Bin columns

This transform takes column of data and divides the values in a given column into the specified number of bins.

tags: [“EDA”]

Parameters

The table gives a brief description about each parameter in Bin columns transform.

Name:

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

Raw 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)

Number of bins:

The total number of bins into which values must be divided.

Target column:

The column that must be excluded from binning.

Output Dataset:

The file name with which the output dataset is created returning the dataset after binning columns. (Required: True, Multiple: False)

Sample input for Bin columns transform:

The output after running the Bin columns 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 Bin columns transform:

binned_col_ds_name = dataset_input_name + "_binned"

transform = Transform()
transform.name = "bin columns"
transform.templateId = bin_cols.id
transform.variables = {
    "inputDataset": dataset_w_one_hot_encoding.name,
    "numOfBins": 5,
    "targetCol": targetCol,
    "OutputDataset": binned_col_ds_name,
}
recipe_bin = project.addRecipe([dataset_w_one_hot_encoding], name="bin_cols")
# recipe_bin.prepareForLocal(transform, contextId="recipe_bin")
recipe_bin.addTransform(transform)
recipe_bin.run()

Requirements

pandas