i’m not a theoretical physicist so i don’t know much about (super) string theory except that it’s supposed to be a unified “theory of everything” (bridging Einstein’s general relativity and the field of Quantum Mechanics: in short, making the “physics of big things” consistent with the “physics of small things” and that there are n-dimensions in reality. i say n because , for some reason, i thought there were 17 and when i consulted the web: some said 10, 11, or 26 (proving you can’t rely on it for accurate information and that my main take away is some things don’t have to seen in order for their existence to be believed). but i digress…

here are samples of “simple” conversions from integer to string in Python:


transmogrify is my favourite word i learned from the comic strip Calvin & Hobbes. i like it because there are sometimes muliple “layers”.

Python has some built-in functions to do some “basic” type conversion. however, i’ve learned “recently” that sometimes additional conversion is required to “prove mastery” (aside from logic) so my next discussions should involve these to be more pragmatic.

i previously knew about the tilde (as i speak Filipino (and can slightly understand a little of two other dialects) which has some words influenced by Spanish) and the umlaut (because of an individual in an organisation i used to work for). i recently found out the general term for it is a diacritic mark in a text mining course.

i’m a computer guy (and not a linguist) that mainly used ASCII – apparently EDCDIC supposedly addresses this “diversity” of letters but have to take this fact “at face value” since i don’t speak other languages.

i can’t help but be reminded of an anecdote of one of my batch mates that matter-of-factly corrected another that chocolate “mouse” isn’t the right way of saying it but since it’s a French word, it should be pronounced as “mouse-say” 😉

initially, i just planned on posting a “short” “blurb” on my blog and GitHub Python page as there seemed to me to be a “virtual triangle” among machine learning, statistics, and data visualisation. i’m still likely to make a brief GitHub “file” but upon serious reflection this post may not be a “cursory” post.

it took me awhile to come up with this post because i was partly busy with an online machine learning course, and, frankly, didn’t know what to write – and i’m finding it difficult to figure out how to do it – it didn’t help that there was a “time-consuming” upgrade of the Jupyter notebook environment that i use to store my ipynb files online.

my last experience started me thinking on how i learn- i still need to reflect more on it. i’ve done “ok” academically but i’ve discovered i can understand better if “alternatives” are provided for me to choose from. programming is, essentially, divergent: that is, sometimes there is more than a single way to arrive at an “acceptable solution”. why can’t “formal” education be that way? i know that the human brain can be easily overloaded by many things but perhaps offering a few choices might result in more students understanding. i’m realistic and pragmatic enough to understand that most teachers are overworked (at least those that care about the development of others) and that maybe there needs to be a more “active” open-source community like coding: sharing can make lighter work.

here is my initial attempt at my updated repository:


i’m not a statistician so kindly bear with my “crudeness”. initially, i just planned on discussing the “3ms”: Mean, Median & Mode. however, aside from these appearing “too short” and after what the describe method returns, it seemed more sensible to cover all the outputs.

as a former educator, i’m open to content being improved : “iteration” is often necessary in endeavouring to present something simpler – so if you have an idea on how to do this “better”, kindly let me know.

here’s the updated GitHub repository:


i’m not a botanist so this is unfamiliar to me – so naturally i googled it.

“serendipitously”, i ran across this “foreign” term in one of my data science courses in learning about a computer language. they are usually green and are the leaf-like vestiges that “protect” the petals of a flower in bud form and act as supports in the blooming process.

i’ve always wondered about this but not until i had to use it in my code did i bother to find out the difference. apparently, it’s just a spelling thing: “grey” is the preferred British way; while some Americans use “gray”.

i was originally from the Philippines and the educational system there is heavily influenced by the Americans, and have migrated to Australia awhile back – hence the “worsening” of my confusion.

it took me awhile to resolve the “s” and “z” (pronounced here as “zed”.

my speech therapist says it’s another “obstacle” for me in learning to speak again as my accent is somewhat “Americanised” and most words are produced differently in Australian-English.