1 00:00:00,000 --> 00:00:14,400 I think it would be interesting to talk about some of the waste data you've put together, 2 00:00:14,400 --> 00:00:15,400 Charles. 3 00:00:15,400 --> 00:00:16,400 And so... 4 00:00:16,400 --> 00:00:17,400 Okay. 5 00:00:17,400 --> 00:00:18,400 So did you download... 6 00:00:18,400 --> 00:00:19,400 Yeah, okay. 7 00:00:19,400 --> 00:00:20,400 This is the latest one. 8 00:00:20,400 --> 00:00:31,400 I think I just updated it yesterday. 9 00:00:31,400 --> 00:00:33,080 Yes, exactly. 10 00:00:33,080 --> 00:00:38,960 So I was tinkering on this throughout the past week, and yes, I saw you just made a 11 00:00:38,960 --> 00:00:42,760 commit that looks like yesterday at some point. 12 00:00:42,760 --> 00:00:48,880 So I've been looking over it, but I may not have all of your latest work. 13 00:00:48,880 --> 00:00:57,360 So if you want to share any of that while we go through it, then that would be awesome. 14 00:00:57,360 --> 00:01:11,280 So essentially, just to give you some background here, Sela, you can do Freedom of Information 15 00:01:11,280 --> 00:01:20,640 requests from the... here in Washington from the Liquor and Cannabis Board, and you'll 16 00:01:20,640 --> 00:01:30,640 get a data dump that looks like this, where they'll... if you're requesting traceability 17 00:01:30,640 --> 00:01:31,640 data. 18 00:01:31,640 --> 00:01:39,720 So they'll return essentially zipped folders of the different endpoints. 19 00:01:39,720 --> 00:01:45,280 So these are the various endpoints of the traceability API. 20 00:01:45,280 --> 00:01:55,320 So for example, licensees record their sales, they record how many plants they have, they 21 00:01:55,320 --> 00:02:04,280 record their lab results, their inventory, and tucked away down here in batches, we've 22 00:02:04,280 --> 00:02:12,640 found that they actually record how much waste they generate. 23 00:02:12,640 --> 00:02:28,560 And there's a company, Better Carbon Solutions, and they're... as far as I know, the only... 24 00:02:28,560 --> 00:02:36,120 well, actually, there's maybe a second one up in Bellevue, Bellingham, but these are 25 00:02:36,120 --> 00:02:44,240 the ones that I personally know that are working on a solution for hemp and cannabis waste. 26 00:02:44,240 --> 00:02:51,360 And they have no idea how much waste there is. 27 00:02:51,360 --> 00:02:57,960 So we're going to try to make some estimates. 28 00:02:57,960 --> 00:03:10,200 So really just to find the right... 29 00:03:10,200 --> 00:03:18,920 So we'll need this data. 30 00:03:18,920 --> 00:03:30,440 Let me actually pause for one second while I load in the data here. 31 00:03:30,440 --> 00:03:31,920 One second here. 32 00:03:31,920 --> 00:03:42,040 I forgot to get the data. 33 00:03:42,040 --> 00:03:56,680 One second. 34 00:03:56,680 --> 00:04:17,160 Okay, so... 35 00:04:17,160 --> 00:04:21,600 Downloaded some data sets here. 36 00:04:21,600 --> 00:04:31,760 The batches, although it looks like it's about one gigabyte of data in the zip folder, and 37 00:04:31,760 --> 00:04:40,640 when you unzip it, you'll see that the data file here, the batches, is 20 gigabytes. 38 00:04:40,640 --> 00:04:43,080 So that's a big... 39 00:04:43,080 --> 00:04:56,120 So there's a large amount of data, and my laptop has 16 gigabytes of RAM, so I cannot 40 00:04:56,120 --> 00:05:02,400 read in this entire data file in one go. 41 00:05:02,400 --> 00:05:11,120 So there's a couple tools we can use to approach this. 42 00:05:11,120 --> 00:05:18,640 So Charles showed me Dask, which is... 43 00:05:18,640 --> 00:05:24,960 It implements the Pandas data frame, so it's similar to working with Pandas. 44 00:05:24,960 --> 00:05:34,360 And so you can think about it as instead of just reading in everything all at once, you're 45 00:05:34,360 --> 00:05:40,000 sort of chunking this. 46 00:05:40,000 --> 00:05:44,320 I... 47 00:05:44,320 --> 00:05:52,240 It may be that Dask hasn't implemented everything, but I actually didn't have the greatest luck 48 00:05:52,240 --> 00:05:57,040 reading in this data with Dask. 49 00:05:57,040 --> 00:06:00,840 Did you run into similar issues, Charles? 50 00:06:00,840 --> 00:06:01,840 Yes. 51 00:06:01,840 --> 00:06:11,280 I had to read it in with Pandas, save it as a parquet file, and then Dask would read it 52 00:06:11,280 --> 00:06:14,440 in. 53 00:06:14,440 --> 00:06:21,560 But I was lucky enough that my Mac has an infinite swap file. 54 00:06:21,560 --> 00:06:26,400 It will just keep expanding it until apparently it uses up the entire disk. 55 00:06:26,400 --> 00:06:28,400 Where on Windows... 56 00:06:28,400 --> 00:06:29,960 You're using Windows, right? 57 00:06:29,960 --> 00:06:30,960 Yes. 58 00:06:30,960 --> 00:06:37,320 So maybe your swap file isn't big enough or Windows has some limit. 59 00:06:37,320 --> 00:06:38,320 I don't know. 60 00:06:38,320 --> 00:06:43,760 Yeah, on my Linux machine, I had to change the size of the swap file in order to get 61 00:06:43,760 --> 00:06:46,160 it to work. 62 00:06:46,160 --> 00:06:48,480 But yeah, it does not like that file. 63 00:06:48,480 --> 00:06:53,240 It will not read it as a CSV. 64 00:06:53,240 --> 00:06:55,280 I don't know if I can... 65 00:06:55,280 --> 00:07:00,320 I'm not sure how big those parquet files are, if I can share them easily. 66 00:07:00,320 --> 00:07:12,280 So it's not the end of the world because that's why we've got Python here, is to sort of make 67 00:07:12,280 --> 00:07:18,440 the unmanageable manageable. 68 00:07:18,440 --> 00:07:22,240 So this is the code that Charles has written here. 69 00:07:22,240 --> 00:07:33,760 So I've just snagged Charles's code from here. 70 00:07:33,760 --> 00:07:37,040 Charles has actually done an awesome analysis here. 71 00:07:37,040 --> 00:07:43,200 And so, Stella, you may want to go through here because here Charles has actually really 72 00:07:43,200 --> 00:07:48,580 just walked through the steps you need to as you're going about data. 73 00:07:48,580 --> 00:07:56,640 So for example, you've read in the data and you started to look at it. 74 00:07:56,640 --> 00:07:58,720 And I love what you did here. 75 00:07:58,720 --> 00:08:05,580 Almost one of the very first things you did was simply describe the data. 76 00:08:05,580 --> 00:08:12,260 So just to see what are we working with here. 77 00:08:12,260 --> 00:08:22,680 So we're working with, what's this, 37 million plants. 78 00:08:22,680 --> 00:08:25,440 That's a lot of plants. 79 00:08:25,440 --> 00:08:30,920 And so that's real cool data that... 80 00:08:30,920 --> 00:08:36,320 They're not really recording 37 million tomato plants out there. 81 00:08:36,320 --> 00:08:46,080 And so it's so cool that we've got such granular data on the cannabis industry. 