🧹 CSV Data Cleaner

What you get: your CSV back as clean dataset rows — whitespace trimmed, empty rows dropped, duplicates removed by the columns you choose, and sorted — ready to export as CSV, JSON or Excel.

Sample output from a real run (2026-10-18, default input: 5 CSV rows including one empty row and one repeated row, 4 rows out):

name	email	city
Alice	alice@example.com	Tokyo
Bob	bob@example.com	New York
Alice	alice@example.com	Tokyo
Charlie	charlie@example.com	London

The empty row was removed. The repeated Alice row stays because the default input sets no dedupColumns; add "dedupColumns": ["email"] to drop it. That run was 4 rows × $0.001 = $0.004 (there is no separate start fee).

Open Input (leave defaults → Start)

Paid first-run JSON ≈ USD 0.002 (2 cleaned rows × USD 0.001). Open Console Input (deep link above) and run this JSON.

{
  "csvData": "name,age\nAlice,30\nBob,25",
  "trimWhitespace": true,
  "removeEmpty": true,
  "dedupColumns": [
    "name"
  ],
  "sortBy": "name",
  "delivery": "dataset",
  "dryRun": false
}

Store Quickstart

Run the bounded paid first-run JSON above first; keep report/export options off unless needed.

{
  "csvData": "name,age\nAlice,30\nBob,25",
  "trimWhitespace": true,
  "removeEmpty": true,
  "dedupColumns": [
    "name"
  ],
  "sortBy": "name",
  "delivery": "dataset",
  "dryRun": false
}

Paste your CSV into csvData (or give a public csvUrl), choose dedupColumns, and Start.

Use with Apify MCP

Exact actor: taroyamada/csv-data-cleaner

Sample input JSON:

{
  "csvData": "name,age\nAlice,30\nBob,25\nAlice,30",
  "trimWhitespace": true,
  "removeEmpty": true,
  "dedupColumns": [
    "name"
  ],
  "sortBy": "name",
  "delivery": "dataset"
}

Data engineers and operations teams can clean user-supplied CSV tables without sending the contents to a third-party enrichment API. The actor trims whitespace, removes empty rows, deduplicates selected columns, sorts records, and returns both structured rows and normalized CSV.

Research teams and analytics pipelines depend on this automated data cleaner to prepare CSV datasets for downstream analysis. Instead of fighting with complex spreadsheet formulas to identify redundant entries, you can pass your raw CSV URL directly to this utility. It systematically scans the file to deduplicate rows based on specific columns—like usernames, email addresses, or phone numbers—ensuring you never analyse duplicate rows as separate records.

Beyond basic deduplication, the tool actively sanitizes the content. It trims invisible whitespace from text fields, drops blank lines generated during interrupted scraping runs, and sorts the final list for easy review. By automating this cleanup phase, you ensure that every exported spreadsheet contains perfectly formatted data. Your final files will have clean website URLs, properly structured bios, and deduplicated social media posts, ready to fuel your next marketing campaign.

Key Features

• 🧹 Trim whitespace — Remove leading/trailing spaces from all cells
• 🗑️ Remove empty rows — Drop rows where all columns are empty
• 🔁 Deduplicate by columns — Remove duplicate rows by specified key columns
• 📊 Sort by column — Output sorted by any column
• 🔗 Dataset or URL input — Apify dataset ID or direct CSV URL
• 🔑 No API key needed — Pure JS, zero dependencies

Use Cases

Who	Why
Data engineers	Clean scraper outputs before downstream processing
BI analysts	Standardize CSV imports from multiple sources
Marketing ops	Clean analyst CSVs before downstream pipeline ingestion
Data migration	Normalize CSV files during system migrations
Apify pipelines	Post-process actor output datasets

Input

Field	Type	Default	Description
csvData	string		CSV text pasted directly (or use csvUrl)
csvUrl	string		Public URL of a CSV file (or use csvData)
delimiter	string	,	Field delimiter
dedupColumns	string[]	[]	Columns for dedup key
trimWhitespace	boolean	true	Trim whitespace
removeEmpty	boolean	true	Remove empty rows
sortBy	string		Column to sort by

Input Example

{
  "csvData": "email,name,created_at\nb@example.com,Bob,2026-01-02\na@example.com,Alice,2026-01-01\nb@example.com,Bob,2026-01-02",
  "dedupColumns": [
    "email"
  ],
  "trimWhitespace": true,
  "removeEmpty": true,
  "sortBy": "created_at"
}

Input Examples

Example: Deduplicate by email

{
  "csvData": "name,email,city\nAlice,alice@example.com,Tokyo\nBob,bob@example.com,New York\nAlice,alice@example.com,Tokyo\n,,\nCharlie,charlie@example.com,London",
  "dedupColumns": [
    "email"
  ]
}

Example: Semicolon-separated file

{
  "csvData": "sku;price\nA-1; 9.90\nA-2;12.00\nA-1;9.90",
  "delimiter": ";",
  "dedupColumns": [
    "sku"
  ],
  "sortBy": "price",
  "sortOrder": "desc"
}

Example: Sort only (keep duplicates)

{
  "csvData": "name,age\nCarol,41\nAlice,30\nBob,25",
  "sortBy": "name"
}

Output

One dataset row per cleaned CSV row. The field names are your CSV header columns.

