Description
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
left = pd.DataFrame(index=[2, 3, 0, 1, 2, 3])
right = pd.DataFrame(range(4), index=range(4), columns=["value"])
merged_on_index = pd.merge(left=left, right=right, left_index=True, right_index=True, how="left")
merged_on_column = pd.merge(left=left.reset_index(), right=right.reset_index(), how="left", on="index").set_index("index")>>> print(merged_on_index)
value
0 0
1 1
2 2
2 2
3 3
3 3
>>> print(merged_on_column)
value
2 2
3 3
0 0
1 1
2 2
3 3
Issue Description
When merging two DataFrames on the index, the order of the rows is not preserved as written in the docs ("how: - left: ... preserve key order")
This unexpected behaviour occurs when the index of the DataFrame on which is merged (here left) contains duplicate values (here 2 and 3). The indices of the resulting DataFrame are then sorted according to the the index of right. Hence the order is not preserved.
The resulting index is merged_on_index.index = [0, 1, 2, 2, 3, 3]
If each index value occurs only once, the behaviour of the merge is as expected.
Expected Behavior
The DataFrames merged_on_index and merged_on_column are identical (except of the index name).
Both DataFrames have the same order:
merged_on_index.index == [2, 3, 0, 1, 2, 3] == merged_on_column.index
Installed Versions
INSTALLED VERSIONS
commit : e8093ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.3
numpy : 1.23.0
pytz : 2022.1
dateutil : 2.8.2
setuptools : 44.0.0
pip : 20.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.1.1
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None