🧹 Scraped Data Cleaner & Converter (No-Code CSV/JSON Tool)

Clean and transform structured datasets from .json or .csv files — no coding required.

This Actor helps you prepare messy scraped data for analysis, import, or enrichment workflows. Whether you're working with leads, profiles, products, or survey results, it removes noise and gives you clean, structured rows you can actually use.

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💰 Pricing – $0.03 Flat Per Run (PPE)

This Actor uses Pay Per Event (PPE) pricing with a flat $0.03 per run — no compute or storage fees.
Want to test it first? Try the Rental version with 7 days free access.

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✅ Features

• Accepts both .csv and .json files (uploaded directly or linked from Apify Key-Value Store)
• Removes duplicate rows based on a field you choose (e.g. email, sku)
• Discards rows missing required data
• Choose whether all required fields must be filled or just one
• Optionally remove rows with no meaningful values
• Filter rows that match a specific field-value pair (e.g. status = active)
• Delete unwanted fields (columns in CSV / keys in JSON)
• Sort rows by one or more fields (text, number, date supported)
• Saves results in both .csv and .json formats — download links shown in log

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🧾 How to Use

The Actor provides a clean no-code interface. Just upload a file and select any combination of cleaning options.

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1.1 📁 Uploaded Data File

Upload a .csv or .json file manually — or enter a full Apify Key-Value Store URL pointing to one.

When uploading directly, you'll see a window titled “Upload file to key-value store” with these options:

• ✅ New temporary storage (recommended) — creates short-lived storage with no additional cost
• 🗂️ New permanent storage — for keeping the file long-term
• 📁 Existing storage — reuse an existing Apify KV store

If you’re cleaning a one-off dataset, just use the default temporary option. It’s lightweight, instant, and cost-free.

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1.2 🧠 Deduplicate By Field

Specify a field name (e.g. email, id) to remove duplicate rows. Only the first occurrence of each unique value is kept.

1.3 🧹 Remove Empty Rows

Enable to discard rows where all fields are blank, null, or empty strings. Works for both JSON and CSV rows.

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2.1.1 🔎 Must-Have Fields

List field names (e.g. email, profile, company) that should contain data. Rows missing those fields will be removed.

2.1.2 🔎 Match All Required Fields

Enable for strict filtering: only rows with all listed fields filled will be kept.
Disable to allow rows with any one field filled.

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2.2.1 🎯 Filter by Field

Specify a field name (e.g. members) to match against a specific value.

2.2.2 🎯 Match Specific Value

Enter the exact value the field should contain (e.g. pro). Only rows with that exact match will be kept.

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3.1 🪓 Remove Columns (optional)

List column names you want deleted from every row (status, id, etc.). Applies to both CSV and JSON files.

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4.1 📌 Sort By Fields

Enter a list of field names to sort by, in order of priority (status, createdAt, email, etc.).

4.2 🔄 Sort Descending

Enable to reverse the sort direction (Z–A, latest-to-earliest, etc.).

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📁 Output

Cleaned results are stored in Apify's Key-Value Store and made available in both .csv and .json formats, regardless of original file type.

You'll see two download links directly in the Actor logs:

• 🟢 JSON format → originalFileName-CLEANED.json
• 🟠 CSV format → originalFileName-CLEANED.csv

Right-click and “Save link as…” to download the cleaned files.

No need to browse datasets, export manually, or convert formats — everything is ready to go.

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🧠 What’s It Good For?

Let’s say you scraped a bunch of data — like contacts, products, survey answers, whatever. This tool helps clean it up and make it actually usable.

You can:

• Get rid of duplicate entries, like the same email showing up twice
• Filter out rows that are missing stuff you care about — like empty emails or profiles
• Keep only the ones with specific values (like people who have status: active or members: pro)
• Delete random columns you don’t need, like internalNotes or debugInfo
• Sort everything — by date, group, name, whatever you want
• Convert between .json and .csv so you can open the file wherever
• Basically, take any messy scraped file and make it clean, neat, and ready to use

It’s like having a smart assistant that tidies your data for you without writing a single line of code.

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🛠 No Coding Required

You don’t need any JavaScript, parsing logic, or scripting knowledge. Just upload your file, tweak a few inputs, and go.

