1 00:00:00,000 --> 00:00:12,280 Well, welcome to the Cannabis Data Science Meetup Group. 2 00:00:12,280 --> 00:00:13,920 It's good to have you both. 3 00:00:13,920 --> 00:00:16,120 So happy to have you, Kyron. 4 00:00:16,120 --> 00:00:22,360 So I'll introduce myself first and then I'd love to hear about what brings you to the 5 00:00:22,360 --> 00:00:23,520 group. 6 00:00:23,520 --> 00:00:25,680 So my name is Keegan. 7 00:00:25,680 --> 00:00:28,720 So I started a company, Candlytics. 8 00:00:28,720 --> 00:00:33,000 And so we principally provide software to laboratories. 9 00:00:33,000 --> 00:00:38,280 And being in this space, we realize there's a high demand for analytics. 10 00:00:38,280 --> 00:00:41,520 And coincidentally, that's what my background is in. 11 00:00:41,520 --> 00:00:45,920 So just thought, OK, time to lend a hand to everyone. 12 00:00:45,920 --> 00:00:52,280 So we help everyone from labs to producers, processors, retailers, crunch numbers, move 13 00:00:52,280 --> 00:00:56,800 data around, just make life simple and easy. 14 00:00:56,800 --> 00:00:58,680 So that's what we do. 15 00:00:58,680 --> 00:01:04,760 And so every Wednesday, we do a Cannabis Data Science Meetup Group where we basically get 16 00:01:04,760 --> 00:01:14,320 some real cannabis data, look at some statistics to show, OK, this is how you would do things. 17 00:01:14,320 --> 00:01:18,760 These are some statistics that you can calculate with data that's out there. 18 00:01:18,760 --> 00:01:22,680 And just have fun, pick each other's brains. 19 00:01:22,680 --> 00:01:24,880 So that's what we're about. 20 00:01:24,880 --> 00:01:29,560 And so I'd love to hear about your angle. 21 00:01:29,560 --> 00:01:33,600 What's your background, Kyron? 22 00:01:33,600 --> 00:01:40,640 Well, I actually am in a data analytics boot camp. 23 00:01:40,640 --> 00:01:45,360 And for one of my mock roles, it's to get started on networking. 24 00:01:45,360 --> 00:01:51,920 Because I'm like an alternative route into the analytics field. 25 00:01:51,920 --> 00:01:56,360 That's going to be a little bit more difficult for me since I have a degree backing it up. 26 00:01:56,360 --> 00:02:00,160 So this is actually my first meetup that I'm doing on the subject. 27 00:02:00,160 --> 00:02:04,800 And I picked this one because it kind of sounds interesting enough to learn about. 28 00:02:04,800 --> 00:02:07,680 But other than that, there really isn't much. 29 00:02:07,680 --> 00:02:12,840 I'm just here to sell it all in. 30 00:02:12,840 --> 00:02:18,680 I think it's one of the, if you're coming from a data perspective, it's one of the best 31 00:02:18,680 --> 00:02:25,200 subjects you could be studying because, believe it or not, there's no data like it. 32 00:02:25,200 --> 00:02:28,800 No one's got public data like cannabis data. 33 00:02:28,800 --> 00:02:37,840 So because the states want to keep a good measure on the cannabis operations, cannabis 34 00:02:37,840 --> 00:02:41,840 activity, because this is something that used to be illegal. 35 00:02:41,840 --> 00:02:45,840 So they want to keep a good eye on it. 36 00:02:45,840 --> 00:02:53,600 And so nobody's tracking tomatoes from seed to sale. 37 00:02:53,600 --> 00:02:59,240 Well maybe to a certain extent, but not to the extent that they are with cannabis. 38 00:02:59,240 --> 00:03:06,880 And then on top of that, a lot of these states are really forthcoming with their data. 39 00:03:06,880 --> 00:03:09,840 They've got public data. 40 00:03:09,840 --> 00:03:14,840 And for the most, the biggest hurdle is just the technical hurdle. 41 00:03:14,840 --> 00:03:17,520 They're happy to provide all the data. 42 00:03:17,520 --> 00:03:20,880 It's just all their mechanisms in place. 43 00:03:20,880 --> 00:03:22,600 And some states have awesome mechanisms. 44 00:03:22,600 --> 00:03:25,840 And so we're going to be exploring some of those today. 45 00:03:25,840 --> 00:03:31,800 So up in the Northeast, like for example, today we'll look at Massachusetts and Finnish 46 00:03:31,800 --> 00:03:32,800 Connecticut. 47 00:03:32,800 --> 00:03:37,320 They've got their data readily available through an API. 48 00:03:37,320 --> 00:03:44,560 So you can just tap in, get some of these cases real time data. 49 00:03:44,560 --> 00:03:49,640 So Connecticut basically is real time data. 50 00:03:49,640 --> 00:03:54,080 And it calculates statistics to your heart's content. 51 00:03:54,080 --> 00:03:55,080 So it's fun. 52 00:03:55,080 --> 00:03:57,200 It's a good space to be in. 53 00:03:57,200 --> 00:03:58,200 Cool. 54 00:03:58,200 --> 00:03:59,200 Sounds good. 55 00:03:59,200 --> 00:04:08,720 So well, without further ado, let's just jump into it. 56 00:04:08,720 --> 00:04:14,240 Because yeah, you know, show don't tell. 57 00:04:14,240 --> 00:04:22,080 So OK, so just to give you a little introduction here. 58 00:04:22,080 --> 00:04:39,960 So we're coming from the we've got a repository here where you can find each week's code and 59 00:04:39,960 --> 00:04:45,480 data that way you can follow along. 60 00:04:45,480 --> 00:04:52,600 So basically last week we were looking at cannabinoids in Connecticut. 61 00:04:52,600 --> 00:05:01,840 And so I figured, OK, before we move on, let's just go ahead and finish out this analysis 62 00:05:01,840 --> 00:05:07,280 with Connecticut data just to be thorough. 63 00:05:07,280 --> 00:05:15,800 So let's essentially do just that. 64 00:05:15,800 --> 00:05:23,240 So here's the script we were working with last time that I've tidied up a little bit. 65 00:05:23,240 --> 00:05:31,680 And so just to remind everyone from last week and then to show you, Kyron, this is the data 66 00:05:31,680 --> 00:05:34,080 source. 67 00:05:34,080 --> 00:05:38,880 So data.ct.gov. 68 00:05:38,880 --> 00:05:51,320 And then you can find your way to the medical cannabis brand registry where they essentially 69 00:05:51,320 --> 00:06:02,680 have real time or at least up to the past day entries for what appears to be every product 70 00:06:02,680 --> 00:06:04,160 sold in Connecticut. 71 00:06:04,160 --> 00:06:10,520 I could be wrong, but it appears to be every product. 72 00:06:10,520 --> 00:06:23,080 So if you're going into the store and you're getting, you know, one of these, you know, 73 00:06:23,080 --> 00:06:35,060 if you're getting Abiflex flower, then you can actually get the image. 74 00:06:35,060 --> 00:06:39,080 So you went and bought some Abiflex flower. 75 00:06:39,080 --> 00:06:43,560 And then you can actually see the certificate of analysis. 76 00:06:43,560 --> 00:06:50,560 So you can actually see, OK, you know, this sample of data passed quality assurance testing, 77 00:06:50,560 --> 00:06:56,080 test microtoxins, you know, there's no heavy metals detected. 78 00:06:56,080 --> 00:07:00,120 You know, there's your cannabinoids and your terpenes. 79 00:07:00,120 --> 00:07:09,340 So and it's verified, signed off on by the lab director here. 80 00:07:09,340 --> 00:07:13,400 So we've got nice official records. 81 00:07:13,400 --> 00:07:21,160 So this is this is a pretty pristine data set, right, because, you know, if if there's 82 00:07:21,160 --> 00:07:28,760 any confusion with the numbers, so for whatever reason, if there was any confusion with any 83 00:07:28,760 --> 00:07:36,120 of these data points, well, you can just refer back to the certificate if you need to. 84 00:07:36,120 --> 00:07:39,720 So cool data set. 85 00:07:39,720 --> 00:07:41,920 They have made it easily accessible. 86 00:07:41,920 --> 00:07:48,960 So you can just come here, you can download it or. 87 00:07:48,960 --> 00:07:55,000 As we like to do when possible, so you can automate things, we can access it through 88 00:07:55,000 --> 00:07:57,040 an API. 89 00:07:57,040 --> 00:08:08,520 So so what's cool here is we'll use the Socrata API. 90 00:08:08,520 --> 00:08:13,160 And with Connecticut, we'll use this package here. 91 00:08:13,160 --> 00:08:20,720 And then the second half of the meetup, we'll get data from Massachusetts and we'll do things 92 00:08:20,720 --> 00:08:21,720 manually. 