🧹 CSV Data Cleaner

Raw scraped data is rarely ready for immediate use. When you run a scraper to extract contact details from Google Maps or pull profiles from platforms like Instagram and Facebook, the resulting export often contains messy text, duplicate entries, and accidental blank rows. This CSV data cleaner transforms those chaotic files into pristine spreadsheets ready for your CRM and marketing tools.

Sales operations and marketing teams use this utility to sanitize datasets before launching outreach campaigns. Instead of manually auditing spreadsheets to find redundant records or invisible spaces, you can schedule this automation to instantly process any CSV URL. You choose the specific columns to deduplicate, and the tool will remove duplicate posts, clean up fragmented web pages, and sort the final results alphabetically or numerically.

Whether you are consolidating leads from multiple search results, trimming trailing whitespace from email addresses, or normalizing website URLs extracted from a browser, this cleaner handles the tedious formatting. Your final exported data will feature beautifully structured fields, completely stripped of messy artifacts and redundant profiles. Integrate this step directly into your lead generation workflow to ensure that every CSV you export contains only unique, validated, and perfectly formatted contact details. Stop wrestling with spreadsheet formulas and automate your post-scraping cleanup today.

Store Quickstart

Start with the Quickstart template (direct CSV URL). For Apify pipelines, use Pipeline Cleaner with datasetId.

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 lead list CSVs before CRM upload
Data migration	Normalize CSV files during system migrations
Apify pipelines	Post-process actor output datasets

Input

Field	Type	Default	Description
csvUrl	string		Direct CSV URL (or use datasetId)
datasetId	string		Apify dataset ID (or use csvUrl)
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

{
  "csvUrl": "https://example.com/data.csv",
  "dedupColumns": ["email"],
  "trimWhitespace": true,
  "removeEmpty": true,
  "sortBy": "created_at"
}

Output

Field	Type	Description
rowNumber	integer	Original row index
data	object	Cleaned row as key-value pairs
changes	string[]	List of cleanings applied to this row
dropped	boolean	Whether the row was removed
dropReason	string	null

Output Example

{
  "inputRows": 1250,
  "outputRows": 1180,
  "duplicatesRemoved": 45,
  "emptyRowsRemoved": 25,
  "cleanedData": [
    {"email": "user1@example.com", "name": "Alice", "created_at": "2026-01-01"},
    {"email": "user2@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 '{ "csvUrl": "https://example.com/data.csv", "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={
  "csvUrl": "https://example.com/data.csv",
  "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({
  "csvUrl": "https://example.com/data.csv",
  "dedupColumns": ["email"],
  "trimWhitespace": true,
  "removeEmpty": true,
  "sortBy": "created_at"
});

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

Tips & Limitations

• Set removeDuplicates: true to deduplicate based on all columns.
• 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:

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• 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:

• actor-start: $0.01 (flat fee per run)
• dataset-item: $0.001 per output item

Example: 1,000 items = $0.01 + (1,000 × $0.001) = $1.01

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

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