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🧹 CSV Data Cleaner

Extracting profile data from social networks often yields fragmented and repetitive spreadsheets. Whether you are running an Instagram profile scraper or pulling business locations from Google Maps, raw CSV exports frequently suffer from duplicate contact details, annoying trailing spaces, and completely empty rows. This CSV data cleaner is designed to instantly polish those raw datasets, converting messy browser outputs into clean, actionable contact lists.

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.

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

Input

Input Example

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

Input Examples

Example: Type detection only

{
"datasetId":"abc123",
"detectTypesOnly":true
}

Example: Full cleanup pass

{
"datasetId":"abc123",
"trimWhitespace":true,
"normalizeNulls":true,
"dedupeRows":true
}

Example: Column-specific transformation

{
"datasetId":"abc123",
"transformations":[
{
"column":"email",
"op":"lowercase"
},
{
"column":"phone",
"op":"e164"
}
]
}

Output

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 =newApifyClient({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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• GitHub Release & Changelog Monitor API — Track GitHub releases, tags, release notes, and changelog drift over time with one summary-first repository row per repo.
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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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