Working with data in Graphext’s low-code mode
10 min tutorial walking you through the basic stuff you need to know about the recipe
ds
, and so the simplest possible recipe is simply
create_project
which accepts a dataset as input and has no output. This is a
special case. Since the result of this step is the creation of a project, it doesn’t generate any output that can be
further processed inside the recipe.
In practice you’ll almost always want to somehow transform or enrich your dataset however, and so you’ll want to add
one or more of the many steps available in Graphext before the final step of project creation.
->
), you provide names for the outputs that the step will generate.
Again, the outputs may be one more columns or datasets.
To differentiate between input datasets and columns, column names need to be prefixed with the name of the dataset it
belongs to, while datasets can be referred to by their name only. In other words, ds
refers to the dataset with the name
“ds” and to pick out a specific column you’d use either ds.my_column
or ds["my_column"]
. The two forms are generally
interchangeable, but the latter is required if a column name contains spaces.
To given an example, a simple step that splits the texts in a given column in two at the first comma, might be written as
"pattern"
is the parameter’s name and ","
its value.
In general, all parameter names must be quoted strings, while values may be