Interpolate Missing Values
This transform fills missing values in the dataset using any of these methods such as linear, time, index and pad.
tags: [“Data Preparation”]
Parameters
The table gives a brief description about each parameter in Interpolate 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 this dataset from the drop-down list to fill the missing values. (Required: True, Multiple: False)
- Output Dataset:
The file name with which the output dataset is created by filling the missing values. (Required: True, Multiple: False)
- method:
Method used to interpolate the values. Possible methods:
linear - Ignores the index and linearly spaces the value.
time - Considers the datatime index to fill the missing values.
index - Uses the numerical values of the index.
Pad - Fills the missing values with the preceding values (forward fill).
(Required: True, Multiple: False, Datatypes: [“STRING”], Options: [“CONSTANT”], Constant_options: [‘linear’,’time’,’index’,’pad’])
The sample input for this transform looks as shown in the screenshot.
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The output after running the Interpolate Missing Values transform on the dataset for linear method appears as below:
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The output after running the Interpolate Missing Values transform on the dataset for pad method appears as below:
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The output after running the Interpolate Missing Values transform on the dataset for index method appears as below:
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The output after running the Interpolate Missing Values transform on the dataset for time method appears as below:
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How to use it in Notebook
The following is the code snippet you must use in the Jupyter Notebook editor to run the Interpolate Missing Values transform:
template=TemplateV2.get_template_by('Interpolate Missing Values')
recipe_Interpolate_Missing_Values= project.addRecipe([car_data, employee_data, temperature_data, only_numeric], name='Interpolate Missing Values')
transform=Transform()
transform.templateId = template.id
transform.name='Interpolate Missing Values'
transform.variables = {
'input_dataset':'car',
'output_dataset':'car_interpolated',
'value_1':"linear"}
recipe_Interpolate_Missing_Values.add_transform(transform)
recipe_Interpolate_Missing_Values.run()
Requirements
pandas