1 00:00:00,720 --> 00:00:03,630 Narrator: To create a useful rain alert application. 2 00:00:03,630 --> 00:00:04,920 We want our script 3 00:00:04,920 --> 00:00:08,640 to detect if it will rain within the next 12 hours 4 00:00:08,640 --> 00:00:11,130 of the Python script being run. 5 00:00:11,130 --> 00:00:13,680 Let's say every day you leave your home at 8:00 AM 6 00:00:13,680 --> 00:00:15,150 in order to go to work, 7 00:00:15,150 --> 00:00:18,990 then by 5:00 or 6:00 PM you might be done with work 8 00:00:18,990 --> 00:00:21,240 and on your way back home. 9 00:00:21,240 --> 00:00:24,000 Given that the free OpenWeatherMap API 10 00:00:24,000 --> 00:00:27,990 gives us the weather forecast in three hour intervals, 11 00:00:27,990 --> 00:00:30,120 we can run the script at 6:00 AM 12 00:00:30,120 --> 00:00:33,240 and check the weather for the next 12 hours. 13 00:00:33,240 --> 00:00:34,560 If there's rain forecast 14 00:00:34,560 --> 00:00:36,690 during the time you're away from home, 15 00:00:36,690 --> 00:00:39,630 you want your Python script to send you a text message, 16 00:00:39,630 --> 00:00:42,060 reminding you to bring an umbrella. 17 00:00:42,060 --> 00:00:45,270 That way you'll get notified before you leave home 18 00:00:45,270 --> 00:00:47,133 and know how to prepare for the day. 19 00:00:48,450 --> 00:00:50,700 In the previous lesson, we saw that the data 20 00:00:50,700 --> 00:00:53,010 that we got back from OpenWeatherMap 21 00:00:53,010 --> 00:00:55,830 contains the weather forecast every three hours 22 00:00:55,830 --> 00:00:57,720 for the next five days. 23 00:00:57,720 --> 00:01:00,960 There are forecasts for 40 different timestamps, 24 00:01:00,960 --> 00:01:04,950 starting from zero and going up all the way to 39. 25 00:01:04,950 --> 00:01:07,650 Now, I dunno about you, but my decision 26 00:01:07,650 --> 00:01:09,420 whether or not to bring an umbrella 27 00:01:09,420 --> 00:01:11,640 really only depends on the forecast 28 00:01:11,640 --> 00:01:13,530 for the next 12 hours. 29 00:01:13,530 --> 00:01:16,410 In the JSON that we got back from OpenWeatherMap, 30 00:01:16,410 --> 00:01:20,250 that would be only the first four forecasts in the list. 31 00:01:20,250 --> 00:01:22,410 If the script is run at 6:00 AM 32 00:01:22,410 --> 00:01:26,520 then a 12-hour window takes us up to 6:00 PM 33 00:01:26,520 --> 00:01:29,430 by which time, we're hopefully ready to head home 34 00:01:29,430 --> 00:01:30,480 and we don't need to worry 35 00:01:30,480 --> 00:01:32,793 if it starts raining much later than that. 36 00:01:33,690 --> 00:01:36,180 If we look at this JSON that we got back, 37 00:01:36,180 --> 00:01:38,550 we don't actually care about the forecasts 38 00:01:38,550 --> 00:01:42,300 that are for tomorrow or those even further in the future. 39 00:01:42,300 --> 00:01:43,650 We just care about the weather 40 00:01:43,650 --> 00:01:46,290 within the next four forecasts. 41 00:01:46,290 --> 00:01:49,260 Looking at the API documentation more closely, 42 00:01:49,260 --> 00:01:51,330 we can actually spot parameters 43 00:01:51,330 --> 00:01:55,140 that will help us to tailor OpenWeatherMaps response, 44 00:01:55,140 --> 00:01:56,850 so that we just get back the data 45 00:01:56,850 --> 00:01:58,590 that we actually care about. 46 00:01:58,590 --> 00:02:03,590 Of course, I'm talking about the cnt or count parameter. 