93 00:08:21,720 --> 00:08:28,600 So here we'll use a package and then in Massachusetts, Massachusetts will just do things manually 94 00:08:28,600 --> 00:08:31,520 just to show you both ways. 95 00:08:31,520 --> 00:08:38,480 So without further ado, let's get this data. 96 00:08:38,480 --> 00:08:52,400 So I'm not as familiar with DS codes. 97 00:08:52,400 --> 00:09:00,800 Console here, terminal, but we'll experiment. 98 00:09:00,800 --> 00:09:07,200 And then I just wrote just a quick charting function. 99 00:09:07,200 --> 00:09:11,800 We'll talk about this when we get to it. 100 00:09:11,800 --> 00:09:14,760 So just a little background work. 101 00:09:14,760 --> 00:09:21,680 I basically looked at all the data points and just defined, OK, these are cannabinoids. 102 00:09:21,680 --> 00:09:24,480 These are terpenes. 103 00:09:24,480 --> 00:09:36,640 So we can define those. 104 00:09:36,640 --> 00:09:44,400 Next it's real easy to get data from this endpoint here. 105 00:09:44,400 --> 00:09:50,960 So essentially all you need to know is this ID right here. 106 00:09:50,960 --> 00:09:55,160 That's essentially the data set ID. 107 00:09:55,160 --> 00:09:58,080 And there we have it right there. 108 00:09:58,080 --> 00:10:13,200 And we can probably get this data. 109 00:10:13,200 --> 00:10:17,040 See if we have any data. 110 00:10:17,040 --> 00:10:19,320 And so there we are. 111 00:10:19,320 --> 00:10:26,160 So there are 10,859 observations at this moment. 112 00:10:26,160 --> 00:10:29,200 And we have read those in with the API. 113 00:10:29,200 --> 00:10:36,600 And so what's cool is as they add records, you can get those through the API. 114 00:10:36,600 --> 00:10:43,320 So you can always have the latest data. 115 00:10:43,320 --> 00:10:50,200 And then you can dive into this code or watch last week's meetup. 116 00:10:50,200 --> 00:10:58,640 But this is basically what we did last week was we spent the majority of the time parsing 117 00:10:58,640 --> 00:11:02,760 the data and getting it into a nice clean format. 118 00:11:02,760 --> 00:11:10,240 So although it's maybe less than 15 lines of code now, this is what we spent the bulk 119 00:11:10,240 --> 00:11:13,560 of our time on last week. 120 00:11:13,560 --> 00:11:23,400 So that's nice that we can just move forward this week with clean data. 121 00:11:23,400 --> 00:11:33,200 And just to show you what does the data look like. 122 00:11:33,200 --> 00:11:42,480 We've got our brand. 123 00:11:42,480 --> 00:11:47,160 We've got the approval date. 124 00:11:47,160 --> 00:11:54,920 And now we've just cleaned all of the cannabinoids, terpenes. 125 00:11:54,920 --> 00:12:02,320 And we added these two columns, total cannabinoids and total terpenes. 126 00:12:02,320 --> 00:12:07,480 Because all those numbers weren't present in the data set. 127 00:12:07,480 --> 00:12:20,840 These numbers, if we look at one of these certificates, I would not be the least bit 128 00:12:20,840 --> 00:12:27,520 surprised to find total cannabinoids. 129 00:12:27,520 --> 00:12:33,760 Well it does not look like total cannabinoids or total terpenes are reported on these COAs. 130 00:12:33,760 --> 00:12:41,040 So perhaps in Massachusetts they aren't. 131 00:12:41,040 --> 00:12:47,080 However in many states you'll see total terpenes and total cannabinoids reported on the certificate 132 00:12:47,080 --> 00:12:49,680 of analysis. 133 00:12:49,680 --> 00:12:54,000 And so this number doesn't make sense, right? 134 00:12:54,000 --> 00:12:57,260 Because you can't have cannabinoids above 100. 135 00:12:57,260 --> 00:13:02,920 So we'll have to account for situations like this. 136 00:13:02,920 --> 00:13:15,320 Where in general we can now have a pretty good measure of those animals. 137 00:13:15,320 --> 00:13:18,400 Now we want to look at the data. 138 00:13:18,400 --> 00:13:28,580 So what we wanted to do last week was we basically said, OK, what is the prevalence of each terpene? 139 00:13:28,580 --> 00:13:32,120 So how often can it be found? 140 00:13:32,120 --> 00:13:38,360 How often does it occur in products? 141 00:13:38,360 --> 00:13:40,880 In Connecticut actually. 142 00:13:40,880 --> 00:13:43,440 This is in Massachusetts. 143 00:13:43,440 --> 00:13:56,760 So this is code we did last week where we basically, we just, the magic here is we just 144 00:13:56,760 --> 00:14:05,680 calculate the prevalence, which is basically the length, so the number of observations 145 00:14:05,680 --> 00:14:16,720 where we have an analyte present divided by the total number of observations. 146 00:14:16,720 --> 00:14:26,920 And so let's show this data sooner rather than later. 147 00:14:26,920 --> 00:14:31,560 Here we are. 148 00:14:31,560 --> 00:14:42,320 Didn't think we were going to be plotting this quite yet. 149 00:14:42,320 --> 00:14:57,360 Anyways, this was basically this chart here, which I have the source code for here, this 150 00:14:57,360 --> 00:14:58,360 chart. 151 00:14:58,360 --> 00:15:07,360 But basically I just tried to generalize to basically, you pass your data frame and a 152 00:15:07,360 --> 00:15:16,280 handful of parameters, and then this generates a fairly good looking chart. 153 00:15:16,280 --> 00:15:25,240 Just as you can tell, sometimes it takes a few lines of code here to make a good chart. 154 00:15:25,240 --> 00:15:32,200 So I encourage you to look through this code on your own if you're interested in making 155 00:15:32,200 --> 00:15:35,380 good looking Matplotlib charts. 156 00:15:35,380 --> 00:15:43,160 So but at the moment I've just sort of abstracted this away for simplicity here because this 157 00:15:43,160 --> 00:15:46,440 isn't really the focus of the meetup. 158 00:15:46,440 --> 00:15:54,800 You're welcome to look at this code here to see how to make the chart. 159 00:15:54,800 --> 00:16:00,760 Anyways, we calculated the data. 160 00:16:00,760 --> 00:16:07,520 So for each analyte here, we've calculated its prevalence, and we've also calculated 161 00:16:07,520 --> 00:16:11,160 its average concentration. 162 00:16:11,160 --> 00:16:17,040 And so just plotted the top 10. 163 00:16:17,040 --> 00:16:24,680 And so as you can see, beta-karyonfylene is in about half of the samples. 164 00:16:24,680 --> 00:16:33,200 Then you have this next group here, which is in about 30%, 30% to 33% of all samples, 165 00:16:33,200 --> 00:16:40,080 the linalool, limonene, humulene, betamersene. 166 00:16:40,080 --> 00:16:49,280 And so betamersene, I believe, is what you may find in lavenders. 167 00:16:49,280 --> 00:16:54,960 So betamersene, betakaryophyllene, I think, are going to be more prevalent in what you 168 00:16:54,960 --> 00:16:57,920 typically would call an indica strain. 169 00:16:57,920 --> 00:17:02,760 And then the limonene, limonool, those may be more present in what you would call a sativa 170 00:17:02,760 --> 00:17:03,760 type strain. 171 00:17:03,760 --> 00:17:09,400 And then these other terpenes, I don't know as much about. 172 00:17:09,400 --> 00:17:19,240 Beta-pinene, alpha-pinene, alpha-bizabolol, osemine, trans-nerolidal. 173 00:17:19,240 --> 00:17:21,440 You do not know as much about these terpenes. 174 00:17:21,440 --> 00:17:26,680 However, they appear in a non-negligible portion of the samples. 175 00:17:26,680 --> 00:17:31,640 So there may be something special going on about these. 176 00:17:31,640 --> 00:17:39,680 So people may be chopping around through these terpenes without knowing it. 177 00:17:39,680 --> 00:17:49,160 So for example, osemine, it would be interesting to dive into the samples that contain osemine. 