Related
When specifying an axis for the unique function, the array is searched for unique vectors along this axis. However a sorted result is difficult to compute when split != axis.
Because this feature might also be interesting on its own it would make sense to implement it as a standalone function.
Feature functionality
Compared to the classic sort the vectorized sorting should keep the values in one vector consistent and only sort the vectors along the sorting axis. The sorting should give the highest priority for the first value, if on this level there are equal values the next value should be compared and so on.
Additional context
Numpy uses some kind of vectorized sorting in their unique function when specifying the axis argument.
Related
When specifying an axis for the
uniquefunction, the array is searched for unique vectors along this axis. However a sorted result is difficult to compute whensplit != axis.Because this feature might also be interesting on its own it would make sense to implement it as a standalone function.
Feature functionality
Compared to the classic sort the vectorized sorting should keep the values in one vector consistent and only sort the vectors along the sorting axis. The sorting should give the highest priority for the first value, if on this level there are equal values the next value should be compared and so on.
Additional context
Numpy uses some kind of vectorized sorting in their
uniquefunction when specifying theaxisargument.