1 00:00:00,090 --> 00:00:07,920 Hello and welcome to this lecture, where we are going to start working with the flight's casual problem 2 00:00:08,250 --> 00:00:12,960 and the first step we need should represent the problem. 3 00:00:13,290 --> 00:00:20,480 This is the part of the implementation that will take more time to implement seals. 4 00:00:20,520 --> 00:00:26,810 We need to prepare and understand all the data as we saw last lecture. 5 00:00:27,090 --> 00:00:33,360 There are six people who will travel from one city to Rome. 6 00:00:33,720 --> 00:00:39,420 So first, we need to define the list of people. 7 00:00:39,780 --> 00:00:43,320 Let's create these variable people. 8 00:00:43,710 --> 00:00:55,320 The Lisbon and I would hear these ladders al, I ask, which means the name of the airports in Lisbon 9 00:00:55,590 --> 00:01:01,980 because we will load our data sets and the dataset is in these four months. 10 00:01:02,280 --> 00:01:03,750 In some minutes, you will. 11 00:01:03,750 --> 00:01:14,820 Badger understands this section, some Madrid's and the name of the airports, and then the third person 12 00:01:15,060 --> 00:01:20,900 buries the name of the airports c the G. 13 00:01:21,330 --> 00:01:31,800 We just needs to close parenthesis here, open and close here for each one of the people. 14 00:01:32,780 --> 00:01:41,960 Then the next one, doubling the name of the airports, the you be. 15 00:01:42,970 --> 00:01:50,520 The next one, Boro sells the name of their words, B, are you? 16 00:01:51,490 --> 00:02:05,680 And finally, the last person London, the name of the airport Al H are we can create this new variable. 17 00:02:07,110 --> 00:02:17,430 We can just type the name people, and we have all this information here when we was access one of the 18 00:02:17,430 --> 00:02:18,180 lines. 19 00:02:18,510 --> 00:02:29,490 We can just type people position five London and we have the name of the person are the name of this 20 00:02:29,490 --> 00:02:32,760 city and the name of the airports. 21 00:02:33,270 --> 00:02:38,740 If we type number three here, we will get the blame. 22 00:02:39,120 --> 00:02:42,460 Just a reminder that's in Python indexes. 23 00:02:42,480 --> 00:02:53,070 It starts from zero position, zero position, one position queue and position three, which is equal 24 00:02:53,070 --> 00:02:53,880 to burbling. 25 00:02:54,510 --> 00:02:58,620 Let's create another variable which will be added. 26 00:02:58,620 --> 00:03:09,360 Destiny FC all, which is the name of the airports in Rome, just better understands how we are going 27 00:03:09,360 --> 00:03:12,930 to define this structure of our data. 28 00:03:13,500 --> 00:03:21,660 We can create a new variable cloud flights and it will be in the format of a dictionary. 29 00:03:21,930 --> 00:03:25,950 For this reason, we open and close breaths. 30 00:03:26,340 --> 00:03:31,620 For example, we will have a flight from Brussels. 31 00:03:32,100 --> 00:03:33,720 Sure run. 32 00:03:34,290 --> 00:03:40,590 The first column is the arranging, and this second column is the destiny. 33 00:03:40,950 --> 00:03:46,260 Then we can define the time of departure. 34 00:03:46,560 --> 00:03:57,390 For example, 15 and 44 and the time of arrival 18 55. 35 00:03:57,750 --> 00:04:09,000 So it means that the flight from Brussels to Rome departures at this time and arrives at this time. 36 00:04:09,450 --> 00:04:13,620 Then we can both hear the price of the flight. 37 00:04:13,920 --> 00:04:24,800 For example, three hundred and eighty two, let's create this variable type, the name of the new variable. 38 00:04:24,810 --> 00:04:27,450 Here we can see the data. 39 00:04:27,870 --> 00:04:36,960 And if you want to access, for example, there is information about the time and the information about 40 00:04:36,960 --> 00:04:37,860 the price. 41 00:04:38,160 --> 00:04:46,890 We can just type here flights and let's type B R you f c o. 42 00:04:47,250 --> 00:04:55,710 These are the indexes of the dictionary, and this list is the values of the dictionary. 43 00:04:56,070 --> 00:04:58,350 Let's run this set of codes. 