If the join operation using polars is slow, there are some additional things you can try to improve performance:

1. Ensure that the join column is sorted: Sorting the join column can significantly speed up the join operation, especially when working with large data frames. In Polars, you can sort a DataFrame using the sort method, like so: df = df.sort("ProductID").
2. Reduce the size of the DataFrame before joining: If you only need a subset of columns from the sub_cat DataFrame, consider selecting only those columns before joining. This can be done using the select method in Polars: sub_cat = sub_cat.select(["ProductID", "SubCatID"]).
3. Ensure that you have enough memory available: If you are running out of memory, it can significantly slow down the join operation. Ensure that you have enough memory available to load both data frames into memory.
4. Parallelize the operation: If you have multiple CPUs available, you can parallelize the join operation using Polars’ join_par method. This method allows you to join two data frames using multiple threads, which can significantly speed up the operation.
5. Optimize your code: Look for other parts of your code that might be slowing down the join operation, and see if there are ways to optimize it. For example, if you are performing other operations on the DataFrame before the join, see if there are ways to reduce the number of operations or make them more efficient.

join_par is a method in Polars that performs a parallel join operation. Here’s how you can use it:

import polars as pl

# create two Polars dataframes
df1 = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df2 = pl.DataFrame({"a": [2, 3, 4], "c": [7, 8, 9]})

# perform a parallel join operation using join_par
join_result = df1.join_par(df2, on="a", how="inner")

# print the result
print(join_result)

The join_par method takes two arguments: the second dataframe to join and the join keys. In the example above, we’re joining df1 with df2 on the a column. We’re performing an inner join, so only the rows with matching values in the a column will be returned.

Parallel joins can be faster than regular joins when working with large dataframes, but keep in mind that they also use more system resources. Also, note that the performance of a parallel join operation can depend on the hardware and number of CPU cores available.