inconsistency with .is_in when filtering string digits

[igormintz]

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

df = pl.DataFrame({'a': ["1","2"], 'b': [1,2]})
print(df.filter(pl.col('a').is_in([1, 2])))
print(df.filter(pl.col('a').is_in(["1", "2"])))
print(df.filter(pl.col('a').is_in(["1", 2])))

Log output

No response

Issue description

the digits in column "a" are strings. not sure that the filtering should work when using ints.

Expected behavior

the digits in column "a" are strings. not sure that the filtering should work when using ints.

Installed versions

--------Version info---------
Polars:              0.19.3
Index type:          UInt32
Platform:            macOS-13.5-arm64-arm-64bit
Python:              3.10.5 (main, Nov 29 2022, 16:45:28) [Clang 14.0.0 (clang-1400.0.29.102)]

----Optional dependencies----
adbc_driver_sqlite:  <not installed>
cloudpickle:         2.2.1
connectorx:          <not installed>
deltalake:           <not installed>
fsspec:              2023.6.0
gevent:              <not installed>
matplotlib:          3.8.0
numpy:               1.26.0
pandas:              2.1.1
pyarrow:             13.0.0
pydantic:            2.4.1
sqlalchemy:          <not installed>
xlsx2csv:            <not installed>
xlsxwriter:          3.1.5

[cmdlineluser]

Seems like it may be specific to the LHS being a string.

df.filter(pl.col('b').is_in(['1', '2']))
# ComputeError: cannot compare Int64 to Utf8 type in 'is_in' operation

pl.select(pl.lit(1).is_in(["1", "2"]))
# ComputeError: cannot compare Int32 to Utf8 type in 'is_in' operation

pl.select(pl.lit("1").is_in([1, 2]))
# shape: (1, 1)
# ┌─────────┐
# │ literal │
# │ ---     │
# │ bool    │
# ╞═════════╡
# │ true    │
# └─────────┘

[alexander-beedie]

I can see where it's happening - it does actually impact some additional dtype pairs, not just string (though lhs string is where you're most likely to hit it. Having a think about how best to handle it as we should be stricter up-front here 🤔