I think it would be interesting to talk about some of the waste data you've put together,
Charles.
And so...
Okay.
So did you download...
Yeah, okay.
This is the latest one.
I think I just updated it yesterday.
Yes, exactly.
So I was tinkering on this throughout the past week, and yes, I saw you just made a
commit that looks like yesterday at some point.
So I've been looking over it, but I may not have all of your latest work.
So if you want to share any of that while we go through it, then that would be awesome.
So essentially, just to give you some background here, Sela, you can do Freedom of Information
requests from the... here in Washington from the Liquor and Cannabis Board, and you'll
get a data dump that looks like this, where they'll... if you're requesting traceability
data.
So they'll return essentially zipped folders of the different endpoints.
So these are the various endpoints of the traceability API.
So for example, licensees record their sales, they record how many plants they have, they
record their lab results, their inventory, and tucked away down here in batches, we've
found that they actually record how much waste they generate.
And there's a company, Better Carbon Solutions, and they're... as far as I know, the only...
well, actually, there's maybe a second one up in Bellevue, Bellingham, but these are
the ones that I personally know that are working on a solution for hemp and cannabis waste.
And they have no idea how much waste there is.
So we're going to try to make some estimates.
So really just to find the right...
So we'll need this data.
Let me actually pause for one second while I load in the data here.
One second here.
I forgot to get the data.
One second.
Okay, so...
Downloaded some data sets here.
The batches, although it looks like it's about one gigabyte of data in the zip folder, and
when you unzip it, you'll see that the data file here, the batches, is 20 gigabytes.
So that's a big...
So there's a large amount of data, and my laptop has 16 gigabytes of RAM, so I cannot
read in this entire data file in one go.
So there's a couple tools we can use to approach this.
So Charles showed me Dask, which is...
It implements the Pandas data frame, so it's similar to working with Pandas.
And so you can think about it as instead of just reading in everything all at once, you're
sort of chunking this.
I...
It may be that Dask hasn't implemented everything, but I actually didn't have the greatest luck
reading in this data with Dask.
Did you run into similar issues, Charles?
Yes.
I had to read it in with Pandas, save it as a parquet file, and then Dask would read it
in.
But I was lucky enough that my Mac has an infinite swap file.
It will just keep expanding it until apparently it uses up the entire disk.
Where on Windows...
You're using Windows, right?
Yes.
So maybe your swap file isn't big enough or Windows has some limit.
I don't know.
Yeah, on my Linux machine, I had to change the size of the swap file in order to get
it to work.
But yeah, it does not like that file.
It will not read it as a CSV.
I don't know if I can...
I'm not sure how big those parquet files are, if I can share them easily.
So it's not the end of the world because that's why we've got Python here, is to sort of make
the unmanageable manageable.
So this is the code that Charles has written here.
So I've just snagged Charles's code from here.
Charles has actually done an awesome analysis here.
And so, Stella, you may want to go through here because here Charles has actually really
just walked through the steps you need to as you're going about data.
So for example, you've read in the data and you started to look at it.
And I love what you did here.
Almost one of the very first things you did was simply describe the data.
So just to see what are we working with here.
So we're working with, what's this, 37 million plants.
That's a lot of plants.
And so that's real cool data that...
They're not really recording 37 million tomato plants out there.
And so it's so cool that we've got such granular data on the cannabis industry.
So enough fawning over Charles's analysis here because we can revisit this here in a
second.
So just to make this manageable here just for the time being, just for the presentation
essentially.
What you can do with pandas is you can just essentially just read in a number of rows
at a time.
And so the way I was going to approach this problem, since I've got a limited bandwidth
here, limited memory, and I'm having problems with Dask.
I was essentially just going to chunk it.
So just read in 10,000 observations at a time or can play with that number.
And essentially the data point we're after is what Charles has calculated here as the
amount of waste by producer by date.
And I was thinking if we group them in 10,000, we can calculate the waste by producer by
date and then save that.
And then we can basically then read in all those files and then essentially combine that
data.
Because there won't be any data loss.
We're just looking for the total so we can break the total up.
So it doesn't really even necessarily matter how the data is sorted.
It'll be nice if the data is kind of nicely in order, but the chunks can split things
up because we're at the end of the day, we're just looking for the total.
So the long story short, just for today's demonstration, we'll just do one with a chunk
of 10,000 and then what I'm going to be working with, unless Charles has a more elegant solution,
but Charles does have a more elegant solution.
I just don't have quite enough memory for it.
