SNOW-1458130 Implement Index.sort_values

[sfc-gh-vbudati]

1. Which Jira issue is this PR addressing? Make sure that there is an accompanying issue to your PR.

   Fixes SNOW-1458130

2. Fill out the following pre-review checklist:

   • I am adding a new automated test(s) to verify correctness of my new code
     • If this test skips Local Testing mode, I'm requesting review from @snowflakedb/local-testing
   • I am adding new logging messages
   • I am adding a new telemetry message
   • I am adding new credentials
   • I am adding a new dependency
   • If this is a new feature/behavior, I'm adding the Local Testing parity changes.

3. Please describe how your code solves the related issue.

   Added support for Index.sort_values. Currently, the key parameter is not supported in Snowpark pandas.

One peculiar feature about Index.sort_values is the option to return an indexer (return_indexer) - this is a numpy array with the old row numbers/positions in their sorted positions.

I reused the sort_index implementation in the query compiler since it's the exact same logic. I added a new parameter include_indexer to retrieve the indexer used to sort the Index values. It is import to note here that pandas uses quicksort (which Snowpark pandas does not support yet) while Snowpark pandas uses stable sort. Therefore, it is not guaranteed that the indexer returned by Snowpark pandas is going to be the same as the one returned by native pandas.

Examples:

>>> res = pd.Index([1, 3, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 3]).sort_values(return_indexer=True)
>>> res[0]
Index([1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3], dtype='int64')
>>> res[1]
array([ 0,  2,  3,  4,  6,  7,  8,  9, 10, 11,  1,  5, 12])

>>> idx = pd.Index([1, 2, 3, 2, 3, 5, 6, 7, 8, 4, 4, 5, 6, 7, 1, 2, 1, 2, 3, 4, 3, 4, 5, 6, 7])
>>> res = idx.sort_values(return_indexer=True)
# Both Snowpark pandas and native pandas return the correct Index.
>>> res[0]
Index([1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8], dtype='int64')
# But the indexer order returned in this example differs since Snowpark pandas uses stable sort
# while native pandas uses quicksort.
>>> res[1]
# Snowpark pandas
array([0, 14, 16, 1, 3, 15, 17, 2, 4, 18, 20, 9, 10, 19, 21, 5, 11, 22, 6, 12, 23, 7, 13, 24, 8])
                  ^ differs
# Native pandas
array([0, 14, 16, 15, 17, 3, 1, 18, 2, 4, 20, 21, 9, 10, 19, 5, 22, 11, 12, 23, 6, 7, 13, 24, 8])
                   ^ differs

[sfc-gh-nkumar]

Looks pretty clean. Thanks for adding descriptive comments.

[sfc-gh-helmeleegy]

Does this comment also apply to Series.sort_values and DataFrame.sort_values? If so, then we should update their docs too. It can be in a separate PR though.

[sfc-gh-vbudati]

Yeah! Should apply there as well, I'll make a separate PR for this

[sfc-gh-vbudati]

https://github.com/snowflakedb/snowpark-python/pull/1916/files
Here's the PR!

[sfc-gh-helmeleegy]

LGTM. Thanks, Varnika.