1 00:00:00,090 --> 00:00:07,660 How long before we do go into this session, let's have a quick recap of what we have done in our project 2 00:00:07,680 --> 00:00:15,210 in so doing, lots of preparation and after we have got this amazing tree map and from this, we can 3 00:00:15,210 --> 00:00:20,580 definitely conclude some inferences as well so that we have find this. 4 00:00:20,940 --> 00:00:27,120 What exactly is a trend with respect to gun phone number of that number of recovered and number of active 5 00:00:27,120 --> 00:00:27,870 cases as well? 6 00:00:28,410 --> 00:00:35,330 So in the session, we had the very first part of a statement in which we have to visualize what exactly 7 00:00:35,340 --> 00:00:38,390 the population to test the ratio. 8 00:00:38,400 --> 00:00:40,980 So we have to visualize this estates. 9 00:00:41,280 --> 00:00:48,840 But you will notice over here in this world underscore DataDot had and if I'm going to show you this 10 00:00:48,840 --> 00:00:55,560 data frame, you don't have any column where you have this state, where you have this population test 11 00:00:55,890 --> 00:00:58,690 done feature over here, you will see you don't have any future. 12 00:00:59,130 --> 00:01:01,560 It means you have to create this feature. 13 00:01:02,190 --> 00:01:07,530 So far, this what I am going to do now over here, let's say I'm just going to define that feature 14 00:01:07,530 --> 00:01:08,580 that features nothing. 15 00:01:08,580 --> 00:01:18,180 But my population will get divided by what exactly the total number of test ongoing, which is exactly 16 00:01:18,180 --> 00:01:18,450 this. 17 00:01:19,140 --> 00:01:24,420 So here I'm just going to say, let's see, very first I have to exit the data frame. 18 00:01:24,420 --> 00:01:30,330 And on this I have to access this population, which is exactly this one. 19 00:01:30,800 --> 00:01:32,940 The data we have to use this operator. 20 00:01:32,940 --> 00:01:34,980 And after it, I have to use this one. 21 00:01:34,980 --> 00:01:39,360 And on this, I'm going to use this total test. 22 00:01:39,360 --> 00:01:45,870 And if I'm going to execute all these things, you can visualize with respect to each and every country, 23 00:01:45,870 --> 00:01:50,620 or you can see with respect to each and every index, you have some values assigned to it. 24 00:01:51,390 --> 00:01:58,800 So for this, we can do let's see, I just need this top 20 index values for this for you guys can call 25 00:01:58,800 --> 00:02:00,300 this index location. 26 00:02:00,510 --> 00:02:08,730 And here I'm going to say zero to 20, because in your top 20, your Mazher countries like India, Brazil, 27 00:02:08,730 --> 00:02:11,350 US, the UK are going to like it here. 28 00:02:12,600 --> 00:02:15,810 So let's see what our stats it will return me. 29 00:02:16,080 --> 00:02:18,120 I'm going to store it somewhere else. 30 00:02:18,170 --> 00:02:18,510 Let's see. 31 00:02:18,510 --> 00:02:26,940 This is exactly my population on the score test, on this score ratio, whatever variable name we want 32 00:02:26,940 --> 00:02:28,770 to assign, it's all up to you. 33 00:02:29,100 --> 00:02:35,340 After what we have to do, we have to simply visualize this data for this. 34 00:02:35,340 --> 00:02:40,980 I'm just going to use my back over here, which is exactly this bar over here. 35 00:02:41,220 --> 00:02:44,160 And these are all the custom parameters of this function. 36 00:02:44,160 --> 00:02:51,150 Your data frame, what you want on your access is what exactly you need on your y axis and all these 37 00:02:51,150 --> 00:02:52,120 kinds of things. 