I had my first experience with Alteryx this week and it was challenging but enjoyable. Alteryx is a code-free data analytics and visualization platform that allows users to prepare, blend, and analyze data from various sources. The user-friendly interface makes it easy for organizations to combine a wide variety of data transformation tools to create efficient workflows. In this blog, I will be taking you through 3 of the most common tools used in Alteryx and how you can use these tools to make informed decisions about your data.

Tool #1: Filter. The filter tool splits your data into 2 separate streams based on a conditional expression. Rows that meet the expression will flow out of the true anchor; the rest will flow out of the false anchor. In the example below I have some mock sales data that I have filtered by countries that equal “Austria”. Simply put, I want to keep all the sales from Austria and exclude everything else. I can still use the data from the false anchor, but it will be separate from my Austria data. The filter tool isn’t just limited to the “equals” function though, you can also use “does not equal, greater than, lesser than, and many more.

Tool #2: Sample. The Sample tool allows you to extract a specific number of rows within your data. This tool is extremely powerful because of how versatile it is. You can take the first N rows, the last N rows, skip 1st N rows, 1 in N chance to include each row, and lastly first N% of rows. In the example below I have taken the last 5 rows of the data and grouped by order date. The output anchor will return the last 5 orders placed for every date. Grouping your data is optional, which adds another layer of versatility to this tool.

Tool #3: Text to Columns. The Text To Columns tool allows you to split text from 1 column into separate rows or columns. This tool is handy if you have too much information/text in 1 column and you want to split it up to make the data easier to digest. In the example below, the “product_category” column has information from the category and sub-category, separated by a semi-colon. We can split this data into 2 separate columns by using the semi-colon as the delimiter (a delimiter is what you want to split the data by, in this case, it’s a semi-colon).

I hope this blog helped you and gave you more insight into some of the basic tools in Alteryx!

The Data School
Author: The Data School