One of the most important aspects of data wrangling is to curate the data carefully from the deluge of streaming data that pours into an organization or business entity from various sources. Lots of data is not always a good thing; rather, data needs to be useful and of high-quality to be effectively used in downstream activities of a data science pipeline such as machine learning and predictive model building. Moreover, one data source can be used for multiple purposes and this often requires different subsets of data to be processed by a data wrangling module. This is then passed on to separate analytics modules.
For example, let's say you are doing data wrangling on US State level economic output. It is a fairly common scenario that one machine learning model may require data for large and populous states (such as California, Texas, and so on), while another model demands processed data for small and sparsely populated states (such as Montana or North...