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.. currentmodule:: xarray
.. _howdoi:
How do I ...
============
.. list-table::
:header-rows: 1
:widths: 40 60
* - How do I...
- Solution
* - 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`
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