Apr-15-2024, 11:07 PM
Hey snippsat,
My apologies for not responding to your earlier, I actually moved away from polars to pandas. In my benchmarking, yes I found that polars is better than pandas at raw math, but it's a lot slower when it comes to splitting/appending dataframes. I'm doing stock backtesting with time based series, however because the times constitute variable row counts of data with differing time slices, none of the polars rolling functions worked out. So I'm stuck with slicing dataframes, and then doing math on sliced dataframes. No single dataframe is greater than perhaps 20000 entries... and the raw math speed provided by polars is negated by the slower dataframe manipulation within polars.
My apologies for not responding to your earlier, I actually moved away from polars to pandas. In my benchmarking, yes I found that polars is better than pandas at raw math, but it's a lot slower when it comes to splitting/appending dataframes. I'm doing stock backtesting with time based series, however because the times constitute variable row counts of data with differing time slices, none of the polars rolling functions worked out. So I'm stuck with slicing dataframes, and then doing math on sliced dataframes. No single dataframe is greater than perhaps 20000 entries... and the raw math speed provided by polars is negated by the slower dataframe manipulation within polars.