44 00:04:58,620 --> 00:05:05,190 We gets the time of departure, the time of arrival and also the price. 45 00:05:05,550 --> 00:05:15,840 As we have a problem where the goal is to minimize the prices, we are going to get all these parts 46 00:05:15,840 --> 00:05:21,480 of the dictionary so we can access each one of the positions. 47 00:05:21,990 --> 00:05:27,840 For example, let's copy and paste this code here. 48 00:05:28,110 --> 00:05:36,660 If we type position zero, we will get only that time of departure. 49 00:05:37,200 --> 00:05:41,370 We can based the code again, position one. 50 00:05:41,730 --> 00:05:45,750 We will get the time of arrival. 51 00:05:46,110 --> 00:05:53,490 And finally, in position number two, we will get the price here. 52 00:05:54,060 --> 00:05:55,540 That is the goal. 53 00:05:55,540 --> 00:05:57,710 Well off our case is 30. 54 00:05:58,320 --> 00:06:06,780 Now that you have understood this structure of this dictionary, we can load the data sets. 55 00:06:07,230 --> 00:06:09,840 We will click this button here. 56 00:06:10,290 --> 00:06:18,570 You can click the uploads and we'll select this dataset flights, not the X. 57 00:06:18,570 --> 00:06:22,650 Then you can download it in the class materials. 58 00:06:23,100 --> 00:06:27,690 Let's open this file and we can see here. 59 00:06:28,230 --> 00:06:36,270 This section column is the arranging the name of the airports in this second column. 60 00:06:36,480 --> 00:06:38,940 We can find the destination. 61 00:06:39,180 --> 00:06:45,990 For example, the first row is a flight from Wrong to Lisbon. 62 00:06:46,560 --> 00:06:54,420 The next column is the departure time, the arrival time and the price. 63 00:06:54,810 --> 00:06:57,090 We can see rule number six. 64 00:06:57,540 --> 00:07:06,180 It is a flight from Lisbon to Rome, and the price is nine denied. 65 00:07:06,870 --> 00:07:10,850 We can also take a look at some other roles. 66 00:07:11,460 --> 00:07:21,960 This is a flight from Paris to Rome, and it costs three hundred and twenty six in this particular case 67 00:07:21,960 --> 00:07:22,550 study. 68 00:07:22,800 --> 00:07:33,000 We are not going to use the time of departure and the time of arrival because our goal is just to walk 69 00:07:33,000 --> 00:07:34,710 with this value. 70 00:07:35,070 --> 00:07:46,740 The price of the airline tickets now in these trades, this file in order to create this complete dictionary 71 00:07:47,010 --> 00:07:53,840 composed of all flights so we can open a new sale of codes. 72 00:07:54,480 --> 00:08:00,090 We will create this variable again and empty dictionary. 73 00:08:00,420 --> 00:08:05,970 Let's implement a for loop for road in open. 74 00:08:06,360 --> 00:08:16,380 And now we just needs to type the name of the datasets, which is here available in the Google CoLab 75 00:08:16,380 --> 00:08:17,400 environments. 76 00:08:17,790 --> 00:08:23,040 We can brands the road just to see the partial results. 77 00:08:23,490 --> 00:08:31,950 They can look here that we are going through each one of the rows now in these two extracts. 78 00:08:32,310 --> 00:08:41,910 The name of the airports and also the price of the airline tickets just to badger understand how we 79 00:08:41,910 --> 00:08:43,290 are going to do that. 80 00:08:43,650 --> 00:08:54,060 We can type role dot splits and when we find a comma, we will split the data. 81 00:08:54,390 --> 00:08:56,280 Let's run this code again. 82 00:08:56,580 --> 00:09:07,470 For example, we have this list here, and when we find a comma, we split that they that in at least 83 00:09:07,470 --> 00:09:08,220 four months. 84 00:09:08,460 --> 00:09:12,300 You can see open and close brackets here. 85 00:09:12,480 --> 00:09:21,990 For example, in the first position of the list, we can see the airports of bringing in this second 86 00:09:21,990 --> 00:09:26,760 position, the destination in the third position. 87 00:09:27,120 --> 00:09:31,370 The time of departure in the next position. 88 00:09:31,740 --> 00:09:33,780 The time for arrival. 89 00:09:33,960 --> 00:09:39,210 And finally, in the last position, the price of the tickets. 