![]() For every distinct Invoice_Number in df, we are joining to multiple rows of Invoice_Number in inv_months, so this will explode out the df table to have the required number of rows per invoice. In the following line (Line 11), we join this table back to the original dataset ( df) on the Invoice_Number field using the pandas merge function. ![]() We now have a table ( inv_months) that contains a row for each month of a given invoice. Line 8: The repeat function from numpy is used to repeat the Invoice_Number value for the associated number of months in the DateDiffMonth field. Line 7: The Invoice_Number and DateDiffMonths fields are subsetted from our dataset and assigned to the variable inv_months. Line 4: This defines the function ( GenerateRowsPrep) that will be applied to the dataset ( df). Here it is broken down with some annotation. ![]() The next chunk is the body of the script that carries out the process. The script depends on functions from those libraries to work. First we load the pandas and numpy Python libraries. Let’s have a look at the contents of the script. The name of the function in the Python script is ‘GenerateRowsPrep’, so this goes in ‘Function Name’.Browse to the Python script on your machine (you can download from my Github). ![]() In the ‘Connect to Tableau Python (TabPy) Server’ dialogue box, connect to port 9004 of ‘localhost’.Set the Connection Type to ‘Tableau Python (TabPy) Server’.To connect to a Python script, you first need to start up TabPy via the command line (see this Tableau blog for information on how to download, install and run TabPy). Next, click ‘Add Script’ in the Prep flow pane. 15 months for Invoice_Number 1), and spread the total invoice value across the invoice billing period.Īfter connecting to the dataset in Prep, the first port of call is to create a calculated field called ‘DateDiffMonths’ that gives us the number of months between the start and end date of each invoice: I want to transform this into a structure that has a row for every month of a given invoice (e.g. As a use case for generating rows in Tableau Prep, take for example this simple mock dataset – a set of invoices with start and end dates: In this blog post I’ll demonstrate how to use TabPy and a short, simple Python script to perform a ‘generate rows’ function, which is currently unavailable in Prep Builder. Aside from equipping the user with the statistical modelling and machine learning capabilities that R/Python are known for, they can also compensate for some of the more basic ETL processing techniques that Tableau Prep Builder, in its current version, is still lacking. The recent integration of TabPy and RServe now allow for Python and R code to be executed from within a Tableau Prep workflow. Tableau Prep Builder is consistently increasing in functionality with every new version release. For more information, click here.| Brian Scally Tableau Prep: Generate rows with TabPy I understand that these countries may not have the same data protection laws as the country from which I provide my personal information. In particular, I consent to the transfer of my personal information to other countries, including the United States, for the purpose of hosting and processing the information as set forth in the Privacy Statement. I agree to the Privacy Statement and to the handling of my personal information. By submitting this form, you confirm that you agree to the storing and processing of your personal data by Salesforce as described in the Privacy Statement. By submitting this form, you acknowledge and agree that your personal data may be transferred to, stored, and processed on servers located outside of the People's Republic of China and that your personal data will be processed by Salesforce in accordance with the Privacy Statement. Reference Materials Toggle sub-navigationīy registering, you confirm that you agree to the processing of your personal data by Salesforce as described in the Privacy Statement.Teams and Organizations Toggle sub-navigation.
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