So basically I'm just going to kind of walk through this stack overflow.
So how can I read in a huge CSV file?
Well basically we're just going to have to iterate over the data frame, read in chunk
by chunk, calculate the waste by producer by date, save it, aggregate it, and then hopefully
at that point we'll have just a nice series.
In fact, we may even aggregate it further and just do total waste by date.
That's I think an informative number we'll do.
We'll revisit the producer by date as well.
But long story short, let's start here with this chunk.
Just for expedience sake, I just gone ahead and read it in here.
So basically I just, this will just save us a tidbit of time, but basically what Charles
has done here is he's defined the columns that we actually need to read in.
This is sort of a little bit of advanced work, or a little bit of a, you know, thanks to
Charles's foresight, we can kind of leapfrog a little bit.
So we already know like what columns are in the data.
But if you were reading this in for the first time, you may not know what columns you even
know.
But long story short, we're going to read in 10,000.
I'm skipping two million observations because for whatever reason, a lot of the observations
at the beginning of the data, there was no waste.
So just for today, I wanted to get a chunk that actually had waste.
So we've got the waste data here.
We can describe it as Charles did.
Here we've just got 10,000 plants and we've got the amount of, you know, well let's actually
see what columns we have here.
For example, we've got the flower dry weight, which of course is what the producers care
about.
But the other half of that is the waste.
And so that's what Better Carbon Solutions cares about.
So they're trying to get that waste off of their hands and compost it essentially on
your pyrolysis.
So they can make products with it instead of it going into the landfill.
And so Charles noted here that we've got some negative values.
And so Charles, I ran into just an error when I ran this line for some reason.
So I just approached this as just essentially taking the absolute value of the waste.
So let me know if that achieves the same outcome.
But basically there were negative values for waste.
Yeah, actually that's a good solution.
I didn't think about that.
Well yours was perfectly fine.
But I just ran into an error.
We'll see here.
I'll show you.
Well that time it worked.
For some reason the first time I ran into an error of some sort.
Anyways, either way works.
So I actually haven't really walked through this line here.
So let's see here.
So let's see.
Okay, so here you're filling in the plant stage.
Oh yeah.
There were some places where it was NA and that will cause problems later on.
So basically we're assuming if there's no plant stage given then it's harvested essentially.
Right.
Oh yeah, and I filled it in with the most common value.
Which is, you know, that's a technique that's pretty common.
Excellent.
Well this is why it's nice to have you here while we walk through this.
Find the amount of waste.
Okay, so now we're just looking at waste for harvested products.
Is that correct?
Yeah.
Because if you take all the different stages, you know, the analysis you get is kind of
murky so if you break it up by the stages you get a sort of a clearer picture.
Can we describe just the waste?
Okay so do we know what the unit of measure is by any chance?
So at this point it should be grams.
There's a point where I converted to kilograms but I don't think, yeah that's not here yet.
Because some of the numbers are really huge and it just sort of made more sense to present
it in kilograms.
Here we are with your analysis.
So we've sort of skipped over some things but like I said you've done some thorough
analysis here.
So here are some charts Charles has presented or prepared.
You have the full data set here.
So there's a lot of data that's, you know, where all the dates are 11, 1900 which of
course doesn't make any sense, doesn't really help.
There's some pre-2012 dates and there's some pre-2018 dates and this data is sort of supposed
to be from 2018 forward.
So I cleared that out and I just kind of went from 2018 forward.
Question I see, did you eventually just basically abandon harvest date and just decide to go
with updated at date essentially?
Yes because if you go up I explain why I do that.
Yeah there's 87, almost 88 percent of the data has a harvest date before 2018.
So it's not really, you know, it's not a good date and there's a lot of harvested dates
that are 11, 1900.
So basically there's not really, they don't do any data checking when they add this data
to the database.
And Sala you may be able to speak to this but I think my experience with the traceability
system I would go exactly with updated at date because this is essentially going to
be a automatically generated number by the traceability system.
As you noted if they, you know, revisited their harvest at a later date, just say they
made an error and then they fix their error then the updated at time would change.
But I think that's a smaller error than whatever's going on here, right?
Because at the end of the day we're trying to minimize our sort of our bias or, you know,
our forecasting.
So when you have entry errors like this that actually introduces statistical bias into
your calculations.
So you know the closer you are to the true value the better.
And so yes, the updated at may, there may still be errors in updated at times but I
think as you settled on it's just going to be a much, I think it's going to be a much
reliable measure of time than the harvest date.