47 00:02:03,660 --> 00:02:06,330 By setting the count to four, 48 00:02:06,330 --> 00:02:09,090 we'll request only four timestamps 49 00:02:09,090 --> 00:02:12,540 covering the 12-hour window that matters to us. 50 00:02:12,540 --> 00:02:14,820 That way we make our own lives easier 51 00:02:14,820 --> 00:02:18,240 and we avoid requesting more data than we actually need. 52 00:02:18,240 --> 00:02:20,550 That's why it's always a good idea to have a read 53 00:02:20,550 --> 00:02:24,000 through the API documentation first. 54 00:02:24,000 --> 00:02:25,950 There are usually some useful nuggets in there 55 00:02:25,950 --> 00:02:29,700 that will make your life as a developer much, much easier. 56 00:02:29,700 --> 00:02:31,530 As I always say, this is like going 57 00:02:31,530 --> 00:02:33,120 into somebody else's house. 58 00:02:33,120 --> 00:02:34,260 Everything is different. 59 00:02:34,260 --> 00:02:36,780 The way that the washing machine works is different. 60 00:02:36,780 --> 00:02:39,060 The way that their dryer works is different. 61 00:02:39,060 --> 00:02:40,440 So you really have 62 00:02:40,440 --> 00:02:43,590 to look at the API documentation when you are working 63 00:02:43,590 --> 00:02:46,380 with a new API, just so that you're not caught out 64 00:02:46,380 --> 00:02:49,170 and you understand exactly what you have to do 65 00:02:49,170 --> 00:02:52,053 in order to be a good API user. 66 00:02:53,370 --> 00:02:55,890 Now, let's go back into our Python code 67 00:02:55,890 --> 00:02:58,350 and provide that extra parameter. 68 00:02:58,350 --> 00:03:00,240 The parameter was called count, 69 00:03:00,240 --> 00:03:04,110 and I'll just add it to our weather_params dictionary. 70 00:03:04,110 --> 00:03:05,730 Let's set count to four, 71 00:03:05,730 --> 00:03:09,630 so that we request only the timestamps in the near future. 72 00:03:09,630 --> 00:03:14,490 Let's run it again and take a look at the JSON response 73 00:03:14,490 --> 00:03:15,810 we get back. 74 00:03:15,810 --> 00:03:17,190 I'm going to copy it again, 75 00:03:17,190 --> 00:03:19,590 go back to the online JSON viewer, 76 00:03:19,590 --> 00:03:22,530 clear that huge amount of data from before 77 00:03:22,530 --> 00:03:24,750 and paste in what I just got back. 78 00:03:24,750 --> 00:03:26,880 You can already see it's much shorter 79 00:03:26,880 --> 00:03:29,580 and if I click on Viewer, we can see that our list 80 00:03:29,580 --> 00:03:31,920 of weather forecasts now only contain 81 00:03:31,920 --> 00:03:33,993 the next four timestamps. 82 00:03:35,070 --> 00:03:36,480 So that's the goal. 83 00:03:36,480 --> 00:03:39,360 And if we head back into our code, 84 00:03:39,360 --> 00:03:42,180 we know that at the moment the response code 85 00:03:42,180 --> 00:03:45,900 that we're getting back from calling this API is 200. 86 00:03:45,900 --> 00:03:47,520 So it's successful. 87 00:03:47,520 --> 00:03:48,750 But we wanna make sure 88 00:03:48,750 --> 00:03:51,900 that we actually catch other response codes. 89 00:03:51,900 --> 00:03:54,750 So we're going to call raise_for_status, 90 00:03:54,750 --> 00:03:57,150 so that if there is a problem 91 00:03:57,150 --> 00:03:59,220 and we don't get a 200 code, 92 00:03:59,220 --> 00:04:01,293 then we actually raise an exception. 93 00:04:02,400 --> 00:04:06,450 Now, next, we wanna save our response.json 94 00:04:06,450 --> 00:04:07,840 as the weather_data 95 00:04:09,480 --> 00:04:11,040 and then we're gonna work with this data 96 00:04:11,040 --> 00:04:13,380 in order to get the particular pieces 97 00:04:13,380 --> 00:04:14,630 that we're interested in. 98 00:04:15,600 --> 00:04:17,519 That's step one done. 99 00:04:17,519 --> 00:04:21,899 The next step is to dig through this hourly forecast 100 00:04:21,899 --> 00:04:24,750 and get hold of the thing that we're interested in, 101 00:04:24,750 --> 00:04:28,170 which is the actual weather condition. 