Pandas VS Cubes

First of all make sure you export the data from Excel into CSV (Comma Separated Value) format which is going to make it much easier to work with from Python. Other person gave a nice link to the python csv docs, if you wanted to get crazy you could also checkout popular data science library Pandas which has a read_csv function which should import your data pretty easily at least

Data extracted using numpy, pandas and json

So far, so good. But what does it look like in concrete terms for ETFs with exposure to GME? Unfortunately, this is not easy to answer, because first a lot of data from different, more or less reliable sources have to be summarized. I have been learning pandas (https://pandas.pydata.org/) lately and wanted to work with a large real-world data set to practice my coding skills. So why not work with such a data set related to GME?

If you really want to store it all (labels included) in one data structure, you should look up pandas.

Another question you may be asking is: "What about skill players?" Well, what about them? Skill players are defined as players that consistently tote the rock. I was able to filter out skill player's performance in different combine events using pandas. For our purposes, the following positions (as listed on PRF) were considered 'skill players': WR, RB, QB, TE, DB, LB. In included linebackers but if you want to not include them, knock yourself out. It kind of only helps my case that the likes of Roquan Smith and Nakobe Dean don't count for the SEC. When only considering skill players, the SEC ranks 2nd to the Big 12 in 40-yard dash times. In the other combine events for which there is data, the SEC ranks first in none of them.

You glossed over the fact that `.pct_change` isn't actually "percent change" as documented. More fun reading: https://github.com/pandas-dev/pandas/issues/20752

I suggest pandas might be a great tool for you as you will be able to read write excel / csv files and process them and see how you get on.

Now to implement this one, we'll have to do some manipulation to our account values. Let's use the power of numpy to help us out here (oh and it's also the same in pandas too. We'll be using np.diff to take the returns of our account values and resampling them.

I'm looking for materials and tools to learn. I'm reading up on OLAP and cubes. I found cubes python package but it hasn't been updated in years. Could you give me some tips on what to learn in 2021?