In the last two decades, the data size of companies and government agencies has increased due to digitalization. This also caused an increase in consistency, errors, and missing values. Data filtering is responsible for handling such issues and optimizing them for management, reporting, and predictions. The filtering process boosts the accuracy, relevance, completeness, consistency, and quality of the data by processing dirty, messy, or coarse datasets. It is a very crucial step for any kind of data management because it can make or break a competitive edge of business. Data scientists need to master the skill of data filtering. Different kinds of data need different kinds of treatment. That's why a systematic approach to data filtering needs to be taken.
In the previous section, we learned about data exploration, while in this section, we will learn about data filtering. Data can be filtered either column-wise or row-wise. Let's explore...
