Data Preparation#

Data preparation is crucial because raw data is rarely perfect.

It often contains errors, inconsistencies, or missing values. For example, marks data may have ‘NA’ or ‘absent’ for non-attendees, which you need to handle.

This section teaches you how to clean up data, convert it to different formats, aggregate it if required, and get a feel for the data before you analyze.