# Pass features into a task

> Pass features into a task - Learn about how features of the same data type may need to be processed
> differently than others in the Blueprint Workshop.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-04-24T16:03:56.252012+00:00` (UTC).

## Primary page

- [Pass features into a task](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-adv.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Code-first tools](https://docs.datarobot.com/en/docs/api/code-first-tools/index.html): Linked from this page.
- [Blueprint Workshop](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/index.html): Linked from this page.

## Documentation content

# Pass features into a task

Certain features of the same data type may need to be processed differently than others. For example, suppose you are working on solving a problem with a dataset containing text features. One of which lends itself well to using word-grams for preprocessing, while the other uses char-grams.

When using Composable ML in DataRobot, you can pass one or more specific features to another task.

**Blueprint Workshop:**
When using project-specific functionality, DataRobot recommends running the following code.

```
w.set_project(project_id="<project_id>")
# or
# w = Workshop(project_id="<project_id>")
```

In this example, select the Age feature, perform missing value imputation, and pass it to the Keras neural network classifier. Note that similar to other pieces of functionality, you may auto-complete feature names with `w.Features.<tab>` to complete available features.

```
features = w.FeatureSelection(w.Features.Age)
pni = w.Tasks.PNI2(features)
keras = w.Tasks.KERASC(pni)
keras_blueprint = w.BlueprintGraph(keras)
```

You may link a blueprint to a specific project if desired, ensuring the blueprint is validated based on the linked project, for example, to confirm that the selected features exist in the dataset associated with the project.

```
# Make sure it is saved at least once, or pass `user_blueprint_id` to `link_to_project`
keras_blueprint.save()
keras_blueprint.link_to_project(project_id="<project_id>")
```

**DataRobot UI:**
To only pass a desired column into a task, add the Task Single Column Converter or Multiple Column Converter. Then, pick the column name from the original dataset as the parameter column_name or column_names. The following task(s) will only receive the selected column(s).

[https://docs.datarobot.com/en/docs/images/bpw-20.png](https://docs.datarobot.com/en/docs/images/bpw-20.png)

Click Update and then Save Blueprint to see the new task referencing the chosen column. Here's an example of a blueprint performing specific preprocessing on certain columns. Notice how each column name is observable.

[https://docs.datarobot.com/en/docs/images/bpw-21.png](https://docs.datarobot.com/en/docs/images/bpw-21.png)

Continuing with this example, you can also pass all columns to another task. To do so, add a new connection from Numeric Variables to the desired task.

You may link a blueprint to a specific project if desired, ensuring the blueprint is validated based on the linked project; for example, to confirm that the selected features exist in the dataset associated with the project

[https://docs.datarobot.com/en/docs/images/bpw-22.png](https://docs.datarobot.com/en/docs/images/bpw-22.png)


Features may also be excluded instead, which is particularly useful when a particular feature should be processed one way, and everything else, processed another way.

**Blueprint Workshop:**
```
without_insurance_type = w.FeatureSelection(w.Features.Insurance_Type, exclude=True)
only_insurance_type = w.FeatureSelection(w.Features.Insurance_Type)
one_hot = w.Tasks.PDM3(without_insurance_type)
ordinal = w.Tasks.ORDCAT2(only_insurance_type)
keras = w.Tasks.KERASC(one_hot, ordinal)
keras_blueprint = w.BlueprintGraph(keras)
```

**DataRobot UI:**
To process certain features in different ways, add the Task Multiple Column Converter. This task lets you select columns. You can give it a list with several columns that you want to include and the rest will be dropped (using the parameter column_names). Alternatively, you can instead provide a list of several columns that you would like to use.

Next, create an edge from the categorical data to the modeler, insert the alternative processing task, then add a second Multiple Column Converter and pick the same column name and change method to be exclude.

Now, one column is processed using one task, and all others are processed with a different task.

[https://docs.datarobot.com/en/docs/images/bpw-23.png](https://docs.datarobot.com/en/docs/images/bpw-23.png)

[https://docs.datarobot.com/en/docs/images/bpw-24.png](https://docs.datarobot.com/en/docs/images/bpw-24.png)