I’m working on a Zapier workflow where I need to parse specific data from a formatted text string. The input contains field names and their corresponding values in a structured format. I want to extract these name-value pairs and transform them into a more usable format for the next automation steps.
Here’s an example of my input data:
@name: Field Username
value: Username Entry
@name: Field Gender
value: Gender Selection
@name: Field Location
value: Street Address Info
@name: Field Birthday
value: Birthday Entry
@name: Field Emergency Contact
value: Emergency Contact Name
@name: Field Email
value: Contact Email Address
@name: Username Entry
value: Smith
@name: Location Info
value: Main Street 42a
@name: Gender Selection
value: male
@name: Phone Number
value: 9876543210
@name: Emergency Contact
value: John Doe
@name: Email Info
value: [email protected]
@name: Phone Emergency
value: 1234567890
What I need is to extract pairs like this:
@name Emergency Contact
value: John Doe
And convert them to: Emergency Contact: { John Doe }
Can someone help me write Python or JavaScript code that works in Zapier to parse this data and create these formatted pairs? I plan to use the output to map values dynamically in subsequent workflow steps.
Been wrestling with similar data extraction in Zapier for months. Your string structure’s consistent enough to handle with basic JavaScript in a code step. I use two passes - first grab all field definitions (‘Field Username’ maps to ‘Username Entry’), then pull the actual values. Strip those 'Field ’ prefixes and be careful with the mapping logic. Map objects work way better than regular objects for this key-value stuff. Getting to ‘Emergency Contact: { John Doe }’ format is easy once you’ve got the pairs. Watch out for fields without matching values - learned that the hard way. Zapier’s JavaScript handles this string manipulation fine without timing out.
I’ve dealt with this exact scenario using Zapier webhooks. Skip the complex regex - pattern recognition works way better here. Build a simple state machine that tracks whether you’re reading field definitions or actual data by watching for ‘@name’ prefixes. Use string contains() to match field names with values instead of complicated mapping logic. For your Emergency Contact example, just strip 'Field ’ during the definition phase, then search for exact matches when processing data. JavaScript’s filter() and find() handle this without any performance hits. Parse everything into a Map first, then format however you need. This approach won’t break when fields are missing and scales nicely when your data structure changes.
just use split() and basic string operations. make two arrays - one for field definitions, one for values. hit ‘@name: Field’? store the mapping. see ‘@name:’ without ‘Field’? grab the value from the next line. way simpler than regex and won’t hit zapier’s timeout limits.
zapier’s code blocks rly good for this. just split the text by lines, loop through each one and grab @name/value pairs usin regex or indexOf. dump everything into an object and ur done. handled similar parsing in zapier b4 - works for most stuff without external tools.
You can fix this parsing issue with vanilla JavaScript in Zapier’s code step. I dealt with something similar when transforming form data last year. Break it into two phases - grab the field definitions first, then match them to values. Build a lookup table where ‘Field Username’ becomes ‘Username Entry’, then when you hit the data section, use that table to find the right values. Something like fieldMap.set(‘Username Entry’, ‘Smith’) does the trick. The annoying part is name normalization - ‘Field Emergency Contact’ might just be ‘Emergency Contact’ in the data. I use substring matching with fuzzy logic to catch most weird cases. Zapier handles the parsing speed fine for normal form sizes.
I’ve hit similar messy data parsing issues in production. Python or JavaScript in Zapier works, but you’ll hit walls with complex regex and data transformation.
Here’s what I’d do - build a proper automation flow:
Set up a webhook endpoint for your raw text data. Use regex to extract @name and value pairs, then run a matching algorithm to connect related fields. The tricky bit is mapping “Field Username” to “Username Entry” values.
So for your example, you want “Emergency Contact” (from “Field Emergency Contact”) to match “John Doe” (from the “Emergency Contact” entry). You need to store intermediate mappings and cross-reference them.
Zapier’s code steps suck for debugging complex parsing and handling edge cases when your data format shifts. Plus you’ll hit execution time limits on bigger datasets.
I’ve automated similar workflows parsing structured text from forms and APIs. The key is building a solid parsing engine that handles format variations and scales.
Latenode gives you the flexibility to build this parsing logic with proper error handling, data validation, and testing. You can create reusable modules for text parsing and data transformation across multiple workflows.
Been down this rabbit hole way too many times. Zapier’s code steps lock you into basic JavaScript with terrible debugging when stuff breaks.
Your data has two parts - field definitions and actual values. You’ve got to connect “Field Emergency Contact” with the later “Emergency Contact” entry containing “John Doe”.
Yeah, you can throw together some JavaScript loops and string matching. But what happens when your data format shifts? Missing fields? Want to use this parsing logic elsewhere?
I built a similar parser for messy form submissions. Started with Zapier, quickly hit walls with error handling and reusability. Moved everything to a proper automation platform.
Latenode crushes this. Build parsing logic with real data validation, create reusable modules for different text formats, and actually debug broken stuff. Better performance on big datasets without Zapier’s execution limits.
Build the text parsing flow once, reuse it everywhere. Way cleaner than copying JavaScript between Zaps.