Summarize Data


Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.

ML Studio (classic) documentation is being retired and may not be updated in the future.

Generates a basic descriptive statistics report for the columns in a dataset

Category: Statistical Functions


Applies to: Machine Learning Studio (classic) only

Similar drag-and-drop modules are available in Azure Machine Learning designer.

Module overview

This article describes how to use the Summarize Data module in Machine Learning Studio (classic), to create a set of standard statistical measures that describe each column in the input table.

Such summary statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:

  • How many missing values are there in each column?
  • How many unique values are there in a feature column?
  • What is the mean and standard deviation for each column?

The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.


You might already know that you can get a short list of statistics by using the Visualize option in Studio (classic). However, this visualization is created based on some top number of rows. In contrast, the Summarize Data module computes its statistics on all rows of data.

How to use Summarize Data

  1. Add the Summarize Data module to your experiment. You can find this module in the Statistical Functions category in Studio (classic).

  2. Connect the dataset for which you want to generate a report.

    If you want to report on only some columns, use the Select Columns in Dataset module to project a subset of columns to work with.

  3. No additional parameters are required. By default, the module analyzes all columns that are provided as input, and depending on the type of values in the columns, outputs a relevant set of statistics as described in the Results section.

  4. Run the experiment, or right-click the module, and select Run selected.


The report from the module can include the following statistics.

  • The exact statistics that are generated depend on the column data type. See the Technical notes section for details.

  • The assumption is made that the instances belong to a representative sample of a population. If you need to compute statistics on a population, use the options in the Compute Elementary Statistics module, which can compute either sample or population statistics.

Column name Description
Feature Name of the column
Count Count of all rows
Unique Value Count Number of unique values in column
Missing Value Count Number of unique values in column
Min Lowest value in column
Max Highest value in column
Mean Mean of all column values
Mean Deviation Mean deviation of column values
1st Quartile Value at first quartile
Median Median column value
3rd Quartile Value at third quartile
Mode Mode of column values
Range Integer representing the number of values between the maximum and minimum values
Sample Variance Variance for column; see Note
Sample Standard Deviation Standard deviation for column; see Note
Sample Skewness Skewness for column; see Note
Sample Kurtosis Kurtosis for column; see Note
P0.5 0.5% percentile
P1 1% percentile
P5 5% percentile
P95 95% percentile
P99.5 99.5% percentile


Output the statistics report as a tabular dataset, so that you can use the data in BI reporting tools, or use the values as input to another operation in the experiment.


For examples of how to use the Summarize Data module in an experiment, see the Azure AI Gallery:

Technical notes

  • For numeric and Boolean columns, you can output the mean, median, mode, and standard deviation.

  • For non-numeric columns, only the values for Count, Unique value count, and Missing value count are computed. For other statistics, a null value is returned.

  • Columns that contain Boolean values are processed using these rules:

    • When calculating Min, a logical AND is applied.

    • When calculating Max, a logical OR is applied

    • When computing Range, the module first checks whether the number of unique values in the column equals 2.

    • When computing any statistic that requires floating-point calculations, values of True are treated as 1.0, and values of False are treated as 0.0.

Expected inputs

Name Type Description
Dataset Data Table Input dataset


Name Type Description
Results dataset Data Table A profile of the input dataset that contains descriptive statistics


Exception Description
Error 0003 Exception occurs if one or more inputs are null or empty.
Error 0020 Exception occurs if the number of columns in some of the datasets passed to the module is too small.
Error 0021 Exception occurs if the number of rows in some of the datasets passed to the module is too small.

For a list of errors specific to Studio (classic) modules, see Machine Learning Error codes.

For a list of API exceptions, see Machine Learning REST API Error Codes.

See also

Statistical Functions
Compute Elementary Statistics