82 00:08:46,080 --> 00:08:54,800 So enough fawning over Charles's analysis here because we can revisit this here in a 83 00:08:54,800 --> 00:08:55,800 second. 84 00:08:55,800 --> 00:09:06,240 So just to make this manageable here just for the time being, just for the presentation 85 00:09:06,240 --> 00:09:07,240 essentially. 86 00:09:07,240 --> 00:09:15,520 What you can do with pandas is you can just essentially just read in a number of rows 87 00:09:15,520 --> 00:09:18,200 at a time. 88 00:09:18,200 --> 00:09:27,960 And so the way I was going to approach this problem, since I've got a limited bandwidth 89 00:09:27,960 --> 00:09:34,280 here, limited memory, and I'm having problems with Dask. 90 00:09:34,280 --> 00:09:38,720 I was essentially just going to chunk it. 91 00:09:38,720 --> 00:09:48,800 So just read in 10,000 observations at a time or can play with that number. 92 00:09:48,800 --> 00:10:05,320 And essentially the data point we're after is what Charles has calculated here as the 93 00:10:05,320 --> 00:10:11,760 amount of waste by producer by date. 94 00:10:11,760 --> 00:10:24,040 And I was thinking if we group them in 10,000, we can calculate the waste by producer by 95 00:10:24,040 --> 00:10:29,960 date and then save that. 96 00:10:29,960 --> 00:10:39,360 And then we can basically then read in all those files and then essentially combine that 97 00:10:39,360 --> 00:10:40,360 data. 98 00:10:40,360 --> 00:10:46,640 Because there won't be any data loss. 99 00:10:46,640 --> 00:10:54,000 We're just looking for the total so we can break the total up. 100 00:10:54,000 --> 00:10:59,240 So it doesn't really even necessarily matter how the data is sorted. 101 00:10:59,240 --> 00:11:08,640 It'll be nice if the data is kind of nicely in order, but the chunks can split things 102 00:11:08,640 --> 00:11:17,160 up because we're at the end of the day, we're just looking for the total. 103 00:11:17,160 --> 00:11:26,040 So the long story short, just for today's demonstration, we'll just do one with a chunk 104 00:11:26,040 --> 00:11:36,600 of 10,000 and then what I'm going to be working with, unless Charles has a more elegant solution, 105 00:11:36,600 --> 00:11:40,920 but Charles does have a more elegant solution. 106 00:11:40,920 --> 00:11:46,080 I just don't have quite enough memory for it. 107 00:11:46,080 --> 00:12:04,000 So basically I'm just going to kind of walk through this stack overflow. 108 00:12:04,000 --> 00:12:06,960 So how can I read in a huge CSV file? 109 00:12:06,960 --> 00:12:13,720 Well basically we're just going to have to iterate over the data frame, read in chunk 110 00:12:13,720 --> 00:12:25,280 by chunk, calculate the waste by producer by date, save it, aggregate it, and then hopefully 111 00:12:25,280 --> 00:12:29,280 at that point we'll have just a nice series. 112 00:12:29,280 --> 00:12:38,200 In fact, we may even aggregate it further and just do total waste by date. 113 00:12:38,200 --> 00:12:43,640 That's I think an informative number we'll do. 114 00:12:43,640 --> 00:12:47,560 We'll revisit the producer by date as well. 115 00:12:47,560 --> 00:12:54,280 But long story short, let's start here with this chunk. 116 00:12:54,280 --> 00:13:06,400 Just for expedience sake, I just gone ahead and read it in here. 117 00:13:06,400 --> 00:13:23,400 So basically I just, this will just save us a tidbit of time, but basically what Charles 118 00:13:23,400 --> 00:13:30,320 has done here is he's defined the columns that we actually need to read in. 119 00:13:30,320 --> 00:13:37,480 This is sort of a little bit of advanced work, or a little bit of a, you know, thanks to 120 00:13:37,480 --> 00:13:44,480 Charles's foresight, we can kind of leapfrog a little bit. 121 00:13:44,480 --> 00:13:48,040 So we already know like what columns are in the data. 122 00:13:48,040 --> 00:13:53,320 But if you were reading this in for the first time, you may not know what columns you even 123 00:13:53,320 --> 00:13:54,320 know. 124 00:13:54,320 --> 00:13:58,160 But long story short, we're going to read in 10,000. 125 00:13:58,160 --> 00:14:10,240 I'm skipping two million observations because for whatever reason, a lot of the observations 126 00:14:10,240 --> 00:14:13,720 at the beginning of the data, there was no waste. 127 00:14:13,720 --> 00:14:23,200 So just for today, I wanted to get a chunk that actually had waste. 128 00:14:23,200 --> 00:14:30,960 So we've got the waste data here. 129 00:14:30,960 --> 00:14:37,520 We can describe it as Charles did. 130 00:14:37,520 --> 00:14:50,320 Here we've just got 10,000 plants and we've got the amount of, you know, well let's actually 131 00:14:50,320 --> 00:15:00,720 see what columns we have here. 132 00:15:00,720 --> 00:15:06,320 For example, we've got the flower dry weight, which of course is what the producers care 133 00:15:06,320 --> 00:15:07,320 about. 134 00:15:07,320 --> 00:15:12,920 But the other half of that is the waste. 135 00:15:12,920 --> 00:15:16,720 And so that's what Better Carbon Solutions cares about. 136 00:15:16,720 --> 00:15:23,480 So they're trying to get that waste off of their hands and compost it essentially on 137 00:15:23,480 --> 00:15:26,320 your pyrolysis. 138 00:15:26,320 --> 00:15:36,120 So they can make products with it instead of it going into the landfill. 139 00:15:36,120 --> 00:15:41,360 And so Charles noted here that we've got some negative values. 140 00:15:41,360 --> 00:15:50,480 And so Charles, I ran into just an error when I ran this line for some reason. 141 00:15:50,480 --> 00:16:01,040 So I just approached this as just essentially taking the absolute value of the waste. 142 00:16:01,040 --> 00:16:08,880 So let me know if that achieves the same outcome. 143 00:16:08,880 --> 00:16:11,600 But basically there were negative values for waste. 144 00:16:11,600 --> 00:16:13,720 Yeah, actually that's a good solution. 145 00:16:13,720 --> 00:16:18,000 I didn't think about that. 146 00:16:18,000 --> 00:16:21,280 Well yours was perfectly fine. 147 00:16:21,280 --> 00:16:23,320 But I just ran into an error. 148 00:16:23,320 --> 00:16:24,320 We'll see here. 149 00:16:24,320 --> 00:16:27,320 I'll show you. 150 00:16:27,320 --> 00:16:33,120 Well that time it worked. 151 00:16:33,120 --> 00:16:39,880 For some reason the first time I ran into an error of some sort. 152 00:16:39,880 --> 00:16:56,360 Anyways, either way works. 153 00:16:56,360 --> 00:17:07,240 So I actually haven't really walked through this line here. 154 00:17:07,240 --> 00:17:08,240 So let's see here. 155 00:17:08,240 --> 00:17:09,240 So let's see. 156 00:17:09,240 --> 00:17:20,880 Okay, so here you're filling in the plant stage. 157 00:17:20,880 --> 00:17:22,800 Oh yeah. 158 00:17:22,800 --> 00:17:28,920 There were some places where it was NA and that will cause problems later on. 159 00:17:28,920 --> 00:17:38,200 So basically we're assuming if there's no plant stage given then it's harvested essentially. 