The documentation for polars is not the best, and figuring out how to do this below took me over an hour. Here's how to read in a headerless csv file into a LazyFrame using scan_csv and write it to a parquet file using sink_parquet. The key is to use with_column_names and schema_overrides. Despite what the documentation says, using schema doesn't work as you might imagine and sink_parquet returns with a cryptic error about the dataframe missing column a.

This is just a simplified version of what I actually am trying to do, but that's the best way to drill down to the issue. Maybe the search engines will find this and save someone else an hour of frustration.

import numpy as np
import polars as pl

df = pl.DataFrame(
    {"a": [str(i) for i in np.arange(10)], "b": np.random.random(10)},
)
df.write_csv("/tmp/stuff.csv", include_header=False)

lf = pl.scan_csv(
    "/tmp/stuff.csv",
    has_header=False,
    schema_overrides={
        "a": pl.String,
        "b": pl.Float64,
    },
    with_column_names=lambda _: ["a", "b"],
)

lf.sink_parquet("/tmp/stuff.parquet")