178 00:17:49,160 --> 00:17:59,600 And see if there is a particular strain or what have you that explains this. 179 00:17:59,600 --> 00:18:02,080 So maybe it's popular. 180 00:18:02,080 --> 00:18:05,080 Maybe it's not popular. 181 00:18:05,080 --> 00:18:09,960 So that's what we essentially did last week. 182 00:18:09,960 --> 00:18:18,280 But I just wanted to run through it again this week just to show you the cleaner code 183 00:18:18,280 --> 00:18:23,680 and we can add on top of it real quick. 184 00:18:23,680 --> 00:18:28,480 So here are some other charts, some of them useful, some of them not so useful, that you 185 00:18:28,480 --> 00:18:30,240 can also make. 186 00:18:30,240 --> 00:18:34,560 And these ones I didn't beautify, I just made. 187 00:18:34,560 --> 00:18:38,000 But they're interesting nonetheless. 188 00:18:38,000 --> 00:18:50,800 So this next one, it was like, OK, how do these terpenes occur with each other? 189 00:18:50,800 --> 00:18:55,320 So that's not what we wanted. 190 00:18:55,320 --> 00:19:00,640 I'm going to run this interactive. 191 00:19:00,640 --> 00:19:11,160 OK, so this is honestly, I wouldn't call this the most informative figure in the world, 192 00:19:11,160 --> 00:19:13,680 but it does have some insights. 193 00:19:13,680 --> 00:19:21,980 So what this is, is this is a choreogram of terpenes in Connecticut. 194 00:19:21,980 --> 00:19:27,960 So this is how correlated various terpenes are with each other. 195 00:19:27,960 --> 00:19:36,760 So what you can think of this is the darker the color, the more common these terpenes 196 00:19:36,760 --> 00:19:38,680 are occurring with each other. 197 00:19:38,680 --> 00:19:41,760 So you can just look at some dark colors here. 198 00:19:41,760 --> 00:19:43,360 So here's one. 199 00:19:43,360 --> 00:19:55,720 So it looks like, OK, this beta-phardazine occurs quite often with phelan... 200 00:19:55,720 --> 00:19:58,920 Phelan... phelandrine. 201 00:19:58,920 --> 00:20:01,200 I'm probably not pronouncing that correctly. 202 00:20:01,200 --> 00:20:04,200 So is that a good insight or is that not a good insight? 203 00:20:04,200 --> 00:20:07,200 I'm not certain. 204 00:20:07,200 --> 00:20:11,360 But there's some interesting ones here. 205 00:20:11,360 --> 00:20:17,480 And so I don't know how these could particularly help you, but maybe they could. 206 00:20:17,480 --> 00:20:18,880 So like, here's another one. 207 00:20:18,880 --> 00:20:29,840 So it's like, OK, for whatever reason, menthol and it looks like puligol looks like those 208 00:20:29,840 --> 00:20:32,520 are correlated with each other. 209 00:20:32,520 --> 00:20:36,520 So that's interesting. 210 00:20:36,520 --> 00:20:46,760 So maybe if you notice that, OK, samples that have menthol perhaps have this particular 211 00:20:46,760 --> 00:20:59,280 taste, who knows what type of strains contain menthol or puligol or what they smell or taste 212 00:20:59,280 --> 00:21:00,280 like. 213 00:21:00,280 --> 00:21:04,520 But perhaps if that was something you were looking for, maybe you could use this data 214 00:21:04,520 --> 00:21:09,960 to your advantage. 215 00:21:09,960 --> 00:21:15,360 Maybe some of these like beta-karyophylline, that would be an interesting one. 216 00:21:15,360 --> 00:21:20,600 OK, so what shows up a lot with beta-karyophylline. 217 00:21:20,600 --> 00:21:28,360 So you may be able to use this when you're selecting strains, if you're trying to breathe 218 00:21:28,360 --> 00:21:32,560 for a particular terpene. 219 00:21:32,560 --> 00:21:33,800 Hard to say. 220 00:21:33,800 --> 00:21:39,560 So honestly, the chart's not as informative as I was... 221 00:21:39,560 --> 00:21:40,560 I don't know. 222 00:21:40,560 --> 00:21:41,560 It exists. 223 00:21:41,560 --> 00:21:55,400 So if you have any insights from this figure, by all means, by all means share. 224 00:21:55,400 --> 00:21:56,400 Moving on. 225 00:21:56,400 --> 00:22:00,920 So I figured, OK, that wasn't the most informative figure in the world. 226 00:22:00,920 --> 00:22:03,320 With another way we can look at this data. 227 00:22:03,320 --> 00:22:06,280 Well we've looked at the terpenes. 228 00:22:06,280 --> 00:22:10,240 Let's look at the cannabinoids. 229 00:22:10,240 --> 00:22:20,560 So what we've looked at in past weeks, particularly in hemp, is OK, what's the CBD to THC ratio? 230 00:22:20,560 --> 00:22:24,040 So let's do just that. 231 00:22:24,040 --> 00:22:37,160 So here we're just going to do a scatter plot of CBD to THCA. 232 00:22:37,160 --> 00:22:44,080 This figure could probably be refined. 233 00:22:44,080 --> 00:22:48,360 So one thing we're doing here is we're just lumping all the sample types together. 234 00:22:48,360 --> 00:22:56,520 It would probably be fruitful to separate out the different sample types, so that way 235 00:22:56,520 --> 00:22:59,080 you're comparing apples to apples. 236 00:22:59,080 --> 00:23:04,880 So what I always like to do is just start with flour, if that's what you're interested 237 00:23:04,880 --> 00:23:11,000 in looking at, to just look at flour samples. 238 00:23:11,000 --> 00:23:17,720 So a lot of times here in the cannabis data science group, we're more about do rather 239 00:23:17,720 --> 00:23:19,780 than talk. 240 00:23:19,780 --> 00:23:25,800 So why don't we see if we can do that? 241 00:23:25,800 --> 00:23:32,000 So the way I would go about doing that is first we want to remind ourselves, OK, what 242 00:23:32,000 --> 00:23:36,320 data points do we actually have here? 243 00:23:36,320 --> 00:23:41,920 A bunch of them. 244 00:23:41,920 --> 00:23:54,640 But it looks, oh yes, that's right, it may be hard for us to isolate. 245 00:23:54,640 --> 00:24:17,840 So I think usage form is, yes, so there's many different types of product categories. 246 00:24:17,840 --> 00:24:25,800 So not to go too deep into this, but I'll give it 30 seconds here. 247 00:24:25,800 --> 00:24:39,600 Let's see if we can just say, OK, we'll say the flour data is the data where the data 248 00:24:39,600 --> 00:24:49,640 is stored in the dosage form as a string. 249 00:24:49,640 --> 00:24:55,200 We want it, I'm not sure if this is going to work, but this is what I would like to 250 00:24:55,200 --> 00:25:05,200 do where the dosage form as a string that's lowered. 251 00:25:05,200 --> 00:25:10,400 This is not going to work, contains flour. 252 00:25:10,400 --> 00:25:14,920 That's what I would love to be able to do. 253 00:25:14,920 --> 00:25:19,120 We're going to have to hit Google real quick. 254 00:25:19,120 --> 00:25:43,000 So we were just doing this last week where we were saying get data, get contains. 255 00:25:43,000 --> 00:25:52,520 OK, there's actually another way we could potentially do this. 256 00:25:52,520 --> 00:25:58,080 OK, so let's see if we can do. 257 00:25:58,080 --> 00:26:05,880 OK, so we can actually do this and we can actually say, OK, let's just get everything 258 00:26:05,880 --> 00:26:15,800 where it contains flour or it contains flour. 259 00:26:15,800 --> 00:26:19,480 Awesome. 260 00:26:19,480 --> 00:26:24,840 So let's see if this makes sense. 261 00:26:24,840 --> 00:26:39,240 So like I said, if you're doing this analysis for research purposes or any serious endeavors, 262 00:26:39,240 --> 00:26:41,600 you'll want to double check all of this stuff. 263 00:26:41,600 --> 00:26:45,920 A lot of times here in the Canvas Data Science group, we're just moving quick, just sort 264 00:26:45,920 --> 00:26:47,980 of doing proof of concepts. 265 00:26:47,980 --> 00:26:54,200 So if you're doing this on your own, you're going to want to dive in here and look at 266 00:26:54,200 --> 00:26:59,920 the flour data and make sure that this makes sense to you. 267 00:26:59,920 --> 00:27:05,360 OK, these are flour observations. 