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 a visualisation course in Python and it has reminded me of red and green colour blindness: both hues appear similar to them.

while they are still granted driver’s licenses as a “strong” convention for traffic lights exist, the position and not just the colour convey information.

this made me think of truly inclusive designs: where a “best effort” is placed that a design is accessible by default (or a “reasonable” alternative or accomodation is provided). this is “good” to know since coming up with a “universal” design can be “problematic” (as more effort can be required) but in media without guidelines this can invaluable.

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:


i also added it to my GitHub repository:


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:


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 :


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.

“Slicing” (that is, creating subsets using indices) DataFrames can be quite useful in partitioning datasets. for those familiar with SQL, this kind of reminds me of the SELECT command that is sometimes paired with an optional WHERE clause.

i know this is a very “basic” treatment but i used to play a lot of basketball and i believe in the importance of fundamentals. i use a lot of this in my own code and from what i’ve seen on the internet this is very common in snippets shared so IMHO it’s important to grasp the “basics” of this – in other words, it’s important to understand this in trying to make sense of sample code (comments are another thing but don’t get me started on that “bugbear”…).

here my updated GitHub repository: