Lambda Function as a Kedro Node
===============================

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...

Date: January 31, 2022

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.

## Examples

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.

``` python
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.