268 00:27:05,360 --> 00:27:12,280 The cannabinoids should all be less than 30, 35 percent. 269 00:27:12,280 --> 00:27:18,160 I'm instantly wondering what's going on with these cannabinoids. 270 00:27:18,160 --> 00:27:25,000 So something's either going wrong with their total cannabinoid calculation or something's 271 00:27:25,000 --> 00:27:27,120 going on there. 272 00:27:27,120 --> 00:27:32,960 Looks like in some cases it's getting it right, some cases what's going on. 273 00:27:32,960 --> 00:27:39,520 So if you're doing this on your own, you're going to want to dive in and figure out, OK, 274 00:27:39,520 --> 00:27:42,640 this is an oil syringe. 275 00:27:42,640 --> 00:27:53,080 This needs to be excluded from the flour data. 276 00:27:53,080 --> 00:28:03,960 Just since we're just moving quick and breaking things, I'll just make our scattered plot 277 00:28:03,960 --> 00:28:08,640 here of flour data. 278 00:28:08,640 --> 00:28:12,280 And then we'll move on to new things. 279 00:28:12,280 --> 00:28:27,280 So we'll just do the scattered plot here. 280 00:28:27,280 --> 00:28:36,800 Looks like I messed something up. 281 00:28:36,800 --> 00:28:42,600 That's probably it. 282 00:28:42,600 --> 00:28:50,600 OK, so I think that's about as much time as we'll dedicate to this particular endeavor. 283 00:28:50,600 --> 00:28:59,680 But here we tried to just isolate the flour data. 284 00:28:59,680 --> 00:29:08,840 You could potentially, this is another little ad hoc thing, we could just say, oh, we'll 285 00:29:08,840 --> 00:29:15,840 just get everything with the total cannabinoids. 286 00:29:15,840 --> 00:29:24,160 Let's just say less than 40, just in case there's some high things in there. 287 00:29:24,160 --> 00:29:31,040 So this is an ad hoc way of just getting those oils out of there. 288 00:29:31,040 --> 00:29:39,400 But similar thing going on where, OK, it looks like there's maybe a slight correlation here 289 00:29:39,400 --> 00:29:49,100 between CBDA and THCA, but maybe not a perfect correlation by any means, because it looks 290 00:29:49,100 --> 00:29:57,000 like you can definitely crank up the THCA without increasing CBDA and perhaps conversely 291 00:29:57,000 --> 00:29:59,600 as well. 292 00:29:59,600 --> 00:30:12,480 So long story short, the data needs a bit more cleaning to do a nice scattered plot 293 00:30:12,480 --> 00:30:14,120 here. 294 00:30:14,120 --> 00:30:24,240 So I thought, OK, one last attempt, let's just look at a scatter plot of cannabinoids 295 00:30:24,240 --> 00:30:29,080 versus total terpenes and see if there's a trade-off there. 296 00:30:29,080 --> 00:30:35,080 Because last week we talked about, oh, maybe if the plant is producing more terpenes, then 297 00:30:35,080 --> 00:30:38,480 maybe it's producing less cannabinoids. 298 00:30:38,480 --> 00:30:41,400 Maybe they have a positive relationship. 299 00:30:41,400 --> 00:30:46,640 So we don't really know, so we can find out. 300 00:30:46,640 --> 00:30:53,400 So this is just a scatter plot here of total cannabinoids, or it should be total terpenes 301 00:30:53,400 --> 00:30:56,400 versus total cannabinoids. 302 00:30:56,400 --> 00:30:58,400 Cool. 303 00:30:58,400 --> 00:31:06,880 Oh, yeah, I also separated this by producer. 304 00:31:06,880 --> 00:31:19,240 So what I would say from this is there's no strong correlation. 305 00:31:19,240 --> 00:31:26,240 You'd actually have to crunch the stats to actually know the correlation. 306 00:31:26,240 --> 00:31:32,080 But just from eyeball, I'd say there's not a strong correlation, but maybe a weak positive 307 00:31:32,080 --> 00:31:35,220 correlation between terpenes and cannabinoids. 308 00:31:35,220 --> 00:31:41,220 So what that would basically suggest to me is just higher quality product. 309 00:31:41,220 --> 00:31:46,840 So just the higher quality product it's going to be is to basically the higher you crank 310 00:31:46,840 --> 00:31:51,960 up the lights, the higher you crank up the nutrients. 311 00:31:51,960 --> 00:31:53,560 I think it's going to say the lights. 312 00:31:53,560 --> 00:31:58,120 So if you really crank those lights up, you're going to get high terpenes. 313 00:31:58,120 --> 00:32:00,640 You're going to get high cannabinoids. 314 00:32:00,640 --> 00:32:08,280 The things on this side of the graph are probably concentrates. 315 00:32:08,280 --> 00:32:12,880 So that's more about processing. 316 00:32:12,880 --> 00:32:15,080 But I think this is interesting. 317 00:32:15,080 --> 00:32:17,960 So here I separated this. 318 00:32:17,960 --> 00:32:27,400 So here let me open this figure up so we can get a better look at it. 319 00:32:27,400 --> 00:32:33,720 So here I separated by the different producers. 320 00:32:33,720 --> 00:32:40,640 And this is what I found so interesting is that Connecticut is similar to Illinois in 321 00:32:40,640 --> 00:32:44,420 that you only have a handful of producers. 322 00:32:44,420 --> 00:32:47,800 You only have four producers in Connecticut. 323 00:32:47,800 --> 00:32:55,340 So I know Connecticut's a small state, but that seems like a small number of producers. 324 00:32:55,340 --> 00:33:03,680 So we'll actually look at Massachusetts next and compare the two. 325 00:33:03,680 --> 00:33:05,720 We'll do the comparison next week. 326 00:33:05,720 --> 00:33:08,040 We'll get Massachusetts dated today. 327 00:33:08,040 --> 00:33:13,240 Again, looking at Massachusetts, then do a comparison next week perhaps. 328 00:33:13,240 --> 00:33:17,640 But for this week, I thought it was interesting that, oh, let's look at what these different 329 00:33:17,640 --> 00:33:19,120 companies are doing. 330 00:33:19,120 --> 00:33:22,880 And not to single out any one of these companies. 331 00:33:22,880 --> 00:33:27,560 But it looks like they kind of have different segments of the market. 332 00:33:27,560 --> 00:33:30,200 They've each found things that work well for them. 333 00:33:30,200 --> 00:33:35,400 So you've got their plant here. 334 00:33:35,400 --> 00:33:41,720 And it looks like they've got, you know, you'd have to do the statistics. 335 00:33:41,720 --> 00:33:48,160 But they have maybe lower terpenes than some of the other players on average. 336 00:33:48,160 --> 00:33:54,840 But wow, they definitely have some of these high canobinoids over here. 337 00:33:54,840 --> 00:34:06,360 So maybe they do a lot of distillates, these high THC distillates perhaps. 338 00:34:06,360 --> 00:34:15,400 And then maybe these companies like Advanced Grow, definitely the Connecticut Pharmaceutical 339 00:34:15,400 --> 00:34:16,400 Solutions. 340 00:34:16,400 --> 00:34:23,840 It looks like they're maybe doing some of these, you know, high terpene concentrates 341 00:34:23,840 --> 00:34:33,480 where sometimes I do believe they'll add terpenes back in after the... 342 00:34:33,480 --> 00:34:40,640 And Heather can maybe speak to this, where this is maybe what consumers are looking for, 343 00:34:40,640 --> 00:34:43,240 these real tasty concentrates. 344 00:34:43,240 --> 00:34:46,920 And so, right, so it's interesting, right? 345 00:34:46,920 --> 00:34:52,840 So here you have, you know, an Orange Connecticut CPS. 346 00:34:52,840 --> 00:34:56,080 And they're doing some and they're not even the highest cannabinoids. 347 00:34:56,080 --> 00:35:00,640 But wow, you know, they've got like some that are like, look, they've got the highest, they've 348 00:35:00,640 --> 00:35:04,880 got one here that's almost 10% terpenes and no one else is up there. 349 00:35:04,880 --> 00:35:09,480 Well, I think that their plant's kind of up there a couple times. 350 00:35:09,480 --> 00:35:15,400 So long story short, I just thought it was interesting to break this out by producer 351 00:35:15,400 --> 00:35:18,400 since you only have four. 352 00:35:18,400 --> 00:35:25,760 And then once again, it looks like there's maybe a slight positive correlation here, 353 00:35:25,760 --> 00:35:34,680 especially when you get into this sort of what I would call the high quality top shelf 354 00:35:34,680 --> 00:35:42,960 flower where you basically got the flower really starting at around 15 to 18% to about, 355 00:35:42,960 --> 00:35:43,960 you know, the high... 356 00:35:43,960 --> 00:35:47,320 Well, this is total cannabinoids. 357 00:35:47,320 --> 00:35:52,120 You know, this is going to be up to like 33, about 35%. 358 00:35:52,120 --> 00:35:55,320 Looks like some of them kind of pushing that. 359 00:35:55,320 --> 00:36:04,480 And that's, you know, just adding all the cannabinoids together, not taking in the mass 360 00:36:04,480 --> 00:36:07,280 factor for THCA or anything like that. 361 00:36:07,280 --> 00:36:11,280 So long story short, this is where the bulk of your flowers shaking out. 362 00:36:11,280 --> 00:36:18,720 And it does look like there's just like positive correlation between terpenes. 363 00:36:18,720 --> 00:36:22,120 So cool things going on in Connecticut. 364 00:36:22,120 --> 00:36:24,800 Awesome data there. 365 00:36:24,800 --> 00:36:33,760 So to step next door, we can start looking at Massachusetts. 366 00:36:33,760 --> 00:36:37,640 So this will be a brand new look here at this data. 367 00:36:37,640 --> 00:36:43,640 I just put together the requests this morning. 368 00:36:43,640 --> 00:36:50,120 So now we can get back into some of the programming things for you, for you program nerds out 369 00:36:50,120 --> 00:36:51,120 there. 370 00:36:51,120 --> 00:36:58,680 So basically if you're using Python, we were using Subcrata, which is a third party package 371 00:36:58,680 --> 00:37:01,760 to access the API. 372 00:37:01,760 --> 00:37:06,160 I was looking around online and some people were kind of complaining. 373 00:37:06,160 --> 00:37:13,280 Subcrata is maybe a little, or not Subcrata, but the SodaPy package is maybe a little dated. 374 00:37:13,280 --> 00:37:19,600 And you can accomplish the same things on your own with simple HTML requests. 375 00:37:19,600 --> 00:37:28,080 So we're just going to, one, this is what can let it stress is simple is that, well, 376 00:37:28,080 --> 00:37:33,720 Python for that matter, simple is better than complex. 377 00:37:33,720 --> 00:37:38,000 Simple solutions just tend to work out better in the long run. 378 00:37:38,000 --> 00:37:47,680 So we can simply request this data with the request package, which is about as simple 379 00:37:47,680 --> 00:37:50,440 and bare bones as you can get. 380 00:37:50,440 --> 00:37:58,280 So basically just going to get these pretty standard packages here. 381 00:37:58,280 --> 00:38:02,700 I am reading in an app token. 382 00:38:02,700 --> 00:38:14,360 You don't need one, but this will make our requests so they're not throttled. 383 00:38:14,360 --> 00:38:17,200 Then we're just going to define our headers. 384 00:38:17,200 --> 00:38:19,320 So basically this is our authentication. 385 00:38:19,320 --> 00:38:22,600 We just pass our app token. 386 00:38:22,600 --> 00:38:26,520 This is the main URL. 387 00:38:26,520 --> 00:38:31,520 So it's just like visiting a web page where you just, it's just a URL. 388 00:38:31,520 --> 00:38:37,120 So this is just a URL that contains a lot of awesome data. 389 00:38:37,120 --> 00:38:43,320 So we can just read those into the terminal here. 390 00:38:43,320 --> 00:38:46,840 And we can start getting these data points. 391 00:38:46,840 --> 00:38:53,920 And so just to show you the data before we, or show you the data source. 392 00:38:53,920 --> 00:39:03,400 So the first one we'll get, we're just going to get the adult use cannabis retail sales 393 00:39:03,400 --> 00:39:07,960 by date and product type in Massachusetts. 394 00:39:07,960 --> 00:39:12,240 So basically we're just going to get all of the Massachusetts data. 395 00:39:12,240 --> 00:39:17,400 We can, and then we'll see what we can do with it at that point. 396 00:39:17,400 --> 00:39:27,680 So as we noted, we really just need this data set ID and then Bob's your uncle. 397 00:39:27,680 --> 00:39:41,320 So we just slap that data set ID onto the base URL, add.json because that way we can 398 00:39:41,320 --> 00:39:50,720 specify that we want our data in JSON requests, allow us to specify our parameters here. 399 00:39:50,720 --> 00:40:06,400 And I will admit that I am not an SQL expert by any means. 400 00:40:06,400 --> 00:40:12,760 Just tell me to move along if I'm getting that too technical here or anyone. 401 00:40:12,760 --> 00:40:28,240 But basically if you want to nerd out, Socrata has good documentation for how to do all of 402 00:40:28,240 --> 00:40:30,080 this. 403 00:40:30,080 --> 00:40:39,200 And so I won't buy into it too much on my own here, but I've put the links up at the 404 00:40:39,200 --> 00:40:40,200 GitHub. 405 00:40:40,200 --> 00:40:48,080 And so then you can, you know, if you're interested, you can read up and see, okay, this is how 406 00:40:48,080 --> 00:41:00,040 the ordering data, or this is how we, you can do many magical things here. 407 00:41:00,040 --> 00:41:08,760 So but anyways, if you're interested, there's many powerful things you can do for our needs 408 00:41:08,760 --> 00:41:09,760 today. 409 00:41:09,760 --> 00:41:18,680 Sorry, let's get back on track here. 410 00:41:18,680 --> 00:41:24,200 For our needs today, we don't really need to limit, but I'm going to add one anyways. 411 00:41:24,200 --> 00:41:26,560 And then we just need to order the data. 412 00:41:26,560 --> 00:41:33,040 So I've already looked at the data set here. 413 00:41:33,040 --> 00:41:35,300 Here we are. 414 00:41:35,300 --> 00:41:51,200 You can see the fields and actually is this the right data set? 415 00:41:51,200 --> 00:41:55,040 Well we'll find out. 416 00:41:55,040 --> 00:42:02,360 But these are the data points and it looks like we want to order by the activity summary 417 00:42:02,360 --> 00:42:03,360 date. 418 00:42:03,360 --> 00:42:12,560 There's a reason I'm saying sales date. 419 00:42:12,560 --> 00:42:19,400 So anyways, it should work, but a little bit of disconnect here. 420 00:42:19,400 --> 00:42:21,240 Sorry, the documentation is not great. 421 00:42:21,240 --> 00:42:23,320 I should have left better comments. 422 00:42:23,320 --> 00:42:33,400 But anyways, for this first one, we're just going to get the products that are in Massachusetts. 423 00:42:33,400 --> 00:42:38,680 So just read 100 of them. 424 00:42:38,680 --> 00:42:43,160 And so these are products that are getting sold. 425 00:42:43,160 --> 00:42:50,360 And so it looks like, okay, these are our product sales by day. 426 00:42:50,360 --> 00:42:56,840 So it looks like we've got data up until the 14th, looks like. 427 00:42:56,840 --> 00:42:59,800 So up and through last week. 428 00:42:59,800 --> 00:43:02,840 So not bad. 429 00:43:02,840 --> 00:43:09,160 And then we just know, okay, these are the amount of buds that were sold. 430 00:43:09,160 --> 00:43:12,080 This is the amount of concentrate that was sold. 431 00:43:12,080 --> 00:43:16,040 This is the amount of edibles that were sold. 432 00:43:16,040 --> 00:43:18,920 So mostly sales data at this point. 433 00:43:18,920 --> 00:43:22,280 However, we can still, we can. 434 00:43:22,280 --> 00:43:29,080 Hold on, there may be. 435 00:43:29,080 --> 00:43:39,160 Anyways, maybe there was someone trying to join, but anyways. 436 00:43:39,160 --> 00:43:45,720 Well this is interesting data because we can maybe start to compare Massachusetts to Connecticut. 437 00:43:45,720 --> 00:43:52,360 And so what we've been trying, we've been working on is, okay, what's the competitiveness 438 00:43:52,360 --> 00:43:54,080 of these different states? 