Panic Exception with map_elements

[GPapagiannopoulos]

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

Log output

thread 'polars-0' panicked at crates/polars-plan/src/plans/conversion/scans.rs:204:56:
could not mmap file: IO { error: Os { code: 19, kind: Uncategorized, message: "No such device" }, msg: None }
--- PyO3 is resuming a panic after fetching a PanicException from Python. ---
Python stack trace below:
--- PyO3 is resuming a panic after fetching a PanicException from Python. ---
Python stack trace below:
PanicException: could not mmap file: IO { error: Os { code: 19, kind: Uncategorized, message: "No such device" }, msg: None }
---------------------------------------------------------------------------
PanicException                            Traceback (most recent call last)
File <command-1692981537052478>, line 1
----> 1 allDerailmentsSegments.collect()

File /local_disk0/.ephemeral_nfs/envs/pythonEnv-119d08cf-b9fa-4f34-b578-0aa914bedafa/lib/python3.11/site-packages/polars/lazyframe/frame.py:1942, in LazyFrame.collect(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, no_optimization, streaming, background, _eager, **_kwargs)
   1939 # Only for testing purposes atm.
   1940 callback = _kwargs.get("post_opt_callback")
-> 1942 return wrap_df(ldf.collect(callback))

PanicException: could not mmap file: IO { error: Os { code: 19, kind: Uncategorized, message: "No such device" }, msg: None }

Issue description

I am currently working with a large dataset as part of a broader project modelling derailments. Because the dataset is too big to work within memory I am working almost exclusively with the Lazy API. This is one of the few exceptions where I have had to use map_elements to utilize a UDF.
For clarity this is the statement that returns the error when collected:
allDerailmentsDistance.with_columns(
pl.struct(["Section_ID", "direction", "Cone_length(m)"]).map_elements(lambda x: special_segments_covered(section_ID = x["Section_ID"],
direction = x["direction"],
cone_length = x["Cone_length(m)"]), return_dtype = pl.Int8).alias("Cone_length(sections)")

The confusing thing is that I only get an error when working with the full dataset but not when working with a subset. What is also very confusing is that this was working as intended on Friday (05/07/2024) and despite not making any changes to my code or the environment in which I am working in it now returns an error. It doesn't appear to be an issue with the RAM and streaming during collection doesn't solve the issue. Working with a DataFrame doesn't solve the issue.

Expected behavior

This is the expected output - which only works when dealing with a subset of the data.

Installed versions

--------Version info---------
Polars:               1.1.0
Index type:           UInt32
Platform:             Linux-5.15.0-1061-azure-x86_64-with-glibc2.35
Python:               3.11.0rc1 (main, Aug 12 2022, 10:02:14) [GCC 11.2.0]

----Optional dependencies----
adbc_driver_manager:  <not installed>
cloudpickle:          2.2.1
connectorx:           <not installed>
deltalake:            <not installed>
fastexcel:            <not installed>
fsspec:               2023.5.0
gevent:               <not installed>
great_tables:         <not installed>
hvplot:               <not installed>
matplotlib:           3.7.2
nest_asyncio:         1.5.6
numpy:                1.23.5
openpyxl:             <not installed>
pandas:               1.5.3
pyarrow:              14.0.1
pydantic:             1.10.6
pyiceberg:            <not installed>
sqlalchemy:           1.4.39
torch:                2.2.2+cpu
xlsx2csv:             <not installed>
xlsxwriter:           <not installed>```

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