27. Video: Removing Data - Other Considerations

One common strategy for working with missing data is to understand the proportion of a column that is missing. If a large proportion of a column is missing data, this is a reason to consider dropping it.

There are easy ways using pandas to create dummy variables to track the missing values, so you can see if these missing values actually hold information (regardless of the proportion that are missing) before choosing to remove a full column.