Data Validation In Pandas | Regex
Dataset & Notes : https://consoleflare-1.gitbook.io/dat...
In this video, we explore how to use Pandas and Regex for validating and cleaning datasets by detecting and filtering out invalid phone numbers and PAN cards. If you’ve ever dealt with dirty data, you know how important it is to filter out incorrect entries to maintain data integrity.
What You'll Learn:
Phone Number Validation:
Extract and validate phone numbers to ensure they follow the correct format: 10 digits, with only numeric characters.
We'll show how to use regex to match valid phone numbers and filter out invalid ones.
String Manipulation with Regex:
Learn the basics of regex and how you can use it to extract, match, or validate specific parts of strings.
Find patterns like digits, letters, and specific formats.
Example Datasets Used:
Phone Number Dataset: Contains a variety of phone numbers with some invalid entries.
PAN Card Dataset: Contains PAN cards with correct and incorrect formats.
We'll load the datasets into Pandas DataFrames and apply regex operations such as str.len(), str.isnumeric(), and str.match() to validate phone numbers and PAN card formats.
You'll see how to use filtering techniques to keep only valid entries.
Why You Should Watch:
Whether you're working with customer data, financial records, or any other kind of structured data, data validation is key to ensuring high-quality, error-free datasets.
Regex is a powerful tool for data manipulation, and by the end of this video, you'll be able to handle common validation tasks like a pro!
Tools Used:
Pandas: Python library for data manipulation
Regex: Regular expressions for pattern matching and string validation