102 00:04:28,170 --> 00:04:30,390 The way that weather services tend 103 00:04:30,390 --> 00:04:34,290 to provide the weather condition is through an id. 104 00:04:34,290 --> 00:04:38,730 And I know this because I read the API documentation. 105 00:04:38,730 --> 00:04:42,630 It's not because I'm some sort of weather geek. (laughs) 106 00:04:42,630 --> 00:04:45,930 Although, I mean that's not a bad thing to be I guess. 107 00:04:45,930 --> 00:04:50,930 If we scroll down in this documentation past the examples, 108 00:04:51,000 --> 00:04:53,010 you can see it provides all of the fields 109 00:04:53,010 --> 00:04:54,600 in the API response. 110 00:04:54,600 --> 00:04:56,280 So these are all of the things 111 00:04:56,280 --> 00:04:59,760 that we could possibly get back and what they mean. 112 00:04:59,760 --> 00:05:01,920 And you can see there's some really interesting things, 113 00:05:01,920 --> 00:05:05,250 like what does the temperature feel like based 114 00:05:05,250 --> 00:05:08,850 on the wind chill and actual ground temperature. 115 00:05:08,850 --> 00:05:13,020 But what we're mostly interested in is the weather data. 116 00:05:13,020 --> 00:05:18,020 So this weather id is a weather condition id, 117 00:05:18,480 --> 00:05:21,660 and when you click on that link, it takes you to this table 118 00:05:21,660 --> 00:05:24,750 that shows you all of the weather condition codes 119 00:05:24,750 --> 00:05:29,103 that we could possibly get back in this particular field. 120 00:05:30,030 --> 00:05:31,860 Now, you can see that all the codes 121 00:05:31,860 --> 00:05:36,860 that start off with 2 means some sort of thunderstorm. 122 00:05:37,230 --> 00:05:38,730 And then starting with 3, 123 00:05:38,730 --> 00:05:40,380 that means some sort of drizzling. 124 00:05:40,380 --> 00:05:42,450 Starting with 5 means rain. 125 00:05:42,450 --> 00:05:44,550 Starting with 6 means snow. 126 00:05:44,550 --> 00:05:47,460 And then afterwards we have the seven hundreds. 127 00:05:47,460 --> 00:05:50,340 So these are atmospheric things like a bit of mist, 128 00:05:50,340 --> 00:05:53,040 a bit of smoke, a bit of dust or fog. 129 00:05:53,040 --> 00:05:55,920 And this is also incidentally the reason why 130 00:05:55,920 --> 00:06:00,000 this weather key actually has a value 131 00:06:00,000 --> 00:06:02,070 that's in the form of a list. 132 00:06:02,070 --> 00:06:05,250 You can see that's denoted by the square brackets here. 133 00:06:05,250 --> 00:06:08,730 So there could actually be multiple weather conditions 134 00:06:08,730 --> 00:06:12,180 for a particular place at a particular hour 135 00:06:12,180 --> 00:06:16,020 and that's because you could maybe have snow, 136 00:06:16,020 --> 00:06:18,753 but you could also have fog at the same time. 137 00:06:19,740 --> 00:06:22,260 Now, when I looked through a lot of the examples 138 00:06:22,260 --> 00:06:26,010 and the documentation, it seems like the first item 139 00:06:26,010 --> 00:06:28,710 in that list is the main condition. 140 00:06:28,710 --> 00:06:30,030 So if it's gonna rain, 141 00:06:30,030 --> 00:06:32,160 then it's gonna be in that first item 142 00:06:32,160 --> 00:06:35,070 in the list of weather conditions. 