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.

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 was composing something in Word and was confused in which was the right preposition for a reference to a month so i Googled it.

i used to “Google” (i mainly restored my browser tab) “fun facts” daily but eventually had a “technical difficulties” so i abandoned this practice.

for a specific time, you’re supposed to use ‘at’. referring to days and dates merits ‘on’, and for other units ‘in.’

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.

since i mainly use a Jupyter notebook for Python coding, i use the print() function a lot to help with “debugging”. Error “detection” has a lot to be desired (that’s one of my only complaints. i lean towards it being used to introduce programming).

here are a “few debugging tips” that would have handy to know in learning how to code in Python:

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

i’ve always been “terminally trivial”. as i am a keen reader (i consume less books now given my vision impairment) and watch a heap of TV/movies (i no longer watch those exclusively with subtitles as the captions are too fast for me to read), the accumulation of factoids can be said to be “eclectic”. sadly, this hasn’t translated to any pub quiz wins and any major prizes in HQ Trivia.

with the advent of Google (and similar technologies) , this predilection for facts seems passé. the ubiquity of search engines and voice assistants like SIRI have resulted in “information at the fingertips” for some. this “JIT” (Just In Time} approach has transformed our relationship with facts – it’s, after all, when (and no longer if) we need it. it’s psychologically more efficient and practical to store information external to your person rather than in your mind (as evidenced by our “over”reliance on our phones). the onus has shifted from the right answers to the right questions. i’ve always believed questions were important but more so now – Jeopardy! was only “tangentially” right.

i asked a former knowledgeable teacher and very smart friend why digital technologies used the Red Green Blue (RGB) palette when i was taught early on that the primary colours were Red Yellow, and Blue – so i was thinking shouldn’t it be RYB instead. i was told that RGB had always been the standard spectrum. i was placated for a while by their answers but it was always in the back of my mind.

one day i was just compelled to do a web search. apparently, RGB are the base additive colours: That is they are “active” and can be combined to form various hues and shades (through the use of such things as lasers). primary colours uses paint and paper to make other colours and are more “passive” – if that makes sense.

it’s no longer just about memorising facts in the digital age as it is, also IMHO, about having the intellectual curiosity to ask “interesting” questions. From now on, i’ll also share the results of my “research” on this blog.