439 00:43:54,080 --> 00:44:01,720 Because they're all, each state is supposedly, it's a little island where they're all operating 440 00:44:01,720 --> 00:44:04,360 like these little isolated environments. 441 00:44:04,360 --> 00:44:07,400 And so we could say, what's the effect of that? 442 00:44:07,400 --> 00:44:14,480 So like Illinois, they only allowed a few dozen licensees. 443 00:44:14,480 --> 00:44:19,120 How does Illinois compare to Michigan, which is, or to Oklahoma? 444 00:44:19,120 --> 00:44:22,560 Like we know Oklahoma in particular is a free for all. 445 00:44:22,560 --> 00:44:25,920 So how do those states compare? 446 00:44:25,920 --> 00:44:30,120 How does Connecticut compare to Massachusetts? 447 00:44:30,120 --> 00:44:40,800 So Connecticut only has four licensees that produce cannabis, four production licenses. 448 00:44:40,800 --> 00:44:46,840 We'll find out how many Massachusetts has, and then does that have any effect on the 449 00:44:46,840 --> 00:44:48,360 competitiveness? 450 00:44:48,360 --> 00:44:55,760 So can we now compare sales in Connecticut to sales in Massachusetts? 451 00:44:55,760 --> 00:44:56,760 Are they different? 452 00:44:56,760 --> 00:45:05,600 Like is there a price discrepancy if we can break that out? 453 00:45:05,600 --> 00:45:09,080 Because that's what is ultimately, that's kind of what I'm coming out with. 454 00:45:09,080 --> 00:45:13,880 My economics background is, what are the effects of these policies? 455 00:45:13,880 --> 00:45:23,760 So yes, they're legalizing and permitting cannabis in various forms, but what's the 456 00:45:23,760 --> 00:45:25,400 effect of that? 457 00:45:25,400 --> 00:45:29,960 Maybe some policies are more optimal than others. 458 00:45:29,960 --> 00:45:36,280 You may have unintended consequences of certain policies. 459 00:45:36,280 --> 00:45:44,480 It's fascinating to see, okay, how do the different states shake out? 460 00:45:44,480 --> 00:45:56,880 And so the do instead of just speak, let's look at the licensees here in Massachusetts. 461 00:45:56,880 --> 00:46:03,880 So there's a hundred of them. 462 00:46:03,880 --> 00:46:06,640 Let's actually just go ahead and get all of them. 463 00:46:06,640 --> 00:46:10,480 I think there are less than a thousand. 464 00:46:10,480 --> 00:46:14,280 So let's see. 465 00:46:14,280 --> 00:46:20,280 Okay, awesome. 466 00:46:20,280 --> 00:46:25,640 So in Massachusetts, there are 898 licensees. 467 00:46:25,640 --> 00:46:27,560 Awesome. 468 00:46:27,560 --> 00:46:29,800 What do they do? 469 00:46:29,800 --> 00:46:31,960 So there's license type. 470 00:46:31,960 --> 00:46:35,280 So let's find out how many of each type there are. 471 00:46:35,280 --> 00:46:38,800 So this is the first time I looked at this data with you. 472 00:46:38,800 --> 00:46:46,440 I promise this is all I've done is I just wrote the code to get the data. 473 00:46:46,440 --> 00:46:53,040 So that's the, getting the data is often the most boring part. 474 00:46:53,040 --> 00:46:55,800 So I've already done sort of the boring part. 475 00:46:55,800 --> 00:47:03,000 And so now we get to get into the fun part, which is looking at the data, cleaning it 476 00:47:03,000 --> 00:47:07,840 up, which is fun depending on who you talk to. 477 00:47:07,840 --> 00:47:13,800 And then visualizing the data, which is if you're a fan of Edward Cuff, this is what 478 00:47:13,800 --> 00:47:14,800 it's all about. 479 00:47:14,800 --> 00:47:19,600 If you've got data, you want to visualize it like that. 480 00:47:19,600 --> 00:47:25,760 Visualize the data should be step one, but you actually have to first, you actually have 481 00:47:25,760 --> 00:47:29,880 to get the data and then you actually have to kind of clean it up. 482 00:47:29,880 --> 00:47:32,760 So inevitably those are the first two steps. 483 00:47:32,760 --> 00:47:36,760 But then you want to look at the data. 484 00:47:36,760 --> 00:47:42,520 So anyways, let's do just that. 485 00:47:42,520 --> 00:47:54,160 So for the license type in the licensees.license type. 486 00:47:54,160 --> 00:47:58,000 So let's just look at all the unique license types. 487 00:47:58,000 --> 00:48:01,720 Let's list them. 488 00:48:01,720 --> 00:48:12,040 For all of these. 489 00:48:12,040 --> 00:48:16,020 Let's find the, you know, the license count for each one. 490 00:48:16,020 --> 00:48:31,360 So that's just going to be the length where our licensees, the license type is equal to 491 00:48:31,360 --> 00:48:33,240 that license type. 492 00:48:33,240 --> 00:48:44,720 So that should get us the, that should get us the license count. 493 00:48:44,720 --> 00:48:49,720 And then we can just print that out. 494 00:48:49,720 --> 00:49:02,360 We can say, okay, so what's the number of this license type? 495 00:49:02,360 --> 00:49:04,600 And that's just going to be the license count. 496 00:49:04,600 --> 00:49:10,160 We don't promise this is going to work. 497 00:49:10,160 --> 00:49:12,240 It looks like it may have. 498 00:49:12,240 --> 00:49:20,880 So this is awesome because now we now have the count of all the licensees here in Massachusetts, 499 00:49:20,880 --> 00:49:27,280 which is already useful because we can already start making comparisons to Connecticut right 500 00:49:27,280 --> 00:49:31,760 off the bat. 501 00:49:31,760 --> 00:49:41,080 There's a lot more cultivators in Massachusetts than there are in Connecticut. 502 00:49:41,080 --> 00:49:44,280 What's the population difference of these two states? 503 00:49:44,280 --> 00:49:56,520 Well, this is a, this is a data point that we've already collected here. 504 00:49:56,520 --> 00:50:06,040 So you can use Fred, Fed to find the population for various states. 505 00:50:06,040 --> 00:50:07,640 And so let's compare these two. 506 00:50:07,640 --> 00:50:21,320 So Massachusetts is 6.9 million, Connecticut's 3.6 million. 507 00:50:21,320 --> 00:50:24,320 So what did we say? 508 00:50:24,320 --> 00:50:31,520 3.6, 6.9, 6.9 minus 3.6. 509 00:50:31,520 --> 00:50:51,160 So Massachusetts is almost twice as large as, and so Massachusetts is a little less 510 00:50:51,160 --> 00:50:57,200 than twice as large as Connecticut. 511 00:50:57,200 --> 00:51:02,760 So that's interesting, but it's got a lot more than twice the number of cultivators, 512 00:51:02,760 --> 00:51:03,760 right? 513 00:51:03,760 --> 00:51:10,880 Remember Connecticut had four and you've got 274 Massachusetts. 514 00:51:10,880 --> 00:51:15,440 So quite a bit different. 515 00:51:15,440 --> 00:51:22,720 And then you have a handful of other companies, you know, you have your processors, you have 516 00:51:22,720 --> 00:51:25,720 your retailers. 517 00:51:25,720 --> 00:51:37,600 And then we can find out, okay, what's the percentage that these are of the whole market 518 00:51:37,600 --> 00:51:38,600 here? 519 00:51:38,600 --> 00:51:45,440 So we want to find out what percent this is of the whole market. 520 00:51:45,440 --> 00:51:55,600 For the percentage of licensees, well, that's just going to be the license count divided 521 00:51:55,600 --> 00:51:57,600 by all licensees. 522 00:51:57,600 --> 00:52:06,800 So let's see if we can't. 523 00:52:06,800 --> 00:52:19,680 Need more decimal places. 524 00:52:19,680 --> 00:52:21,680 Cool. 525 00:52:21,680 --> 00:52:25,640 So now we can even break it down. 526 00:52:25,640 --> 00:52:29,400 So we've done this in other states. 527 00:52:29,400 --> 00:52:34,520 So now this is cool because now we can start comparing Massachusetts to other states. 528 00:52:34,520 --> 00:52:39,880 We can say, okay, look at this. 529 00:52:39,880 --> 00:52:44,880 Almost 40% of the licensees are retailers. 530 00:52:44,880 --> 00:52:48,200 That's much higher than we've noted in other states. 531 00:52:48,200 --> 00:52:55,640 I want to say we were just looking at these statistics in Oklahoma and maybe only 15% 532 00:52:55,640 --> 00:52:58,480 of the licensees were retailers. 