38 00:02:52,230 --> 00:02:59,190 So now, very first, I have to mention what exactly my data, which is exactly what data after it. 39 00:02:59,200 --> 00:03:08,160 Now I have to mention in this data frame, I just need the top 20 index for this. 40 00:03:08,160 --> 00:03:09,270 I have to use this. 41 00:03:09,270 --> 00:03:12,390 I like over here zero to 20. 42 00:03:12,400 --> 00:03:15,870 So this is exactly my entire data frame over here. 43 00:03:15,870 --> 00:03:22,560 Let's say on X axis, I have to set some value, which is exactly my country. 44 00:03:22,560 --> 00:03:25,260 So you can just pass over there. 45 00:03:25,270 --> 00:03:25,770 That's it. 46 00:03:26,100 --> 00:03:27,870 Operate on y axis. 47 00:03:27,870 --> 00:03:29,520 You have to set some values. 48 00:03:29,820 --> 00:03:35,100 So here on y axis, I'm going to say whatever value I have over here, that's it. 49 00:03:35,610 --> 00:03:42,960 And if I'm going to pass shift staff to check my all the custom parameters here, you have a very cool 50 00:03:42,960 --> 00:03:44,850 parameter, which is exactly this. 51 00:03:45,600 --> 00:03:53,070 So it means according to your color or you can see on the basis of what feature you have to assign color 52 00:03:53,070 --> 00:03:53,780 to your boss. 53 00:03:54,030 --> 00:04:01,680 So let's say I'm going to say color equals to, let's say country, class, region, and let's see if 54 00:04:01,680 --> 00:04:03,560 I have to assign some title as well. 55 00:04:03,880 --> 00:04:08,940 So let's my title is nothing, but actually my title is Population to Test. 56 00:04:08,940 --> 00:04:16,020 Don't they should say this is exact population to test done that. 57 00:04:16,110 --> 00:04:21,140 This is exactly my title, so I'm just going to store it somewhere else. 58 00:04:21,150 --> 00:04:26,310 Let's say in figure and figure is exactly an object, Utterback thought. 59 00:04:26,610 --> 00:04:29,490 And after it, I'm just going to call this show over here. 60 00:04:29,520 --> 00:04:37,650 And before executing this, you have to pass over here on Axis because over here in this data frame, 61 00:04:37,650 --> 00:04:40,800 you will figure out you had just stopped on the index. 62 00:04:40,800 --> 00:04:47,610 It means on y axis, you have to say, yeah, I have to pass that top 20 values for this. 63 00:04:47,610 --> 00:04:51,570 I have to say zero to to twenty. 64 00:04:51,570 --> 00:04:52,170 That's it. 65 00:04:52,170 --> 00:04:55,470 And just execute the set and it will return you. 66 00:04:55,470 --> 00:04:58,910 This beautiful, this amazing is overhead. 67 00:04:59,280 --> 00:04:59,840 Now you will. 68 00:05:00,250 --> 00:05:03,880 With respect to this Bangladesh, you have that much. 69 00:05:04,840 --> 00:05:07,430 You can say population to test and issue. 70 00:05:07,460 --> 00:05:12,820 Similarly, with respect to this Mexico, similarly with respect to India, you don't have that much 71 00:05:13,060 --> 00:05:16,510 good test and issue of arrest with respect to Russia. 72 00:05:16,510 --> 00:05:19,560 Definitely you have good test and issue. 73 00:05:19,570 --> 00:05:25,990 Similarly, with respect to U.K., with Saudi Arabia, it is in all the Western countries, you have 74 00:05:25,990 --> 00:05:29,470 definitely a good population to test on the issue. 75 00:05:29,520 --> 00:05:33,100 So that's the type of inference you can fetch from this Vizsla. 76 00:05:33,520 --> 00:05:40,180 So let's go ahead with our next analysis in which we have to find what are the 20 countries that are 77 00:05:40,180 --> 00:05:44,380 badly affected by this coronavirus so far? 78 00:05:44,380 --> 00:05:46,000 This what we can do. 79 00:05:46,000 --> 00:05:53,050 Let's say I'm just going to call our columns over here to showcase what are those columns that will 80 00:05:53,050 --> 00:05:56,590 play a very major role over here in this problem statement. 