160 00:17:38,200 --> 00:17:39,200 Right. 161 00:17:39,200 --> 00:17:45,640 Oh yeah, and I filled it in with the most common value. 162 00:17:45,640 --> 00:17:53,240 Which is, you know, that's a technique that's pretty common. 163 00:17:53,240 --> 00:17:56,840 Excellent. 164 00:17:56,840 --> 00:18:02,320 Well this is why it's nice to have you here while we walk through this. 165 00:18:02,320 --> 00:18:05,080 Find the amount of waste. 166 00:18:05,080 --> 00:18:10,120 Okay, so now we're just looking at waste for harvested products. 167 00:18:10,120 --> 00:18:11,800 Is that correct? 168 00:18:11,800 --> 00:18:12,800 Yeah. 169 00:18:12,800 --> 00:18:19,560 Because if you take all the different stages, you know, the analysis you get is kind of 170 00:18:19,560 --> 00:18:27,760 murky so if you break it up by the stages you get a sort of a clearer picture. 171 00:18:27,760 --> 00:18:40,160 Can we describe just the waste? 172 00:18:40,160 --> 00:18:50,080 Okay so do we know what the unit of measure is by any chance? 173 00:18:50,080 --> 00:18:53,600 So at this point it should be grams. 174 00:18:53,600 --> 00:19:00,840 There's a point where I converted to kilograms but I don't think, yeah that's not here yet. 175 00:19:00,840 --> 00:19:07,800 Because some of the numbers are really huge and it just sort of made more sense to present 176 00:19:07,800 --> 00:19:12,560 it in kilograms. 177 00:19:12,560 --> 00:19:14,160 Here we are with your analysis. 178 00:19:14,160 --> 00:19:19,800 So we've sort of skipped over some things but like I said you've done some thorough 179 00:19:19,800 --> 00:19:29,040 analysis here. 180 00:19:29,040 --> 00:19:36,280 So here are some charts Charles has presented or prepared. 181 00:19:36,280 --> 00:19:38,480 You have the full data set here. 182 00:19:38,480 --> 00:19:47,160 So there's a lot of data that's, you know, where all the dates are 11, 1900 which of 183 00:19:47,160 --> 00:19:52,720 course doesn't make any sense, doesn't really help. 184 00:19:52,720 --> 00:20:02,520 There's some pre-2012 dates and there's some pre-2018 dates and this data is sort of supposed 185 00:20:02,520 --> 00:20:05,960 to be from 2018 forward. 186 00:20:05,960 --> 00:20:13,360 So I cleared that out and I just kind of went from 2018 forward. 187 00:20:13,360 --> 00:20:20,900 Question I see, did you eventually just basically abandon harvest date and just decide to go 188 00:20:20,900 --> 00:20:24,840 with updated at date essentially? 189 00:20:24,840 --> 00:20:30,640 Yes because if you go up I explain why I do that. 190 00:20:30,640 --> 00:20:41,520 Yeah there's 87, almost 88 percent of the data has a harvest date before 2018. 191 00:20:41,520 --> 00:20:47,560 So it's not really, you know, it's not a good date and there's a lot of harvested dates 192 00:20:47,560 --> 00:20:50,560 that are 11, 1900. 193 00:20:50,560 --> 00:20:57,280 So basically there's not really, they don't do any data checking when they add this data 194 00:20:57,280 --> 00:21:00,280 to the database. 195 00:21:00,280 --> 00:21:10,440 And Sala you may be able to speak to this but I think my experience with the traceability 196 00:21:10,440 --> 00:21:15,640 system I would go exactly with updated at date because this is essentially going to 197 00:21:15,640 --> 00:21:23,800 be a automatically generated number by the traceability system. 198 00:21:23,800 --> 00:21:31,880 As you noted if they, you know, revisited their harvest at a later date, just say they 199 00:21:31,880 --> 00:21:39,160 made an error and then they fix their error then the updated at time would change. 200 00:21:39,160 --> 00:21:50,520 But I think that's a smaller error than whatever's going on here, right? 201 00:21:50,520 --> 00:21:59,360 Because at the end of the day we're trying to minimize our sort of our bias or, you know, 202 00:21:59,360 --> 00:22:01,800 our forecasting. 203 00:22:01,800 --> 00:22:13,480 So when you have entry errors like this that actually introduces statistical bias into 204 00:22:13,480 --> 00:22:15,240 your calculations. 205 00:22:15,240 --> 00:22:18,440 So you know the closer you are to the true value the better. 206 00:22:18,440 --> 00:22:28,240 And so yes, the updated at may, there may still be errors in updated at times but I 207 00:22:28,240 --> 00:22:35,400 think as you settled on it's just going to be a much, I think it's going to be a much 208 00:22:35,400 --> 00:22:41,800 reliable measure of time than the harvest date. 209 00:22:41,800 --> 00:22:45,680 Yeah. 210 00:22:45,680 --> 00:22:49,480 And that's why, you know, rule number one is look at the data. 211 00:22:49,480 --> 00:22:57,600 So fantastic, fantastic work there Charles because all you really need to do is you plot 212 00:22:57,600 --> 00:23:05,600 those and now we know how we can keep track of time. 213 00:23:05,600 --> 00:23:11,560 So I think this is a real cool chart here where this is just showing the amount of waste 214 00:23:11,560 --> 00:23:20,120 generated I think essentially are these like essentially. 215 00:23:20,120 --> 00:23:21,120 By day. 216 00:23:21,120 --> 00:23:25,760 Okay, so these are totals by day or by producer by day? 217 00:23:25,760 --> 00:23:26,760 By day. 218 00:23:26,760 --> 00:23:33,960 We get down to by producer later on. 219 00:23:33,960 --> 00:23:35,400 Interesting. 220 00:23:35,400 --> 00:23:42,720 So maybe some of these are when the larger producers harvest and there's a lot of waste. 221 00:23:42,720 --> 00:23:45,640 Oh, cool charts here. 222 00:23:45,640 --> 00:23:48,520 Are these new? 223 00:23:48,520 --> 00:23:53,960 Probably I've been, you know, I've been, you know, sort of thinking about it and updating 224 00:23:53,960 --> 00:23:59,320 it, you know, and like how can I improve it? 225 00:23:59,320 --> 00:24:00,320 This is fantastic. 226 00:24:00,320 --> 00:24:03,680 I haven't seen this chart yet. 227 00:24:03,680 --> 00:24:11,040 So I think this you may have added this since I was looking at it because this is exactly 228 00:24:11,040 --> 00:24:14,000 this is exactly what needed to be plotted. 229 00:24:14,000 --> 00:24:16,160 So thank you Charles. 230 00:24:16,160 --> 00:24:19,720 Like awesome work here. 231 00:24:19,720 --> 00:24:23,720 This is essentially what everybody's been curious about. 232 00:24:23,720 --> 00:24:25,840 I don't think anybody's plotted this. 233 00:24:25,840 --> 00:24:35,920 In fact, I'll have to share an article with you, Charles. 234 00:24:35,920 --> 00:24:39,480 There's an article in The Stranger. 