Add Rows

 

Updated: September 21, 2017

Appends a set of rows from an input dataset to the end of another dataset

Category: Data Transformation / Manipulation

This article describes how to use the Add Rows module in Azure Machine Learning to concatenate two datasets. In concatenation, the rows of the second dataset are added to the end of the first dataset.

Concatenation of rows is useful in scenarios such as these:

  • You have generated a series of evaluation statistics, and you want to combine them into one table for easier reporting.

  • You have been working with different datasets, and you want to combine the datasets to create a final dataset.

  1. Drag the Add Rows module into your experiment, You can find it under Data Transformation, Manipulate.

  2. Connect the datasets to the two input ports. The dataset that you want to append should be connected to the second (right) port.

    When adding two datasets together, these rules apply:

    • To concatenate rows together, the rows must have the same schema. This means, the same number of columns, and the same type of data in the columns.

    • If you add the same dataset to both inputs of the Add Rows module, the dataset will be duplicated.

  3. Run the experiment.

    The number of rows in the output dataset should equal the sum of the rows of both input datasets.

This section describes implementation details and common questions.

  • You cannot filter the source dataset when adding rows. All the rows from both datasets provided as inputs are concatenated when you use Add Rows.

    If you want to add only a few rows, use Partition and Sample to define a condition by which to filter the rows and generate a dataset with only the rows you want.

You can see examples of how this module is used by exploring these sample experiments in the Model Gallery:

NameTypeDescription
Dataset1Data TableDataset rows to be added to the output dataset first
Dataset2Data TableDataset rows to be appended to the first dataset
NameTypeDescription
Results datasetData TableDataset that contains all rows of input datasets

For a list of all exceptions, see Module Error Codes.

ExceptionDescription
Error 0003An exception occurs if one or more of input datasets is null or empty.
Error 0010An exception occurs if input datasets have column names that should match but do not.
Error 0016An exception occurs if input datasets passed to the module should have compatible column types but do not.
Error 0008An exception occurs if the parameter is not in range.

Manipulation
Data Transformation
A-Z Module List

Show: