1 00:00:00,000 --> 00:00:12,000 Welcome to the Cannabis Data Science Meetup Group for the 1st of September. 2 00:00:12,000 --> 00:00:14,000 I hope everybody's doing well. 3 00:00:14,000 --> 00:00:19,000 Crunching numbers here in Oklahoma again. 4 00:00:19,000 --> 00:00:29,000 So I've prepared some data that I can share with you essentially trying to look at the market competitiveness on a county by county basis here. 5 00:00:29,000 --> 00:00:39,000 As we do our national analysis, thought I would drill down here real quick since we have county level data. 6 00:00:39,000 --> 00:00:46,000 But before we dive into that, I guess, you know, welcome to the group, Katie. 7 00:00:46,000 --> 00:00:54,000 And of course, Heather is here. And then, Katie, you wouldn't mind introducing yourself if you want. 8 00:00:54,000 --> 00:00:59,000 You don't have to, but just just curious. 9 00:00:59,000 --> 00:01:06,000 What avenue you're coming at the group from. 10 00:01:06,000 --> 00:01:11,000 Oh, it's okay. Always, always happy to have people listen. 11 00:01:11,000 --> 00:01:33,000 So without further ado, I'll share with you some of the work that I've been working on and we can maybe even spearhead and do some new development. 12 00:01:33,000 --> 00:01:40,000 Okay. So. 13 00:01:40,000 --> 00:01:43,000 We've started. 14 00:01:43,000 --> 00:01:46,000 As you can see, we've got a blank map here. 15 00:01:46,000 --> 00:01:54,000 So we're just now starting to essentially try to populate data. 16 00:01:54,000 --> 00:02:00,000 All the public cannabis data that we can scrounge up for each state. 17 00:02:00,000 --> 00:02:09,000 So, of course, the states, many states here in the east, the Eastern seaboard. 18 00:02:09,000 --> 00:02:12,000 As well as some here in the south. 19 00:02:12,000 --> 00:02:17,000 Don't permit cannabis. 20 00:02:17,000 --> 00:02:25,000 Neither some of them, neither medicinally nor recreationally, and then many states don't allow cannabis recreationally. 21 00:02:25,000 --> 00:02:39,000 So we're going to be collecting all the data that the states provide on either and or recreational and or medicinal cannabis. 22 00:02:39,000 --> 00:02:45,000 So, for example, Washington State, Oregon. 23 00:02:45,000 --> 00:02:50,000 You still have medicinal programs as well as recreational programs. 24 00:02:50,000 --> 00:03:00,000 So we'll try to track that data. Same for Colorado and then states like Oklahoma just have medicinal sales. 25 00:03:00,000 --> 00:03:04,000 And then, you know, we've got Illinois, Michigan. 26 00:03:04,000 --> 00:03:12,000 And then we'll we'll take a look at Maryland and we're looking at some of the northeastern states as well. 27 00:03:12,000 --> 00:03:15,000 So Maine has some good public data. 28 00:03:15,000 --> 00:03:19,000 We'll see if Massachusetts has any data. 29 00:03:19,000 --> 00:03:24,000 And then, you know, some of the southern states do have medicinal markets. 30 00:03:24,000 --> 00:03:27,000 So Florida has medicinal cannabis sales. 31 00:03:27,000 --> 00:03:30,000 So we'll see if there's any data there. 32 00:03:30,000 --> 00:03:34,000 And then, of course, we can't forget Alaska and Hawaii. 33 00:03:34,000 --> 00:03:42,000 I think Hawaii has medicinal and then Alaska has. 34 00:03:42,000 --> 00:03:47,000 They have permitted cannabis. I forget if it's recreational and or medicinal. 35 00:03:47,000 --> 00:03:53,000 And then we may even look at a few other territories, essentially. 36 00:03:53,000 --> 00:04:03,000 So Puerto Rico has some sort of permitted cannabis, I believe. 37 00:04:03,000 --> 00:04:07,000 And then I was looking in their Guam has cannabis. 38 00:04:07,000 --> 00:04:11,000 So they may be worth looking at as well. 39 00:04:11,000 --> 00:04:19,000 So for now, we were going to drill down on Oklahoma. 40 00:04:19,000 --> 00:04:28,000 And so a pardon that this map is small. 41 00:04:28,000 --> 00:04:33,000 I'll be working to render this larger in the future. 42 00:04:33,000 --> 00:04:37,000 And so that's Texas County, Oklahoma. 43 00:04:37,000 --> 00:04:43,000 And so essentially, we'll want to look at a county by county level analysis. 44 00:04:43,000 --> 00:04:53,000 And so one measure of market competitiveness that was brought up at the cannabis conference was, 45 00:04:53,000 --> 00:05:02,000 OK, let's just look at the total number of licensees per capita. 46 00:05:02,000 --> 00:05:09,000 So that could give you an idea if there are any markets that may be underserved. 47 00:05:09,000 --> 00:05:22,000 So if there's a high number of licensees per county, then that county may be getting saturated. 48 00:05:22,000 --> 00:05:39,000 Whereas if there's a low number of licensees per county, then that county may be ripe for 49 00:05:39,000 --> 00:05:42,000 for trying to set up a new license. 50 00:05:42,000 --> 00:05:44,000 Or there may be a reason for that. 51 00:05:44,000 --> 00:05:50,000 There may be or there may be hard to do licensing in that county. 52 00:05:50,000 --> 00:05:53,000 So there could be a variety of factors. 53 00:05:53,000 --> 00:05:59,000 So what tools and data do we need? 54 00:05:59,000 --> 00:06:06,000 Well, first, started to just go ahead and write. 55 00:06:06,000 --> 00:06:10,000 So first, just posed our question. 56 00:06:10,000 --> 00:06:13,000 So, OK, we're looking at licensees per capita in Oklahoma. 57 00:06:13,000 --> 00:06:19,000 We would like to look at licensees per capita by county. 58 00:06:19,000 --> 00:06:28,000 Of course, our null hypothesis is there's just an even distribution of licensees per state. 59 00:06:28,000 --> 00:06:53,000 I mean, no one county has a statistically significant difference than another. 60 00:06:53,000 --> 00:07:04,000 And we will reject this if we can conclude that any counties do have a statistically significant number of licensees than another. 61 00:07:04,000 --> 00:07:09,000 So that's a research question. 62 00:07:09,000 --> 00:07:15,000 So let's get to work. 63 00:07:15,000 --> 00:07:24,000 So first things first, we need the number of licensees in Oklahoma. 64 00:07:24,000 --> 00:07:41,000 I have Fortanlytics put together a script that collects data, public data, public cannabis data for Oklahoma. 65 00:07:41,000 --> 00:07:48,000 And so this is MIT licensed so anybody can use it for any purpose they wish. 66 00:07:48,000 --> 00:08:01,000 Just make sure to give a link back to Canlytics as the copyright holder. 67 00:08:01,000 --> 00:08:14,000 However, this script essentially just collects data from various sources. 68 00:08:14,000 --> 00:08:16,000 So we're not looking at revenue at the moment. 69 00:08:16,000 --> 00:08:18,000 We're just looking at licensees. 70 00:08:18,000 --> 00:08:29,000 So essentially, Oklahoma provides a running list here of licensees. 71 00:08:29,000 --> 00:08:47,000 So essentially what the script does is, OK, just downloads all of these PDFs, parses this data into a usable format. 72 00:08:47,000 --> 00:08:53,000 So there's the raw data. 73 00:08:53,000 --> 00:09:13,000 And then you can use this script here and you can get a decent list of licensees in Oklahoma. 74 00:09:13,000 --> 00:09:24,000 So now we know, OK, we've got the licensee, their trade name. 75 00:09:24,000 --> 00:09:42,000 So a lot of companies may, not a lot, but a handful of companies may do business under a different name than their registered business, their licensed business name. 76 00:09:42,000 --> 00:09:50,000 So, for example, here we've got a license that's licensed under Donald P. Northcutt. 77 00:09:50,000 --> 00:09:55,000 However, they're going to be packaging as Big Bud. 78 00:09:55,000 --> 00:10:00,000 So you're going to know them at the store as Big Bud. 79 00:10:00,000 --> 00:10:06,000 So this is not uncommon in the cannabis industry. 80 00:10:06,000 --> 00:10:08,000 Here's Toe, Oklahoma. 81 00:10:08,000 --> 00:10:24,000 So just to say what anyone's doing per se, but a lot of times you may see it essentially to help with banking. 82 00:10:24,000 --> 00:10:28,000 So it's still tough for cannabis businesses to get banked. 83 00:10:28,000 --> 00:10:35,000 So these people could be doing things for different reasons. 84 00:10:35,000 --> 00:10:44,000 However, it would probably be easier to get banked under agency properties versus Toe, Oklahoma. 85 00:10:44,000 --> 00:10:52,000 And that's just the tragic nature of the state of affairs. 86 00:10:52,000 --> 00:11:03,000 And so, OK, but to long story short, we have many companies here in Oklahoma. 87 00:11:03,000 --> 00:11:22,000 So some include including every licensee that's plant touching in some way, you know, from waste disposal to laboratories to dispensaries to the growers themselves. 88 00:11:22,000 --> 00:11:25,000 And of course, processors and the whole shebang. 89 00:11:25,000 --> 00:11:34,000 There's almost 13,000 plant touching businesses in Oklahoma. 90 00:11:34,000 --> 00:11:41,000 So where are they? Are they all right outside of Oklahoma City? 91 00:11:41,000 --> 00:11:43,000 Are they all in the country? 92 00:11:43,000 --> 00:11:56,000 So we know what city they're in. However, actually, we can even look at the zip code. 93 00:11:56,000 --> 00:12:00,000 So I didn't even realize that we could get that granular. 94 00:12:00,000 --> 00:12:06,000 So for this analysis, we'll just be looking at the county. 95 00:12:06,000 --> 00:12:13,000 So we'll try to get a county by county count. 96 00:12:13,000 --> 00:12:19,000 So without further ado, 97 00:12:19,000 --> 00:12:23,000 let's try to do just that. 98 00:12:23,000 --> 00:12:38,000 So it's just running quick Python script here. 99 00:12:38,000 --> 00:12:51,000 OK, so essentially, we're just reading in the licensees data. 100 00:12:51,000 --> 00:13:00,000 So this is the data we just saw in Excel. 101 00:13:00,000 --> 00:13:04,000 So we can just do some sanity checks here. 102 00:13:04,000 --> 00:13:21,000 For example, let's look at OK, what are all of the license types that we did in fact get? 103 00:13:21,000 --> 00:13:29,000 So like we expected, we've got the growers, the labs, the waste disposals and dispensaries. 104 00:13:29,000 --> 00:13:34,000 And we also have processors and transporters. 105 00:13:34,000 --> 00:13:42,000 So you could find out how many of each there are. 106 00:13:42,000 --> 00:13:48,000 So if you wanted to do that, which I mean, why not? 107 00:13:48,000 --> 00:13:55,000 So I'm a little curious. OK, how many of each are there and what's the percent of the total? 108 00:13:55,000 --> 00:14:10,000 Because we can do a little exploratory data analysis since this is the first real good time that I've had to look at the licensees data here. 109 00:14:10,000 --> 00:14:13,000 So let's look at it together. 110 00:14:13,000 --> 00:14:16,000 So the way I would do this is OK. 111 00:14:16,000 --> 00:14:18,000 So let's look at each of these types. 112 00:14:18,000 --> 00:14:26,000 So for the license type. 113 00:14:26,000 --> 00:14:29,000 OK, so what do we want to do here? 114 00:14:29,000 --> 00:14:43,000 So we want to isolate data for that type. 115 00:14:43,000 --> 00:14:52,000 So let's just locate the licensees where the licensees license type. 116 00:14:52,000 --> 00:14:55,000 Equal to the license type. 117 00:14:55,000 --> 00:14:58,000 OK, not too bad. 118 00:14:58,000 --> 00:15:00,000 And then what do we want to do? 119 00:15:00,000 --> 00:15:04,000 Well, let's just print out some statistics here. 120 00:15:04,000 --> 00:15:07,000 So one, a statistic is a total. 121 00:15:07,000 --> 00:15:30,000 So so we could just say, OK, what's the total number of license type? 122 00:15:30,000 --> 00:15:38,000 That's just going to be the total number that we just found. 123 00:15:38,000 --> 00:15:47,000 And then I'm also curious, OK, what's that as a percentage of the total? 124 00:15:47,000 --> 00:15:51,000 So let's just say. 125 00:15:51,000 --> 00:15:56,000 OK, so what's that as a percent of the total? 126 00:15:56,000 --> 00:16:07,000 Well, we're just going to be the length divided by all licensees. 127 00:16:07,000 --> 00:16:22,000 Then I'm just going to add an extra space here just so things print out decently. 128 00:16:22,000 --> 00:16:33,000 And then let's also maybe just format this decently. 129 00:16:33,000 --> 00:16:45,000 So let's round this times 100 in two decimal places. 130 00:16:45,000 --> 00:16:52,000 OK, so now the numbers look a little cleaner here. 131 00:16:52,000 --> 00:16:56,000 OK, so this is interesting. 132 00:16:56,000 --> 00:17:07,000 We can start to, you know, these would be good numbers for a pie chart here. 133 00:17:07,000 --> 00:17:12,000 So that way you can actually see, OK, what are the pieces of the pie? 134 00:17:12,000 --> 00:17:16,000 For now, we'll just look at the just the raw numbers. 135 00:17:16,000 --> 00:17:19,000 So OK, so this is actually surprising. 136 00:17:19,000 --> 00:17:23,000 So I knew it was going to be a high percentage. 137 00:17:23,000 --> 00:17:30,000 For some reason, I was thinking higher, just my. 138 00:17:30,000 --> 00:17:35,000 My. What do you call it? 139 00:17:35,000 --> 00:17:39,000 My naive prior would have been higher. 140 00:17:39,000 --> 00:17:44,000 However, it's about 70 percent of all licensees are growers. 141 00:17:44,000 --> 00:17:48,000 About 12 percent are processors. 142 00:17:48,000 --> 00:17:53,000 That's interesting. I would have thought higher, but about 12 percent. 143 00:17:53,000 --> 00:17:58,000 That's about right. I would have thought about 20 percent, but. 144 00:17:58,000 --> 00:18:00,000 That sounds about right. 145 00:18:00,000 --> 00:18:03,000 Well, yeah, so maybe maybe that's more growers than I was expecting. 146 00:18:03,000 --> 00:18:08,000 So you have about 12 percent processors. 147 00:18:08,000 --> 00:18:16,000 Well, should we try to do a quick pie chart just for the heck of it? 148 00:18:16,000 --> 00:18:21,000 Since since we're crunching numbers here and that's what we do. 149 00:18:21,000 --> 00:18:29,000 So let's do a map plot live pie chart and see if it's not the hardest thing in the world. 150 00:18:29,000 --> 00:18:32,000 Because if we can get a pie chart. 151 00:18:32,000 --> 00:18:37,000 You know, thrown together. 152 00:18:37,000 --> 00:18:43,000 You know, in a couple of minutes, then. 153 00:18:43,000 --> 00:18:49,000 Also, I'm going to spin this down. 154 00:18:49,000 --> 00:18:52,000 Yeah, I think we could look at some of this data here. 155 00:18:52,000 --> 00:18:55,000 So let's let's look at a quick pie chart here. 156 00:18:55,000 --> 00:18:59,000 So right, because we're already looping through this. 157 00:18:59,000 --> 00:19:11,000 So all we have to do here is just collect these points that we're. 158 00:19:11,000 --> 00:19:13,000 We're trying to do, right? 159 00:19:13,000 --> 00:19:20,000 So we just have to collect the what's called what they're calling the sizes here. 160 00:19:20,000 --> 00:19:24,000 So the size of each piece of the pie. 161 00:19:24,000 --> 00:19:30,000 Right. So all we have to do. 162 00:19:30,000 --> 00:19:34,000 Is keep track of the proportion. 163 00:19:34,000 --> 00:19:39,000 Each license type makes up and look, we're already calculating the proportion. 164 00:19:39,000 --> 00:19:47,000 So that's easy enough. 165 00:19:47,000 --> 00:19:57,000 We don't really need to explode anything. In fact, we're trying to do the exact opposite. 166 00:19:57,000 --> 00:20:02,000 And then it looks like. 167 00:20:02,000 --> 00:20:13,000 They have their labels as a tuple, but we're going to try to do our labels as a list and see if that works. 168 00:20:13,000 --> 00:20:28,000 No one caught me. We need to append these not. 169 00:20:28,000 --> 00:20:50,000 OK. 170 00:20:50,000 --> 00:21:03,000 So let's see if we can't make a quick pie chart just to get a bit better visualization. 171 00:21:03,000 --> 00:21:05,000 OK. And look at that. 172 00:21:05,000 --> 00:21:08,000 So now we can actually get the data. 173 00:21:08,000 --> 00:21:24,000 So. 174 00:21:24,000 --> 00:21:28,000 So we've got the growers. 175 00:21:28,000 --> 00:21:36,000 Making up about 68 percent processors, 12 percent. 176 00:21:36,000 --> 00:21:40,000 The dispensaries, about 18 percent. 177 00:21:40,000 --> 00:21:43,000 So. 178 00:21:43,000 --> 00:21:46,000 That's quite interesting. 179 00:21:46,000 --> 00:21:50,000 About 30 percent here. 180 00:21:50,000 --> 00:21:52,000 Processors, dispensaries. 181 00:21:52,000 --> 00:21:55,000 And then look, just this tiny little sliver. 182 00:21:55,000 --> 00:22:07,000 You have about one percent of. 183 00:22:07,000 --> 00:22:15,000 So you have a very small number of these almost ancillary. 184 00:22:15,000 --> 00:22:20,000 Plant touching businesses so that still make the. 185 00:22:20,000 --> 00:22:25,000 Make the industry function at the end of the day. Right. 186 00:22:25,000 --> 00:22:29,000 So you need transporters to get products from point A to point B. 187 00:22:29,000 --> 00:22:35,000 In certain states like Oklahoma, you really need to pay attention to waste disposal. 188 00:22:35,000 --> 00:22:40,000 So there's waste disposal companies and you need your your quality control. 189 00:22:40,000 --> 00:22:49,000 So you've got the laboratories often doing the state mandated quality control tests. 190 00:22:49,000 --> 00:22:52,000 So. So that's the breakdown of licensees. 191 00:22:52,000 --> 00:22:55,000 Let's go ahead and move on. 192 00:22:55,000 --> 00:22:58,000 You know, trying to get to bogged down in that. 193 00:22:58,000 --> 00:23:03,000 So we've got our number of licensees. 194 00:23:03,000 --> 00:23:08,000 And like let's not forget lose sight of what we're after here. 195 00:23:08,000 --> 00:23:16,000 We're after the county by county breakdown. 196 00:23:16,000 --> 00:23:20,000 OK, let's get a couple of data points before we continue. 197 00:23:20,000 --> 00:23:24,000 So because we were trying to get the population. 198 00:23:24,000 --> 00:23:31,000 So a quick aside. 199 00:23:31,000 --> 00:23:38,000 The place you want to go if you want to get reliable. 200 00:23:38,000 --> 00:23:41,000 Credible. 201 00:23:41,000 --> 00:23:49,000 Economic data points for economic indicators such as population. 202 00:23:49,000 --> 00:23:55,000 The place you want to go is the Federal Reserve and Fred. 203 00:23:55,000 --> 00:23:59,000 So this is economic data repository. 204 00:23:59,000 --> 00:24:03,000 Public open to the you know, it's free. 205 00:24:03,000 --> 00:24:09,000 And you can use it via API, which is what we'll do here momentarily. 206 00:24:09,000 --> 00:24:12,000 And. 207 00:24:12,000 --> 00:24:18,000 This is a trusted data source, so no one will be too hard on you. 208 00:24:18,000 --> 00:24:31,000 If you use the Fed Fred data, you know, of course, they may say, oh, why did you use, you know, the not seasonally adjusted series or this or that? 209 00:24:31,000 --> 00:24:42,000 However, they won't you know, they won't try to undermine the data source itself. So so that's why it's credible and reliable. 210 00:24:42,000 --> 00:24:44,000 So. 211 00:24:44,000 --> 00:24:48,000 But in that regard, I would still hedge the data in that. 212 00:24:48,000 --> 00:24:52,000 OK, the data is still probably not probably. 213 00:24:52,000 --> 00:24:56,000 It is an estimate of the population here. 214 00:24:56,000 --> 00:25:01,000 So, you know, it's probably not spot on to a T. 215 00:25:01,000 --> 00:25:05,000 However, you know, we can get a decent. 216 00:25:05,000 --> 00:25:09,000 Measure of the of the population here. 217 00:25:09,000 --> 00:25:12,000 And so, for example. 218 00:25:12,000 --> 00:25:19,000 Here we just have OK, here's just the total population in Oklahoma. 219 00:25:19,000 --> 00:25:23,000 Times 1000, so this is thousands of people. 220 00:25:23,000 --> 00:25:26,000 So. 221 00:25:26,000 --> 00:25:31,000 Just shy of four million. 222 00:25:31,000 --> 00:25:35,000 People in Oklahoma. 223 00:25:35,000 --> 00:25:40,000 And then you can get this on a county by county level basis. 224 00:25:40,000 --> 00:25:46,000 And so, for example, this is LaFleur County. 225 00:25:46,000 --> 00:25:53,000 Has almost 50,000 people. 226 00:25:53,000 --> 00:25:56,000 So. 227 00:25:56,000 --> 00:26:04,000 So long story short, we can go ahead and get those data points just using the, you know, the Federal Reserve. 228 00:26:04,000 --> 00:26:09,000 API. 229 00:26:09,000 --> 00:26:15,000 So, you know, we can get the population. 230 00:26:15,000 --> 00:26:17,000 Of Oklahoma. 231 00:26:17,000 --> 00:26:21,000 And then we can actually get the licensees per capita. 232 00:26:21,000 --> 00:26:29,000 So this number on itself may not be that. 233 00:26:29,000 --> 00:26:31,000 Informative slash interesting. 234 00:26:31,000 --> 00:26:38,000 However, what would be interesting would be to calculate this number licensees per capita. 235 00:26:38,000 --> 00:26:44,000 For. 236 00:26:44,000 --> 00:26:49,000 For all of the states that way we can see, OK, which states. 237 00:26:49,000 --> 00:26:53,000 You know, how do the different states shake out? 238 00:26:53,000 --> 00:26:56,000 So, for example. 239 00:26:56,000 --> 00:27:04,000 Illinois, I think there's only, you know, a handful of dozen. 240 00:27:04,000 --> 00:27:06,000 So, you know. 241 00:27:06,000 --> 00:27:12,000 A couple dozen to maybe more licensees in the whole state. 242 00:27:12,000 --> 00:27:16,000 And so if you think about the population of Illinois. 243 00:27:16,000 --> 00:27:25,000 Then this number licensees per capita is going to be much smaller than it will be in Oklahoma. 244 00:27:25,000 --> 00:27:28,000 And so the scale will be interesting. 245 00:27:28,000 --> 00:27:32,000 So the scale there will be interesting. 246 00:27:32,000 --> 00:27:35,000 So. 247 00:27:35,000 --> 00:27:37,000 We can't. 248 00:27:37,000 --> 00:27:40,000 Well, yeah, we can't do state to state yet. 249 00:27:40,000 --> 00:27:44,000 So let's just let's look at county to county. 250 00:27:44,000 --> 00:27:47,000 And so. 251 00:27:47,000 --> 00:27:55,000 Basically, what I've done here is I've just gone ahead and gotten the federal reserve. 252 00:27:55,000 --> 00:28:00,000 That Fred code here for looking up each county. 253 00:28:00,000 --> 00:28:03,000 And so this will be the first time I've done this. 254 00:28:03,000 --> 00:28:11,000 So long story short, let's try to look at the licensees per capita in each county. 255 00:28:11,000 --> 00:28:16,000 And ambitiously, we may try to plot this. 256 00:28:16,000 --> 00:28:20,000 It could be a wild one, but we're going to try. 257 00:28:20,000 --> 00:28:23,000 OK, so. 258 00:28:23,000 --> 00:28:29,000 Without further ado, let's start looping over these counties. 259 00:28:29,000 --> 00:28:34,000 So so we'll just kind of view this on the fly, right? 260 00:28:34,000 --> 00:28:37,000 So OK, so we want to do it for each county. 261 00:28:37,000 --> 00:28:38,000 We know that. 262 00:28:38,000 --> 00:28:43,000 So for county and counties. 263 00:28:43,000 --> 00:28:46,000 Well, what do we want to do? 264 00:28:46,000 --> 00:28:55,000 We want to find all the licensees in that county. 265 00:28:55,000 --> 00:28:59,000 Then we want to get the population. 266 00:28:59,000 --> 00:29:01,000 Of that county. 267 00:29:01,000 --> 00:29:14,000 And then at that point, we just want to calculate the licensees per capita. 268 00:29:14,000 --> 00:29:18,000 In that county. 269 00:29:18,000 --> 00:29:21,000 And we want to keep track of this. 270 00:29:21,000 --> 00:29:25,000 So. 271 00:29:25,000 --> 00:29:32,000 I'm just going to create a dictionary here. 272 00:29:32,000 --> 00:29:41,000 We've got a dictionary of states and because just heads up that I kind of know this is how I want the data presented here in a second. 273 00:29:41,000 --> 00:29:44,000 So that's why I'm formatting it like this. 274 00:29:44,000 --> 00:29:48,000 So so we then do licensees per capita. 275 00:29:48,000 --> 00:29:52,000 OK, so first things first, we want all the licensees in that county. 276 00:29:52,000 --> 00:29:54,000 So. 277 00:29:54,000 --> 00:29:56,000 So the county licensees. 278 00:29:56,000 --> 00:29:58,000 Oh, well, that'll be simple enough. 279 00:29:58,000 --> 00:30:02,000 That's just going to be the licensees. 280 00:30:02,000 --> 00:30:06,000 Where the licensees. 281 00:30:06,000 --> 00:30:08,000 County. 282 00:30:08,000 --> 00:30:10,000 Correct. 283 00:30:10,000 --> 00:30:14,000 It's equal to that county. 284 00:30:14,000 --> 00:30:16,000 Simple enough. 285 00:30:16,000 --> 00:30:20,000 And we want to get the population of that county. 286 00:30:20,000 --> 00:30:25,000 Well, we know how to get the fed spread data. 287 00:30:25,000 --> 00:30:30,000 So we've got the county population. 288 00:30:30,000 --> 00:30:37,000 And then we got the county code here. 289 00:30:37,000 --> 00:30:39,000 That's right. 290 00:30:39,000 --> 00:30:44,000 And I want to make sure this is equal equal to the county name. 291 00:30:44,000 --> 00:30:47,000 Because we've built a good county. 292 00:30:47,000 --> 00:30:50,000 I just call it pop graph. 293 00:30:50,000 --> 00:30:53,000 So. 294 00:30:53,000 --> 00:31:01,000 Long story short, we want to get this. 295 00:31:01,000 --> 00:31:04,000 OK. 296 00:31:04,000 --> 00:31:18,000 And then we would just want to calculate the licensees per capita in that county, which we did for the state. So we can use that same logic once again. 297 00:31:18,000 --> 00:31:23,000 So this is just going to be county licensees per capita. 298 00:31:23,000 --> 00:31:29,000 And that's just going to be. 299 00:31:29,000 --> 00:31:34,000 County licensees. 300 00:31:34,000 --> 00:31:38,000 Divided by the county population. 301 00:31:38,000 --> 00:31:43,000 That's a thousand since these are in thousands of people. 302 00:31:43,000 --> 00:31:44,000 Cool. 303 00:31:44,000 --> 00:31:47,000 So we now should have that. 304 00:31:47,000 --> 00:31:50,000 And then. 305 00:31:50,000 --> 00:31:53,000 Basically, we want to keep track. 306 00:31:53,000 --> 00:32:02,000 Of the data and the way I'm doing it is in the dictionary of county. 307 00:32:02,000 --> 00:32:11,000 And then I actually want some variables here. And then basically I want to keep track of this as. 308 00:32:11,000 --> 00:32:18,000 Licensees per capita. 309 00:32:18,000 --> 00:32:23,000 That's how I'll keep track. 310 00:32:23,000 --> 00:32:27,000 Of this data. 311 00:32:27,000 --> 00:32:32,000 So. 312 00:32:32,000 --> 00:32:35,000 That should work. 313 00:32:35,000 --> 00:32:38,000 Let's just go ahead and. 314 00:32:38,000 --> 00:32:41,000 Print some. 315 00:32:41,000 --> 00:32:51,000 Print some data out here just so we can kind of see what we're doing as we're going. So we're just going to say OK. 316 00:32:51,000 --> 00:32:56,000 OK, what's the county. 317 00:32:56,000 --> 00:33:04,000 With the population of the county. 318 00:33:04,000 --> 00:33:08,000 And. 319 00:33:08,000 --> 00:33:10,000 What's. 320 00:33:10,000 --> 00:33:20,000 The number of licensees. 321 00:33:20,000 --> 00:33:29,000 And then finally, what is the number of licensees per capita. 322 00:33:29,000 --> 00:33:41,000 So just go ahead and print that out for each county. 323 00:33:41,000 --> 00:33:48,000 And. 324 00:33:48,000 --> 00:33:59,000 I'm going to go ahead and just print a separating line here. 325 00:33:59,000 --> 00:34:10,000 And let's see what happens. 326 00:34:10,000 --> 00:34:23,000 We got an error and that's because I was not using this county name here. So let's just go ahead and define a new variable in the Pythonic way. 327 00:34:23,000 --> 00:34:27,000 County name. 328 00:34:27,000 --> 00:34:39,000 So simple enough. 329 00:34:39,000 --> 00:34:53,000 And we are cranking out some calculations here. 330 00:34:53,000 --> 00:34:55,000 Awesome. 331 00:34:55,000 --> 00:34:58,000 So we just printed out. 332 00:34:58,000 --> 00:35:01,000 Probably printed too many lines here. 333 00:35:01,000 --> 00:35:05,000 We just. 334 00:35:05,000 --> 00:35:15,000 I probably should have once again, print. Oh well, print county name. OK, so long story short, we've got our counties here. 335 00:35:15,000 --> 00:35:18,000 We've got the population. 336 00:35:18,000 --> 00:35:26,000 As of 2020. So keep in mind, this is the 2020 population. So all of these numbers are done with 2020 population. 337 00:35:26,000 --> 00:35:29,000 So. 338 00:35:29,000 --> 00:35:35,000 So we now have the number of licensees and then we also. 339 00:35:35,000 --> 00:35:38,000 So, for example. 340 00:35:38,000 --> 00:35:49,000 You can really start to compare these counties, right? And so this is where you may start to get a measure of competitiveness, right? 341 00:35:49,000 --> 00:35:57,000 Because, OK, a DARE County has already issued 18 license, 118 licenses. 342 00:35:57,000 --> 00:36:02,000 A TOCA County has issued 76 licenses. 343 00:36:02,000 --> 00:36:05,000 However. 344 00:36:05,000 --> 00:36:16,000 A TOCA may be slightly more saturated with licensees than a DARE. 345 00:36:16,000 --> 00:36:31,000 Just, just, just slightly. And so, so this is where, OK, you may not necessarily just want to look at the total number of licensees issued per county. 346 00:36:31,000 --> 00:36:38,000 But you may actually want to measure them. 347 00:36:38,000 --> 00:36:41,000 Based on. 348 00:36:41,000 --> 00:36:46,000 On licensees per capita. 349 00:36:46,000 --> 00:36:48,000 So. 350 00:36:48,000 --> 00:36:55,000 So we now have this data and so now we want to look at this data. 351 00:36:55,000 --> 00:37:03,000 So there's two ways we can do this, the easy and the hard way. 352 00:37:03,000 --> 00:37:13,000 So the easy way is just, if this just works with my JavaScript. 353 00:37:13,000 --> 00:37:26,000 If this just works with my JavaScript map here, if not, we'll have to make a map in Python. 354 00:37:26,000 --> 00:37:35,000 So I'm going to my try. Yes. So right now I just need to print. 355 00:37:35,000 --> 00:37:43,000 Right now I just need to print the licensees per capita. 356 00:37:43,000 --> 00:37:49,000 And then let's see if this data. 357 00:37:49,000 --> 00:37:54,000 Just works nicely. 358 00:37:54,000 --> 00:38:03,000 With 359 00:38:03,000 --> 00:38:08,000 this Oklahoma County map that I've been tinkering with. 360 00:38:08,000 --> 00:38:13,000 So 361 00:38:13,000 --> 00:38:19,000 hopefully 362 00:38:19,000 --> 00:38:31,000 this will be a bit of a problem. 363 00:38:31,000 --> 00:38:55,000 Okay, not the end of the world here. So real quick like I just need to format. 364 00:38:55,000 --> 00:39:07,000 The name where the spaces are replaced with underscores. 365 00:39:07,000 --> 00:39:18,000 Sort of a pain here and I'm sorry Fred that I'm about to make a bunch of more API calls, but that's what we're going to do. 366 00:39:18,000 --> 00:39:28,000 Okay. 367 00:39:28,000 --> 00:39:46,000 But for some reason I don't think we'll have a chloro plant right out of the box. 368 00:39:46,000 --> 00:39:59,000 Unless we're somehow able to. 369 00:39:59,000 --> 00:40:01,000 Yeah. 370 00:40:01,000 --> 00:40:06,000 Okay. 371 00:40:06,000 --> 00:40:16,000 So I'll try to do this. 372 00:40:16,000 --> 00:40:23,000 This could be interesting. So long story short, we're going to try to visualize this data. 373 00:40:23,000 --> 00:40:31,000 Which is tricky right because it's state data, it's geographical data, but 374 00:40:31,000 --> 00:40:40,000 we're going to try here. So long story short, we are trying to color this map. 375 00:40:40,000 --> 00:40:49,000 So we're trying to color this with places with more licensees to be darker than the places with less license. 376 00:40:49,000 --> 00:40:54,000 So going to show you a little bit of web programming here. 377 00:40:54,000 --> 00:41:08,000 So we're going to just dump in our data just for the time being normally you would get your data from the server, you know. 378 00:41:08,000 --> 00:41:16,000 Right. Normally you get that from your database. Okay, so we've got our data here. 379 00:41:16,000 --> 00:41:27,000 What we can do is we can actually add a little tool tip. 380 00:41:27,000 --> 00:41:47,000 Bear with me. 381 00:41:47,000 --> 00:41:59,000 So okay, so the first thing first, let's just see if we can't just bought all this data here on the map. 382 00:41:59,000 --> 00:42:06,000 Okay, cool. So now we can at least see the numbers. 383 00:42:06,000 --> 00:42:14,000 However, we want to get them colored. 384 00:42:14,000 --> 00:42:27,000 Also, I wonder if we can do something like, I forget what it is off the top of my head, but 385 00:42:27,000 --> 00:42:33,000 I don't think it was that. 386 00:42:33,000 --> 00:42:41,000 Well, this is what Google's for. 387 00:42:41,000 --> 00:42:57,000 Oh, it's too fixed. Okay. 388 00:42:57,000 --> 00:43:03,000 All right. And let's give it a few more decimal places here. 389 00:43:03,000 --> 00:43:07,000 Awesome. 390 00:43:07,000 --> 00:43:14,000 So now we have licensees per capita 391 00:43:14,000 --> 00:43:16,000 by county in Oklahoma. 392 00:43:16,000 --> 00:43:22,000 And so we've got 15 minutes left. Let's see if we can't color these. 393 00:43:22,000 --> 00:43:27,000 So the 394 00:43:27,000 --> 00:43:50,000 proper sometimes use a proper way and improper way to do things. So the proper way would be if somehow 395 00:43:50,000 --> 00:44:01,000 we could 396 00:44:01,000 --> 00:44:05,000 I wonder 397 00:44:05,000 --> 00:44:15,000 if this variable is going to be accessible right here. 398 00:44:15,000 --> 00:44:37,000 Doesn't look like it. 399 00:44:37,000 --> 00:44:42,000 Interesting. 400 00:44:42,000 --> 00:45:07,000 Okay, so we may have to resort. 401 00:45:07,000 --> 00:45:25,000 Okay. 402 00:45:25,000 --> 00:45:38,000 So the problem is, this is probably like much 403 00:45:38,000 --> 00:45:56,000 smaller than number necessary to give us a good range. 404 00:45:56,000 --> 00:46:05,000 Okay, so this may end up waiting to the future or we can maybe try a Python chloropleth real quick. 405 00:46:05,000 --> 00:46:17,000 Just trying to scale this a bit. 406 00:46:17,000 --> 00:46:35,000 Last shot here. 407 00:46:35,000 --> 00:46:49,000 Hmm. 408 00:46:49,000 --> 00:47:06,000 Okay, so we may not be able to get this JavaScript working right this second. So let's see if we can't. 409 00:47:06,000 --> 00:47:22,000 I'm not confident that I can get a chloropleth going here in a in 10 minutes in Python. 410 00:47:22,000 --> 00:47:37,000 Well, okay, here's the deal. We'll try it. We'll give it 10 minutes and see if we can't make a chloropleth of this data in Oklahoma in Python. And then if not, I'll finish this later. 411 00:47:37,000 --> 00:47:46,000 I'll finish this plot later today and then post that link. So this is how you make a chloropleth in Python. 412 00:47:46,000 --> 00:48:14,000 So 413 00:48:14,000 --> 00:48:21,000 Well, it may not actually be as hard as one may think. 414 00:48:21,000 --> 00:48:28,000 So let's see if we can use this handy dandy 415 00:48:28,000 --> 00:48:47,000 package and do this work for us, right, because we're not trying to reinvent the wheel here. Okay, so let's see what this yields us. 416 00:48:47,000 --> 00:48:50,000 Okay, do we just have our county names here? 417 00:48:50,000 --> 00:48:58,000 Okay, those look like the Oklahoma counties. So that's cool. 418 00:48:58,000 --> 00:49:10,000 We already have the licensees. 419 00:49:10,000 --> 00:49:22,000 And so what was this chloropleth doing? 420 00:49:22,000 --> 00:49:36,000 So let's just grab all of this. 421 00:49:36,000 --> 00:49:42,000 And so we've got our county names, and we're just trying to make this chloropleth. 422 00:49:42,000 --> 00:49:49,000 Okay, so we're, so it says, okay, so now we define the county shapes. 423 00:49:49,000 --> 00:49:57,000 And let's hope these are defined already through BOKA. 424 00:49:57,000 --> 00:50:01,000 It looks like they are. That's awesome. 425 00:50:01,000 --> 00:50:09,000 Looks like we need a couple of BOKA's plotting tools. 426 00:50:09,000 --> 00:50:24,000 That's okay with us. 427 00:50:24,000 --> 00:50:38,000 Okay, so the main thing we need are values. That actually shouldn't be the hardest thing to do. 428 00:50:38,000 --> 00:50:44,000 Maybe just partition our values. 429 00:50:44,000 --> 00:50:49,000 So, you know, 430 00:50:49,000 --> 00:50:54,000 let's just do it in the order that county names is in. 431 00:50:54,000 --> 00:51:04,000 So we basically want to define our values here. 432 00:51:04,000 --> 00:51:15,000 And so we'll want to just account for maybe just in case any of these counties don't have any. 433 00:51:15,000 --> 00:51:26,000 So what we want to get licensees per county. 434 00:51:26,000 --> 00:51:32,000 So we want to get the value for that county name. 435 00:51:32,000 --> 00:51:42,000 If we don't find anything, 436 00:51:42,000 --> 00:51:57,000 then we don't want it. 437 00:51:57,000 --> 00:52:02,000 And this is just because this is how we, 438 00:52:02,000 --> 00:52:06,000 well, how I formatted this data above. 439 00:52:06,000 --> 00:52:10,000 Okay, actually, I'm going to need to just do something here. 440 00:52:10,000 --> 00:52:14,000 Okay, so this just didn't work. 441 00:52:14,000 --> 00:52:23,000 I may revisit this later. 442 00:52:23,000 --> 00:52:26,000 Trying to do this on the quick. I think I can finish. 443 00:52:26,000 --> 00:52:31,000 Okay, so we've got our licensees per capita. 444 00:52:31,000 --> 00:52:40,000 And then let's just. 445 00:52:40,000 --> 00:52:45,000 Let's just do this simple, right? That's sort of the Candlesticks model. 446 00:52:45,000 --> 00:52:49,000 And I'm going to have to clean up all this code later. 447 00:52:49,000 --> 00:52:57,000 Okay, so we've got a much more simple dictionary now, which is the county name value. 448 00:52:57,000 --> 00:53:04,000 So that should make making these values much easier to get. 449 00:53:04,000 --> 00:53:12,000 Right now, our value is just going to be licensees per capita. 450 00:53:12,000 --> 00:53:16,000 Okay, much simpler. 451 00:53:16,000 --> 00:53:30,000 So we've got all our values. 452 00:53:30,000 --> 00:53:47,000 Okay, so this is the Oklahoma cannabis licensees per capita. 