Field	Type	Description
<column>	string	One field per CSV header column, with the cleaned value

Output Example

Rows from a real run of the Input Example above (2026-10-18): see the sample table at the top of this page for the default input.

[
  {
    "email": "a@example.com",
    "name": "Alice",
    "created_at": "2026-01-01"
  },
  {
    "email": "b@example.com",
    "name": "Bob",
    "created_at": "2026-01-02"
  }
]

API Usage

Run this actor programmatically using the Apify API. Replace YOUR_API_TOKEN with your token from Apify Console → Settings → Integrations.

cURL

curl -X POST "https://api.apify.com/v2/acts/taroyamada~csv-data-cleaner/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"csvData": "email,name,created_at\nb@example.com,Bob,2026-01-02\na@example.com,Alice,2026-01-01\nb@example.com,Bob,2026-01-02", "dedupColumns": ["email"], "trimWhitespace": true, "removeEmpty": true, "sortBy": "created_at"}'

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("taroyamada/csv-data-cleaner").call(run_input={
  "csvData": "email,name,created_at\nb@example.com,Bob,2026-01-02\na@example.com,Alice,2026-01-01\nb@example.com,Bob,2026-01-02",
  "dedupColumns": ["email"],
  "trimWhitespace": true,
  "removeEmpty": true,
  "sortBy": "created_at"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript / Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor('taroyamada/csv-data-cleaner').call({
  "csvData": "email,name,created_at\nb@example.com,Bob,2026-01-02\na@example.com,Alice,2026-01-01\nb@example.com,Bob,2026-01-02",
  "dedupColumns": ["email"],
  "trimWhitespace": true,
  "removeEmpty": true,
  "sortBy": "created_at"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Tips & Limitations

• Set dedupColumns to the columns that identify a record (for example ["email"]); rows with the same values are kept once.
• Use delimiter to handle TSV (\t) or semicolon-separated files.
• Combine with Phone Validator and Email Checker for full lead-data cleansing.
• Output dataset is ready for direct import into CRMs or databases.

FAQ

What CSV dialects are supported?

Standard RFC 4180 CSV: comma-delimited, quoted fields, CRLF line endings. TSV not supported directly.

Max CSV file size?

In-memory processing. Works well up to ~100 MB / 1M rows. Larger files need chunking.

Does it validate data types?

No — cleaning operations only. For type validation, combine with validation libraries.

Can I use this in Apify pipelines?

Yes — provide datasetId from a prior actor run to clean that dataset directly.

What's the max file size?

Limited by actor memory (1024 MB by default). Tested up to 100k rows.

Can I upload a local CSV?

Provide a public URL via csvUrl. Use a service like file.io or S3 presigned URLs for private files.

Related Actors

DevOps & Tech Intel cluster — explore related Apify tools:

• 🌐 DNS Propagation Checker — Check DNS propagation across 8 global resolvers (Google, Cloudflare, Quad9, OpenDNS).
• 🔍 Subdomain Finder — Discover subdomains for any domain using Certificate Transparency logs (crt.
• 📦 NPM Package Analyzer — Analyze npm packages: download stats, dependencies, licenses, deprecation status.
• 💬 Reddit Scraper — Scrape Reddit posts and comments from any subreddit via official JSON API.
• GitHub Release & Changelog Monitor API — Track GitHub releases, tags, release notes, and changelog drift over time with one summary-first repository row per repo.
• Docs & Changelog Drift Monitor API — Monitor release notes, changelog pages, migration guides, and key docs pages with one summary-first target row per monitored repo, SDK, or product.
• Tech Events Calendar API | Conferences + CFP — Aggregate tech conferences and CFPs across multiple sources into a deduplicated event calendar for DevRel and recruiting workflows.
• 🔒 OSS Vulnerability Monitor — Monitor open-source packages for known security vulnerabilities using OSV and GitHub Security Advisories.

Cost

Pay Per Event (current Store prices):

• Result row: $0.001 per row written to the default dataset (no separate start fee)

Example: 1,000 rows = 1,000 × $0.001 = $1.00

No subscription required — you only pay for what you use.

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