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

Clean and filter scraped datasets from .json or .csv files — no coding required.

This Actor helps you transform messy data into clean, analysis-ready results. Whether you're working with leads, profiles, product listings, or survey results, it removes the noise and gives you back structured rows that actually matter.

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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)
• Pushes cleaned results to the Apify dataset for flexible exporting

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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 rows are pushed to your Apify dataset.
You can export in any format — CSV, JSON, Excel, HTML Table, RSS, JSONL, and more — from the Actor run page or via API.

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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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🧪 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.