Use clean steps to assign the correct data types into the separate flows. The ISDATE function can find string fields that are actual dates. You could also use a non-Regular Expression way to isolate the numbers and nulls, by filtering with FLOAT( )>0 OR ISNULL(), and IsNull(FLOAT()) AND Not ISNULL() to find just the strings. This would be a very straightforward process had the data been in Excel. I could also add an additional pivot, if I wanted to have a column for Total vs Wins and the pivoted named for individual vs team. However, its quite likely to see multiple rows in your dataset for the same transaction. All versions: Click the plus icon, and select Add Pivot from the context menu. Tableau then shows you your pivot names and the values. Tableau Prep Builder Version 2019.4.2 and later and on the web: In the Profile pane, select the fields that you want to pivot, then right-click or Ctrl-click (MacOS) and select Pivot Columns to Rows from the menu. I can then drag and drop the fields that I would like to be pivoted. I will configure my Pivot to Columns to Rows. Click the drop-down arrow next to the column name, and then select Pivot. To filter out the string fields in our data, all of which begin with a capital letter, the following formula in a filter works: REGEXP_MATCH(,'.*’)Īnd to filter out the nulls and numbers into a separate field, you can use the following formula in a filter: NOT REGEXP_MATCH(,'.*’) OR ISNULL() The objective is to get all the Managers and Reps into one column. For Tableau Prep, I will use a Pivot step. After you have set up the data source, in the grid, select two or more columns. For more on Regular Expressions, engage in our Tableau Prep Builder: Practitioner learning program. Regular Expressions give us a great option for separating the numeric data from the string data, and could also be used to separate date data. Prep Builder will automatically start sampling your data at just over one million records, but you can set your flow to sample regardless of whether this threshold has been met. In scenarios like this, you can sample to speed things up. In our example, we have a mix of string and numbers. Say you just need to pivot rows to columns, or union a few tables together. We need something to indicate the type of data. But first we need to assign data types, and create a way to automate this process. Now the correct column names are associated with their respective rows, and we will later pivot this back into columns. Transform Filtering missing, blank, or unusable values (e.g., null cells) Reformatting for consistency (e.g., dates, row and column headings) Removing.
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