What does the term "data wrangling" refer to?

The term "data wrangling" refers specifically to the process of cleaning and transforming raw data into a usable format. This is a crucial step in the data analysis workflow, as raw data is often messy, unstructured, or inconsistent, making it difficult to derive meaningful insights. During data wrangling, various techniques are employed, such as removing duplicates, correcting inaccuracies, handling missing values, and reformatting data types, to ensure that the data is organized and structured properly for analysis.

By focusing on preparing the data efficiently, data wrangling allows analysts and data scientists to spend more time on deriving insights rather than dealing with the complexities of unprocessed data. This foundational step is vital for effective data exploration, modeling, and visualization, which depend on reliable and well-structured data.