1 00:00:00,270 --> 00:00:03,360 Now we're finally ready to tackle our project, 2 00:00:03,510 --> 00:00:08,510 and this project is based on a real-life struggle that I have. Often when I'm 3 00:00:09,480 --> 00:00:13,830 calling a company and over the phone I have to give some sort of details 4 00:00:13,830 --> 00:00:16,680 right? They'll ask me, what is your name, and can you spell it? 5 00:00:16,890 --> 00:00:21,360 So I'll have to be like A-N-G-E-L-A. And they're like, wait, was that an E? 6 00:00:21,390 --> 00:00:24,390 Was that a B, what did you say? In these cases 7 00:00:24,390 --> 00:00:29,070 often I have to sort of default to some sort of Naval alphabet. where 8 00:00:29,070 --> 00:00:32,220 I say A is the alfa, N for November, G for golf. 9 00:00:32,610 --> 00:00:36,870 And then at some point I forget what E stands for and I'm like E for.. elephant? 10 00:00:37,530 --> 00:00:42,180 And there have been times where I've just completely taken the MIG and said 11 00:00:42,180 --> 00:00:46,170 things like M for mnemonic, P for pneumonia, W for wrench, 12 00:00:46,440 --> 00:00:47,880 which doesn't make any sense at all 13 00:00:47,880 --> 00:00:52,880 because these are silent letters. And I've had some very angry people, 14 00:00:52,950 --> 00:00:54,570 um, but it was quite fun. 15 00:00:55,110 --> 00:00:59,610 So now what we want to create is we want to create a tool that takes the NATO 16 00:00:59,610 --> 00:01:03,840 phonetic alphabet which was generated because it's often really, 17 00:01:03,840 --> 00:01:08,840 really important for the other side to know exactly what it is that you said and 18 00:01:08,970 --> 00:01:13,970 which letters you're spelling out so that they don't mistaken your E for a B or 19 00:01:15,000 --> 00:01:16,500 your T for a C 20 00:01:16,530 --> 00:01:20,700 cause it all sounds really similar. Head over to the course resources page 21 00:01:20,760 --> 00:01:24,120 where you can find the starting files for the NATO alphabet project. 22 00:01:24,990 --> 00:01:29,730 I want you to practice with list comprehension and with dictionary 23 00:01:29,730 --> 00:01:31,980 comprehension. But in this case, 24 00:01:32,010 --> 00:01:34,800 we're not going through a dictionary per se, 25 00:01:34,830 --> 00:01:39,830 but we're actually going through a data frame because our data lives inside a 26 00:01:39,930 --> 00:01:43,830 CSV. So we're going to be reading it using pandas. 27 00:01:44,130 --> 00:01:49,130 and then we're going to end up with a data frame. Instead of having a bog 28 00:01:49,410 --> 00:01:54,150 standard dictionary comprehension where our keywords were new_key: 29 00:01:54,180 --> 00:01:59,180 new_value for key, value in dict.items. 30 00:02:05,130 --> 00:02:08,430 Now we're going to change this for-loop 31 00:02:08,490 --> 00:02:11,330 so instead of using the simple for loop where 32 00:02:11,410 --> 00:02:12,990 we're just looping through a dictionary, 33 00:02:13,380 --> 00:02:17,670 we're going to use this particular loop. So then in this case, 34 00:02:17,700 --> 00:02:22,700 we're saying for index row in our data frame, 35 00:02:24,300 --> 00:02:28,740 and then it's .iterrows as the method. 36 00:02:29,220 --> 00:02:34,220 So basically I want you to use this different loop in order to loop through our 37 00:02:34,440 --> 00:02:39,440 data frame and then create a new dictionary using a new key and new value. 38 00:02:41,130 --> 00:02:45,390 So what exactly is it that I want you to do with this? Well, 39 00:02:45,420 --> 00:02:49,380 I want you to be able to take the CSV of NATO phonetic alphabet, 40 00:02:49,830 --> 00:02:51,690 and I want you to do two things to it. 41 00:02:52,140 --> 00:02:57,140 The first thing is for you to create a dictionary in this format. 42 00:03:01,000 --> 00:03:06,000 The format is basically the key is going to be the actual letter, 43 00:03:06,130 --> 00:03:11,130 so maybe A. And then the value is going to be the corresponding code for the NATO 44 00:03:12,460 --> 00:03:15,700 phonetic alphabet. So A actually is for alpha. 45 00:03:16,150 --> 00:03:20,530 And then the next one is B, which stands for Bravo. 46 00:03:21,490 --> 00:03:22,180 Essentially, 47 00:03:22,180 --> 00:03:26,830 we're going to take the CSV and we're going to convert it into a dictionary in 48 00:03:26,830 --> 00:03:30,190 this particular format where the keys 49 00:03:30,190 --> 00:03:34,330 are the letters inside the CSV, so that first column, 50 00:03:34,690 --> 00:03:39,690 and then the values are the corresponding code words for each of those letters. 51 00:03:40,630 --> 00:03:45,250 And the reason why we're talking about iterrows and had to loop through rows of a 52 00:03:45,250 --> 00:03:49,570 data frame instead of just getting you to get hold of the data frame 53 00:03:49,900 --> 00:03:52,270 and then just say to_dict 54 00:03:52,420 --> 00:03:56,590 like what we did before, is because it's not going to give it to you in this 55 00:03:56,590 --> 00:03:57,640 particular format. 56 00:03:58,000 --> 00:04:01,210 So you're going to have to use what you've learned about dictionary 57 00:04:01,210 --> 00:04:05,770 comprehension in order to create a dictionary in this particular format. 58 00:04:06,370 --> 00:04:08,830 And then once you've created a dictionary in this format, 59 00:04:08,860 --> 00:04:11,200 then the next step is a lot easier. 60 00:04:11,500 --> 00:04:16,500 You are going to create a list of the phonetic code words from a word that the 61 00:04:19,450 --> 00:04:20,620 user inputs. 62 00:04:21,940 --> 00:04:26,410 That way the user can use the input to type a word. 63 00:04:26,830 --> 00:04:30,430 So for example, Thomas, and then when they hit enter, 64 00:04:30,550 --> 00:04:32,740 they get a list printed out 65 00:04:33,010 --> 00:04:38,010 that is the phonetic alphabet code word for each of the letters in their input. 66 00:04:39,190 --> 00:04:41,800 So Tango, Hotel, Oscar Mike, Alpha Sierra, 67 00:04:41,830 --> 00:04:45,160 which they can then read back using our little tool. 68 00:04:46,210 --> 00:04:50,290 I would say that to do step 1 is a lot harder than to do step 2. 69 00:04:50,620 --> 00:04:54,070 But essentially, to do step 1 is a dictionary comprehension, 70 00:04:54,160 --> 00:04:58,630 creating a dictionary in this format from the CSV, and to do 71 00:04:58,630 --> 00:05:03,630 number 2 is basically completing the project and being able to create and 72 00:05:03,910 --> 00:05:08,860 print out a list of the code words based on the word that the user inputs. 73 00:05:09,400 --> 00:05:13,570 So I want you to pause the video, have a think about how you might solve this, 74 00:05:13,900 --> 00:05:18,900 and then go ahead to the course resources and download this starting zip file so 75 00:05:20,170 --> 00:05:24,370 that you can open it up inside PyCharm and create your project. 76 00:05:24,880 --> 00:05:26,770 Pause the video and give that a go now.