BUG: pd.concat produces frames with inconsistent order when concating the ones with categorical indices #44099
Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the master branch of pandas.
Reproducible Example
import pandas df1 = pandas.DataFrame( {"col1": ["a_val", "b_val", "c_val"]}, index=pandas.CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c"]), ) df2 = pandas.DataFrame( {"col1": ["b_val", "a_val", "c_val"]}, # The same categories as in the first frame, but the values are in a different order index=pandas.CategoricalIndex(["b", "a", "c"], categories=["b", "a", "c"]), ) print(pandas.concat([df1, df2])) # Output: # col1 # a a_val # b b_val # c c_val # a b_val <-- index doesn't math the original value # b a_val <-- index doesn't math the original value # c c_valIssue Description
When concatenating frames with categorical indices that have the same values but they are in a different order, the order of indices does not match the order of values from the original frame.
In the reproducer above each "x" index value corresponds to the "x_val" value in the frame ("a" -> "a_val", "b" -> "b_val", ...), but in the concated frame this order is broken, the index values appears to be sorted, when the actual frame's values don't.
Expected Behavior
An expected result would be either properly sorted result:
col1 a a_val b b_val c c_val a a_val b b_val c c_valor the one that is unsorted at all:
col1 a a_val b b_val c c_val b b_val a a_val c c_valInstalled Versions
INSTALLED VERSIONS
commit : 945c9ed
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.8
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 2021.08.1
scipy : None
sqlalchemy : 1.4.23
tables : None
tabulate : None
xarray : 0.19.0
xlrd : 2.0.1
xlwt : None
numba : None