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The RegEx tool is kind of like the Swiss Army Knife of parsing in Alteryx; there are a whole lot of ways you can use it to do things faster or more effectively, but even if you just use the blade it's still immensely useful. Sometimes that's all you need, but if you do take the time to figure out how to use a few other tools in that knife, you'll start to see that there isn't much you can't do with it.
Date/Time data can appear in your data in string formats (text fields) or date formats. The DateTime Tool standardizes and formats such data so that it can be used in expressions and functions from the Formula or Filter Tools (e.g. calculating the number of days that have elapsed since a start date). It can also be used to convert dates in datetime format to strings to use for reporting purposes.
Dynamic Input threw error: ParseError: Invalid document structure at line 1 and column 1 while processing "AliasFileXml" on the workflow with "Root element is missing." in a popup. Upon clicking OK, a different pop-up comes up saying "Internal error in SRC_GetCosmeticName_Raw"
How do you convert a string with a $ sign into a number?
1. First remove the dollar sign and commas out of the string
2. Convert () to a negative sign "-"
3. ToNumber() function
There are many ways to do this in Alteryx, here are a few:
Formula Tool - ToNumber(Replace(Replace(Replace(Replace([Data], '$', ''), ',' , ''), ')' , ''), '(' , '-'))
Formula Tool - ToNumber(REGEX_Replace(REGEX_Replace([Data], "\$|,|\)", ""), "\(", "-"))
RegEx Tool #1 - Replace \$|,|\) with Replacement Text "blank", then
RegEx Tool #2 - Replace \( with Replacement Text "-"
Select Tool - Change Type to Fixed Decimal
See attached workflow.
These methods should work in most versions of Alteryx.
One of the most common issues we see from clients trying to read in a .csv file is that they are receiving an error starting “Too many fields in row x”, and because of this Alteryx can’t read in the file.
There are a couple of different ways you can resolve this error.
First, in the Input Data tool, you can tell Alteryx to treat read errors as warnings to allow the file to be read in.
This will change the Error to a Warning so that Alteryx can read in the file so you can investigate.
The other method is to read the file in with no delimiter. You can do this by changing the delimiter in the Input Data tool to \0.
Once you have your data parsed back out into its fields, you can use the Dynamic Rename tool to correct your field names, a select tool to remove the original field, and a simple Trim() function to remove the extra delimiter from your data.
This process is illustrated in the attached workflow, created in version 10.1.
When importing a delimited file, have you ever run across an error saying ‘Too many fields in record…’ and it’s something like record number 1478254 and you think to yourself, “how the heck am I going to find that record and correct it?”
Well, let us help you with that.
This message is essentially telling us that some particular row of data, when parsed, has more fields that the first row of data (usually caused by an extra delimiter in that row).
The first step in identifying the row is to change the delimiter in the Input Data configuration window. Change the delimiter from the current delimiter to \0 (that is a backslash zero, which stands for no delimiter). This will read in your file but instead of parsing, it’ll just read in the data as is.
Now just add a RecordID tool and Filter tool so we can filter on RecordID 2 (or 1478254) to find the row that is holding us up.
Now that you’ve identified the row that is causing issues, you could go back to your original data, correct it and try importing it again.
If you were looking closely at the example data, you may have noticed that record 4 also had a different number of fields as row 1. After correcting row 2 and importing , again, we would get an error for row 4. Just follow the same procedure to correct additional errors. This could probably be done through an automated/iterative process, but that will have to wait for another time.
(attached is an example workflow created in Alteryx 10.0)
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.
Recently a client reached out to us, saying that "all of a sudden" curly quotes were coming out of his keyboard, and Alteryx was rejecting them as unrecognizable. These are called SmartQuotes, and they are purely aesthetic. This can also happen when cutting/ pasting from other programs. Either way, Alteryx doesn't like them.
To change them back to the standard, useful straight quotes we all know and rely on, the first thing to try is CTRL SHIFT '
This will toggle between quote types.
An extra precaution is to make sure that your 'Regional Language' setting is English (United States); this is different than US English International. To check, in your control panel, go to Region and Language, Keyboards and Languages tab, and click on Change keyboards:
Make sure that your installed services is English (United States). If you do happen to require another input language, under the Advanced Key Settings, it will show you the key sequence to toggle between those keyboards.
This last step is likely unnecessary, but it's a good option to have in case CTRL SHIFT ' doesn't work (and I haven't yet seen it not work).
Now, back to writing formulas and queries with ease!
Easily the most used tool in the parsing category , the Text To Columns Tool makes for an extremely quick dicing of delimited fields. To use it you only need to specify a delimited field, delimiter(s), whether you’re parsing to rows or columns (you’ll need to specify a number of columns to parse into with this selection) and you’re off. Any way you slice it, this tool has you covered:
Question I have a table of sales data with each column being a week's worth of sales. I only want records that have data in each of those fields and want to filter out all records that have Null values. How can I do this?
Answer There are two basic elements necessary to make this happen. The first is that all records in the original table have a unique ID. If you do not have a unique ID in your data, go ahead and add a Record ID Tool.
In the sample data you can see we will want data from Rows 1 and 6 while filtering out each of the other records because they contain null values.
From here we will use the Transpose Tool to pivot your data into 3 separate columns. In the transpose field choose your unique ID as the KEY FIELD and make sure all other records are selected as DATA FIELDS.
The result is that you will have your unique ID field, a field called [Name] which contains the names of each of the fields in your data, repeated for every unique ID in your original data, and a [Value] field which contains the individual values for each of the records for each of the columns in the original data.
Now we want to search for Nulls, and get a comprehensive list of the UniqueID values that do not contain Null values. Now is the time to bring in a Summarize tool and GroupBy your unique ID field, and then use the CountNull action.
The result is a list of how many nulls exist in each of your unique ID groups.
Next we can simply filter out the fields that have 0 null values in them and then use the unique IDs to join back to the original data, and pull only those records.
It's important to note here that because I'm only interested in the original fields I intentionally chose to deselect the unique ID and the Null Count fields from the output of the join so that I am left with only those records that have data in all of the weeks.
See the attached v10.5 workflow for an example of the approach above.
One of the greatest strengths of modern web APIs is their flexible, developer-friendly nature, which provides numerous options for both the provider and the user. However, this flexibility can make it more intimidating for business users to deal with the various data formats that these APIs provide. The purpose of this article is to familiarize you with the main data formats used by the vast majority of web APIs, and provide the basic knowledge that will allow you to confidently process the data they return into a typical tabular format.
If you're using the Formula Tool in v11.0+, be sure to familiarize yourself with the tool's interface redesign!
Often, clients try to format a plain numeric value to a currency format. A simple way to accomplish this is to use the optional "addCommas" parameter in the ToString function. This function converts a numeric value to a string, using a user-specified number of decimal places [numDec]. Though Alteryx doesn’t require the third parameter, if called, the third parameter inserts the appropriate commas (1 or true means format with commas).
ToString([original_value], 2, 1)
Since the new value is a String, simply add in the dollar symbol:
'$'+ToString([original_value], 2, 1)