453 00:53:47,000 --> 00:53:54,000 Well, let's see what happens. 454 00:53:54,000 --> 00:54:00,000 We've got five minutes left to debug. 455 00:54:00,000 --> 00:54:04,000 If anything goes askew. 456 00:54:04,000 --> 00:54:08,000 And it's not held get the JavaScript up and running today. 457 00:54:08,000 --> 00:54:11,000 So thanks for bearing with. 458 00:54:11,000 --> 00:54:17,000 Thanks for bearing with as we went through this exploration here, but you saw us firsthand. 459 00:54:17,000 --> 00:54:29,000 How we go about scrounging the licensees data, supplementing it with population data from the Federal Reserve. 460 00:54:29,000 --> 00:54:33,000 Calculating licensees per capita. 461 00:54:33,000 --> 00:54:36,000 And. 462 00:54:36,000 --> 00:54:43,000 Ideally, providing a visualization here. 463 00:54:43,000 --> 00:54:55,000 And we've got an obscure error that I'm not certain I can debug right here on the fly. 464 00:54:55,000 --> 00:55:14,000 One can hope it's just some problem with these tooltips or something. 465 00:55:14,000 --> 00:55:25,000 But. 466 00:55:25,000 --> 00:55:47,000 So we unfortunately. 467 00:55:47,000 --> 00:56:15,000 They don't think it's the value so there must be something. 468 00:56:15,000 --> 00:56:19,000 What. 469 00:56:19,000 --> 00:56:34,000 What is going so. 470 00:56:34,000 --> 00:56:48,000 So let's just try to district. 471 00:56:48,000 --> 00:57:13,000 Oh. 472 00:57:13,000 --> 00:57:34,000 Probably because we don't have a plot. 473 00:57:34,000 --> 00:57:44,000 That's so bizarre. 474 00:57:44,000 --> 00:58:13,000 So. 475 00:58:13,000 --> 00:58:21,000 So we may have to give it one shot here in spider just. 476 00:58:21,000 --> 00:58:25,000 Just to see if there's something just going on with the plotting. 477 00:58:25,000 --> 00:58:29,000 But otherwise, I may have to send it to. 478 00:58:29,000 --> 00:58:34,000 The final output to you afterwards. 479 00:58:34,000 --> 00:58:49,000 Just a little bit of a letdown because I was real curious to see this data and talk about it here. 480 00:58:49,000 --> 00:59:17,000 Let's just do one quick Google search and then we may have to just. 481 00:59:17,000 --> 00:59:21,000 Oh, that's right. So it gives us. 482 00:59:21,000 --> 00:59:23,000 This HTML. 483 00:59:23,000 --> 00:59:31,000 Hold on. We may just have a plot here. 484 00:59:31,000 --> 00:59:36,000 That's bizarre. 485 00:59:36,000 --> 00:59:40,000 We may just have a plot. 486 00:59:40,000 --> 00:59:48,000 That would be bizarre if. 487 00:59:48,000 --> 00:59:53,000 Well, by golly. 488 00:59:53,000 --> 00:59:56,000 Something happened. 489 00:59:56,000 --> 01:00:10,000 I don't think this is what we quite what we quite wanted. It was it. So this appears to be Oklahoma. 490 01:00:10,000 --> 01:00:18,000 With with only some of the county shaded in. 491 01:00:18,000 --> 01:00:26,000 And and so. 492 01:00:26,000 --> 01:00:31,000 So long story short, going to need to touch this up a bit. 493 01:00:31,000 --> 01:00:37,000 But what you can see right off the bat here. 494 01:00:37,000 --> 01:00:45,000 So there's going to be a lot of cleanup that needs to be done. So thank you for bearing with this initial for a. 495 01:00:45,000 --> 01:00:48,000 But this is I mean, this is interesting right off the bat. 496 01:00:48,000 --> 01:00:51,000 So it's going to need to be cleaned up a bit. 497 01:00:51,000 --> 01:00:55,000 But as you can see. 498 01:00:55,000 --> 01:01:01,000 The counties that even permit. It looks like cannabis licensees are limited. 499 01:01:01,000 --> 01:01:06,000 And so it looks like I want to get the actual numbers here and get the shaded in. 500 01:01:06,000 --> 01:01:18,000 But surprisingly, it looks like a wide swath of the licensees are in this. 501 01:01:18,000 --> 01:01:21,000 Eastern Crescent. 502 01:01:21,000 --> 01:01:23,000 Here. 503 01:01:23,000 --> 01:01:26,000 So. 504 01:01:26,000 --> 01:01:31,000 So I apologize that these maps were. 505 01:01:31,000 --> 01:01:39,000 Finicky and not behaving well. And so that's that's the thing with geographical data like it is. 506 01:01:39,000 --> 01:01:48,000 A wrangling process to get these maps to work. And so I'm going to spend a bit of time today and get these cleaned up because this is a little embarrassing. 507 01:01:48,000 --> 01:01:52,000 And so for next week, I can point you at. 508 01:01:52,000 --> 01:02:02,000 I can point you to. 509 01:02:02,000 --> 01:02:05,000 State data trove. 510 01:02:05,000 --> 01:02:09,000 As we fill in state by state. 511 01:02:09,000 --> 01:02:15,000 And so and hopefully county by county. 512 01:02:15,000 --> 01:02:23,000 So it's going to be a little bit of a journey here getting all this data. 513 01:02:23,000 --> 01:02:26,000 Processed and presentable. 514 01:02:26,000 --> 01:02:29,000 However, we're on the right road. 515 01:02:29,000 --> 01:02:32,000 So. 516 01:02:32,000 --> 01:02:38,000 I did my best to present this data will will give it another go again next week. 517 01:02:38,000 --> 01:02:43,000 So thanks for bearing with this ad hoc data presentation here. 518 01:02:43,000 --> 01:02:49,000 And I'm going to go ahead and wrap it up there. 519 01:02:49,000 --> 01:02:53,000 If there are any. 520 01:02:53,000 --> 01:03:02,000 Questions I can field a couple of questions before concluding. 521 01:03:02,000 --> 01:03:05,000 Yeah, I actually had a question. 522 01:03:05,000 --> 01:03:21,000 By all means, so sorry about earlier. I was having trouble getting my microphone unblocked, but I'm pretty new to data science. I was just wondering why you decided to pick Python to examine your data. 523 01:03:21,000 --> 01:03:27,000 Good question. That's sort of my tool of choice. So. 524 01:03:27,000 --> 01:03:41,000 It's just what I'm most comfortable with. So I work with a couple people and they like to use our so that's their first go to choice. So for me. 525 01:03:41,000 --> 01:03:53,000 I find it simple. It just generally reads like English. I find it powerful. Basically, I find if I want to do something with Python. 526 01:03:53,000 --> 01:04:02,000 And it can be done on a computer. I can do it, which is awesome because then like you saw today. 527 01:04:02,000 --> 01:04:18,000 The Oklahoma licensees data is in a bunch of PDFs. And so I found okay well that's on a computer. I would normally download this PDFs and put them into Excel by hand. 528 01:04:18,000 --> 01:04:28,000 Manually with Python, I can download all those PDFs scrounge all the data, get it into Excel with the click of a button. 