235 00:24:39,480 --> 00:24:46,440 Washington State has a I don't know like a I forget what the title is like Washington 236 00:24:46,440 --> 00:24:50,680 State has a million ton waste problem or something like that. 237 00:24:50,680 --> 00:24:52,800 I'll share the article with you. 238 00:24:52,800 --> 00:25:02,120 And so essentially the author was calling around to like recycling places trying to 239 00:25:02,120 --> 00:25:10,560 figure out like what the actual weight of waste is. 240 00:25:10,560 --> 00:25:16,360 And he just sort of made estimates because like this data well he actually wrote this 241 00:25:16,360 --> 00:25:20,200 article back in 2017. 242 00:25:20,200 --> 00:25:23,720 Especially before this data was there. 243 00:25:23,720 --> 00:25:28,520 As we found out the data is tough to wrangle. 244 00:25:28,520 --> 00:25:38,520 So although the data is there and as you showed we can calculate them I don't know if anybody 245 00:25:38,520 --> 00:25:39,520 has. 246 00:25:39,520 --> 00:25:46,800 And so this Charles you really may be the first person who's plotted harvest weight 247 00:25:46,800 --> 00:25:48,920 by month. 248 00:25:48,920 --> 00:25:55,840 So good work. 249 00:25:55,840 --> 00:26:00,200 And so this is it. 250 00:26:00,200 --> 00:26:04,080 I'll just give you my hot take my first look here. 251 00:26:04,080 --> 00:26:15,280 It's incredibly interesting to observe this this large amount in 2018. 252 00:26:15,280 --> 00:26:23,360 And I want to say that that may be producers essentially entering their essentially initial 253 00:26:23,360 --> 00:26:26,560 inventory into the traceability system. 254 00:26:26,560 --> 00:26:27,560 Okay. 255 00:26:27,560 --> 00:26:30,160 I wondered about that. 256 00:26:30,160 --> 00:26:35,080 Actually if you go down to the next chart I cut off February and March and then I also 257 00:26:35,080 --> 00:26:40,000 cut off January of 2021 because that was incomplete. 258 00:26:40,000 --> 00:26:43,280 And then I thought the other two were outliers. 259 00:26:43,280 --> 00:26:47,120 So I think this is more representative of the actual data. 260 00:26:47,120 --> 00:26:49,480 Yes. 261 00:26:49,480 --> 00:26:52,040 I think yeah I think I think you're right. 262 00:26:52,040 --> 00:26:59,440 This is and this way the scale you have a better idea of the scale. 263 00:26:59,440 --> 00:27:06,600 So just to explain I think so basically the traceability system was introduced in January 264 00:27:06,600 --> 00:27:18,440 or February of 2018 and they had a contingency period where they had to essentially enter 265 00:27:18,440 --> 00:27:29,120 all because these were companies that were already operational with the bio track traceability 266 00:27:29,120 --> 00:27:30,320 system. 267 00:27:30,320 --> 00:27:35,200 So the before leaf data systems there was bio track. 268 00:27:35,200 --> 00:27:43,360 They're already operational and they're given a contingency period through roughly April 269 00:27:43,360 --> 00:27:45,120 of 2018. 270 00:27:45,120 --> 00:27:52,400 I'll actually want to double check the I'll have to do some because if you look at the 271 00:27:52,400 --> 00:28:00,200 press releases they may have pushed back the contingency period but around April of 2018 272 00:28:00,200 --> 00:28:06,880 it was sort of the deadline to get all your inventory into the system. 273 00:28:06,880 --> 00:28:11,680 So that's real curious. 274 00:28:11,680 --> 00:28:19,160 So maybe people just had like a bunch of waste sitting around and they just had to get that 275 00:28:19,160 --> 00:28:21,520 entered. 276 00:28:21,520 --> 00:28:30,560 If you look there's definitely a lot of pre 2018 dates in some of this data. 277 00:28:30,560 --> 00:28:38,320 So I think yeah I think they just must have had entered data maybe from the previous year 278 00:28:38,320 --> 00:28:47,160 or but yeah anyway just kind of those couple months sort of skewed things so I just decided 279 00:28:47,160 --> 00:28:53,400 to drop them because if you drop them you sort of see kind of a trend. 280 00:28:53,400 --> 00:28:57,560 This is where you get to the art of data science. 281 00:28:57,560 --> 00:29:04,880 So one thing we're going to be getting to here is forecasting right. 282 00:29:04,880 --> 00:29:17,920 So we'll want to forecast this forward you know 12 months you know to kind of predict 283 00:29:17,920 --> 00:29:22,320 2021. 284 00:29:22,320 --> 00:29:29,000 My technique of for it well not my technique if technique of forecasting is you know a 285 00:29:29,000 --> 00:29:41,400 Rima forecasting box Jenkins where you basically you just take historic data and you basically 286 00:29:41,400 --> 00:29:47,520 use statistics to calculate the sort of like a moving average and you just basically continue 287 00:29:47,520 --> 00:29:50,380 the moving average out into the future. 288 00:29:50,380 --> 00:30:00,320 So it's a theoretical and it's it forecasts fairly well with time series surprisingly 289 00:30:00,320 --> 00:30:04,920 well so. 290 00:30:04,920 --> 00:30:11,540 But one thing if you were doing box Jenkins forecasting if you included all these observations 291 00:30:11,540 --> 00:30:18,040 in there you may get wonky forecasts because. 292 00:30:18,040 --> 00:30:25,080 If you conclude these observations the box Jenkins may like put like this negative trend 293 00:30:25,080 --> 00:30:35,280 on waste but if you really exclude those prior years and you just look at the more recent 294 00:30:35,280 --> 00:30:44,400 data you know it's not really obvious there's a negative trend per se. 295 00:30:44,400 --> 00:30:54,480 So you know it's not a big thing perhaps but not not like there was at first glance. 296 00:30:54,480 --> 00:31:04,160 So just to kind of I guess look through some more of your analysis here I guess before 297 00:31:04,160 --> 00:31:08,960 we get to forecasting. 298 00:31:08,960 --> 00:31:16,840 So this is so I guess let's just look at the totals here so. 299 00:31:16,840 --> 00:31:24,600 This is 30,000 kilograms. 300 00:31:24,600 --> 00:31:27,720 It's a lot. 301 00:31:27,720 --> 00:31:32,360 It's hard to quantify so. 302 00:31:32,360 --> 00:31:40,120 What would be interesting analysis is so actually somebody at better carbon solutions recommended 303 00:31:40,120 --> 00:31:42,120 this so. 304 00:31:42,120 --> 00:31:48,960 I forget the amount of weight he said but basically. 305 00:31:48,960 --> 00:31:56,120 To help visualize this you may want to think about like barrels so I forget the amount 306 00:31:56,120 --> 00:32:03,440 of weight but like let's say you can like fit like 50. 307 00:32:03,440 --> 00:32:08,480 Like say 50 pounds in a barrel or something like that it would be I think it would be 308 00:32:08,480 --> 00:32:18,960 easier to visualize if you said oh there were you know a thousand barrels of waste. 