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⇱ Data Cleaner & Normalizer — Clean & Dedupe JSON/CSV · Apify

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Data Cleaner & Normalizer (JSON/CSV)

Pricing

from $1.00 / 1,000 item cleaneds

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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

Rosario Vitale

Maintained by Community

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2

Total users

1

Monthly active users

7 days ago

Last modified

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Clean and normalize messy datasets in one step. Feed a JSON array of records and get back tidy, consistent data — perfect as the cleaning step before deduplication, import, or analysis.

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

FieldTypeDescription
itemsarrayJSON array of objects to clean (required).
trimWhitespacebooleanTrim spaces. Default true.
collapseSpacesbooleanCollapse inner whitespace. Default true.
lowercaseEmailsbooleanLowercase email values. Default true.
cleanPhonesbooleanStrip phone formatting. Default true.
normalizeDatesbooleanConvert dates to ISO. Default false.
removeEmptyValuesbooleanDrop null/empty fields. Default false.
dropEmptyRowsbooleanSkip empty records. Default true.
dedupeKeystringField 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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