Yeah.
And that's why, you know, rule number one is look at the data.
So fantastic, fantastic work there Charles because all you really need to do is you plot
those and now we know how we can keep track of time.
So I think this is a real cool chart here where this is just showing the amount of waste
generated I think essentially are these like essentially.
By day.
Okay, so these are totals by day or by producer by day?
By day.
We get down to by producer later on.
Interesting.
So maybe some of these are when the larger producers harvest and there's a lot of waste.
Oh, cool charts here.
Are these new?
Probably I've been, you know, I've been, you know, sort of thinking about it and updating
it, you know, and like how can I improve it?
This is fantastic.
I haven't seen this chart yet.
So I think this you may have added this since I was looking at it because this is exactly
this is exactly what needed to be plotted.
So thank you Charles.
Like awesome work here.
This is essentially what everybody's been curious about.
I don't think anybody's plotted this.
In fact, I'll have to share an article with you, Charles.
There's an article in The Stranger.
Washington State has a I don't know like a I forget what the title is like Washington
State has a million ton waste problem or something like that.
I'll share the article with you.
And so essentially the author was calling around to like recycling places trying to
figure out like what the actual weight of waste is.
And he just sort of made estimates because like this data well he actually wrote this
article back in 2017.
Especially before this data was there.
As we found out the data is tough to wrangle.
So although the data is there and as you showed we can calculate them I don't know if anybody
has.
And so this Charles you really may be the first person who's plotted harvest weight
by month.
So good work.
And so this is it.
I'll just give you my hot take my first look here.
It's incredibly interesting to observe this this large amount in 2018.
And I want to say that that may be producers essentially entering their essentially initial
inventory into the traceability system.
Okay.
I wondered about that.
Actually if you go down to the next chart I cut off February and March and then I also
cut off January of 2021 because that was incomplete.
And then I thought the other two were outliers.
So I think this is more representative of the actual data.
Yes.
I think yeah I think I think you're right.
This is and this way the scale you have a better idea of the scale.
So just to explain I think so basically the traceability system was introduced in January
or February of 2018 and they had a contingency period where they had to essentially enter
all because these were companies that were already operational with the bio track traceability
system.
So the before leaf data systems there was bio track.
They're already operational and they're given a contingency period through roughly April
of 2018.
I'll actually want to double check the I'll have to do some because if you look at the
press releases they may have pushed back the contingency period but around April of 2018
it was sort of the deadline to get all your inventory into the system.
So that's real curious.
So maybe people just had like a bunch of waste sitting around and they just had to get that
entered.
If you look there's definitely a lot of pre 2018 dates in some of this data.
So I think yeah I think they just must have had entered data maybe from the previous year
or but yeah anyway just kind of those couple months sort of skewed things so I just decided
to drop them because if you drop them you sort of see kind of a trend.
This is where you get to the art of data science.
So one thing we're going to be getting to here is forecasting right.
So we'll want to forecast this forward you know 12 months you know to kind of predict
2021.
My technique of for it well not my technique if technique of forecasting is you know a
Rima forecasting box Jenkins where you basically you just take historic data and you basically
use statistics to calculate the sort of like a moving average and you just basically continue
the moving average out into the future.
So it's a theoretical and it's it forecasts fairly well with time series surprisingly
well so.
But one thing if you were doing box Jenkins forecasting if you included all these observations
in there you may get wonky forecasts because.
If you conclude these observations the box Jenkins may like put like this negative trend
on waste but if you really exclude those prior years and you just look at the more recent
data you know it's not really obvious there's a negative trend per se.
So you know it's not a big thing perhaps but not not like there was at first glance.
So just to kind of I guess look through some more of your analysis here I guess before
we get to forecasting.
So this is so I guess let's just look at the totals here so.
This is 30,000 kilograms.
It's a lot.
It's hard to quantify so.
What would be interesting analysis is so actually somebody at better carbon solutions recommended
this so.
I forget the amount of weight he said but basically.
To help visualize this you may want to think about like barrels so I forget the amount
of weight but like let's say you can like fit like 50.
Like say 50 pounds in a barrel or something like that it would be I think it would be
easier to visualize if you said oh there were you know a thousand barrels of waste.
In a given month because you know that way you know people could visualize sort of what
you know a thousand barrels of waste would look like like that would be like you know
fill up like a warehouse.
But when you just say you know 30,000 kilograms.
It's really hard to have any like I don't have I mean maybe some people they were a
bit more metric mindset may have a bit more mental framework for that but I can't really
visualize that.
