Handling `missing` data?

All datasets have missing values.

These can be in the form of NaN, undefined or null — or maybe something else?

But what should we do with them? Should we drop those rows or should we replace these values? Let’s see how to get rid of these missing values in a sensical manner.

Data is everywhere, but also with missing values. [Photo by Stephen Dawson on Unsplash]