Build a recipe¶

Building a recipe is the first step in preparing your data. When you start a Wrangle session, DataRobot connects to your data source, pulls a live random sample, and performs exploratory data analysis on that sample. When you add operations to your recipe, the transformation is applied to the sample and the exploratory data insights are recalculated, allowing you to quickly iterate on and profile your data before publishing.

See the associated considerations for important additional information.

Configure the live sample¶

By default, DataRobot retrieves 10000 rows for the live sample, however, you can modify this number in the wrangling settings. Note that the more rows you retrieve, the longer it will take to render the live sample.

To configure the live sample:

1. Click Settings in the right panel and open Interactive sample.

   

2. Enter the number of rows (under 10000) you want to include in the live sample and click Resample. The live sample updates to display the specified number of rows.

   

Analyze the live sample¶

During data wrangling, DataRobot performs exploratory data analysis on the live sample, generating table- and column-level summary statistics and visualizations that help you profile the dataset and recognize data quality issues as you apply operations. For more information on interacting with the live sample, see the section on Exploratory Data Insights.

Add operations¶

A recipe is composed of operations—transformations that will be applied to the source data to prepare it for modeling. Note that operations are applied sequentially, so you may need to reorder the operations in your recipe to achieve the desired result.

The table below describes the wrangling operations currently available in Workbench:

To add an operation to your recipe:

1. With Recipe selected, click Add Operation in the right panel.

   

2. Select and configure an operation. Then, click Add to recipe.

   The live sample updates after DataRobot retrieves a new sample from the data source and applies the operation, allowing you to review the transformation in realtime.

3. Continue adding operations while analyzing their effect on the live sample; when you're done, the recipe is ready to be published.

   

Compute a new feature¶

Use the Compute new feature operation to create a new output feature from existing features in your dataset. By applying domain knowledge, you can create features that do a better job of representing your business problem to the model than those in the original dataset.

To compute a new feature:

1. Click Compute new feature in the right panel.

   

2. Enter a name for the new feature, and under Expression, use Snowflake scalar subqueries, scalar functions, or window functions to define the feature.

   

   This example uses REGEXP_SUBSTR, to extract the first number from the [<age_range_start> - <age_range_end>) from the age column, and to_number to convert the output from a string to a number.

3. Click Add to recipe.

Filter row¶

Use the Filter row operation to filter the rows in your dataset according to specified value(s) and conditions.

To filter rows:

1. Click Filter row in the right panel.

   

2. Decide if you want to keep the rows that match the defined conditions or exclude them.

3. Define the filter conditions, by choosing the feature you want to filter, the condition type, and the value you want to filter by. DataRobot highlights the selected column.

   

4. (Optional) Click Add condition to define additional filtering criteria.

5. Click Add to recipe.

De-duplicate row¶

Use the De-duplicate rows operation to automatically remove all rows with duplicate information from the dataset.

To de-duplicate rows, click De-duplicate rows in the right panel. This operation is immediately added to your recipe and applied to the live sample.

Find and replace¶

Use the Find and replace operation to quickly replace specific feature values in a dataset. This is helpful to, for example, fix typos in a dataset.

To find and replace a feature value:

1. Click Find and replace in the right panel.

   

2. Under Select feature, click the dropdown and choose the feature that contains the value you want to replace. DataRobot highlights the selected column.

   

3. Under Find, choose the match criteria—Exact, Partial, or Regular Expression—and enter the feature value you want to replace. Then, under Replace, enter the new value.

   

4. Click Add to recipe.

Rename features¶

Use the Rename features operation to rename one or more features in the dataset.

To rename features:

1. Click Rename features in the right panel.

   

   Rename specific features from the live sample

   Alternatively, you can click the More options icon next to the feature you want to rename. This opens the operation parameters in the right panel with the feature field already filled in.

   

2. Under Feature name, click the dropdown and choose the feature you want to rename. Then, enter the new feature name in the second field.

   

3. (Optional) Click Add feature to rename additional features.

4. Click Add to recipe.

Remove features¶

Use the Remove features operation to remove features from the dataset.

To remove features:

1. Click Remove features in the right panel.

   

   Remove specific features from the live sample

   Alternatively, you can click the More options icon next to the feature you want to remove. This opens the operation parameters in the right panel with the feature field already filled in.

   

2. Under Feature name, click the dropdown and either start typing the feature name or scroll through the list to select the feature(s) you want to remove. Click outside of the dropdown when you're done selecting features.

   

3. Click Add to recipe.

Reorder operations¶

All operations in a wrangling recipe are applied sequentially, therefore, the order in which they appear affects the results of the output dataset.

To move an operation to a new location, click and hold the operation you want to move, and then drag it to a new position.

The live sample updates to reflect the new order.

Quit wrangling¶

At any point, you can click Quit Wrangling to end your wrangling session, however, any operations applied to the dataset will be removed.

Next steps¶

From here, you can:

• Publish the recipe to the data source, generating a new output dataset.

Read more¶

To learn more about the topics discussed on this page, see:

• Snowflake scalar functions for computing features.
• Snowflake window functions for computing features.
• Description of summary statistics and histograms in DataRobot Classic.