uproot4 to uproot5 TTree to DataFrame arrays slower

[masterfelu]

Hello.

I run a script to load a TTree as a DataFrame with 5 million rows and 3 columns, and measure the time taken. Here's an MWE for ipython:

%timeit uproot.open('tree.root')['time'].arrays(['year','month','day'],library='pd')

For uproot 5.3.7 with pandas 2.2.2, 19.2 s ± 177 ms per loop
For uproot 4.1.9 with pandas 1.3.5, 1.61 s ± 9.27 ms per loop

The value reported is mean ± std. dev. of 7 runs, 1 loop each. We can see uproot5 is 4 times slower than uproot4. The discrepancy increases further when more columns are loaded, leading to more than ten minutes of time for what previously took seconds.

I just want to know if there has been any major change that can cause such a reduction of load time for large TTree to DataFrame. Also, if any more checks should be done before I reach a conclusion, that is super helpful.

Thanks a lot for your time.

[vvsagar]

It might also be important to mention the differences in the pandas versions in order to reproduce the results?