BUG: .unstack(sort=False) reorders columns labels without the column values #62816
Description
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Reproducible Example
import pandas as pd # Sample DataFrame df = pd.DataFrame({ "Department": ["HR", "HR", "Finance", "Finance", "IT", "IT", "Finance", "HR", "IT", "Finance"], "Gender": ["F", "M", "M", "F", "M", "F", "M", "F", "M", "F"], "Location": ["NY", "CA", "CA", "NY", "CA", "NY", "CA", "CA", "CA", "NY"] }) # Counts combinations of three columns, but wrong column labels assigned: print( df .value_counts(subset=["Department", "Gender", "Location"]) .unstack(fill_value=0, sort=False) ) # output: Location CA NY Department Gender Finance F 0 2 M 2 0 HR F 1 1 M 1 0 IT F 0 1 M 2 0Issue Description
In Pandas (v2.3), I have noticed an issue with .unstack(fill_value=0, sort=False) where incorrect column labels are assigned to column values.
For example, if you add the sort=False, the counts are not correct:
print( df .value_counts(subset=["Department", "Gender", "Location"]) .unstack(fill_value=0, sort=False) )The output is:
Location NY CA Department Gender Finance F 0 2 M 2 0 IT M 2 1 HR F 1 0 M 0 1 IT F 1 0 Note that the dataframe contains no female in the Finnance department in CA.
I have seen related bug reports (#55516 and #53910) where this argument is supposed to be deprecated. But it seems that no warning is being issued for the deprecation.
Expected Behavior
The following works as expected:
# note: use the above "reproducible" code to generate the `df` print( df .value_counts(subset=["Department", "Gender", "Location"]) .unstack(fill_value=0) # without sort argument )output:
Location CA NY Department Gender Finance F 0 2 M 2 0 HR F 1 1 M 1 0 IT F 0 1 M 2 0 Installed Versions
INSTALLED VERSIONS
commit : 2cc3762
python : 3.12.7
python-bits : 64
OS : Linux
OS-release : 4.18.0-372.32.1.el8_6.x86_64
Version : #1 SMP Thu Oct 27 15:18:36 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.3.0
numpy : 2.2.6
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.2
Cython : None
sphinx : None
IPython : 9.3.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.4
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.3
numba : 0.61.2
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.2
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None