Clean Messy Data Instantly without Regex

Data cleaning is 80% of a data scientist's job. It's also a headache for marketers, sales ops, and developers.

Usually, it involves writing complex Regular Expressions (Regex) or complex Excel formulas.

^(\+\d{1,2}\s)?\(?\d{3}\)?[\s.-]\d{3}[\s.-]\d{4}$

Does that look fun to you? No.

Natural Language Processing

With Rephrase, you can use plain English to transform data.

Example 1: Formatting Phone Numbers

Input:

555-1234
(555) 987-6543
555.111.2222

Prompt: "Format these as standard US phone numbers: (XXX) XXX-XXXX"

Output:

(555) 555-1234
(555) 987-6543
(555) 111-2222

Example 2: Extracting Emails

Input: "Contact John at [email protected] or Sarah ([email protected]) for more info."

Prompt: "Extract all email addresses as a list."

Output:

• [email protected]
• [email protected]

Example 3: Capitalization

Input:

john doe
JANE SMITH
bOb joNes

Prompt: "Fix capitalization to Title Case."

Output:

John Doe
Jane Smith
Bob Jones

No Code Required

You don't need to be a Python wizard to clean data. You just need to know what you want. Rephrase bridges the gap between messy input and clean output, instantly.