Detect Non-Missing Values

This transform will detect the existing (non-missing) values and return boolean values that are True for non-missing values and False for the missing values in all columns in a dataset.

tags: [“EDA”]

Parameters

The table gives a brief description about each parameter in Detect Non-Missing Values 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 to flag the non-missing values. (Required: True, Multiple: False)

Output Dataset:

The file name with which the output dataset is created. This file returns the dataset flagging missing values as False and non-missing values as True.(Required: True, Multiple: False)

The sample input for this transform looks as below:

The output after running the Detect non-missing values 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 Detect Non-Missing Values transform:

template=TemplateV2.get_template_by('Detect Non-Missing Values')

recipe_Detect_Non_Missing_Values= project.addRecipe([car_data, employee_data, temperature_data, only_numeric], name='Detect Non-Missing Values')

transform=Transform()
transform.templateId = template.id
transform.name='Detect Non-Missing Values'
transform.variables = {
'input_dataset':'car',
'output_dataset':'car_notna'}
recipe_Detect_Non_Missing_Values.add_transform(transform)
recipe_Detect_Non_Missing_Values.run()

Requirements

pandas