Karthikeyan Singaravelan <tir.kar...@gmail.com> added the comment:
@Serhiy I just checked the docs to add an example and it's explained with an
example at [0] :)
```
Sorts are guaranteed to be stable. That means that when multiple records have
the same key, their original order is preserved.
This wonderful property lets you build complex sorts in a series of sorting
steps. For example, to sort the student data by descending grade and then
ascending age, do the age sort first and then sort again using grade:
>>> s = sorted(student_objects, key=attrgetter('age')) # sort on secondary
>>> key
>>> sorted(s, key=attrgetter('grade'), reverse=True) # now sort on
>>> primary key, descending
[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
The Timsort algorithm used in Python does multiple sorts efficiently because it
can take advantage of any ordering already present in a dataset.
```
As noted TimSort makes this efficient by taking advantage of the sorting in the
first run though it was not done as a single pass. If I am bench marking
correctly in the attached file then for a list with 400 items using multiple
pass sort is 4-5x faster than the suggested sorting in Python 3.7 and also more
readable (though my lambda call is messy :)
Multiple pass sort :
3.871509724
Suggested sort :
17.237952677
[0]
https://docs.python.org/3/howto/sorting.html#sort-stability-and-complex-sorts
----------
Added file: https://bugs.python.org/file47874/bpo35010_1.py
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