Quantifying Randomness, quantifying of how random a newly discovered infinite series is and be able to get a more complete list on quantifying random?

A very interesting topic, "quantification of randomness" in mathematics it is sometimes reffered to as "complex theory" (although it is more about pseudorandom than randomness) that is based on saying that a complicated series is more random and then there are tests for randomness in Statistics and perhaps the most intriguing test related to information theory -"entropy"(as also being of relevence to and result of second law of thermodynamics), while there are also random numbers generators (pseudorandom numbers generators) and true random numbers generators using quantum computing.

So, what I've been trying to, is making a complete list of all available algorithms or books or even random number generators that will allow me to tell me how much random a series is, allowing me to "quantify randomness".

There are 125 unique infinite series which are pseudorandom that I have discovered and generated based on a rule, now how do I test for randomness and quantify it? If the series is random or there is probably a pattern, or something that will allow me to predict the next number in the series given I don't know what the next number is.

Now, do anyone know of any github links based on any of the above? ^ (like anything related to quantifying randomness in general that you think will be helpful). A book/books on quantifying randomness will be very very helpful too. Actually anything at all...

(Also, Any coding language will do if it is able to make use of any existing algorithms for computing randomness, I have used Python for generating 125 unique infinite series which are pseudorandom-like, so is there a way already available to check how much random these series are (programmatically) or what do you suggest I should do...)

I didn't try much as of yet but what I'm planning to is first being able to plot the data (infinite sequences) get images (descriptive statistics to the rescue) to tell me if the data is first looking like random noise, although it is kind of already as from the way I computed it but essentially being able to convert one kind of data to another and see if it is behaving like noise or not.

Then there are measures such as based on compression's (something like Kolmogorov complexity) and the ways algorithms might have been used to determine how much random pi could be? I'm looking into Entropy as well, books, reading stuff and also exploring cryptographic ways but all of these are research work, am yet to arrive at developing proper and more complete ways of also being able to implement all of it programmatically and I need as many ways of being able to do it as possible.