Beyond simple examples of showing empty rows or missing dates, there are cases where you can use data densification to solve problems or get around limitations of the data, which would be very difficult otherwise.
Consider, for example, if you had a data that indicated dates when certain generators were turned On or Off:
| Generator | Date | Action |
| A | 1//2017 | On |
| B | 1/22/2017 | On |
| C | 1/25/2017 | On |
| D | 1/25/2017 | On |
| B | 1/27/2017 | Off |
| E | 1/29/2017 | On |
| A | 1/30/2017 | Off |
| C | 1/30/2017 | Off |
What if you wanted a visualization that showed how many generators were On for any given date? The challenge is that the dataset is sparse. That is, there are only records for dates when an On or Off action occurred. ...
