Multiple

Dealing with multiple files.

Polars can deal with multiple files differently depending on your needs and memory strain.

Let's create some files to give us some context:

{{code_block('user-guide/io/multiple','create',['write_csv'])}}

Reading into a single DataFrame

To read multiple files into a single DataFrame, we can use globbing patterns:

{{code_block('user-guide/io/multiple','read',['read_csv'])}}

--8<-- "python/user-guide/io/multiple.py:create"
--8<-- "python/user-guide/io/multiple.py:read"

To see how this works we can take a look at the query plan. Below we see that all files are read separately and concatenated into a single DataFrame. Polars will try to parallelize the reading.

{{code_block('user-guide/io/multiple','graph',['show_graph'])}}

--8<-- "python/user-guide/io/multiple.py:creategraph"

Reading and processing in parallel

If your files don't have to be in a single table you can also build a query plan for each file and execute them in parallel on the Polars thread pool.

All query plan execution is embarrassingly parallel and doesn't require any communication.

{{code_block('user-guide/io/multiple','glob',['scan_csv'])}}

--8<-- "python/user-guide/io/multiple.py:glob"