Data quality

This transform checks for issues in the dataset and displays the first five rows, last five rows in the dataset, rows & columns size, missing values, data types, duplicates, cardinality, duplication rate, column name analysis, normality, and other potential data errors.

tags: [“Data Preparation”]

Parameters

The table gives a brief description about each parameter in Data quality 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 check its data quality. (Required: True, Multiple: False)

The sample input for this transform looks as below:

The output after running the Data quality 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 Data quality transform:

Requirements

pandas