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How to clean web-extracted data?
TL;DR
Web-extracted data requires cleaning: remove HTML artifacts, normalize formats (dates, currencies), handle missing values, and validate records. Manual cleaning is tedious; Firecrawl Agent handles most cleaning automatically—returning typed, normalized data rather than raw text.
How to clean web-extracted data?
Raw scraped data is messy. Prices include symbols and commas. Dates appear in various formats. Text contains entities and extra whitespace.
| Issue | Solution |
|---|---|
HTML artifacts (&) | Decode entities |
| Extra whitespace | Trim and normalize |
Price formats ($1,234) | Parse to number |
| Date variations | Convert to ISO |
Missing values (N/A, "") | Standardize to null |
Schema-based extraction reduces cleaning work—Firecrawl returns typed data automatically:
result = app.scrape_url(url, {
"formats": ["extract"],
"extract": {
"schema": {
"properties": {
"price": {"type": "number"} # Returns numeric, not "$29.99"
}
}
}
})Key Takeaways
Data cleaning normalizes formats and removes artifacts. Schema-based extraction APIs like Firecrawl handle this automatically—prices as numbers, booleans as booleans, text without HTML artifacts.
Last updated: Feb 09, 2026
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