Hello, I am a new to open source contribution. Can I take up this enhancement?

Hi @ezerkar,
This seems reasonable to me, since we do this for sort_index as well. (see #39451)
@vishalkumarprasad Thanks for volunteering to contribute to pandas. Feel free to take up this issue. You can find our contributing guidelines here. https://pandas.pydata.org/docs/development/contributing.html and if you need help, don't hesitate to ping me :).

take

You can use sorted() function here.
Code:

a = ("h", "b", "a", "c", "f", "d", "e", "g")
x = sorted(a)
print(x)

Output:
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']

Hi Eric, I am already working on this enhancement.

Hey, is this issue still open or taken?

Hi all, while working on this problem, i figured another issue where if you put series.sort_values ascending parameter to 1 or 0, it fails. However according to the code, it shouldn't fail.
Can someone confirm?

Hello all! @vishalkumarprasad I have tried the test case you have described and it indeed fails. However, I have noticed that the is_bool() function - in pandas,core-, which raises the error, also fails with 0, 1 as input arguments. It seems to me that series.sort_values() works as expected.

Hi all, @ninasiam If you look into the function definition of series.sort_values(), its clearly written that the ascending argument takes either bool or int or a sequence of bool or int. Hence there is mismatch somewhere, either in the function definition or is_bool() function or the implementation of is_bool() function. Please let me know your thoughts.

Hello! @vishalkumarprasad I see your point, I have probably missed that :) I think the mismatch is probably due to the way is_bool() function is implemented. If I try:

pd.core.dtypes.common.is_bool(True)
pd.core.dtypes.common.is_bool(1)  

The output is:

True
False

respectively. An easy fix, to avoid confusion for the time being, could be the update of the sort_values() function definition, to accept only True/False as input for the ascending parameter.

Hello @ninasiam,
So there is one more point which actually led me towards this issue. sort_values for series does not work currently for 1 and 0, but sort_values for dataframe works perfectly with 1 and 0. Shouldn't we make both of them to work in the same way?

take