But any who.
I was something else that just occurred to me that I don't know if it would contribute
to the outliers but at least when I was working at North with cannabis solutions.
The folks who finance and kind of like own everything were these Russian dudes and I
remember that one year they lost like over half their crop because they didn't know what
they were doing.
And so I don't know if that would contribute at all to sort of like their waste production
but that is something that just occurred to me is like I remember they had really serious
issues with growing and a lot of their crop just like wouldn't make it and they would
lose entire swaths of like plants at a time until they kind of dialed it in and got it
figured out.
And they're a pretty large producer as well from what I understand.
So it's possible that kind of thing could contribute but definitely for sure 30,000
kilograms is like a wild number even as somebody who has like worked in cannabis and seen literally
like entire dumpster sized like containers of like plant matter and stuff it's pretty
wild but.
Wow, that's interesting that kind of helps make sense of some of this data because there
are some like huge losses I can't, it's hard to picture but that does help.
That's a good anecdotal story from the ground floor because it helps.
That's what I think we were talking about last week is part of data science is telling
us the story of the data.
Right.
So that's like a real good story where you kind of highlight just some of these individual
days where oh like there were maybe like hundreds or thousands of grams of waste in a given
day and you're like you're trying to tell the story of what went on there.
And one thing Charles and Selle that would be an interesting analysis would essentially
you could essentially run a regression of this data on month.
So you would you basically do dummy variables for each month.
And so then when you run the regression you basically exclude one of the variables so
you'd you'd exclude January.
That way you would just basically be comparing everything to January but you can exclude
what every month you'd want but essentially see what months are the statistically highest
you know the statistically larger amount of waste.
Because I mean my Bayesian hypothesis would have been like October November but just naively
looking at the data like I mean these jump out to me here and this is like like late winter
spring January February March and then again over here you see a spike and then this is
this is like spring again where you've got but this is later spring like April May.
So I'm just curious if there's any seasonality.
You think it's related to 420?
Well you see sales spike but I'm curious if that would just be so strange for like waste
to spike.
Well I mean obviously they have to ramp up inventory right and it's not an out I mean
there's a lot of this is outdoors but there's so much indoor that it's basically you know
12 months a year you probably have some sort you know every month somebody has some harvest
assuming they've timed everything out.
So like I said when you run the and that's why the regression is interesting here is
because when you run the regression you know we may there may not even be there may not
even be a correlation for given months.
It's worth revisiting.
I couldn't really see much of a pattern it just I think like September through December
sort of were lower but like the peak months changed from year to year so I don't know
why that is.
It's worth revisiting.
Seasonality still something we're trying to wrap their heads around but it's a pickle
one.
So you've got the top producers here once again I think and that was just the what the
person at Better Carbon Solutions suggested and it was I think it was an interesting an
interesting advice because it wasn't something I would have thought of but you know stressing
on the story I think the barrels is a little bit of a you know almost have to have like
a legend that would say oh you know assuming one barrel is a hundred pounds or something
like that but that's how they they do gasoline right.
So but anyways it looks like there's really this pack of one two three.
It's really like this pack of seven who were like out in the lead and then you know it
looks like these are well it looks like this is almost a secondary pack here you know another
pack of six then you know these these may just be like you know these firms look similar
in size you know this is real interesting.
And just look at some of the rest of your analysis here while we've got a bit of time
but this is my first time seeing it so pardon me for for sort of my hot takes but we'll
see what you with the data you've wrangled up here.
This is so cool Charles this is the first analysis I've ever seen.
And it is like even to like you know a casual like me this is like I can still understand
and glean like what you're getting at.
So I think that speaks to your ability.
Thank you.
Interesting so here you basically have the top five.
So I don't understand this concept of growing waste like what does that mean?
Oh this is like that's basically like I mean so you may know better than I but I think
that's basically when they're just sweeping up like plant matter off of the floor.
Yeah there's when you grow cannabis one of the great enemies of cannabis like production
is mold and so there's like a lot of defoliation that happens in the growing process to ensure
the highest like exposure of bud sites to light because that's like you know where the
money is.
And so and sometimes you know they cull plants that just aren't making it or there is definitely
like growing waste produced in the life cycle of the plant as well.
Okay thanks that yeah I mean to me this is a bunch of data I don't always know what these
terms mean and it's hard for me to wrap my head around what is it you know what am I
actually looking at and what am I looking for?
