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The Multi-Row Formula Tool functions much like the normal Formula Tool but adds the ability to reference multiple rows of data within one expression . Say, for example, someone was on the ground floor of a house and had a Formula Tool. They would only be able to talk to the people also on the ground floor. If they had a Multi-Row Formula Tool, though, they would also be able to talk to the people upstairs, in the attic, and in the basement as well.
This short, but packed demonstration will show you why tens of thousands of data analysts from more than 1,800 companies rely on Alteryx daily to prep, blend, and analyze data, to deliver deeper business insights in hours, not weeks.
The Find Replace Tool is one of those tools that goes relatively unused and uncelebrated until you stumble into a data blending technique that would be extremely difficult without it – at which point, it becomes your favorite tool in the Designer. You can find it in the Join Category and it’ll make easy string substitutions in your data that would otherwise require herculean effort to work around. Today, we celebrate Find Replace as a hero.
When your Python libraries don't work the way they should in Python tool, restoring the tool to it's original state could be the solution. This article walks through how to restore Python libraries and the virtual environment associated with the Python tool.
A large component of data blending is applying mathematical or transformational processes to subsets of your data. Often, this requires isolating the data that complies with a certain criteria that you’ve set. The Conditional functions build expressions that allow you to apply processes to data that satisfy conditions you set.
What’s the difference between Scheduling a workflow from its original location on disk vs as a copy in the Scheduler DB?
Run a copy of the workflow stored in the scheduler DB creates a copy of the workflow and stores it in the scheduler’s database. When the time comes to run the workflow, it is pulled up from the database and run.
When scheduling as a copy stored in the scheduler DB, it is important to make sure that any dependencies (input files, macros, etc) are available at run time. If the location of the dependencies cannot be reached by the Scheduler you may run into errors stating things such as “File not found” or “unable to open macro”. These errors mean the Scheduler is unable to see the file paths you have referenced in your workflow, and therefore cannot run the process successfully.
To get around these errors, check your dependencies. If you are using relative paths you may just need to set them to Absolute. You can do this by going to Options – Advanced Options – Workflow Dependencies.
When a workflow is stored in the database, it becomes static. Any changes made in Alteryx to this workflow are independent to the Scheduled version and will require you to upload a new instance of the workflow or replace the current one.
Run the workflow from its original location on disk calls the actual .yxmd file from where you originally saved it and runs the process.
You’ll still need to make sure the dependencies are accessible by the scheduler, including the location of the workflow itself.
The difference with this option is the scheduler is pulling the live version of the workflow and input files so any changes made are reflected the next time the workflow is run and there is no need to create a new schedule.
For more information on the Scheduler check out the Scheduler FAQ in the help documentation here.
I’m proud to show off some of the great features of the Interactive Chart tool . Using the new Interactive Chart tool, users can immediately validate the configuration options selected to ensure the desired chart is created. There is no longer a need to re-run the workflow to see changes reflected in the chart.
Far more than just a window to your data, the Browse Tool has a catalog of features to best view, investigate, and copy/save data at any checkpoint you place it. That introspection to your data anywhere in your blending gives valuable feedback that often speeds workflow development and makes it easier to learn tools by readily visualizing their transforms. Be equipped, and browse through the catalog of useful applications below!
Scientific notation , or E notation , is used to more simply represent values that are very large or very small. Rather than represent the vertical distance from the top of Mount Everest to the bottom of the Marianas Trench as 19795000 millimeters (why millimeters, you ask? Well, why not?), expressing this distance in scientific notation, 1.9795e+7 mm, provides a more accessible way to understand the magnitude and precision of that value. When databases and spreadsheets format data in scientific notation, that formatting may be carried over into Alteryx. For some users, data in scientific notation can be problematic, especially if the data type is read in Alteryx as a string. Some Alteryx users have posted their helpful ideas on dealing with converting data in scientific notation to the full numeric value, and the links to those discussion threads are provided below. This article summarizes and demonstrates their ideas.
Between the RegEx , Text To Columns , and XML Parse Tools , the Alteryx data artisan already has an exceptionally robust selection of tools to help parse uniquely delimited data. However, there are still some data sets so entangled in formatting that it’s labor intensive to parse even for them. Enter the Find and Replace Tool , which captures the ability to find your nightmarish parsing workflows and replace them with sweet color by number pictures. Just kidding, it finds bad jokes and replaces them with good ones. Seriously, though, you could do both if you wanted to because this tool has the capability to look up a table of any number of specified targets to find in your data and will replace them with a table of specified sources. With the help of a few quick configuration steps, this tool can simplify some parsing use cases significantly.
One of the best things about Alteryx is the ability to read in multiple files very easily and automatically combine them into a single dataset. This becomes a bit trickier when dealing with files that have different schemas or Excel files with multiple tabs. Adding both multiple excel files with multiple tabs, and having the schema change within each tab takes it to another level.
For most tools that already have “dynamic” in the name, it would be redundant to call them one of the most dynamic tools in the Designer. That’s not the case for Dynamic Input. With basic configuration, the Dynamic Input Tool allows you to specify a template (this can be a file or database table) and input any number of tables that match that template format (shape/schema) by reading in a list of other sources or modifying SQL queries. This is especially useful for periodic data sets, but the use of the tool goes far beyond its basic configuration. To aid in your data blending, we’ve gone ahead and cataloged a handful of uses that make the Dynamic Input Tool so versatile:
The Input Data Tool is where it all starts in the Designer. Sure, you can bring in webscraped or API data with the Download Tool (master it here ) and our prebuilt Connector Tools , but the tool that makes it a breeze to grab data from your most used file formats and databases is the Input Data Tool.