533 00:52:58,480 --> 00:53:04,280 And maybe 60 to 70% were cultivators. 534 00:53:04,280 --> 00:53:07,400 Here only 30% are cultivators. 535 00:53:07,400 --> 00:53:13,800 The manufacturers is a little higher than we saw in Oklahoma, but comparable. 536 00:53:13,800 --> 00:53:18,880 And then of course, your other businesses are comparable where you only have a small 537 00:53:18,880 --> 00:53:23,920 number of laboratories. 538 00:53:23,920 --> 00:53:26,400 This may be higher than it was in Oklahoma. 539 00:53:26,400 --> 00:53:32,680 This is greater than 1%. 540 00:53:32,680 --> 00:53:38,640 So let's put this in percentages. 541 00:53:38,640 --> 00:53:46,760 But that's not what we want. 542 00:53:46,760 --> 00:53:54,280 Exactly. 543 00:53:54,280 --> 00:54:00,480 So I think in Oklahoma, maybe less than 1% of the licensees were laboratories. 544 00:54:00,480 --> 00:54:03,320 Here you have a little more than 1%. 545 00:54:03,320 --> 00:54:08,760 Not sure if that's a significant difference. 546 00:54:08,760 --> 00:54:14,560 You've got just a small number of transporters, et cetera. 547 00:54:14,560 --> 00:54:20,640 I just think it's interesting that you have a much different breakdown of retailers, of 548 00:54:20,640 --> 00:54:26,040 the percent of retailers and cultivators than you do in Oklahoma. 549 00:54:26,040 --> 00:54:33,240 And so I would like to think, OK, does this have to do with time? 550 00:54:33,240 --> 00:54:39,120 Is Massachusetts a more mature market than Oklahoma? 551 00:54:39,120 --> 00:54:41,720 And trend that. 552 00:54:41,720 --> 00:54:44,960 So you could start to make predictions. 553 00:54:44,960 --> 00:54:50,600 So maybe you could look at Massachusetts early on and maybe look at Massachusetts like a 554 00:54:50,600 --> 00:54:56,400 year ago, two years ago, and see, OK, maybe the percent of retailers has increased over 555 00:54:56,400 --> 00:55:00,920 time and the percent of cultivators has decreased over time. 556 00:55:00,920 --> 00:55:04,640 Just a conjecture, but that may be the case. 557 00:55:04,640 --> 00:55:09,440 And so if that's the case, you can basically learn from Massachusetts. 558 00:55:09,440 --> 00:55:13,400 And you could maybe use that to apply to Oklahoma. 559 00:55:13,400 --> 00:55:22,080 And you say, OK, maybe over time, Oklahoma may wind up with a higher percentage of retailers 560 00:55:22,080 --> 00:55:24,760 and a lower percentage of cultivators. 561 00:55:24,760 --> 00:55:26,760 May, may not. 562 00:55:26,760 --> 00:55:30,680 And it may be a policy effect. 563 00:55:30,680 --> 00:55:31,680 Right? 564 00:55:31,680 --> 00:55:41,680 So if you look at them, you watch them over time, and time doesn't appear to be a factor, 565 00:55:41,680 --> 00:55:46,680 which would be like maturity, learning by doing, et cetera, then maybe there's another 566 00:55:46,680 --> 00:55:48,680 explanatory factor. 567 00:55:48,680 --> 00:55:49,680 Right? 568 00:55:49,680 --> 00:55:52,840 Maybe it's the policy. 569 00:55:52,840 --> 00:56:00,520 Maybe there's policies in place in Massachusetts that just encourage a large percentage of 570 00:56:00,520 --> 00:56:04,200 retail versus Oklahoma. 571 00:56:04,200 --> 00:56:11,560 So already, already, we've barely gotten the data. 572 00:56:11,560 --> 00:56:14,000 We've barely scratched the surface. 573 00:56:14,000 --> 00:56:15,000 Right? 574 00:56:15,000 --> 00:56:20,320 We've basically taken like, we're talking about a conditional average. 575 00:56:20,320 --> 00:56:22,400 We've taken a conditional count. 576 00:56:22,400 --> 00:56:27,680 Like, we made like one conditional statistic here. 577 00:56:27,680 --> 00:56:34,240 And we already have groundbreaking insights. 578 00:56:34,240 --> 00:56:37,680 I mean, you could borderline already write a paper on this. 579 00:56:37,680 --> 00:56:42,360 Like say, oh, like this is, right, because what we're doing is we're basically looking 580 00:56:42,360 --> 00:56:45,800 at the industrial organization of these different states. 581 00:56:45,800 --> 00:56:56,800 And you can start to see how does the policies and behavior of the organizations and players, 582 00:56:56,800 --> 00:57:00,960 how does that affect the structure? 583 00:57:00,960 --> 00:57:03,840 So fascinating stuff here. 584 00:57:03,840 --> 00:57:09,960 So we'll kind of bring it to a head here, but basically just want to show you the rest 585 00:57:09,960 --> 00:57:12,400 of the data points. 586 00:57:12,400 --> 00:57:18,880 That way you can start picking your brains about what statistics could be made for next 587 00:57:18,880 --> 00:57:19,880 week. 588 00:57:19,880 --> 00:57:29,440 So that way we can continue to uncover insights and maybe continue our interstate comparison. 589 00:57:29,440 --> 00:57:39,320 So just to show you the other data points here, you can also get prices. 590 00:57:39,320 --> 00:57:43,440 So just get a hundred prices here. 591 00:57:43,440 --> 00:57:45,760 Awesome. 592 00:57:45,760 --> 00:57:58,360 So you basically have the average price of an ounce, unbelievably a flower, by day. 593 00:57:58,360 --> 00:58:01,320 So I don't think my order worked. 594 00:58:01,320 --> 00:58:05,600 Oh, yes, this is actually by month. 595 00:58:05,600 --> 00:58:13,400 So this is a really aggregated statistic, but statistics on prices are hard to come 596 00:58:13,400 --> 00:58:14,480 by. 597 00:58:14,480 --> 00:58:19,000 We've got them in Washington state because we can do Freedom of Information Act requests. 598 00:58:19,000 --> 00:58:23,960 We probably get them in other states, but prices are hard to come by. 599 00:58:23,960 --> 00:58:27,200 So this is good data. 600 00:58:27,200 --> 00:58:29,120 I'm happy to have it. 601 00:58:29,120 --> 00:58:35,160 And so here you just have the average price of an ounce per month. 602 00:58:35,160 --> 00:58:52,960 And then just to show you real quick what you can do with that, we want to sort this 603 00:58:52,960 --> 00:58:59,640 on the place. 604 00:58:59,640 --> 00:59:02,480 Okay. 605 00:59:02,480 --> 00:59:10,680 Let's go to our good friend Google real quick. 606 00:59:10,680 --> 00:59:19,560 Just see how do we sort a data frame. 607 00:59:19,560 --> 00:59:21,840 We use sort values. 608 00:59:21,840 --> 00:59:26,520 So let's do sort the values. 609 00:59:26,520 --> 00:59:28,240 Okay. 610 00:59:28,240 --> 00:59:32,240 That looks chronological. 611 00:59:32,240 --> 00:59:33,240 Okay. 612 00:59:33,240 --> 00:59:41,320 I'll be wrapping up here, but basically just to show you this data. 613 00:59:41,320 --> 00:59:49,240 Oh, that's not what we want. 614 00:59:49,240 --> 00:59:58,160 Here we are. 615 00:59:58,160 --> 01:00:02,520 So you can plot prices over time. 616 01:00:02,520 --> 01:00:04,040 This is real interesting. 617 01:00:04,040 --> 01:00:12,560 So they have this data on their website, but as many of you noticed, there was something 618 01:00:12,560 --> 01:00:18,000 unusual about April of 2020. 619 01:00:18,000 --> 01:00:26,080 I'll let you be the judge of what was unusual about that month, but it had an effect on 620 01:00:26,080 --> 01:00:28,080 prices. 621 01:00:28,080 --> 01:00:36,560 It looks like it was a transitory effect on prices. 622 01:00:36,560 --> 01:00:47,240 So as you can see, everybody's running through the store to get cannabis. 623 01:00:47,240 --> 01:00:55,960 And for whatever reason, you'd actually think prices would rise, but for whatever reason, 624 01:00:55,960 --> 01:01:04,320 prices fell dramatically that month, April of 2020. 625 01:01:04,320 --> 01:01:12,000 But it was transitory and by May, the prices had stabilized. 626 01:01:12,000 --> 01:01:18,600 As you can see, this is what you'd call an increase in volatility, where you basically 627 01:01:18,600 --> 01:01:28,200 have pretty stable prices and then, whoa, we've got a volatile market. 