90 00:09:39,750 --> 00:09:46,080 So now we can create a variable for each one of the positions. 91 00:09:46,650 --> 00:09:54,120 The orange in the destiny departure, the arrival and the price. 92 00:09:54,570 --> 00:09:58,410 It will be equal to zero thoughts. 93 00:09:58,500 --> 00:10:00,300 Blitz comma. 94 00:10:00,990 --> 00:10:10,260 If you want to see the results, we can just bring into the variables our engine, destiny, departure, 95 00:10:10,670 --> 00:10:13,020 arrival and the price. 96 00:10:13,410 --> 00:10:17,910 Let's run this code again and we can see the results. 97 00:10:18,180 --> 00:10:25,260 Each one of the positions are starts in each one of these new variables. 98 00:10:25,740 --> 00:10:30,260 Now we can access our dictionary flights. 99 00:10:30,890 --> 00:10:42,390 Let's set the to initialize the dictionary, and the key will be orange and destiny, so we can put 100 00:10:42,390 --> 00:10:54,780 their origin and destiny similar to this structure here, because in this data sets that are more than 101 00:10:54,780 --> 00:10:59,700 one option off flights from Brussels to Rome. 102 00:11:00,270 --> 00:11:03,470 So we need the name of the airports. 103 00:11:03,480 --> 00:11:09,600 Ask is and then we will boards all the available flights. 104 00:11:09,990 --> 00:11:12,480 Let's finish this implementation. 105 00:11:12,780 --> 00:11:15,360 So it will be easier to understand. 106 00:11:15,570 --> 00:11:22,230 We will initialize the dictionary with empty spaces are empty list. 107 00:11:22,590 --> 00:11:28,500 We can brands the flights just to see the partial results. 108 00:11:28,860 --> 00:11:31,350 See, that's there are a lot of options. 109 00:11:31,680 --> 00:11:39,090 For example, FCO should Lisbon, FCO, Julius Balagan and so on. 110 00:11:39,390 --> 00:11:46,230 Now it needs to fill these empty lists here in order to do that. 111 00:11:46,530 --> 00:11:51,660 We can just access our flights dictionary. 112 00:11:52,140 --> 00:11:53,490 Let's put here. 113 00:11:53,880 --> 00:12:05,970 They are changing and the destiny and OWWA bands icons in the list, the departure de arrival and let's. 114 00:12:06,330 --> 00:12:11,900 Words the price should integer, let's run this quote. 115 00:12:12,440 --> 00:12:15,210 Now it will be easier to see the results. 116 00:12:15,500 --> 00:12:17,720 I will type flights. 117 00:12:18,290 --> 00:12:23,570 We can see here, for example, from Rome, actually is boom. 118 00:12:23,960 --> 00:12:29,780 And in this list here we can see all available flights. 119 00:12:30,050 --> 00:12:38,780 For example, the cheapest flight from Rome to Lisbon is one hundred and thirty six. 120 00:12:39,080 --> 00:12:45,590 And the more expensive is these first one, two hundred and thirty nine. 121 00:12:45,830 --> 00:12:57,530 In a similar way, we can see from London to Rome, all available flights from Lisbon to Rome, from 122 00:12:57,710 --> 00:13:09,260 my three children and so on, and we can see that there are one two three four five six seven eight 123 00:13:09,260 --> 00:13:16,610 nine 10 flights that are then flights in all these options here. 124 00:13:16,880 --> 00:13:27,500 And the goal is to select assets off flights that have the cheapest value if you want to visualize data 125 00:13:27,800 --> 00:13:31,040 about a specific destination. 126 00:13:31,520 --> 00:13:41,810 We can type flights, for example, if you wants to see the available flights from Lisbon short run, 127 00:13:42,920 --> 00:13:47,960 we can see all options here and in a similar way. 128 00:13:48,170 --> 00:14:01,130 We can also see the flights from FCO, from Rome to Lisbon brand escorts and we can see the options 129 00:14:01,400 --> 00:14:11,500 and remembering again, that's the goal of this case study is to select a flight from Lisbon to Rome 130 00:14:11,810 --> 00:14:16,100 and then the other flights from wrong to Lisbon. 131 00:14:16,460 --> 00:14:19,400 And the same for all other cities. 132 00:14:19,760 --> 00:14:24,110 And in the next lecture, we will finish this implementation. 133 00:14:24,440 --> 00:14:28,250 And then we will apply genetic algorithms. 134 00:14:28,610 --> 00:14:29,300 See you there!