ColumnNotFoundError in polars.DataFrame.join with how='right' and different left_on and right_on columns

[kartoria]

Checks

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of Polars.

Reproducible example

import polars as pl

df = pl.DataFrame(
    {
        "foo": [1, 2, 3],
        "bar": [6.0, 7.0, 8.0],
        "ham1": ["a", "b", "c"],
    }
)
other_df = pl.DataFrame(
    {
        "apple": ["x", "y", "z"],
        "ham2": ["a", "b", "d"],
    }
)

df.join(other_df, left_on="ham1", right_on="ham2", how="right")

Log output

Traceback (most recent call last):
  File "test.py", line 19, in <module>
    df.join(other_df, left_on="ham1", right_on="ham2", how="right")
  File "lib/python3.11/site-packages/polars/dataframe/frame.py", line 6828, in join
    .collect(_eager=True)
     ^^^^^^^^^^^^^^^^^^^^
  File "lib/python3.11/site-packages/polars/lazyframe/frame.py", line 1942, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: ham1

Issue description

This does not occur when 'on' is used instead of 'left_on', 'right_on', and only how is 'right'
And it is an error only when 'how' is 'right'.

I can't speak English, so I wrote it using a translator.

Expected behavior

import pandas as pd

# same dataframe
pd.merge(df, other_df, left_on="ham1", right_on="ham2", how="right")

Installed versions

--------Version info---------

Polars: 1.2.1
Index type: UInt32
Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
Python: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0]

----Optional dependencies----

adbc_driver_manager:
cloudpickle: 3.0.0
connectorx:
deltalake:
fastexcel:
fsspec: 2024.6.1
gevent:
great_tables:
hvplot:
matplotlib: 3.8.3
nest_asyncio: 1.6.0
numpy: 1.26.4
openpyxl: 3.1.2
pandas: 2.2.2
pyarrow: 15.0.2
pydantic:
pyiceberg:
sqlalchemy:
torch:
xlsx2csv:
xlsxwriter:
pl ver: None

[ritchie46]

Thanks for the report. That was silly. Fix coming up.

[kartoria]

보고해주셔서 감사합니다. 고맙습니다. 곧 수정하겠습니다.

Very fast! thanks you.