As jqurious suggested the default behavior of Polars write_csv is to cast a Polars df to a string, if not output file is given:

polarsdf.write_csv(separator='\t')

And for Shiny for Python this is my solution now:

def download_something():
    yield polarsdf.write_csv(separator='\t')

To append to a CSV file for example - you can pass a file object e.g.

import polars as pl

df1 = pl.DataFrame({"a": [1, 2], "b": [3 ,4]})
df2 = pl.DataFrame({"a": [5, 6], "b": [7 ,8]})

with open("out.csv", mode="a") as f:
   df1.write_csv(f)
   df2.write_csv(f, include_header=False)

>>> from pathlib import Path
>>> print(Path("out.csv").read_text(), end="")
a,b
1,3
2,4
5,7
6,8

Further discussion: "Append on export"

• https://github.com/pola-rs/polars/issues/21880

Polars already parses csv files as fast as it can. It already uses all threads, so trying to spin separate processes will only hurt performance.

You can set rechunk=False. That will save an expensive reallocation after reading.

Other things you can try is set the schema to smaller data types, so that you will have less memory and cache pressure.