Pricing

from $1.00 / 1,000 item cleaneds

Data Cleaner & Normalizer (JSON/CSV)

Clean and normalize JSON/CSV data: trim whitespace, lowercase emails, normalize phone numbers and dates, drop empty values/rows, and deduplicate by a field.

Pricing

from $1.00 / 1,000 item cleaneds

Rating

0.0

(0)

Developer

Rosario Vitale

Actor stats

Bookmarked

Total users

Monthly active users

3 days ago

Last modified

Features

✂️ Trim & collapse whitespace in every string field.
📧 Lowercase emails so Mario.Rossi@EXAMPLE.com becomes mario.rossi@example.com.
☎️ Normalize phone numbers — strips spaces, dashes and brackets, keeps a leading +.
📅 Normalize dates to ISO YYYY-MM-DD (optional, best-effort).
🧹 Drop empty fields and empty rows.
🔁 Deduplicate by any field (e.g. keep one record per email).

Input

Field	Type	Description
`items`	array	JSON array of objects to clean (required).
`trimWhitespace`	boolean	Trim spaces. Default `true`.
`collapseSpaces`	boolean	Collapse inner whitespace. Default `true`.
`lowercaseEmails`	boolean	Lowercase email values. Default `true`.
`cleanPhones`	boolean	Strip phone formatting. Default `true`.
`normalizeDates`	boolean	Convert dates to ISO. Default `false`.
`removeEmptyValues`	boolean	Drop null/empty fields. Default `false`.
`dropEmptyRows`	boolean	Skip empty records. Default `true`.
`dedupeKey`	string	Field to deduplicate by (optional).

Example input

{
    "items": [
        { "name": "  Mario Rossi ", "email": "Mario.Rossi@EXAMPLE.com ", "phone": "+39 (333) 123-4567" },
        { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567" }
    ],
    "dedupeKey": "email"
}

Output

One cleaned record per kept item:

{ "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567" }

Export as JSON, CSV, or Excel, or pull via the Apify API.

Common use cases

Clean scraped leads before importing to a CRM.
Normalize emails and phone numbers for matching and dedup.
Tidy any dataset before deduplication, analysis, or upload.
A reliable cleaning step in an automated data pipeline.

Notes

Pure in-memory processing — no external services, nothing to break over time.
Date normalization is best-effort; ambiguous formats may not convert.

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