`Dataset.reindex_like`, :py:meth:`Dataset.interp_like`, :py:meth:`Dataset.broadcast_like`, :py:meth:`DataArray.reindex_like`, :py:meth:`DataArray.interp_like`, :py:meth:`DataArray.broadcast_like` |
| replace NaNs with other values |
:py:meth:`Dataset.fillna`, :py:meth:`Dataset.ffill`, :py:meth:`Dataset.bfill`, :py:meth:`Dataset.interpolate_na`, :py:meth:`DataArray.fillna`, :py:meth:`DataArray.ffill`, :py:meth:`DataArray.bfill`, :py:meth:`DataArray.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:class:`~xarray.DataArray` containing datetime64 or cftime values. See :ref:`dt_accessor` for more. |
| round off time values to a specified frequency |
obj.dt.ceil, obj.dt.floor, obj.dt.round. See :ref:`dt_accessor` for more. |
make a mask that is True where an object contains any of the values in a array |
:py:meth:`Dataset.isin`, :py:meth:`DataArray.isin` |