add a DataArray to my dataset as a new variable |
my_dataset[varname] = my_dataArray or Dataset.assign() (see also Dictionary like methods)
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add variables from other datasets to my dataset |
Dataset.merge()
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add a new dimension and/or coordinate |
DataArray.expand_dims(), Dataset.expand_dims()
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add a new coordinate variable |
DataArray.assign_coords()
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change a data variable to a coordinate variable |
Dataset.set_coords()
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change the order of dimensions |
DataArray.transpose(), Dataset.transpose()
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reshape dimensions |
DataArray.stack(), Dataset.stack(), Dataset.coarsen.construct(), DataArray.coarsen.construct()
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remove a variable from my object |
Dataset.drop_vars(), DataArray.drop_vars()
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remove dimensions of length 1 or 0 |
DataArray.squeeze(), Dataset.squeeze()
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remove all variables with a particular dimension |
Dataset.drop_dims()
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convert non-dimension coordinates to data variables or remove them |
DataArray.reset_coords(), Dataset.reset_coords()
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rename a variable, dimension or coordinate |
Dataset.rename(), DataArray.rename(), Dataset.rename_vars(), Dataset.rename_dims(),
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convert a DataArray to Dataset or vice versa |
DataArray.to_dataset(), Dataset.to_dataarray(), Dataset.to_stacked_array(), DataArray.to_unstacked_dataset()
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extract variables that have certain attributes |
Dataset.filter_by_attrs()
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extract the underlying array (e.g. NumPy or Dask arrays) |
DataArray.data
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convert to and extract the underlying NumPy array |
DataArray.to_numpy
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convert to a pandas DataFrame |
Dataset.to_dataframe
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sort values |
Dataset.sortby
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find out if my xarray object is wrapping a Dask Array |
dask.is_dask_collection()
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know how much memory my object requires |
DataArray.nbytes, Dataset.nbytes
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Get axis number for a dimension |
DataArray.get_axis_num()
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convert a possibly irregularly sampled timeseries to a regularly sampled timeseries |
DataArray.resample(), Dataset.resample() (see Resampling and grouped operations for more)
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apply a function on all data variables in a Dataset |
Dataset.map()
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write xarray objects with complex values to a netCDF file |
Dataset.to_netcdf(), DataArray.to_netcdf() specifying engine="h5netcdf" or Dataset.to_netcdf(), DataArray.to_netcdf() specifying engine="netCDF4", auto_complex=True
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make xarray objects look like other xarray objects |
ones_like(), zeros_like(), full_like(), Dataset.reindex_like(), Dataset.interp_like(), Dataset.broadcast_like(), DataArray.reindex_like(), DataArray.interp_like(), DataArray.broadcast_like()
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Make sure my datasets have values at the same coordinate locations |
xr.align(dataset_1, dataset_2, join="exact")
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replace NaNs with other values |
Dataset.fillna(), Dataset.ffill(), Dataset.bfill(), Dataset.interpolate_na(), DataArray.fillna(), DataArray.ffill(), DataArray.bfill(), DataArray.interpolate_na()
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extract the year, month, day or similar from a DataArray of time values |
obj.dt.month for example where obj is a DataArray containing datetime64 or cftime values. See Datetime components for more.
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round off time values to a specified frequency |
obj.dt.ceil, obj.dt.floor, obj.dt.round. See Datetime components for more.
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make a mask that is True where an object contains any of the values in a array |
Dataset.isin(), DataArray.isin()
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Index using a boolean mask |
Dataset.query(), DataArray.query(), Dataset.where(), DataArray.where()
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preserve attrs during (most) xarray operations |
xr.set_options(keep_attrs=True)
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