309 00:32:18,960 --> 00:32:26,120 In a given month because you know that way you know people could visualize sort of what 310 00:32:26,120 --> 00:32:31,960 you know a thousand barrels of waste would look like like that would be like you know 311 00:32:31,960 --> 00:32:34,400 fill up like a warehouse. 312 00:32:34,400 --> 00:32:40,480 But when you just say you know 30,000 kilograms. 313 00:32:40,480 --> 00:32:46,440 It's really hard to have any like I don't have I mean maybe some people they were a 314 00:32:46,440 --> 00:32:53,120 bit more metric mindset may have a bit more mental framework for that but I can't really 315 00:32:53,120 --> 00:32:55,120 visualize that. 316 00:32:55,120 --> 00:33:02,000 But any who. 317 00:33:02,000 --> 00:33:05,880 I was something else that just occurred to me that I don't know if it would contribute 318 00:33:05,880 --> 00:33:11,320 to the outliers but at least when I was working at North with cannabis solutions. 319 00:33:11,320 --> 00:33:16,640 The folks who finance and kind of like own everything were these Russian dudes and I 320 00:33:16,640 --> 00:33:19,840 remember that one year they lost like over half their crop because they didn't know what 321 00:33:19,840 --> 00:33:21,840 they were doing. 322 00:33:21,840 --> 00:33:27,000 And so I don't know if that would contribute at all to sort of like their waste production 323 00:33:27,000 --> 00:33:31,000 but that is something that just occurred to me is like I remember they had really serious 324 00:33:31,000 --> 00:33:35,680 issues with growing and a lot of their crop just like wouldn't make it and they would 325 00:33:35,680 --> 00:33:42,560 lose entire swaths of like plants at a time until they kind of dialed it in and got it 326 00:33:42,560 --> 00:33:44,560 figured out. 327 00:33:44,560 --> 00:33:48,120 And they're a pretty large producer as well from what I understand. 328 00:33:48,120 --> 00:33:53,800 So it's possible that kind of thing could contribute but definitely for sure 30,000 329 00:33:53,800 --> 00:34:00,240 kilograms is like a wild number even as somebody who has like worked in cannabis and seen literally 330 00:34:00,240 --> 00:34:09,080 like entire dumpster sized like containers of like plant matter and stuff it's pretty 331 00:34:09,080 --> 00:34:10,680 wild but. 332 00:34:10,680 --> 00:34:18,480 Wow, that's interesting that kind of helps make sense of some of this data because there 333 00:34:18,480 --> 00:34:28,480 are some like huge losses I can't, it's hard to picture but that does help. 334 00:34:28,480 --> 00:34:39,960 That's a good anecdotal story from the ground floor because it helps. 335 00:34:39,960 --> 00:34:46,280 That's what I think we were talking about last week is part of data science is telling 336 00:34:46,280 --> 00:34:49,520 us the story of the data. 337 00:34:49,520 --> 00:34:50,520 Right. 338 00:34:50,520 --> 00:34:51,520 Right. 339 00:34:51,520 --> 00:35:01,720 So that's like a real good story where you kind of highlight just some of these individual 340 00:35:01,720 --> 00:35:09,280 days where oh like there were maybe like hundreds or thousands of grams of waste in a given 341 00:35:09,280 --> 00:35:16,280 day and you're like you're trying to tell the story of what went on there. 342 00:35:16,280 --> 00:35:23,760 And one thing Charles and Selle that would be an interesting analysis would essentially 343 00:35:23,760 --> 00:35:32,380 you could essentially run a regression of this data on month. 344 00:35:32,380 --> 00:35:44,000 So you would you basically do dummy variables for each month. 345 00:35:44,000 --> 00:35:48,880 And so then when you run the regression you basically exclude one of the variables so 346 00:35:48,880 --> 00:35:54,600 you'd you'd exclude January. 347 00:35:54,600 --> 00:35:59,720 That way you would just basically be comparing everything to January but you can exclude 348 00:35:59,720 --> 00:36:10,720 what every month you'd want but essentially see what months are the statistically highest 349 00:36:10,720 --> 00:36:17,360 you know the statistically larger amount of waste. 350 00:36:17,360 --> 00:36:29,120 Because I mean my Bayesian hypothesis would have been like October November but just naively 351 00:36:29,120 --> 00:36:40,600 looking at the data like I mean these jump out to me here and this is like like late winter 352 00:36:40,600 --> 00:36:53,120 spring January February March and then again over here you see a spike and then this is 353 00:36:53,120 --> 00:37:02,640 this is like spring again where you've got but this is later spring like April May. 354 00:37:02,640 --> 00:37:06,400 So I'm just curious if there's any seasonality. 355 00:37:06,400 --> 00:37:12,680 You think it's related to 420? 356 00:37:12,680 --> 00:37:21,920 Well you see sales spike but I'm curious if that would just be so strange for like waste 357 00:37:21,920 --> 00:37:23,520 to spike. 358 00:37:23,520 --> 00:37:31,200 Well I mean obviously they have to ramp up inventory right and it's not an out I mean 359 00:37:31,200 --> 00:37:36,880 there's a lot of this is outdoors but there's so much indoor that it's basically you know 360 00:37:36,880 --> 00:37:42,840 12 months a year you probably have some sort you know every month somebody has some harvest 361 00:37:42,840 --> 00:37:45,000 assuming they've timed everything out. 362 00:37:45,000 --> 00:37:50,840 So like I said when you run the and that's why the regression is interesting here is 363 00:37:50,840 --> 00:37:59,320 because when you run the regression you know we may there may not even be there may not 364 00:37:59,320 --> 00:38:04,080 even be a correlation for given months. 365 00:38:04,080 --> 00:38:07,640 It's worth revisiting. 366 00:38:07,640 --> 00:38:13,800 I couldn't really see much of a pattern it just I think like September through December 367 00:38:13,800 --> 00:38:22,800 sort of were lower but like the peak months changed from year to year so I don't know 368 00:38:22,800 --> 00:38:26,240 why that is. 369 00:38:26,240 --> 00:38:29,240 It's worth revisiting. 370 00:38:29,240 --> 00:38:35,640 Seasonality still something we're trying to wrap their heads around but it's a pickle 371 00:38:35,640 --> 00:38:38,840 one. 372 00:38:38,840 --> 00:38:49,560 So you've got the top producers here once again I think and that was just the what the 373 00:38:49,560 --> 00:38:55,480 person at Better Carbon Solutions suggested and it was I think it was an interesting an 374 00:38:55,480 --> 00:39:02,480 interesting advice because it wasn't something I would have thought of but you know stressing 375 00:39:02,480 --> 00:39:11,760 on the story I think the barrels is a little bit of a you know almost have to have like 376 00:39:11,760 --> 00:39:18,680 a legend that would say oh you know assuming one barrel is a hundred pounds or something 377 00:39:18,680 --> 00:39:25,440 like that but that's how they they do gasoline right. 