Remove Duplicate Rows component
This article describes a component in Azure Machine Learning designer.
Use this component to remove potential duplicates from a dataset.
For example, assume your data looks like the following, and represents multiple records for patients.
| PatientID | Initials | Gender | Age | Admitted |
|---|---|---|---|---|
| 1 | F.M. | M | 53 | Jan |
| 2 | F.A.M. | M | 53 | Jan |
| 3 | F.A.M. | M | 24 | Jan |
| 3 | F.M. | M | 24 | Feb |
| 4 | F.M. | M | 23 | Feb |
| F.M. | M | 23 | ||
| 5 | F.A.M. | M | 53 | |
| 6 | F.A.M. | M | NaN | |
| 7 | F.A.M. | M | NaN |
Clearly, this example has multiple columns with potentially duplicate data. Whether they are actually duplicates depends on your knowledge of the data.
For example, you might know that many patients have the same name. You wouldn't eliminate duplicates using any name columns, only the ID column. That way, only the rows with duplicate ID values are filtered out, regardless of whether the patients have the same name or not.
Alternatively, you might decide to allow duplicates in the ID field, and use some other combination of files to find unique records, such as first name, last name, age, and gender.
To set the criteria for whether a row is duplicate or not, you specify a single column or a set of columns to use as keys. Two rows are considered duplicates only when the values in all key columns are equal. If any row has missing value for keys, they will not be considered duplicate rows. For example, if Gender and Age are set as Keys in above table, row 6 and 7 are not duplicate rows given they have missing value in Age.
When you run the component, it creates a candidate dataset, and returns a set of rows that have no duplicates across the set of columns you specified.
Important
The source dataset is not altered; this component creates a new dataset that is filtered to exclude duplicates, based on the criteria you specify.
How to use Remove Duplicate Rows
Add the component to your pipeline. You can find the Remove Duplicate Rows component under Data Transformation, Manipulation.
Connect the dataset that you want to check for duplicate rows.
In the Properties pane, under Key column selection filter expression, click Launch column selector, to choose columns to use in identifying duplicates.
In this context, Key does not mean a unique identifier. All columns that you select using the Column Selector are designated as key columns. All unselected columns are considered non-key columns. The combination of columns that you select as keys determines the uniqueness of the records. (Think of it as a SQL statement that uses multiple equalities joins.)
Examples:
- "I want to ensure that IDs are unique": Choose only the ID column.
- "I want to ensure that the combination of first name, last name, and ID is unique": Select all three columns.
Use the Retain first duplicate row checkbox to indicate which row to return when duplicates are found:
- If selected, the first row is returned and others discarded.
- If you uncheck this option, the last duplicate row is kept in the results, and others are discarded.
Submit the pipeline.
To review the results, right-click the component, and select Visualize.
Tip
If the results are difficult to understand, or if you want to exclude some columns from consideration, you can remove columns by using the Select Columns in Dataset component.
Next steps
See the set of components available to Azure Machine Learning.