Ideal for:

• Marketers analyzing scraped leads
• Researchers organizing field data
• Journalists working with tabular records
• Data-driven workflows powered by no-code integrations

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🔍 File Format Notes

• If your input is a .json array, the Actor auto-converts it to tabular .csv as well
• If your input is a .csv, the Actor also generates .json output for flexible reuse
• Result filenames follow this pattern:
  yourFileName-CLEANED.csv and yourFileName-CLEANED.json

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🧪 Sample Pre-Filled Input

To try it instantly, use the example CSV file provided in the interface or paste this Apify URL:
https://api.apify.com/v2/key-value-stores/9oIROyE5tcs83ZqP5/records/data-example.csv

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📊 Input vs Output Examples

📄 Original CSV

group	name	email	linkedin	status	members
3	Bob	bob@example.com		active	basic
1	Eve	eve@example.com		active	pro
1	Charlie		https://linkedin.com/in/charlie	inactive
2	Dana			pending	guest
1	Eve	eve@example.com		active	pro
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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1.2 🧠 Deduplicate By Field: email

Duplicate row for eve@example.com is removed.

group	name	email	linkedin	status	members
3	Bob	bob@example.com		active	basic
1	Eve	eve@example.com		active	pro
1	Charlie		https://linkedin.com/in/charlie	inactive
2	Dana			pending	guest
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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1.3 🧹 Remove Empty Rows

Removes the row with no values (between Charlie and Dana).

group	name	email	linkedin	status	members
3	Bob	bob@example.com		active	basic
1	Eve	eve@example.com		active	pro
1	Charlie		https://linkedin.com/in/charlie	inactive
2	Dana			pending	guest
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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2.1.1 🔎 Required Fields: email, linkedin

2.1.2 🔎 Match All Required Fields: 🔘 OFF — keeps rows with at least one of the fields filled

group	name	email	linkedin	status	members
3	Bob	bob@example.com		active	basic
1	Eve	eve@example.com		active	pro
1	Charlie		https://linkedin.com/in/charlie	inactive
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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2.1.1 🔎 Required Fields: email, linkedin

2.1.2 🔎 Match All Required Fields: 🟢 ON — keeps only rows with both fields filled

group	name	email	linkedin	status	members
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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2.2.1 🎯 Filter by Field: members

2.2.2 🎯 Match Specific Value: pro

Keeps only rows where the members field is exactly pro.

group	name	email	linkedin	status	members
1	Eve	eve@example.com		active	pro
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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3.1 🪓 Remove Columns: linkedin, status

Removes those columns from all rows.

group	name	email	members
3	Bob	bob@example.com	basic
1	Eve	eve@example.com	pro
1	Charlie
2	Dana		guest
1	Alice	alice@example.com	pro

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4.1 📌 Sort By Fields: group, then name

4.2 🔄 Sort Descending: 🔘 OFF — lowest group first, A–Z within group

group	name	email	linkedin	status	members
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro
1	Charlie		https://linkedin.com/in/charlie	inactive
1	Eve	eve@example.com		active	pro
2	Dana			pending	guest
3	Bob	bob@example.com		active	basic

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4.1 📌 Sort By Fields: group, then name

4.2 🔄 Sort Descending: 🟢 ON — highest group first, Z–A within group

group	name	email	linkedin	status	members
3	Bob	bob@example.com		active	basic
2	Dana			pending	guest
1	Eve	eve@example.com		active	pro
1	Charlie		https://linkedin.com/in/charlie	inactive
1	Alice	alice@example.com	https://linkedin.com/in/alice	active	pro

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⚖️ Pay Per Event (PPE) vs Rental – Which Version Should You Use?

Feature	🟢 PPE Version	🔵 Rental Version
Pricing Model	Pay Per Event	Monthly Subscription
Cost	$0.03 per run	$2.90/month
Usage Charges	✅ No compute/storage fees	⚠️ Usage billed separately (CU, dataset)
Free Trial	❌ None	✅ 7 days free
Output Storage	Key-Value Store (CSV + JSON)	Dataset Export

If you clean data occasionally and want zero billing surprises, use the PPE version — simple, predictable pricing.
If you run this frequently (e.g. 100+ runs/month), the Rental version offers better long-term value, and includes a 7-day free trial.

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💬 Feedback & Ideas

Want new filtering modes, regex support, or nested data handling?
Have ideas to make it even simpler for non-coders? Just send me a message — I’d love to hear how you're using the tool.