Summarize Data

This article describes a module of Azure Machine Learning designer.

Use the Summarize Data module to create a set of standard statistical measures that describe each column in the input table.

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.

How to configure Summarize Data

  1. Add the Summarize Data module to your pipeline. You can find this module in the Statistical Functions category in the designer.

  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. Submit the pipeline.

Results

The report from the module can include the following 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

Technical notes

  • 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.

Next steps

See the set of modules available to Azure Machine Learning.