143 00:06:35,070 --> 00:06:39,630 Inside that list, we have a dictionary or many dictionaries. 144 00:06:39,630 --> 00:06:42,870 Each of those contain a weather condition id, 145 00:06:42,870 --> 00:06:46,260 the main condition name and the description. 146 00:06:46,260 --> 00:06:49,410 So if we look at this id code 802, 147 00:06:49,410 --> 00:06:51,780 we can decode it in this table 148 00:06:51,780 --> 00:06:54,540 and you can see it means scattered clouds. 149 00:06:54,540 --> 00:06:57,903 20 to 50% of the sky is covered in clouds, basically. 150 00:06:59,370 --> 00:07:01,500 Based on this list, we can say that, 151 00:07:01,500 --> 00:07:05,550 well, anything that has a code less than 700, 152 00:07:05,550 --> 00:07:08,730 then we probably will need an umbrella. 153 00:07:08,730 --> 00:07:11,400 I'm not sure how you stand on the umbrella 154 00:07:11,400 --> 00:07:15,180 in snow situation, but I personally do like 155 00:07:15,180 --> 00:07:17,130 to hold an umbrella when it's snowing, 156 00:07:17,130 --> 00:07:18,780 especially because I live in a country 157 00:07:18,780 --> 00:07:22,130 where the snow is not crazy, it's just sort of... 158 00:07:23,220 --> 00:07:26,670 It's never sort of the beautiful snow 159 00:07:26,670 --> 00:07:29,310 where it's thick and it gets caught on your eyelashes. 160 00:07:29,310 --> 00:07:33,810 It's the sort of annoying slush that's just sort of snow, 161 00:07:33,810 --> 00:07:37,380 but it's sort of like somebody spitting at you. 162 00:07:37,380 --> 00:07:38,940 In my case, I would prefer 163 00:07:38,940 --> 00:07:41,820 to have an umbrella if it was gonna snow. 164 00:07:41,820 --> 00:07:44,040 I'm going to check for the codes 165 00:07:44,040 --> 00:07:47,010 that we get back from the OpenWeatherMap API, 166 00:07:47,010 --> 00:07:49,890 and if the code is less than 700, 167 00:07:49,890 --> 00:07:53,553 then I'm gonna advise my user to bring an umbrella. 168 00:07:55,290 --> 00:07:57,690 Here's a challenge for you. 169 00:07:57,690 --> 00:08:00,150 Can you figure out how to look 170 00:08:00,150 --> 00:08:01,800 through the data that we get back, 171 00:08:01,800 --> 00:08:05,073 which remember looks something like this, 172 00:08:05,910 --> 00:08:08,910 and then look at the weather 173 00:08:08,910 --> 00:08:13,410 and the first item in the weather list and also the id? 174 00:08:13,410 --> 00:08:16,800 So you're going to go all the way down to this, 175 00:08:16,800 --> 00:08:19,830 but remember you're gonna do that for all of the items 176 00:08:19,830 --> 00:08:22,410 and you're going to check for the condition code. 177 00:08:22,410 --> 00:08:26,700 Now if any of those id codes are less than 700, 178 00:08:26,700 --> 00:08:30,330 then you want to be able to print out, bring an umbrella. 179 00:08:30,330 --> 00:08:32,340 So that's the goal 180 00:08:32,340 --> 00:08:35,460 and the actual implementation, I'll leave up to you 181 00:08:35,460 --> 00:08:37,799 'cause there's quite a few ways that you can do this, 182 00:08:37,799 --> 00:08:40,710 but I'm sure by now, you are well prepared 183 00:08:40,710 --> 00:08:44,400 with all of your tools in Python to figure this out. 184 00:08:44,400 --> 00:08:48,180 If you wanna make sure that your code is actually working, 185 00:08:48,180 --> 00:08:50,730 you can switch to a latitude and longitude 186 00:08:50,730 --> 00:08:52,860 that is definitely raining. 187 00:08:52,860 --> 00:08:56,130 So if you go to ventusky.com, 188 00:08:56,130 --> 00:08:59,340 they actually show you the live weather forecast. 