What is the main activity involved in data wrangling?

Data wrangling primarily involves the process of transforming raw data into a suitable format for analysis. This transformation includes cleaning the data, handling missing values, filtering out unnecessary information, and restructuring the data into a coherent format that can be easily analyzed. The purpose is to ensure that the data is accurate, consistent, and formatted correctly so that analysts can extract meaningful insights effectively.

While collecting data from various sources is an important part of the overall data lifecycle, it usually precedes wrangling. Similarly, visualizing data and storing it in databases are subsequent steps that rely on wrangling to ensure the data is in the right condition for those tasks. Hence, the focus on transforming raw data highlights the essential function of data wrangling within the field of data analytics.