529 01:04:28,000 --> 01:04:37,000 I'm sure you can do this with other programming languages. So I'm sure you could potentially do this with our. 530 01:04:37,000 --> 01:04:42,000 You could definitely do this with C or. 531 01:04:42,000 --> 01:04:51,000 Or who knows? I'm not well versed with programming languages that you know there could be Java or. 532 01:04:51,000 --> 01:05:02,000 Potentially PHP, what have you. So it's to me I see them as tools, you know means to an end. So. 533 01:05:02,000 --> 01:05:14,000 I just picked Python because I knew I could. Well, I didn't quite get the job done, but I knew I could get given more time I could get the job done. So I knew I could get the job done with Python. 534 01:05:14,000 --> 01:05:24,000 So that's why I chose in. So does that answer your question? Yeah, for sure. Thank you. 535 01:05:24,000 --> 01:05:33,000 How about yourself? Do you have any go to tools that you use for data science? So far, no. 536 01:05:33,000 --> 01:05:45,000 I'm going through the Thinkful program at the moment and I finished with Excel and then I'm pretty close to finishing SQL, but I haven't started with the other ones yet. 537 01:05:45,000 --> 01:05:54,000 OK, so the SQL will be OK. That's where OK, yes, we need to store our data in the database. 538 01:05:54,000 --> 01:06:03,000 By myself, I'm not the biggest SQL expert. I a lot of the times use no SQL Firebase. 539 01:06:03,000 --> 01:06:15,000 And so that's like the JSON like dictionary format formatted data for me. It's a bit more natural and. 540 01:06:15,000 --> 01:06:33,000 I gravitate towards. So my recommendation, of course, I'm biased, but my recommendation would be what you may want to try to install Python and try to do a little bit. 541 01:06:33,000 --> 01:06:48,000 Of course, try to figure out, OK, what are some of the repetitive tasks that you find yourself doing? Oh, I'm downloading this data set on every week or oh. 542 01:06:48,000 --> 01:06:58,000 I keep searching for these journal articles or this or that and just find things that you do repeatedly and automate them. 543 01:06:58,000 --> 01:07:07,000 And so you could set up what's called a cron job, so that's just have your Python script run automatically. 544 01:07:07,000 --> 01:07:13,000 So you could have your Python script run every Monday and. 545 01:07:13,000 --> 01:07:19,000 Get you data for the week, so. That's my personal recommendation. 546 01:07:19,000 --> 01:07:30,000 Python's got a close place to my heart. So maybe maybe it'll find a place near your heart. Yeah, hopefully. 547 01:07:30,000 --> 01:07:38,000 I think we're learning that next, so OK, well, my recommendation is. 548 01:07:38,000 --> 01:07:44,000 Make it make it your own. So like a lot of times. 549 01:07:44,000 --> 01:07:52,000 You know you get out of things which put into them, and so that's why I got so I learned Python in a. 550 01:07:52,000 --> 01:08:06,000 Computational economics course and it was one of those things where I just fell in love with it and I realized, oh wow, I can make really cool plots with Python. 551 01:08:06,000 --> 01:08:15,000 And so I just went out of my way to whenever I needed to make a good looking plot. 552 01:08:15,000 --> 01:08:21,000 I would do it with Python, no matter how hard or arduous. 553 01:08:21,000 --> 01:08:28,000 It was so. But it was worth it. It paid off because. 554 01:08:28,000 --> 01:08:33,000 The cool thing is, once you write the code, you have it so. 555 01:08:33,000 --> 01:08:38,000 Although it wasn't that clean rate, I was digging up this old. 556 01:08:38,000 --> 01:08:47,000 Boca chloroplet chart that we had done months months ago, and so with a bit more tinkering, we could get it to work. 557 01:08:47,000 --> 01:08:55,000 But long story short, you know you can and that's the that's the importance to writing clean code is so you can just pick it back up months later and use it. 558 01:08:55,000 --> 01:09:00,000 That was not clean code could not pick it back up and use it. That was unfortunate. 559 01:09:00,000 --> 01:09:07,000 So long story short, you can write these awesome tools for yourself, which will. 560 01:09:07,000 --> 01:09:12,000 Be there for perpetuity and. 561 01:09:12,000 --> 01:09:16,000 Sky's the limit, so that's my. 562 01:09:16,000 --> 01:09:20,000 Promo for Python. Thank you. 563 01:09:20,000 --> 01:09:23,000 Yes. 564 01:09:23,000 --> 01:09:30,000 On that note, future Python East is I want to go ahead and wrap it up for today. 565 01:09:30,000 --> 01:09:33,000 Just don't want to just. 566 01:09:33,000 --> 01:09:38,000 Just stay, you know, just spread it too long, but. 567 01:09:38,000 --> 01:09:46,000 Like I said, there's still more cleanup to be done here. So for next week we can have some real beautiful looking charts. 568 01:09:46,000 --> 01:09:54,000 And so that's what I'll be tinkering on, but you can at least I think it's important to see the process because it's not like. 569 01:09:54,000 --> 01:10:05,000 Just someone waves a magic wand or you know flashes their little curtain or what have you and then. 570 01:10:05,000 --> 01:10:14,000 Yeah, so pleasure people don't just wave a wand and things are made there's there's tinkering to be done and there's mistakes to be made along the way. 571 01:10:14,000 --> 01:10:19,000 And so, as you saw, we've got some ugly looking charts along the way we're making some mistakes. 572 01:10:19,000 --> 01:10:23,000 We're we're figuring this out, but. 573 01:10:23,000 --> 01:10:31,000 You'll see next week the sausage will hopefully we'll get some good visualizations out of it. 574 01:10:31,000 --> 01:10:36,000 So that's the idea. 575 01:10:36,000 --> 01:10:38,000 On that note. 576 01:10:38,000 --> 01:10:48,000 Thank you both for attending for coming today and definitely feel free to reach out if you have anything you want specifically covered in in the coming weeks. 577 01:10:48,000 --> 01:11:04,000 And then next week we'll be moving on to more states and just trying to fill in this map and get all the public cannabis data that we can and make it available. 578 01:11:04,000 --> 01:11:10,000 All right, I'm going to end the recording here and see you both next week.