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:

https://github.com/LinsAbadia/Python/blob/master/Transformations/BI_String_Integer.ipynb

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.

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:

https://github.com/LinsAbadia/Python/blob/master/Machine%20Learning/Learning.ipynb

i know the Aussie expression (it is Kiwi depending on whom you ask) of: yeah, nah, yeah can differ based on context and the actual variant used – in this case it indicates ambivalence.

it’s hard to believe that it’s been nearly 12 years since i stopped work. don’t get me wrong – i was really glad to see them and i had seen some of them over the years but something was slightly different this time.

i had to reflect on it to figure out why. i don’t know if it was because my wife now worked for the same organisation, the place where we ate was just a stone’s throw away from our offices, they were “purely” social visits before, it was something else, or a combination of some of these factors. there were moments (admittedly, few and far in between) when the conversation was lightly peppered with “shop talk”.

this sounds like i’m nostalgic for work but i still recall it was a hardly a “bed of roses”. the core group is still around even after all these years (which is a testament to my former boss’ management skills). i think, to some extent, i miss the challenge – sure the team sometimes handled things differently, but our goal was, ultimately, to arrive at a singular mitigation strategy.

i guess i felt a little frustrated that stopping wasn’t really on my own terms and it wasn’t a conscious decision on my part: in short i didn’t have a choice.

i feel there were still things i could’ve “accomplished” and my contribution would’ve been much “greater”. Oh, well…

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:

https://github.com/LinsAbadia/Python/blob/master/Statistics/Descriptive.ipynb

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’m currently taking: Applied Plotting, Charting & Data Representation in Python and have been introduced to a “relevant” model.

as validated by my years of professional experience in ICT, communication is a major part. as technologist, we almost only always focus on the processing and analyses of information. i’m glad that Data Science “explicitly emphasises” the importance of also communication of results. most people just refer to it as IT (but that IMHO is an “antiquated” form of thinking}. not just because it was “recently” rebranded as ICT by some governments and agencies, but because it highlights the other part of the equation and is a much more holistic approach to technology.

for your reference, here’s the Visualization Whee/ by Alberto Cairo:

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i also added it to my GitHub repository:

https://github.com/LinsAbadia/Python/blob/master/Visualisation/VisualizationWheelAlbertoCairo.jpeg

i’ve “shared” something a bit unusual as this Jupyter notebook is comprised of all “Markdown” (and no Python Code) cells as it mainly talks about the initial step required referred to as data “cleaning”. some “transformations” are warranted after importing datasets before working with them or performing Exploratory Data Analyses.

as it is mainly words it may be “ambiguous” ( as everything seems “obvious” to me ) to some. kindly let me know if there are things that aren’t “clear” or can be explained much better so i can post these. or if you know of supplementary (hyper)links or other resources “freely” available online, please let me know so i can make sure to include them.

my updated GitHub repository is at:

https://github.com/LinsAbadia/Python/blob/master/Analyses/Transformation.ipynb

to “complete” “slicing” DataFrames, i discuss loc and iloc. i think this enough to cover the “basics” of Python. as you know, i will start trying to delve into statistics to a.) further my skills, and b.) see if i can be “useful” to my wife.

i was always planning to tackle “advanced” topics -it was just “accelerated” sooner rather later.

here’s something i “shared” so i can “move on” to statistics :

https://github.com/LinsAbadia/Python/blob/master/DataFrames/LocVIloc.ipynb

That said, i can consider revisiting “past” topics based on feedback.

i did a lot of coding in my time and was introduced to neural networks at school so it wasn’i really a stretch learning Python. i only knew aspects of statistics so it became obvious to me that it was something i had to strengthen to upgrade my data science skills because i had a lot of exposure to programming and a little background on artificial intelligence – let me preface it by saying, it’s been awhile since i’ve “actively” done both and technology has advanced, that said, i’ve been developing a GitHub repository because i believe the expression that says you teach best what you need to learn.

to brush on the basics and truly understand Descriptive Statistics i’m perusing version 2 of the ebook Think Stats: Exploratory Data Analysis by Allen B. Downey. it’s supposedly framed for programmers and better suited for them in learning statistics.

aside from personal growth, my wife (although she’s well versed in machine learning and teaching programming) and her work team are looking at doing some research that may require this. so there’s a greater incentive to study this.