And I like I said like I wasn't super involved in growing or anything like that so it's possible
that the growing waste is coming from like a specific practice industry practice or some
kind of thing that they're doing but that's what I would assume that they're referring
to anyway.
It looks like the entry of it fell off so does it look like they're entering growing
waste anymore doesn't it?
Right although it's not listed as depreciated in the manual so but it looks like it's being
depreciated.
Okay so it may not be required to record it anymore so we'll have to double check.
In flower waste there was like three entries and so again what is that and obviously they're
not tracking it or those are mislabeled entries and then the seedling waste also falls off.
It also falls off and then at one point there's like 30,000 kilograms of seedlings I mean
how much does a seedling weigh?
30,000 kilograms I mean that would be like that would be a lot of seedlings.
Yeah for sure.
It looks like okay this would make sense in July of 2019 they updated the traceability
system and so at that time I think is when they deprecated certain fields and it looks
like they essentially just deprecated needing to record that waste and my guess is some
people just kept on recording it even though they didn't have to but then they kind of
failed off.
Okay that's helpful that explains a lot of what I'm seeing.
And similar thing here so growing waste so they're recording it up to July of 2019 and
then that one trails off.
So really what we just need to focus in here is this pervist waste.
Out of curiosity do you know how strict they are about requiring producers to, sorry low
key cat emergency.
Maybe try to catch a bird through the window that was dumb.
Sorry do you know how strict they are about requiring producers to report these numbers
and like whether or not they audit them or if there's like requirements by the state
for that?
I don't off the top of my head I don't know.
That's an excellent question.
Yeah I was just wondering it's not that I expect you to know that necessarily but I
feel like you know people when their when homework is assigned and required and part
of your grade it's a little different.
So I know for sure that like at least when I was working for Northwest Canada Solutions
they didn't always do it by the book you know and so I'm wondering if how reliable some
of the numbers could be or if they don't like penalize any of the growers like or incentivize
their reporting for certain numbers like how that would affect the overall outcome of the
numbers.
I think that's an excellent excellent point and I think that's and that sort of drives
home sort of a recurring theme of the meetup group is so we haven't actually prepared any
forecasts yet but that's one thing that I always always hinge my forecasts because as
you noted it you know junk data in you know junk data out so it's sort of it's true.
You can you know statistically show that if there is measurement error then that does
bias your results it depends on the amount of measurement error so you know a small amount
of measurement error doesn't really lead to too much bias but as you noted I mean if you
know if people are just eyeballing this or if you have entire licensees who may not even
be entering it I mean these numbers could be unreliable.
So I think I think that's an excellent observation and should always be taken into consideration
so if you were doing analysis of this or if you were presenting forecasts I think you
should definitely edge this you should say okay producers are principally care you know
they principally care about selling their cannabis so they're probably going to record
their you know price, labor, weight.
So I think if any good analysis should bring that up just to counter I just always feel
like well I heard somewhere that you know if you're not measuring it you're not managing
it so you know any you know sometimes any measure is better than no measure so I think
you should take take it on face value so these numbers could be wild at least we have some
numbers so that's where you know where you get your confidence intervals so at least
we have a measure of how much waste may be being produced and we can kind of see where
it fluctuates and so like if you were doing a monthly analysis perhaps the entry error
is the same month to month to month and then you could just say oh well we can still estimate
the month effect but we just don't know the magnitude right or we're biased in the magnitude
but you know our month effect you know that wouldn't be biased so but that takes a little
hand waving so I you know I think there's two sides to the coin you know it's basically
yeah you I think you need to acknowledge that there's noise in the data incredibly imperfect
data and the producers don't even really have the incentive to enter it in accurately but
you know we need some measure of the waste. Having spent a lot of time you're going to
chime in Charles. There's no way that anybody could audit anybody I mean it is really really
messy I should I need to publish that the amount of time it takes for like lab results
because I got to a point with it where I was like you know I'm just making stuff up because
I'm just having to fiddle with this data so much to make something out of it but it's
just you know it's just a made-up story yeah there's no way this is really the data is
really inconsistent there doesn't seem to be anything that there doesn't seem to be
any requirements of like you know like checking the data when it goes into the database I
think people enter whatever and I think that they enter stuff like they don't enter it
daily or some people might enter it daily some people might enter it weekly some people
might enter it monthly which I think maybe explains some of those you know where the
spikes in the amount of waste change like from April to May because people it's just
depending on when they entered the data totally I feel like if anything it's an argument to
you know encourage or advocate for that sort of like I guess incentivization of like reporting
that information just because like I totally get that I think you it's really clear even
to me based on like the really helpful charts and graphs that you've made that like yeah
there is you know as Keegan said like a lot of noise in the data and it would probably
be beneficial on a lot of different levels to require and incentivize producers to report
that more accurately and like for sure based on my personal experience like I said just
working for that one producer I think you can bet your bottom dollar that it's your
your mileage will vary like I really can't imagine that they were reporting a lot of
the stuff accurately or consistently based on kind of like how they were running things
when I was there and how they have been running things before I was there so I think you're
totally right and I feel like if anything it's an argument to kind of like advocate
for that stuff to have better data in the future.
