Also, it can use a **with** statement to reduce the code and guarantee the call to the close function to save changes: ```python from netcdf import netcdf as nc import numpy as np with nc.loader('new_file.nc') as joined_root: with nc.loader(['file01.nc', 'file02.nc', 'file03.nc']) as root: data = nc.getvar(root, 'data')
One of the main advantages of NetCDF4 include its support for larger files, unlimited dimensions (e.g. the time dimension), something called groups, and compression ability using zlib. Using Jeff Whittaker's NetCDF4 Python module is slightly different than the two previous methods discussed, but still easy to pick up.