378 00:39:25,440 --> 00:39:44,080 So but anyways it looks like there's really this pack of one two three. 379 00:39:44,080 --> 00:39:50,480 It's really like this pack of seven who were like out in the lead and then you know it 380 00:39:50,480 --> 00:39:59,840 looks like these are well it looks like this is almost a secondary pack here you know another 381 00:39:59,840 --> 00:40:08,060 pack of six then you know these these may just be like you know these firms look similar 382 00:40:08,060 --> 00:40:17,760 in size you know this is real interesting. 383 00:40:17,760 --> 00:40:21,760 And just look at some of the rest of your analysis here while we've got a bit of time 384 00:40:21,760 --> 00:40:31,160 but this is my first time seeing it so pardon me for for sort of my hot takes but we'll 385 00:40:31,160 --> 00:40:37,280 see what you with the data you've wrangled up here. 386 00:40:37,280 --> 00:40:43,600 This is so cool Charles this is the first analysis I've ever seen. 387 00:40:43,600 --> 00:40:49,880 And it is like even to like you know a casual like me this is like I can still understand 388 00:40:49,880 --> 00:40:51,440 and glean like what you're getting at. 389 00:40:51,440 --> 00:40:55,800 So I think that speaks to your ability. 390 00:40:55,800 --> 00:40:57,800 Thank you. 391 00:40:57,800 --> 00:41:15,280 Interesting so here you basically have the top five. 392 00:41:15,280 --> 00:41:22,400 So I don't understand this concept of growing waste like what does that mean? 393 00:41:22,400 --> 00:41:30,160 Oh this is like that's basically like I mean so you may know better than I but I think 394 00:41:30,160 --> 00:41:38,320 that's basically when they're just sweeping up like plant matter off of the floor. 395 00:41:38,320 --> 00:41:43,520 Yeah there's when you grow cannabis one of the great enemies of cannabis like production 396 00:41:43,520 --> 00:41:49,560 is mold and so there's like a lot of defoliation that happens in the growing process to ensure 397 00:41:49,560 --> 00:41:54,640 the highest like exposure of bud sites to light because that's like you know where the 398 00:41:54,640 --> 00:41:56,520 money is. 399 00:41:56,520 --> 00:42:02,920 And so and sometimes you know they cull plants that just aren't making it or there is definitely 400 00:42:02,920 --> 00:42:08,480 like growing waste produced in the life cycle of the plant as well. 401 00:42:08,480 --> 00:42:13,580 Okay thanks that yeah I mean to me this is a bunch of data I don't always know what these 402 00:42:13,580 --> 00:42:18,120 terms mean and it's hard for me to wrap my head around what is it you know what am I 403 00:42:18,120 --> 00:42:23,080 actually looking at and what am I looking for? 404 00:42:23,080 --> 00:42:27,880 And I like I said like I wasn't super involved in growing or anything like that so it's possible 405 00:42:27,880 --> 00:42:33,280 that the growing waste is coming from like a specific practice industry practice or some 406 00:42:33,280 --> 00:42:37,480 kind of thing that they're doing but that's what I would assume that they're referring 407 00:42:37,480 --> 00:42:39,640 to anyway. 408 00:42:39,640 --> 00:42:52,080 It looks like the entry of it fell off so does it look like they're entering growing 409 00:42:52,080 --> 00:42:56,040 waste anymore doesn't it? 410 00:42:56,040 --> 00:43:01,960 Right although it's not listed as depreciated in the manual so but it looks like it's being 411 00:43:01,960 --> 00:43:02,960 depreciated. 412 00:43:02,960 --> 00:43:18,520 Okay so it may not be required to record it anymore so we'll have to double check. 413 00:43:18,520 --> 00:43:25,400 In flower waste there was like three entries and so again what is that and obviously they're 414 00:43:25,400 --> 00:43:37,640 not tracking it or those are mislabeled entries and then the seedling waste also falls off. 415 00:43:37,640 --> 00:43:45,800 It also falls off and then at one point there's like 30,000 kilograms of seedlings I mean 416 00:43:45,800 --> 00:43:48,720 how much does a seedling weigh? 417 00:43:48,720 --> 00:43:56,240 30,000 kilograms I mean that would be like that would be a lot of seedlings. 418 00:43:56,240 --> 00:43:59,640 Yeah for sure. 419 00:43:59,640 --> 00:44:14,280 It looks like okay this would make sense in July of 2019 they updated the traceability 420 00:44:14,280 --> 00:44:26,080 system and so at that time I think is when they deprecated certain fields and it looks 421 00:44:26,080 --> 00:44:35,280 like they essentially just deprecated needing to record that waste and my guess is some 422 00:44:35,280 --> 00:44:44,280 people just kept on recording it even though they didn't have to but then they kind of 423 00:44:44,280 --> 00:44:49,960 failed off. 424 00:44:49,960 --> 00:44:55,120 Okay that's helpful that explains a lot of what I'm seeing. 425 00:44:55,120 --> 00:45:05,760 And similar thing here so growing waste so they're recording it up to July of 2019 and 426 00:45:05,760 --> 00:45:12,200 then that one trails off. 427 00:45:12,200 --> 00:45:27,560 So really what we just need to focus in here is this pervist waste. 428 00:45:27,560 --> 00:45:37,120 Out of curiosity do you know how strict they are about requiring producers to, sorry low 429 00:45:37,120 --> 00:45:41,440 key cat emergency. 430 00:45:41,440 --> 00:45:46,360 Maybe try to catch a bird through the window that was dumb. 431 00:45:46,360 --> 00:45:52,000 Sorry do you know how strict they are about requiring producers to report these numbers 432 00:45:52,000 --> 00:45:55,360 and like whether or not they audit them or if there's like requirements by the state 433 00:45:55,360 --> 00:45:59,560 for that? 434 00:45:59,560 --> 00:46:04,840 I don't off the top of my head I don't know. 435 00:46:04,840 --> 00:46:11,160 That's an excellent question. 436 00:46:11,160 --> 00:46:14,080 Yeah I was just wondering it's not that I expect you to know that necessarily but I 437 00:46:14,080 --> 00:46:19,080 feel like you know people when their when homework is assigned and required and part 438 00:46:19,080 --> 00:46:21,160 of your grade it's a little different. 439 00:46:21,160 --> 00:46:26,680 So I know for sure that like at least when I was working for Northwest Canada Solutions 440 00:46:26,680 --> 00:46:32,160 they didn't always do it by the book you know and so I'm wondering if how reliable some 441 00:46:32,160 --> 00:46:38,280 of the numbers could be or if they don't like penalize any of the growers like or incentivize 442 00:46:38,280 --> 00:46:43,720 their reporting for certain numbers like how that would affect the overall outcome of the 443 00:46:43,720 --> 00:46:45,760 numbers. 