One-Hot Encoder Python Data Function for Spotfire® 1.1.0

Overview

This data function uses one-hot-encoding to transform categorical columns into multiple numeric columns containing 0's and 1's, based on the presence of a column-value combination. This method is commonly used to transform data prior to model creation in machine learning because models often require all numeric datasets as input.

Installing the data function

Follow the online guide available here to register a data function in Spotfire.

Configuring the data function

Each data function may require inputs from the Spotfire analysis and will return outputs to the Spotfire analysis. For each data function, these need to be configured once the data function is registered. To learn about how to configure data functions in Spotfire please view this video:

For more information on Spotfire visit the Spotfire training page.

Data function library

There exists a large number of data functions covering various features. Feel free to review what is available on the Data Function Library.