189 00:08:59,340 --> 00:09:03,270 So we can look at precipitation, which is basically rain, 190 00:09:03,270 --> 00:09:06,000 and we can find some sort of unfortunate place, 191 00:09:06,000 --> 00:09:09,060 which seems to be really heavily raining. 192 00:09:09,060 --> 00:09:11,313 For example, this place in Poland, Lodz, 193 00:09:12,270 --> 00:09:16,145 and if I spell that name correctly. 194 00:09:16,145 --> 00:09:19,200 (keyboard typing) 195 00:09:19,200 --> 00:09:22,560 Lodz and then I'll add the country code Poland 196 00:09:22,560 --> 00:09:24,750 and just to make sure in the map, 197 00:09:24,750 --> 00:09:27,510 it's actually found the correct place. 198 00:09:27,510 --> 00:09:29,280 Yet that looks pretty much it, 199 00:09:29,280 --> 00:09:32,310 then I can switch out my latitude and longitude 200 00:09:32,310 --> 00:09:34,260 with this rainy place 201 00:09:34,260 --> 00:09:37,470 and that way I know that at least one of the results 202 00:09:37,470 --> 00:09:39,480 I get back is definitely going 203 00:09:39,480 --> 00:09:41,463 to contain some sort of rain. 204 00:09:44,400 --> 00:09:46,620 Now if we look at their hourly data, 205 00:09:46,620 --> 00:09:48,450 you can see that the weather 206 00:09:48,450 --> 00:09:51,780 is basically just rain, rain, rain. 207 00:09:51,780 --> 00:09:55,020 So between looking at places which are sunny 208 00:09:55,020 --> 00:09:58,020 and places which are rainy, you should be able 209 00:09:58,020 --> 00:09:59,370 to get your code to work, 210 00:09:59,370 --> 00:10:02,610 so that it tells you in the next 12 hours, 211 00:10:02,610 --> 00:10:07,350 if any of those condition codes are less than 700, 212 00:10:07,350 --> 00:10:10,263 which means it's got some form of precipitation. 213 00:10:11,310 --> 00:10:13,173 Pause the video and give this a go. 214 00:10:18,300 --> 00:10:22,290 All right, so let's narrow down into this weather. 215 00:10:22,290 --> 00:10:24,090 If we want to get the weather, 216 00:10:24,090 --> 00:10:28,320 then we have to tap into the first item in our list 217 00:10:28,320 --> 00:10:30,030 and then we get hold of the weather 218 00:10:30,030 --> 00:10:32,010 and then we get hold of the first item 219 00:10:32,010 --> 00:10:37,010 in that list under the id key, we'll get the actual value. 220 00:10:37,050 --> 00:10:40,050 We've talked about this in detail in previous lessons. 221 00:10:40,050 --> 00:10:42,030 If you've skipped a lot of lessons 222 00:10:42,030 --> 00:10:43,860 and you've come here directly, 223 00:10:43,860 --> 00:10:46,050 then it's gonna be a little bit confusing 224 00:10:46,050 --> 00:10:48,630 and I recommend to review the previous lessons 225 00:10:48,630 --> 00:10:50,580 before you continue. 226 00:10:50,580 --> 00:10:53,820 Instead of printing the weather data, let's drill down. 227 00:10:53,820 --> 00:10:56,250 Let's get to the first item here. 228 00:10:56,250 --> 00:10:58,650 If we provide a set of square brackets 229 00:10:58,650 --> 00:11:00,630 and then we can access the value 230 00:11:00,630 --> 00:11:04,500 inside the list key, like this. 231 00:11:04,500 --> 00:11:08,910 And now, we've got a list with all of the hourly data 232 00:11:08,910 --> 00:11:11,613 and it looks pretty much like this. 233 00:11:12,780 --> 00:11:14,250 Now, you can confirm this 234 00:11:14,250 --> 00:11:17,790 by also pasting this into the JSON viewer, 235 00:11:17,790 --> 00:11:20,460 replacing the previous text that was there. 