444 00:46:45,760 --> 00:46:54,120 I think that's an excellent excellent point and I think that's and that sort of drives 445 00:46:54,120 --> 00:47:03,400 home sort of a recurring theme of the meetup group is so we haven't actually prepared any 446 00:47:03,400 --> 00:47:09,800 forecasts yet but that's one thing that I always always hinge my forecasts because as 447 00:47:09,800 --> 00:47:23,680 you noted it you know junk data in you know junk data out so it's sort of it's true. 448 00:47:23,680 --> 00:47:30,280 You can you know statistically show that if there is measurement error then that does 449 00:47:30,280 --> 00:47:37,560 bias your results it depends on the amount of measurement error so you know a small amount 450 00:47:37,560 --> 00:47:46,600 of measurement error doesn't really lead to too much bias but as you noted I mean if you 451 00:47:46,600 --> 00:47:51,880 know if people are just eyeballing this or if you have entire licensees who may not even 452 00:47:51,880 --> 00:48:01,520 be entering it I mean these numbers could be unreliable. 453 00:48:01,520 --> 00:48:06,600 So I think I think that's an excellent observation and should always be taken into consideration 454 00:48:06,600 --> 00:48:12,040 so if you were doing analysis of this or if you were presenting forecasts I think you 455 00:48:12,040 --> 00:48:20,120 should definitely edge this you should say okay producers are principally care you know 456 00:48:20,120 --> 00:48:23,960 they principally care about selling their cannabis so they're probably going to record 457 00:48:23,960 --> 00:48:36,600 their you know price, labor, weight. 458 00:48:36,600 --> 00:48:53,200 So I think if any good analysis should bring that up just to counter I just always feel 459 00:48:53,200 --> 00:48:57,480 like well I heard somewhere that you know if you're not measuring it you're not managing 460 00:48:57,480 --> 00:49:10,960 it so you know any you know sometimes any measure is better than no measure so I think 461 00:49:10,960 --> 00:49:19,920 you should take take it on face value so these numbers could be wild at least we have some 462 00:49:19,920 --> 00:49:26,080 numbers so that's where you know where you get your confidence intervals so at least 463 00:49:26,080 --> 00:49:34,560 we have a measure of how much waste may be being produced and we can kind of see where 464 00:49:34,560 --> 00:49:44,240 it fluctuates and so like if you were doing a monthly analysis perhaps the entry error 465 00:49:44,240 --> 00:49:51,800 is the same month to month to month and then you could just say oh well we can still estimate 466 00:49:51,800 --> 00:49:58,000 the month effect but we just don't know the magnitude right or we're biased in the magnitude 467 00:49:58,000 --> 00:50:06,240 but you know our month effect you know that wouldn't be biased so but that takes a little 468 00:50:06,240 --> 00:50:14,960 hand waving so I you know I think there's two sides to the coin you know it's basically 469 00:50:14,960 --> 00:50:25,800 yeah you I think you need to acknowledge that there's noise in the data incredibly imperfect 470 00:50:25,800 --> 00:50:36,520 data and the producers don't even really have the incentive to enter it in accurately but 471 00:50:36,520 --> 00:50:46,080 you know we need some measure of the waste. Having spent a lot of time you're going to 472 00:50:46,080 --> 00:50:55,800 chime in Charles. There's no way that anybody could audit anybody I mean it is really really 473 00:50:55,800 --> 00:51:02,520 messy I should I need to publish that the amount of time it takes for like lab results 474 00:51:02,520 --> 00:51:07,560 because I got to a point with it where I was like you know I'm just making stuff up because 475 00:51:07,560 --> 00:51:14,880 I'm just having to fiddle with this data so much to make something out of it but it's 476 00:51:14,880 --> 00:51:21,840 just you know it's just a made-up story yeah there's no way this is really the data is 477 00:51:21,840 --> 00:51:28,120 really inconsistent there doesn't seem to be anything that there doesn't seem to be 478 00:51:28,120 --> 00:51:33,720 any requirements of like you know like checking the data when it goes into the database I 479 00:51:33,720 --> 00:51:38,120 think people enter whatever and I think that they enter stuff like they don't enter it 480 00:51:38,120 --> 00:51:42,240 daily or some people might enter it daily some people might enter it weekly some people 481 00:51:42,240 --> 00:51:49,000 might enter it monthly which I think maybe explains some of those you know where the 482 00:51:49,000 --> 00:51:56,040 spikes in the amount of waste change like from April to May because people it's just 483 00:51:56,040 --> 00:52:02,760 depending on when they entered the data totally I feel like if anything it's an argument to 484 00:52:02,760 --> 00:52:09,200 you know encourage or advocate for that sort of like I guess incentivization of like reporting 485 00:52:09,200 --> 00:52:14,520 that information just because like I totally get that I think you it's really clear even 486 00:52:14,520 --> 00:52:19,560 to me based on like the really helpful charts and graphs that you've made that like yeah 487 00:52:19,560 --> 00:52:25,520 there is you know as Keegan said like a lot of noise in the data and it would probably 488 00:52:25,520 --> 00:52:31,800 be beneficial on a lot of different levels to require and incentivize producers to report 489 00:52:31,800 --> 00:52:35,800 that more accurately and like for sure based on my personal experience like I said just 490 00:52:35,800 --> 00:52:41,000 working for that one producer I think you can bet your bottom dollar that it's your 491 00:52:41,000 --> 00:52:46,480 your mileage will vary like I really can't imagine that they were reporting a lot of 492 00:52:46,480 --> 00:52:52,160 the stuff accurately or consistently based on kind of like how they were running things 493 00:52:52,160 --> 00:52:57,280 when I was there and how they have been running things before I was there so I think you're 494 00:52:57,280 --> 00:53:01,680 totally right and I feel like if anything it's an argument to kind of like advocate 495 00:53:01,680 --> 00:53:06,040 for that stuff to have better data in the future. 496 00:53:06,040 --> 00:53:12,120 Yeah so Oregon is supposed to be really strict but I've never seen their their actual raw 497 00:53:12,120 --> 00:53:13,120 data. 498 00:53:13,120 --> 00:53:26,640 So those are excellent points across the board and yeah like you said show us the data because 499 00:53:26,640 --> 00:53:34,800 you're right it would be like for Oregon it'd be interesting because it does seem like they 500 00:53:34,800 --> 00:53:48,440 have a good handle on their data but I'm curious yeah to the amount of noise but you know it 501 00:53:48,440 --> 00:53:52,680 just takes good data wranglers and so that's why Charles when you're doing your analysis 502 00:53:52,680 --> 00:54:01,600 it's so good for you to explain like you know why things are so messy and the effects of 503 00:54:01,600 --> 00:54:07,320 it like you said it you know it's going to be tough to audit anything because you know 504 00:54:07,320 --> 00:54:15,680 it doesn't seem like there's much validation of the data. 505 00:54:15,680 --> 00:54:26,480 You know for example like a like a like a valid harvest date for example. 