Dataset Merge, Dedup & Transform – Clean Data
Pricing
Pay per usage
Merge several Apify datasets, remove duplicates by any field, filter, rename, pick or flatten columns, and write one clean dataset. Limited permissions.
Pricing
Pay per usage
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Innovative Cookie
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1
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6 hours ago
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Pick one or more datasets (for example the results of several scraper runs). The Actor only gets READ access to the datasets you pick. Items are read in this order.
Optional items pasted as a JSON array. They are processed before the datasets. Handy for a quick test or for small lists.
[]Items with the same values in all these fields are duplicates. Use dot paths for nested fields (address.city). Leave empty to keep everything.
[]When no field is given above, remove items that are exactly identical (all fields).
first = keep the first item seen (streaming, low memory). last = keep the most recent one. merge = keep the first and fill its empty fields from the duplicates.
Treat 'Example.com' and 'example.com' as the same value.
Trim and collapse whitespace in text values before comparing.
Keep only items matching ALL conditions. Each condition is {"field": "price", "op": "lt", "value": 20}. Ops: eq, neq, contains, notContains, startsWith, endsWith, regex, gt, gte, lt, lte (numbers or dates), exists, notExists, empty, notEmpty, in, notIn (value = array).
[]Output only these fields, in this order (dot paths allowed). Leave empty to keep all fields.
[]Fields to drop from the output (dot paths allowed).
[]Map old name to new name, applied last. Example: {"title": "name", "stock.count": "stock_count"}
{}Turn {"stock": {"count": 2}} into {"stock.count": 2}. Useful for CSV and Google Sheets.
With flattening on, also turn arrays into numbered keys (images.0, images.1...).
Drop fields starting with # (debug data) when reading datasets.
Add a _source field with the dataset ID each item came from (or 'inline').