236 00:11:20,460 --> 00:11:24,330 You can see we now have our items in this list. 237 00:11:24,330 --> 00:11:25,650 To drill further down, 238 00:11:25,650 --> 00:11:28,260 let's get hold of the first item in that list 239 00:11:28,260 --> 00:11:30,960 by providing a square bracket 240 00:11:30,960 --> 00:11:33,990 and then the index, which is zero. 241 00:11:33,990 --> 00:11:36,660 Now, we are into the first item 242 00:11:36,660 --> 00:11:39,120 and this is what the data looks like. 243 00:11:39,120 --> 00:11:40,980 It's a lot shorter. 244 00:11:40,980 --> 00:11:44,820 Now, we wanna tap into the weather condition, 245 00:11:44,820 --> 00:11:47,340 so that means yet another set of square brackets 246 00:11:47,340 --> 00:11:50,823 and then the name of the key, which is weather. 247 00:11:52,560 --> 00:11:53,730 Now, it's pretty simple. 248 00:11:53,730 --> 00:11:58,470 It's simply giving us a list with only one item. 249 00:11:58,470 --> 00:12:02,820 Let's tap into that one item by using again, 250 00:12:02,820 --> 00:12:05,070 square brackets, zero. 251 00:12:05,070 --> 00:12:10,070 And now, we've got just a simple dictionary, essentially. 252 00:12:10,470 --> 00:12:12,330 So if we wanna get hold of the id, 253 00:12:12,330 --> 00:12:15,570 then it's the final square bracket 254 00:12:15,570 --> 00:12:18,450 and the key, which is id. 255 00:12:18,450 --> 00:12:21,690 Now, what if I created a new list 256 00:12:21,690 --> 00:12:24,600 that contains the condition codes? 257 00:12:24,600 --> 00:12:26,610 We know that we've got a list 258 00:12:26,610 --> 00:12:31,470 of all of the weather conditions for the next 12 hours. 259 00:12:31,470 --> 00:12:36,470 How can we loop through that list to find out the actual id 260 00:12:36,780 --> 00:12:39,873 of the weather condition for each of those hours? 261 00:12:40,770 --> 00:12:42,570 Well, we can create a for loop 262 00:12:42,570 --> 00:12:46,590 that looks at each of the data from the hour. 263 00:12:46,590 --> 00:12:50,640 So for hour_data in weather_data list, 264 00:12:50,640 --> 00:12:53,070 so this is gonna go through the 12 hours. 265 00:12:53,070 --> 00:12:56,220 And then for each, we're going to tap into the hour data 266 00:12:56,220 --> 00:12:58,830 and try to get hold of the item 267 00:12:58,830 --> 00:13:01,653 that is in the weather key. 268 00:13:03,510 --> 00:13:04,413 Like this. 269 00:13:05,370 --> 00:13:09,180 Now, I'm going to print each of these 270 00:13:09,180 --> 00:13:12,360 and you can see, we've now got a list 271 00:13:12,360 --> 00:13:17,360 of all of the weather conditions for each of those hours. 272 00:13:17,760 --> 00:13:20,790 Now, let's narrow down that a little bit further. 273 00:13:20,790 --> 00:13:24,300 Let's get hold of the first item of each of these lists. 274 00:13:24,300 --> 00:13:26,580 And you can see, in fact, there's only one item 275 00:13:26,580 --> 00:13:27,930 in all of these lists. 276 00:13:27,930 --> 00:13:31,200 It's very rare that you have multiple weather conditions 277 00:13:31,200 --> 00:13:32,463 for each hour. 278 00:13:33,420 --> 00:13:36,420 Now that gives us a Python dictionary 279 00:13:36,420 --> 00:13:38,100 for each of those hours 280 00:13:38,100 --> 00:13:41,310 and we can tap into that final value we're interested in 281 00:13:41,310 --> 00:13:43,800 under the key id. 282 00:13:43,800 --> 00:13:47,760 So now, we've looped through the next 12 hours 283 00:13:47,760 --> 00:13:51,810 and got hold of the weather condition id 284 00:13:51,810 --> 00:13:53,223 for each of those hours. 