FireDucks: Pandas but Faster

It's a bit sad for me. I find the biggest issue for me with pandas is the API, not the speed.

So many foot guns, poorly thought through functions, 10s of keyword arguments instead of good abstractions, 1d and 2d structures being totally different objects (and no higher-order structures). I'd take 50% of the speed for a better API.

I looked at Polars, which looks neat, but seems made for a different purpose (data pipelines rather than building models semi-interactively).

To be clear, this library might be great, it's just a shame for me that there seems no effort to make a Pandas-like thing with better API. Maybe time to roll up my sleeves...

What about the polars API doesn't work well for your use case?

Polars is missing a crucial feature for replacing pandas in Finance: first class timeseries handling. Pandas allows me to easily do algebra on timeseries. I can easily resample data with the resample(...) method, I can reason about the index frequency, I can do algebra between timeseries, etc.

You can do the same with Polars, but you have to start messing about with datetimes and convert the simple problem "I want to calculate a monthly sum anchored on the last business day of the month" to SQL-like operations.

Pandas grew a large and obtuse API because it provides specialized functions for 99% of the tasks one needs to do on timeseries. If I want to calculate an exponential weighted covariance between two time series, I can trivially do this with pandas: series1.ewm(...).cov(series2). I welcome people to try and do this with Polars. It'll be a horrible and barely readable contraption.

YC is mostly populated by technologists, and technologists are often completely ignorant about what makes pandas useful and popular. It was built by quants/scientists, for doing (interactive) research. In this respect it is similar to R, which is not a language well liked by technologists, but it is (surprise) deeply loved by many scientists.

I don't know what exponential weighted covariance is, but I've had pretty good luck converting time series-based analyses from pandas to polars (for patient presentations to my emergency department -- patients per hour, per day, per shift, etc.). Resample has a direct (and easier IMO) replacement in polars, and there is group_by_dynamic.

I've had trouble determining whether one timestamp falls between two others across tens of thousands of rows (with the polars team suggesting I use a massive cross product and filter -- which worked but excludes the memory requirement), whereas in pandas I was able to sort the timestamps and thereby only need to compare against the preceding / following few based on the index of the last match.

The other issue I've had with resampling is with polars automatically dropping time periods with zero events, giving me a null instead of zero for the count of events in certain time periods (which then gets dropped from aggregations). This has caught me a few times.

But other than that I've had good luck.