Artificial Intelligence & Machine Learning
Data Wrangling
Definition
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics.
Why It Matters
Real-world data is almost always messy, inconsistent, and incomplete. Data wrangling is a crucial and often time-consuming step in any data science project to clean and structure the data before it can be analyzed or used to train a model.
Contextual Example
The data wrangling process might involve removing duplicate rows, handling missing values (e.g., by filling them with the mean), correcting typos, and converting data types.
Common Misunderstandings
- Data scientists often report that data wrangling takes up 80% of their time on a project.
- Libraries like pandas are essential tools for data wrangling in Python.