1 00:00:00,510 --> 00:00:05,550 Hi, in this video, I want to hand you an exercise that you need to solve now. 2 00:00:06,030 --> 00:00:12,510 It's a challenging exercise, but even if you don't solve it, it will be excellent training in terms 3 00:00:12,510 --> 00:00:17,160 of accessing APIs and working with Jason data. 4 00:00:18,090 --> 00:00:19,540 So I will show you two things. 5 00:00:19,740 --> 00:00:22,620 First of all, demonstrate you've expected output. 6 00:00:22,980 --> 00:00:25,350 That's your script should generate. 7 00:00:25,950 --> 00:00:32,640 And then I'll show you the API that you should use for this exercise so that you don't just go out in 8 00:00:32,640 --> 00:00:34,890 the wild and search for APIs. 9 00:00:35,550 --> 00:00:38,190 So I want to help you focus on this exercise. 10 00:00:38,430 --> 00:00:42,240 So let's start with showing you first the expected output. 11 00:00:43,680 --> 00:00:53,430 So I manually created this data dot, text, file and I place you a city comma time, comma temperature, 12 00:00:53,430 --> 00:00:54,420 comma condition. 13 00:00:55,080 --> 00:00:56,670 So please do the same. 14 00:00:57,450 --> 00:00:58,470 This is the first step. 15 00:00:58,830 --> 00:01:04,200 Just creates an empty file and then place the cursor in the next line. 16 00:01:04,560 --> 00:01:10,050 Now, if I go to the bottom of this will run the main data profile where the scripts are. 17 00:01:10,060 --> 00:01:16,230 That's I developed three sides, so I'll not show you the script, but I'll just executed and you'll 18 00:01:16,290 --> 00:01:17,250 see what happens. 19 00:01:17,490 --> 00:01:22,200 So as soon as I execute the script, it populates. 20 00:01:23,510 --> 00:01:26,150 This does not file with Delta. 21 00:01:27,170 --> 00:01:31,430 So these are the dots are they are added under the header. 22 00:01:31,790 --> 00:01:39,560 So I had to place the cursor in here and Python adds the dots on, so it appends the data to this text 23 00:01:39,560 --> 00:01:39,950 file. 24 00:01:40,430 --> 00:01:42,410 For that, we use an open function. 25 00:01:42,650 --> 00:01:45,890 We a flag, not a W flag. 26 00:01:46,250 --> 00:01:49,640 So when we append delta to the X files, that's a clue. 27 00:01:50,590 --> 00:01:51,430 So what is this? 28 00:01:51,700 --> 00:02:00,400 Well, we have data for a city weather forecast data, and I use Aleppo, which is a city in Syria. 29 00:02:00,940 --> 00:02:01,870 It's warm there. 30 00:02:02,890 --> 00:02:11,080 So these are the full data points, which are repeated every three hours. 31 00:02:11,260 --> 00:02:14,890 So we have the city, we have the time and date. 32 00:02:15,760 --> 00:02:18,310 So time is for that. 33 00:02:19,420 --> 00:02:27,880 And then we have degrees Celsius degrees, the temperature and then we have the condition of the sky. 34 00:02:28,690 --> 00:02:29,290 It's over. 35 00:02:29,620 --> 00:02:30,790 Gas clouds. 36 00:02:32,110 --> 00:02:39,790 And this is a repeated every three hours and we have forecasts for the next five days. 37 00:02:40,180 --> 00:02:42,850 So at an interval of three hours. 38 00:02:43,840 --> 00:02:44,870 So we have a temperature. 39 00:02:44,890 --> 00:02:47,260 We have the sky condition and so on. 40 00:02:47,950 --> 00:02:50,560 Now how do we get those dots up? 41 00:02:50,590 --> 00:02:52,660 This is what I explain you next. 42 00:02:53,530 --> 00:02:56,570 So you should go to open weather map. 43 00:02:56,610 --> 00:02:57,430 That's all. 44 00:02:57,850 --> 00:03:00,850 And then go over to the API menu. 45 00:03:03,660 --> 00:03:10,350 And if you scroll down, you'll see we have this five day, three hour forecast. 46 00:03:10,860 --> 00:03:12,660 So you want to go to subscribe. 47 00:03:13,680 --> 00:03:17,900 And then under the free plan, you want to get some API. 48 00:03:18,630 --> 00:03:24,240 And then you want to fill in these data that will give you the API key. 49 00:03:25,510 --> 00:03:26,730 So once you get your API. 50 00:03:27,130 --> 00:03:35,620 Now I want to show you an example of a query to get a three hour weather forecast data for five days. 51 00:03:35,920 --> 00:03:38,140 So this is an example of URL. 52 00:03:39,500 --> 00:03:44,150 And this is the same URL that I'm using inside my Python script. 53 00:03:44,990 --> 00:03:55,580 So we have this main URL up to forecasts question mark queue is equal to where you enter the city so 54 00:03:55,580 --> 00:03:59,420 you can enter any city in here and then you have. 55 00:04:00,510 --> 00:04:03,690 And API is equal to. 56 00:04:03,900 --> 00:04:09,570 And this is your API key until this and symbol. 57 00:04:10,720 --> 00:04:19,360 And then we have and units a sequel to Metric, you can also use FT, but for more information, you 58 00:04:19,360 --> 00:04:30,160 can go back to the API menu page in here and go to the API doc and then you want to scroll down because 59 00:04:30,370 --> 00:04:33,870 this is about getting data for a latitude and longitude. 60 00:04:33,910 --> 00:04:41,830 If you scroll down, you'll see the documentation for getting data based on a city. 61 00:04:46,020 --> 00:04:48,930 Here, built in API request by city name. 62 00:04:49,110 --> 00:04:54,240 And that is the same, you are a format that I'm using that is a format that I show you. 63 00:04:54,870 --> 00:04:55,890 In this example. 64 00:04:57,090 --> 00:05:05,010 So that will give you data for the next five days and then you should store all those data in this text 65 00:05:05,100 --> 00:05:05,490 file. 66 00:05:07,130 --> 00:05:13,100 That's the existence now in the next video, I'll show you my version of the exercise to try to solve 67 00:05:13,100 --> 00:05:18,380 it, because even if you don't solve it, you'll learn something and I'll see you in the next video.