Columns Covariance

This transform computes pairwise covariance of columns, excluding null values and returns the dataset with the covariance matrix of columns.

tags: [“EDA”]

Parameters

The table gives a brief description about each parameter in Columns Covariance transform.

Name:

By default, the transform name is populated. You can also add a custom name for the transform.

Input Dataset:

The file name of the input dataset. You can select the dataset that was uploaded from the drop-down list. (Required: True, Multiple: False)

Output Dataset:

The file name with which the output dataset is created with the covariance matrix of columns. (Required: True, Multiple: False)

Sample input for Columns Covariance transform:

The output after running the Columns Covariance transform on the dataset appears as below:

How to use it in Notebook

The following is the code snippet you must use in the Jupyter Notebook editor to run the Columns Covariance transform:

template=TemplateV2.get_template_by('Columns Covariance')

recipe_Columns_Covariance= project.addRecipe([car_data, employee_data, temperature_data, only_numeric], name='Columns Covariance')

transform=Transform()
transform.templateId = template.id
transform.name='Columns Covariance'
transform.variables = {
'input_dataset':'car',
'output_dataset':'car_cov'}
recipe_Columns_Covariance.add_transform(transform)
recipe_Columns_Covariance.run()

Requirements

pandas