What does data wrangling involve?

Data wrangling refers to the process of cleaning, restructuring, and preparing raw data for analysis. It involves transforming and integrating data from various sources, ensuring the data is in a suitable format for analysis, and addressing any inconsistencies or inaccuracies present in the raw data. This process is crucial because it allows data scientists and analysts to derive meaningful insights from the data without being hindered by unorganized or messy datasets.

The focus of data wrangling is on making the data usable rather than merely collecting new data or automating processes. For instance, one might remove duplicates, fill in missing values, or change data formats during data wrangling. This foundational step is essential in ensuring that the analysis conducted later is based on high-quality and reliable data.