1 00:00:00,210 --> 00:00:07,050 Halaal, before going ahead in this session, let's have a quick recap of what we have done in this 2 00:00:07,050 --> 00:00:08,250 particular project. 3 00:00:08,260 --> 00:00:14,430 So from collecting the data, are doing lots of analysis, doing lots of preparation on data. 4 00:00:14,670 --> 00:00:18,970 And in the previous session, we have basically performed this trend. 5 00:00:18,990 --> 00:00:19,250 Yeah. 6 00:00:19,320 --> 00:00:23,580 How exactly room price per night were I over the moon? 7 00:00:23,610 --> 00:00:29,350 So this is exactly my train for the resort and this is exactly my train for the city hotel as well. 8 00:00:29,640 --> 00:00:33,430 And apart from we have had some meaningful insight as well. 9 00:00:33,870 --> 00:00:40,110 So in this session, we have again do some lots of analysis on data, the very first one, which are 10 00:00:40,110 --> 00:00:46,920 the most busiest month and all you can see on which month, my guests are highest. 11 00:00:47,250 --> 00:00:52,680 So this is exactly you have to understand in what month guests are coming from. 12 00:00:52,770 --> 00:00:55,590 It means you have to consider both the data framed. 13 00:00:55,590 --> 00:00:59,340 It is back to your resort hotel as well as with respect to City Hotel. 14 00:00:59,730 --> 00:01:05,190 So let me give you a quick overview of how exactly mine data looks like. 15 00:01:05,190 --> 00:01:08,800 So you will figure out over here this is with respect to your resort hotel. 16 00:01:08,910 --> 00:01:16,170 Now, in this data and the ski resort, what you have to do, you have to count in each and every month 17 00:01:16,320 --> 00:01:20,850 what exactly the total number of count of guest far is what I have to do. 18 00:01:20,860 --> 00:01:27,180 Very first, I have to access my data and resort, which is exactly this one, so that if I have to 19 00:01:27,180 --> 00:01:33,840 exit this feature and on this I have to just call my value on the school council, just execute this 20 00:01:33,850 --> 00:01:35,340 stuff over now. 21 00:01:35,460 --> 00:01:40,770 And this is amazing stats simply you have to just convert it into some data frames. 22 00:01:40,800 --> 00:01:45,720 I'm just going to say reset underscore index again, executed. 23 00:01:46,080 --> 00:01:47,590 This is your amazing stats. 24 00:01:47,640 --> 00:01:49,620 Lexapro, boosterism, modification. 25 00:01:49,830 --> 00:01:54,100 Let's say this is exactly my thought on a scale resort. 26 00:01:55,020 --> 00:01:58,800 And now what I have to do, I have to simply rename my column name. 27 00:01:58,800 --> 00:02:02,370 So I'm going to say that Gollum's is close to nothing more. 28 00:02:02,380 --> 00:02:04,830 The very first column name is exactly my mom. 29 00:02:05,370 --> 00:02:10,200 The second column name is nothing but number of guest after eight. 30 00:02:10,230 --> 00:02:15,360 What I have to do, I have to simply print my russianness resided a framed exit. 31 00:02:15,660 --> 00:02:20,400 So this is that amazing data frame that you have to consider for you. 32 00:02:21,030 --> 00:02:26,810 And after it, what I have to do, I have to simply consider my data, underscore city. 33 00:02:27,180 --> 00:02:33,060 So here I'm just going to say it is exactly my rush on this score city. 34 00:02:33,450 --> 00:02:36,840 It is also my rush on a score city. 35 00:02:37,200 --> 00:02:41,670 It is also my rush on the city just to execute the set. 36 00:02:41,910 --> 00:02:46,820 And you will see the difference with respect to this resort hotel. 37 00:02:46,830 --> 00:02:51,990 You have that much number of guests in August, but again, you have a similar problem. 38 00:02:51,990 --> 00:02:54,450 Again, you don't have a proper hierarchy in your data. 39 00:02:54,450 --> 00:03:00,240 It means whenever you are going to conclude from this data, you get some improper conclusion. 40 00:03:00,240 --> 00:03:06,120 It means you have to assign a proper hierarchy to this month column. 41 00:03:06,540 --> 00:03:13,290 So for this, we have defined a function which is exactly my short data, which is exactly this one. 42 00:03:13,530 --> 00:03:18,370 But very first, you have to merge both these data frames. 43 00:03:18,370 --> 00:03:20,220 So far, this is what I have to do. 44 00:03:20,220 --> 00:03:25,740 I'm just going to say Rush, underscore, resort, not much. 45 00:03:25,740 --> 00:03:27,360 And here are my second data. 46 00:03:27,360 --> 00:03:30,360 Feinglass Rush, underscore city. 47 00:03:30,510 --> 00:03:34,050 Now, I have to say on what column basis I have to merge it. 48 00:03:34,050 --> 00:03:37,730 So basically on the basis of my month column, that's it. 49 00:03:38,040 --> 00:03:43,890 So after having all this stuff, I'm going to say its name is nothing but fine and a school trash, 50 00:03:44,250 --> 00:03:45,480 so just executed. 51 00:03:45,480 --> 00:03:52,650 And if I'm going to print my fun and rush, you will see these are all my data frame, Aleksi. 