Yeah so Oregon is supposed to be really strict but I've never seen their their actual raw
data.
So those are excellent points across the board and yeah like you said show us the data because
you're right it would be like for Oregon it'd be interesting because it does seem like they
have a good handle on their data but I'm curious yeah to the amount of noise but you know it
just takes good data wranglers and so that's why Charles when you're doing your analysis
it's so good for you to explain like you know why things are so messy and the effects of
it like you said it you know it's going to be tough to audit anything because you know
it doesn't seem like there's much validation of the data.
You know for example like a like a like a valid harvest date for example.
So I think your analysis here has been fruitful and I think there's you know a lot of lessons
learned here with with with data analysis you know you spend you know 90 plus percent
of your time just working with the data and you know you really need to be upfront with
your audience about you know what steps you took you know like Charles was saying like
with the lab result data like you know you can't just you know you can't just squeeze
things into a box and like you know just you know wave your hands about the things that
don't make sense you know you have to be upfront with your audience about okay we've got maybe
not the best data entry here or you know not the best incentives to enter the right data
you know the data entry may be delayed but I think I'm just just reiterating what you
two have already said here.
But anyways I think you two actually captured the heat of things the meat of things so I
think I'll go ahead and wrap it up there for the day and just you know let people get on
and start exploring the data on their own but for next week we can work on some forecasting
and I'm going to have a look at you know wrangling this data myself because I want to sort of
replicate Charles's analysis because you've done an excellent job.
Thanks.
Yeah thanks for sharing Charles this is super duper cool I learned a lot today y'all thanks
so much for walking me through what y'all do on the regular.
Oh yeah thanks for joining Selle we just sometimes we get a bit more hands on but like this data
set is giant so we're a little limited in scope but it was it's awesome that Charles
put together these charts because rule number one look at the data so that's what we did
today.
And they are beautiful charts but anyways in the future you know if things are more
hands on I definitely don't mind observing you and Charles kind of in your like natural
I guess environment working on stuff and collaborating because I would also find that helpful so
don't please don't feel like you have to teach to the back of the class so to speak.
I'm just really excited to kind of have exposure just because in my personal like tech journey
a lot of it is just like studying JavaScript or studying whatever it is and studying I'm
still trying to learn a passion for something so I think this is definitely a step in that
right direction especially in terms of like exposure for specialization in the future
so I really appreciate it.
Well it's entirely open so if we want to look at charts and talk about Charles's analysis
by all means are you going to chime in Charles?
Yeah and really your insights from working you know in the industry that was really helpful
I mean there's you know because when I look at this data I don't you know again I don't
have anybody to go to and ask you know like why is this or but you know you gave me some
good insight and now I kind of I can have a better understanding of what's going on.
Well I'm glad I could help in my own way so you know let me know if there's anything else
I can do on the sort of like off time in terms of like getting a little more familiar I'm
definitely like kind of rubbing my hands together and looking at Python as like my next language
I'd like to learn but given how JavaScript is going that may be in some time yet so we'll
just have to see.
I think Python is easier.
I don't know but it looks super I love I honestly love optimizing stuff and I love automating
things tasks as much as possible and so Python is looking pretty delectable these days so
it's really cool to see the kind of heavy lifting that y'all are accomplishing with
it so I definitely am starting to like feel those kinds of like connections hip happening
and that's super exciting just because I'm not gonna lie just learning JavaScript in
a vacuum is kind of a slog so getting to see actual practical applications for various
languages is like super exciting and very energizing so I really really appreciate it.
There's room in the world for both so they you know you know you got a hammer you got
a screwdriver you know so it's you know they each have their own they have their own purposes
definitely yeah very very cool y'all thank you so much again for your time awesome great
job Charles this thing was so cool.
Yeah thank you.
All right y'all catch you next week.
Have an awesome day until next week.
Thanks you too.
Bye.