506 00:54:26,480 --> 00:54:35,280 So I think your analysis here has been fruitful and I think there's you know a lot of lessons 507 00:54:35,280 --> 00:54:44,720 learned here with with with data analysis you know you spend you know 90 plus percent 508 00:54:44,720 --> 00:54:52,000 of your time just working with the data and you know you really need to be upfront with 509 00:54:52,000 --> 00:55:01,880 your audience about you know what steps you took you know like Charles was saying like 510 00:55:01,880 --> 00:55:13,920 with the lab result data like you know you can't just you know you can't just squeeze 511 00:55:13,920 --> 00:55:21,760 things into a box and like you know just you know wave your hands about the things that 512 00:55:21,760 --> 00:55:26,920 don't make sense you know you have to be upfront with your audience about okay we've got maybe 513 00:55:26,920 --> 00:55:35,400 not the best data entry here or you know not the best incentives to enter the right data 514 00:55:35,400 --> 00:55:41,880 you know the data entry may be delayed but I think I'm just just reiterating what you 515 00:55:41,880 --> 00:55:48,160 two have already said here. 516 00:55:48,160 --> 00:56:02,600 But anyways I think you two actually captured the heat of things the meat of things so I 517 00:56:02,600 --> 00:56:08,920 think I'll go ahead and wrap it up there for the day and just you know let people get on 518 00:56:08,920 --> 00:56:16,960 and start exploring the data on their own but for next week we can work on some forecasting 519 00:56:16,960 --> 00:56:24,160 and I'm going to have a look at you know wrangling this data myself because I want to sort of 520 00:56:24,160 --> 00:56:29,000 replicate Charles's analysis because you've done an excellent job. 521 00:56:29,000 --> 00:56:30,000 Thanks. 522 00:56:30,000 --> 00:56:36,440 Yeah thanks for sharing Charles this is super duper cool I learned a lot today y'all thanks 523 00:56:36,440 --> 00:56:42,520 so much for walking me through what y'all do on the regular. 524 00:56:42,520 --> 00:56:51,160 Oh yeah thanks for joining Selle we just sometimes we get a bit more hands on but like this data 525 00:56:51,160 --> 00:56:57,080 set is giant so we're a little limited in scope but it was it's awesome that Charles 526 00:56:57,080 --> 00:57:03,600 put together these charts because rule number one look at the data so that's what we did 527 00:57:03,600 --> 00:57:05,800 today. 528 00:57:05,800 --> 00:57:09,760 And they are beautiful charts but anyways in the future you know if things are more 529 00:57:09,760 --> 00:57:15,160 hands on I definitely don't mind observing you and Charles kind of in your like natural 530 00:57:15,160 --> 00:57:20,640 I guess environment working on stuff and collaborating because I would also find that helpful so 531 00:57:20,640 --> 00:57:26,440 don't please don't feel like you have to teach to the back of the class so to speak. 532 00:57:26,440 --> 00:57:32,840 I'm just really excited to kind of have exposure just because in my personal like tech journey 533 00:57:32,840 --> 00:57:38,400 a lot of it is just like studying JavaScript or studying whatever it is and studying I'm 534 00:57:38,400 --> 00:57:43,520 still trying to learn a passion for something so I think this is definitely a step in that 535 00:57:43,520 --> 00:57:47,520 right direction especially in terms of like exposure for specialization in the future 536 00:57:47,520 --> 00:57:49,680 so I really appreciate it. 537 00:57:49,680 --> 00:57:56,080 Well it's entirely open so if we want to look at charts and talk about Charles's analysis 538 00:57:56,080 --> 00:58:00,360 by all means are you going to chime in Charles? 539 00:58:00,360 --> 00:58:06,840 Yeah and really your insights from working you know in the industry that was really helpful 540 00:58:06,840 --> 00:58:11,600 I mean there's you know because when I look at this data I don't you know again I don't 541 00:58:11,600 --> 00:58:17,960 have anybody to go to and ask you know like why is this or but you know you gave me some 542 00:58:17,960 --> 00:58:23,880 good insight and now I kind of I can have a better understanding of what's going on. 543 00:58:23,880 --> 00:58:28,280 Well I'm glad I could help in my own way so you know let me know if there's anything else 544 00:58:28,280 --> 00:58:33,360 I can do on the sort of like off time in terms of like getting a little more familiar I'm 545 00:58:33,360 --> 00:58:38,560 definitely like kind of rubbing my hands together and looking at Python as like my next language 546 00:58:38,560 --> 00:58:44,960 I'd like to learn but given how JavaScript is going that may be in some time yet so we'll 547 00:58:44,960 --> 00:58:45,960 just have to see. 548 00:58:45,960 --> 00:58:49,760 I think Python is easier. 549 00:58:49,760 --> 00:58:58,200 I don't know but it looks super I love I honestly love optimizing stuff and I love automating 550 00:58:58,200 --> 00:59:05,480 things tasks as much as possible and so Python is looking pretty delectable these days so 551 00:59:05,480 --> 00:59:10,240 it's really cool to see the kind of heavy lifting that y'all are accomplishing with 552 00:59:10,240 --> 00:59:15,000 it so I definitely am starting to like feel those kinds of like connections hip happening 553 00:59:15,000 --> 00:59:19,560 and that's super exciting just because I'm not gonna lie just learning JavaScript in 554 00:59:19,560 --> 00:59:24,720 a vacuum is kind of a slog so getting to see actual practical applications for various 555 00:59:24,720 --> 00:59:32,720 languages is like super exciting and very energizing so I really really appreciate it. 556 00:59:32,720 --> 00:59:39,480 There's room in the world for both so they you know you know you got a hammer you got 557 00:59:39,480 --> 00:59:44,000 a screwdriver you know so it's you know they each have their own they have their own purposes 558 00:59:44,000 --> 00:59:51,120 definitely yeah very very cool y'all thank you so much again for your time awesome great 559 00:59:51,120 --> 00:59:53,080 job Charles this thing was so cool. 560 00:59:53,080 --> 00:59:55,080 Yeah thank you. 561 00:59:55,080 --> 00:59:58,280 All right y'all catch you next week. 562 00:59:58,280 --> 01:00:00,280 Have an awesome day until next week. 563 01:00:00,280 --> 01:00:01,280 Thanks you too. 564 01:00:01,280 --> 01:00:23,280 Bye.