BUG: Series.value_counts with sort=False returns result sorted on values for Series with string dtype

[CynthiaLuijkx]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

series = pd.Series(data=['a', 'b', 'c', 'b'])

series.astype('string').value_counts(sort=False)

which results in:

b    2
a    1
c    1
Name: count, dtype: Int64

Issue Description

Series.value_counts with sort=False returns result sorted on values for Series with string dtype.

The output returned is sorted on values, while using sort=False, according to the documentation, should ensure that the result is ordered by the order in which the different categories are encountered.

From a brief dive into the code I believe the error is found in core\algorithms.py in value_couns_internal; on line 906, in case the values are an extension array

result = Series(values, copy=False)._values.value_counts(dropna=dropna)

is called and the sort keyword is not passed on.

Expected Behavior

The expected behavior is that the results are ordered by the order in which the categories were encountered, rather than by value, as stated in the value_counts documentation. This does happen correctly when the series still has the object dtype:

import pandas as pd

series = pd.Series(data=['a', 'b', 'c', 'b'])

series.value_counts(sort=False)

which results in:

a    1
b    2
c    1
Name: count, dtype: int64

Workaround

This code snippet does return the expected values:

import pandas as pd

series = pd.Series(data=['a', 'b', 'c', 'b'])

series.groupby(series, sort=False).count()

Result:

a    1
b    2
c    1
dtype: int64

Installed Versions

INSTALLED VERSIONS

commit : e86ed37
python : 3.10.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 166 Stepping 0, GenuineIntel
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : Dutch_Netherlands.1252

pandas : 2.1.1
numpy : 1.24.3
pytz : 2023.3
dateutil : 2.8.2
setuptools : 65.5.0
pip : 22.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.15.0
pandas_datareader : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat: None
fastparquet : 2023.8.0
fsspec : 2023.9.0
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 13.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report! Confirmed on main. PRs to fix are most welcome.

[wangdong2023]

Hi, I'm a Pandas user but never contributed, I would like to take this issue, is it okay?

[yusharth]

take

[wangdong2023]

:D now I know how it works

[tirth-hihoriya]

@wangdong2023 Are you still working on it?

[wangdong2023]

@tirth-hihoriya I think @yusharth took the task

[yusharth]

I am working on it @tirth-hihoriya . Thanks for asking, will let you know if I need help.