628 01:01:28,200 --> 01:01:30,560 Prices are going all over the place. 629 01:01:30,560 --> 01:01:36,200 People like, this is what you see from a rise in uncertainty. 630 01:01:36,200 --> 01:01:39,160 So people are less certain about what's going on. 631 01:01:39,160 --> 01:01:40,160 Oh no. 632 01:01:40,160 --> 01:01:42,200 Is this good? 633 01:01:42,200 --> 01:01:43,200 Is this bad? 634 01:01:43,200 --> 01:01:45,040 We don't really know. 635 01:01:45,040 --> 01:01:54,080 This is what I would call increased volatility, where you see it's just greater variation 636 01:01:54,080 --> 01:02:00,240 in prices, not quite so stable. 637 01:02:00,240 --> 01:02:07,600 So who knows if this is going to persist, but that's how I would characterize this past 638 01:02:07,600 --> 01:02:08,600 year. 639 01:02:08,600 --> 01:02:16,160 And you could actually look at the statistics, actually compare the variance of this period 640 01:02:16,160 --> 01:02:18,280 versus the variance of this period. 641 01:02:18,280 --> 01:02:25,520 I know you only have so many observations, so you probably couldn't get the statistical 642 01:02:25,520 --> 01:02:30,400 claim one way or the other, but you could still look at the variance and maybe we'll 643 01:02:30,400 --> 01:02:31,880 do that next week. 644 01:02:31,880 --> 01:02:35,960 But long story short, you can look at the trend. 645 01:02:35,960 --> 01:02:41,560 So there's cool things you can do, even though it's just a simple series. 646 01:02:41,560 --> 01:02:48,240 And then the last set of data points here, which can't be forgotten because this is some 647 01:02:48,240 --> 01:02:51,320 real meat right here. 648 01:02:51,320 --> 01:02:56,480 So we'll dive into this meat and potatoes next week. 649 01:02:56,480 --> 01:03:11,280 But just to show you what the data looks like, let's just look at a couple of these. 650 01:03:11,280 --> 01:03:15,320 So here is real cool data. 651 01:03:15,320 --> 01:03:17,960 So you've got the date. 652 01:03:17,960 --> 01:03:22,360 Yeah, so you've got daily data here. 653 01:03:22,360 --> 01:03:25,480 You've got plant counts. 654 01:03:25,480 --> 01:03:27,600 You've got you know what stage they're in. 655 01:03:27,600 --> 01:03:32,880 So you know how many flowering plants there are, how many vegetative plants there are, 656 01:03:32,880 --> 01:03:37,160 how many plants are being harvested. 657 01:03:37,160 --> 01:03:40,560 Just you've got the sales total. 658 01:03:40,560 --> 01:03:44,280 You know how many packages there are. 659 01:03:44,280 --> 01:03:50,760 Packages are kind of a little vague, but nonetheless, interesting data point. 660 01:03:50,760 --> 01:03:54,680 And you know strains is interesting. 661 01:03:54,680 --> 01:03:57,200 You know how many employees there are. 662 01:03:57,200 --> 01:04:02,760 This is an incredibly interesting data point because it's real. 663 01:04:02,760 --> 01:04:05,240 There's ways to measure capital. 664 01:04:05,240 --> 01:04:11,440 So in economics, we talk about two inputs to the production function, primarily. 665 01:04:11,440 --> 01:04:14,080 You've got capital and labor. 666 01:04:14,080 --> 01:04:17,320 And you know, there's many ways to measure capital. 667 01:04:17,320 --> 01:04:21,480 And I've done it in the past and we'll do it maybe next week where we can just kind 668 01:04:21,480 --> 01:04:27,640 of proxy capital with things like plants or what have you. 669 01:04:27,640 --> 01:04:30,120 There's ways to do it. 670 01:04:30,120 --> 01:04:34,600 But actually knowing the number of employees, the labor, of course, yes, it would be nice 671 01:04:34,600 --> 01:04:37,080 to know the breakdown of the labor. 672 01:04:37,080 --> 01:04:39,200 What are these employees doing? 673 01:04:39,200 --> 01:04:41,200 What are their wages? 674 01:04:41,200 --> 01:04:44,960 However, we'll take what we get. 675 01:04:44,960 --> 01:04:49,400 So we've been given the total number of employees. 676 01:04:49,400 --> 01:04:52,920 So I think this is an extraordinarily interesting data point. 677 01:04:52,920 --> 01:05:03,960 So you know, just to run just a little bit longer here, let's just do a similar thing 678 01:05:03,960 --> 01:05:12,920 we did with prices and just plot employees real quick because I'm super, super interested 679 01:05:12,920 --> 01:05:24,120 what that may look like by total employees. 680 01:05:24,120 --> 01:05:26,080 So let's just end on this note. 681 01:05:26,080 --> 01:05:29,600 Oh, oh no. 682 01:05:29,600 --> 01:05:33,240 I want to check. 683 01:05:33,240 --> 01:05:41,400 OK, that is chronological order. 684 01:05:41,400 --> 01:05:46,200 So this is worrisome. 685 01:05:46,200 --> 01:05:50,760 At least it is to me. 686 01:05:50,760 --> 01:05:58,280 If unless I somehow plotted this in the wrong direction, which is not unfathomable. 687 01:05:58,280 --> 01:06:11,720 Yes, I think I may have plotted the data backwards here. 688 01:06:11,720 --> 01:06:20,840 So let's double check on this next week because right off the bat, I'm worried I may have 689 01:06:20,840 --> 01:06:26,400 plotted this in the wrong direction because this would look like, oh no, our total employees 690 01:06:26,400 --> 01:06:28,640 are falling off a cliff here. 691 01:06:28,640 --> 01:06:39,200 But I've got a feeling if you look here at the data, we've got 2021, 06, 05, and we have 692 01:06:39,200 --> 01:06:40,200 8,000. 693 01:06:40,200 --> 01:06:45,240 And then here we have 2021, 09, 14. 694 01:06:45,240 --> 01:06:46,600 We have 9,000. 695 01:06:46,600 --> 01:06:50,880 So I think this is reversed. 696 01:06:50,880 --> 01:06:53,120 So that's OK. 697 01:06:53,120 --> 01:06:56,120 So got a little sloppy there at the end. 698 01:06:56,120 --> 01:07:06,240 So next week, we'll finish cleaning this data and then dive into more analysis. 699 01:07:06,240 --> 01:07:13,680 So we've got all these awesome data points here so we can have a field day next week. 700 01:07:13,680 --> 01:07:18,960 So I'm going to stop the presentation there. 701 01:07:18,960 --> 01:07:22,920 But thank you both for listening to that big launch feel. 702 01:07:22,920 --> 01:07:29,640 Did any questions, comments come to mind? 703 01:07:29,640 --> 01:07:33,840 As far as the presentation went, it was a great watch. 704 01:07:33,840 --> 01:07:38,200 Everything kind of went over my head because, like I said, I'm still learning about this. 705 01:07:38,200 --> 01:07:41,520 I haven't even dipped more than a pinky toe into Python yet. 706 01:07:41,520 --> 01:07:48,000 But I was going to ask after we're done here if I could get like five minutes of your time 707 01:07:48,000 --> 01:07:49,280 to ask a couple of questions. 708 01:07:49,280 --> 01:07:51,520 It's also for my school thing. 709 01:07:51,520 --> 01:07:52,520 Oh, yes. 710 01:07:52,520 --> 01:07:55,120 Always happy to talk. 711 01:07:55,120 --> 01:07:58,400 This is sort of our approach is yet thrown in on the deep end. 712 01:07:58,400 --> 01:08:02,160 So like I said, it's just sort of a cursory approach. 713 01:08:02,160 --> 01:08:10,320 In practice, you'd go much slower and really parse things out and be real certain with 714 01:08:10,320 --> 01:08:11,320 things. 715 01:08:11,320 --> 01:08:14,480 But this is what we're sort of doing just real quick and easy. 716 01:08:14,480 --> 01:08:17,640 We're just showing, OK, it's possible. 717 01:08:17,640 --> 01:08:18,800 The data is there. 718 01:08:18,800 --> 01:08:19,960 You can get the data. 719 01:08:19,960 --> 01:08:21,120 You can print it. 720 01:08:21,120 --> 01:08:24,680 And so this group is more for fun. 721 01:08:24,680 --> 01:08:30,600 And then exactly, yes, if you're trying to get serious about data analytics, then I'm 722 01:08:30,600 --> 01:08:34,640 always happy to be a resource for you. 723 01:08:34,640 --> 01:08:57,880 It's good to hear.