Capture the tools of Cross tab and Transpose (tools with inverse functions) by answering the following four questions and an example.

  1. WHY

The row data comes in different shape and format, it often occurs that we need to reshape and reorganize data to gain better insights and facilitate further analysis. In Alteryx, the Cross Tab and Transpose tools are powerful for restructuring and transforming data, allowing the users to manipulate data to derive valuable insights and support data-driven decision-making.

  1. WHAT

The Cross Tab tool creates 1 new column for each categorical value held in a single existing column, pivoting the data from a vertical layout to a more horizontal layout.Below is a conceptual representation of how the Cross Tab tool transforms the data.

The Transpose tool pivots the data so that a wide dataset becomes a narrower dataset.All the columns selected as Data columns will have their values stacked into a single column called Value, while the names of each corresponding variable will be stored alongside in a column called Name. The Key columns will remain unchanged, and all other columns will be dropped from the output.Below is a conceptual representation of how the Transpose tool transforms the data.

  1. WHEN

By cross-tabulating data, you can examine the relationships and patterns between different variables in a structured manner. It helps you understand the distribution of data across various categories and identify any trends or dependencies. Cross tabulation is commonly used to create contingency tables, which display the joint frequencies or proportions of two or more categorical variables. Contingency tables provide a clear representation of the relationship between variables, making it easier to identify associations or dependencies. They are often used in statistical analysis, hypothesis testing, and exploratory data analysis.

Transposing data is helpful when you need to transform the structure of your dataset. It allows you to convert rows into columns or columns into rows, making the data more suitable for specific analysis or reporting purposes. For example, transposing data can help create time series datasets, pivot tables, or input formats required for certain statistical models or visualization tools.

Cross tabulation and transposition can be useful when integrating data from multiple sources or systems. By aligning data in a standardized format, you can combine and compare information more easily. It helps in merging datasets, matching variables, and preparing data for further analysis or modeling.

  1. HOW

Configuring the tool’s settings is the last and the most important part in a successful application of Cross Tab and Transpose tools. For Cross Tab the configuration settings are shown below.

Group data by these values: These will be grouped together so that there is one row per unique value.

Change Column Headers: A new column will be created for each unique value contained within the column selected.

Values for New Columns: This column will be used to populate the new columns created by the “Header column”. Since there may be multiple rows that are being grouped together, a Method for aggregating values must be selected.


For Cross Tab the configuration settings are shown below.

The columns checked in the Key Columns section will not be changed but will be duplicated on the new rows created by the transposed data columns. Any columns not checked in the Key columns or Data columns sections will be dropped from the table.

The Value column type will be auto determined to be the shortest possible type to accommodate the data columns selected. A mix of numeric and string data types will result in a string column.


Cross Tab and Transpose are effectively inverses of each other. Here is an example.

The input data is:

Configure the settings of Transpose tool as:

The table will be longer and narrower after transposing.

Four key columns are Product, Category, Suggested Age Range and Average Monthly Sales.

The contents in Name column are January, February, March, etc.

The content in Value column of would be the value in the original table.


Let’s check the table after transposing:

Then configure the settings of Cross Tab tool as:

The table would be wider after cross-tabulating.

The grouping columns are Product, Category, Suggested Age Range, Headers are the Name of input table.

Values for New Columns are the sum of value from input table.



Let’s check the table after cross-tabulating:

Since the Cross Tab tool groups and aggregates data, it is not always possible to do the reverse.



The Data School
Author: The Data School