I keep my nodes short and sweet. They do one thing and do it well. I turn almost every DataFrame transformation into its own node. It makes it must easier to pull catalog entries, than firing up the pipeline, running it, and starting a debugger. For this reason many of my nodes can be built from inline lambdas.
Here are two examples, the first one
lambda x: x is sometimes referred
to as an identity function. This is super common to use in the early
phases of a project. It lets you follow standard layering conventions,
without skipping a layer, overthinking if you should have the layer or
not, and leaves a good placholder to fill in later when you need it.
Many times I just want to get the data in as fast as possible, learn about it, then go back and tidy it up.
from kedro.pipeline import node my_first_node = node( func=lambda x: x, inputs='raw_cars', output='int_cars', tags=['int',] ) my_first_node = node( func=lambda cars: cars[['mpg', 'cyl', 'disp',]].query('disp>200'), inputs='raw_cars', output='int_cars', tags=['pri',] )
Note: try not to take the idea of a one liner too far. If your one line function wraps several lines down it probably deserves to be a real function for readability and a good docstring.