Column Correlation Python Data Function for Spotfire® 1.2.0

Overview

This Python data function calculates the correlation coefficients between columns of data. Correlation analysis is an important step in comparing data to determine whether it is highly correlated or not, and if so is that negatively or positively correlated. This can help determine relationships in data, as well as aid in data reduction by removing highly correlated data in use cases such as data science model building.

In this data function, three methods are available. These are: Pearson, Spearman and Kendall. All methods return a correlation score between -1 and 1 indicating the correlation score between two columns of data. A score of -1 is the maximum negative correlation, a score of 1 is the maximum positive correlation and a score of 0 means there is no correlation.

More info can be found here.

Also available as a Statistica data function.

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.