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Enter Data Manually component

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This article describes a component in Azure Machine Learning designer.

Use the Enter Data Manually component to create a small dataset by typing values. The dataset can have multiple columns.

This component can be helpful in scenarios such as:

  • Generating a small set of values for testing.
  • Creating a short list of labels.
  • Typing a list of column names to insert in a dataset.

Create a dataset

  1. Add the Enter Data Manually component to your pipeline. You can find this component in the Data Input and Output category in Azure Machine Learning.

  2. For DataFormat, select one of the following options. These options determine how the data that you provide should be parsed. The requirements for each format differ greatly, so be sure to read the related topics.

    • ARFF: Attribute-relation file format used by Weka.
    • CSV: Comma-separated values format. For more information, see Convert to CSV.
    • SVMLight: Format used by Vowpal Wabbit and other machine learning frameworks.
    • TSV: Tab-separated values format.

    If you choose a format and do not provide data that meets the format specifications, a runtime error occurs.

  3. Click inside the Data text box to start entering data. The following formats require special attention:

    • CSV: To create multiple columns, paste in comma-separated text, or type multiple columns by using commas between fields.

      If you select the HasHeader option, you can use the first row of values as the column heading.

      If you deselect this option, the column names (Col1, Col2, and so forth) are used. You can add or change columns names later by using Edit Metadata.

    • TSV: To create multiple columns, paste in tab-separated text, or type multiple columns by using tabs between fields.

      If you select the HasHeader option, you can use the first row of values as the column heading.

      If you deselect this option, the column names (Col1, Col2, and so forth) are used. You can add or change columns names later by using Edit Metadata.

    • ARFF: Paste in an existing ARFF format file. If you're typing values directly, be sure to add the optional header and required attribute fields at the beginning of the data.

      For example, the following header and attribute rows can be added to a simple list. The column heading would be SampleText. Note that the String type is not supported.

      % Title: SampleText.ARFF  
% Source: Enter Data component  
@ATTRIBUTE SampleText NUMERIC  
@DATA  
\<type first data row here>

    • SVMLight: Type or paste in values by using the SVMLight format.

      For example, the following sample represents the first couple of lines of the Blood Donation dataset, in SVMLight format:

      # features are [Recency], [Frequency], [Monetary], [Time]  
1 1:2 2:50 3:12500 4:98   
1 1:0 2:13 3:3250 4:28

      When you run the Enter Data Manually component, these lines are converted to a dataset of columns and index values as follows:

      Col1 Col2 Col3 Col4 Labels
      0.00016 0.004 0.999961 0.00784 1
      0 0.004 0.999955 0.008615 1

  4. Select the Enter key after each row, to start a new line.

    If you select Enter multiple times to add multiple empty trailing rows, the empty rows will be removed or trimmed.

    If you create rows with missing values, you can always filter them out later.

  5. Connect the output port to other components, and run the pipeline.

    To view the dataset, right-click the component and select Visualize.

Next steps

See the set of components available to Azure Machine Learning.