52 00:03:52,650 --> 00:04:01,230 I have to do manipulation is still let me rename my column name so far and as Goldrush dot columns, 53 00:04:01,300 --> 00:04:05,100 nothing but the very first one is exactly my next month. 54 00:04:05,430 --> 00:04:09,480 The second one is, let's see my number of Gastón results. 55 00:04:09,480 --> 00:04:13,650 I'm going to say number of guest in resort. 56 00:04:13,650 --> 00:04:22,860 And after the third column name is exactly my number of guest in, let's say, City Hotel so that these 57 00:04:22,860 --> 00:04:24,940 are exactly all the stuff after it. 58 00:04:24,960 --> 00:04:28,710 I have to just print my final score. 59 00:04:29,070 --> 00:04:32,010 So I'm just going to bring this frame now. 60 00:04:32,130 --> 00:04:35,820 You will see over here all your stuff gets modified over here. 61 00:04:35,820 --> 00:04:41,130 What you have to do, you have to just assign proper hierarchy over here for this. 62 00:04:41,130 --> 00:04:46,890 I have to just call my short on a data function that what I have defined in the previous session. 63 00:04:46,890 --> 00:04:49,350 So here, if you will, press shift goosestep. 64 00:04:49,710 --> 00:04:51,720 The very first is your data frame. 65 00:04:51,810 --> 00:04:56,340 The second is what column name to which you have to assign you a proper header. 66 00:04:57,120 --> 00:04:59,940 So my data frame is nothing but underscore. 67 00:05:00,090 --> 00:05:09,480 Rush, my column name is nothing, but which is exactly a month, so I have to just execute this function, 68 00:05:09,870 --> 00:05:12,150 it will it is this amazing stretch. 69 00:05:12,180 --> 00:05:18,570 Let me just save as finite and as gold rush, which is exactly this one. 70 00:05:18,870 --> 00:05:27,160 And if I am going to print my final rush, so this is exactly my final data frame that I have to consider. 71 00:05:27,390 --> 00:05:30,090 So now I exactly need. 72 00:05:30,300 --> 00:05:30,740 Yeah. 73 00:05:30,900 --> 00:05:33,390 Which are my most busiest month. 74 00:05:33,390 --> 00:05:37,320 It means up to some extent I need some kind of trend. 75 00:05:37,560 --> 00:05:42,520 So whenever you need some kind of trend, always go for your landlord. 76 00:05:42,900 --> 00:05:46,900 So here I'm just going to say pics dot line. 77 00:05:46,920 --> 00:05:53,570 So here I'm going to start line press, postop data frame and all these custom parameters. 78 00:05:53,580 --> 00:05:55,130 You have to just assign some value. 79 00:05:55,470 --> 00:05:57,180 That's a piece of collecting. 80 00:05:57,480 --> 00:06:02,750 So I'm going to say, which is nothing more final and a rush on X-axis. 81 00:06:02,760 --> 00:06:09,040 What I have to assign I have to basically assign my month next simple on Y-axis, what I have to assign. 82 00:06:09,270 --> 00:06:14,310 So here I'm going to save on this gold rush dot column. 83 00:06:14,350 --> 00:06:22,450 So you will figure out these are exactly both the columns that you have to assign on your Y-axis. 84 00:06:22,450 --> 00:06:27,630 So I'm going to just paste over there and let me assign some title as well. 85 00:06:27,630 --> 00:06:31,110 So I'm going to say my title is nothing. 86 00:06:31,110 --> 00:06:38,620 But let's say total number of guest for one of these are exactly my title. 87 00:06:39,000 --> 00:06:40,650 So just execute the set. 88 00:06:40,650 --> 00:06:43,410 It will return as this amazing statistics. 89 00:06:43,920 --> 00:06:50,430 You'll see this is with respect to your city hotel, this one, whereas with respect to your resort 90 00:06:50,430 --> 00:06:57,540 hotel, you have that much changed because I have to conclude from this, then I can see the City Hotel 91 00:06:57,660 --> 00:07:04,500 has more guest during obviously spring and autumn in July and August. 92 00:07:04,500 --> 00:07:09,630 There are less visitors you will observe, although prices are lower. 93 00:07:09,660 --> 00:07:17,160 Whereas with respect to this resort hotel, you can say Ghast, member for this resort hotel, go down 94 00:07:17,160 --> 00:07:19,440 slightly from June to September. 95 00:07:19,440 --> 00:07:24,240 You will figure out this one and definitely which is also when the prices are highest. 96 00:07:24,390 --> 00:07:27,860 That's what we have analyzed in the previous session, which is exactly this one. 97 00:07:28,290 --> 00:07:31,290 So here you can also analyze in the zone. 98 00:07:31,590 --> 00:07:36,210 Obviously, both hotels have the fewest guest during this winter. 99 00:07:37,050 --> 00:07:42,570 So that's the type of analysis, that's the type of influence you can extract from your visual. 100 00:07:42,750 --> 00:07:44,040 So that's all about the session. 101 00:07:44,040 --> 00:07:45,270 I hope you love it very much. 102 00:07:45,510 --> 00:07:46,200 Thank you. 103 00:07:46,200 --> 00:07:47,130 Have a nice day. 104 00:07:47,280 --> 00:07:48,170 Keep learning. 105 00:07:48,180 --> 00:07:49,080 Keep growing. 106 00:07:49,230 --> 00:07:50,100 Keep practicing.