The non-zero count functions (non_zero_count, low_non_zero_count, and high_non_zero_count) allow the handling of count-based analysis, but also allow for accurate modeling in cases where the data may be sparse and you would not want the non-existence of data to be explicitly treated as zero, but rather as null. In other words, a dataset in time, which looks like the following:

4,3,0,0,2,0,5,3,2,0,2,0,0,1,0,4

Data with the non_zero_count functions will be interpreted as the following:

4,3,2,5,3,2,2,1,4

The act of treating zeros as null can be useful in cases where the non-existence of measurements at regular intervals is expected. Some practical examples of this are as follows:

• The number of airline tickets purchased per month by an individual
• The number of times a server reboots in a day
• The number of login attempts on a system per hour