Data wrangling is the process of transforming raw data into a clean format that can be analysed. Data wrangling is a crucial phase in the data preprocessing process that involves tasks such as data importing, data cleaning, data structuring, string processing, HTML parsing, handling dates and times, missing data management, and text mining.
Data Wrangling:
• Importation Cleaning Structuring
• Missing Data
• Dates & Time HTML Parsing
• String Processing Text Mining
For any data scientist, the practice of data wrangling is crucial. In a data science endeavor, data is rarely readily available for analysis. The information is more likely to be stored in a file, a database, or retrieved from documents like web pages, tweets, or PDFs. Knowing how to wrangle and clean data will allow you to extract crucial insights from your data that would otherwise go unnoticed.