285 00:13:54,120 --> 00:13:57,870 Now, all we need to do is to save this 286 00:13:57,870 --> 00:14:02,870 instead of printing it and we'll call it the condition_code. 287 00:14:02,871 --> 00:14:06,270 (keyboard typing) 288 00:14:06,270 --> 00:14:07,710 And remember previously we said 289 00:14:07,710 --> 00:14:11,340 that if the condition code is less than 700, 290 00:14:11,340 --> 00:14:15,000 then we're going to print, bring an umbrella. 291 00:14:15,000 --> 00:14:17,940 So we can check if condition_code, 292 00:14:17,940 --> 00:14:21,540 which remember at this point is still a string. 293 00:14:21,540 --> 00:14:25,350 So we have to turn that into an integer 294 00:14:25,350 --> 00:14:29,010 in order to be able to compare it against another number. 295 00:14:29,010 --> 00:14:33,570 If that condition_code as an integer is less than 700, 296 00:14:33,570 --> 00:14:37,173 then we're going to print, bring an umbrella. 297 00:14:39,510 --> 00:14:42,420 And you can see that for the next 12 hours, 298 00:14:42,420 --> 00:14:46,470 there's rain at this particular place in Lodz in Poland 299 00:14:46,470 --> 00:14:49,860 that I've put into the latitude and longitude. 300 00:14:49,860 --> 00:14:51,270 Now, if I don't want 301 00:14:51,270 --> 00:14:54,720 to call this print statement many times, 302 00:14:54,720 --> 00:14:57,718 then we can define a variable outside the for loop 303 00:14:57,718 --> 00:14:59,135 called will_rain, 304 00:15:00,000 --> 00:15:03,180 and we can set that to false to begin with. 305 00:15:03,180 --> 00:15:06,570 Now if during the next 12 hours the condition_code 306 00:15:06,570 --> 00:15:08,310 is less than 700, 307 00:15:08,310 --> 00:15:12,540 then we'll switch that to equal true instead. 308 00:15:12,540 --> 00:15:16,140 Now, after the for loop has completed, 309 00:15:16,140 --> 00:15:17,850 then we can check to see 310 00:15:17,850 --> 00:15:20,130 if it will_rain in the next 12 hours. 311 00:15:20,130 --> 00:15:22,503 And if so, it will print, bring an umbrella. 312 00:15:23,760 --> 00:15:26,790 This way we'll only get one print statement being called, 313 00:15:26,790 --> 00:15:28,560 instead of every single time 314 00:15:28,560 --> 00:15:32,280 we land on a condition_code for rain. 315 00:15:32,280 --> 00:15:36,330 So this involved quite a bit of JSON passing 316 00:15:36,330 --> 00:15:41,330 and also understanding how to work with parameters and APIs. 317 00:15:41,970 --> 00:15:44,490 Now, I hope you managed to get this far by yourself, 318 00:15:44,490 --> 00:15:47,370 but if not, be sure to review what I've written 319 00:15:47,370 --> 00:15:49,710 and fix your code as required. 320 00:15:49,710 --> 00:15:51,330 If any of it was confusing, 321 00:15:51,330 --> 00:15:54,570 be sure to review previous lessons before you continue 322 00:15:54,570 --> 00:15:56,340 because we're now building heavily 323 00:15:56,340 --> 00:15:57,870 on your previous knowledge 324 00:15:57,870 --> 00:16:00,390 that you learned through the course. 325 00:16:00,390 --> 00:16:01,800 Now, in the next lesson, 326 00:16:01,800 --> 00:16:03,930 we're gonna be looking at how we can, 327 00:16:03,930 --> 00:16:06,600 instead of printing, bring an umbrella 328 00:16:06,600 --> 00:16:09,870 to send a SMS text message 329 00:16:09,870 --> 00:16:12,993 that notifies you to bring an umbrella instead. 330 00:16:13,860 --> 00:16:15,960 So for all of that and more, 331 00:16:15,960 --> 00:16:17,560 I'll see you on the next lesson.