81 00:05:56,890 --> 00:06:03,430 So let's say on a basis of this serious on the basis of this critical and definitely on the basis of 82 00:06:03,430 --> 00:06:05,120 total less total record. 83 00:06:05,320 --> 00:06:11,330 So these are those features that will impact highly in this analysis. 84 00:06:11,710 --> 00:06:14,710 So let's say I'm just going to use a bar plot over here. 85 00:06:14,710 --> 00:06:20,800 And if I'm going to shift crosstab, these are all the custom parameters that you all are already aware 86 00:06:20,800 --> 00:06:21,310 about it. 87 00:06:21,730 --> 00:06:27,340 So let's say I have to need I just need the 20 countries for this. 88 00:06:27,340 --> 00:06:31,840 I have to say, I look and here I have to say zero to 10, 20. 89 00:06:31,840 --> 00:06:32,350 That's it. 90 00:06:32,770 --> 00:06:38,560 And definitely I have to pass something on x axis and some values on X Y axis as well. 91 00:06:38,920 --> 00:06:46,750 So on X axis, I have to mention this country reason because on this X axis, you will figure out here 92 00:06:46,750 --> 00:06:47,770 you have some countries. 93 00:06:48,040 --> 00:06:55,240 So here I would say on Axis I have all this stuff and definitely on Y axis, I'm just going to say I 94 00:06:55,240 --> 00:06:56,380 have to pass my data. 95 00:06:56,380 --> 00:07:00,070 And the list to the very first is, you see this. 96 00:07:00,220 --> 00:07:02,480 The second one is you're critical. 97 00:07:02,500 --> 00:07:10,870 So I'm just going to copy all these stats over here, all these stuffs, and make sure you have to separate 98 00:07:10,870 --> 00:07:17,440 it using comma after doing all these things that hard feature that will definitely impact over here, 99 00:07:17,440 --> 00:07:19,690 which is exactly what total let's say. 100 00:07:19,690 --> 00:07:23,080 I'm just going to copy from here, just going to paste to it here. 101 00:07:23,440 --> 00:07:26,290 The fourth feature that will play a major role over here. 102 00:07:26,290 --> 00:07:30,780 My recovered case is my active cases and my total cases. 103 00:07:31,390 --> 00:07:35,010 So these are my total recovered cases. 104 00:07:35,230 --> 00:07:37,240 So these are exactly my total record. 105 00:07:37,240 --> 00:07:39,420 These are my total active cases. 106 00:07:39,820 --> 00:07:43,050 So these are exactly my total active cases. 107 00:07:43,060 --> 00:07:48,700 I'm going to say these are my active cases and these are my all the total cases. 108 00:07:48,700 --> 00:07:57,610 So I'm going to say these are exactly my all the total cases over here ones I have all these estates 109 00:07:57,610 --> 00:07:58,270 over here. 110 00:07:58,750 --> 00:07:59,770 What do we have to do? 111 00:07:59,770 --> 00:08:02,280 We have to just execute this solution here. 112 00:08:02,290 --> 00:08:05,530 So this is your amazing statistics over here. 113 00:08:05,530 --> 00:08:11,470 And if you want to assign some titles and if you want to update your layout, you can definitely do 114 00:08:11,470 --> 00:08:11,740 that. 115 00:08:11,900 --> 00:08:15,360 That's that's not a very clean task there. 116 00:08:15,370 --> 00:08:17,140 That's just like a piece of a cake. 117 00:08:17,650 --> 00:08:19,780 So here you will observe this. 118 00:08:20,110 --> 00:08:22,630 This is exactly estaba chart. 119 00:08:22,930 --> 00:08:27,550 So you will figure out this this arrangement with the respective total cases. 120 00:08:27,760 --> 00:08:31,720 Where is this purple with respect to see this critical? 121 00:08:32,350 --> 00:08:36,440 This green one is exactly it is totally covered. 122 00:08:36,850 --> 00:08:42,550 There is all these things with respect to that and all other things you will visualize already here. 123 00:08:42,580 --> 00:08:44,350 This is with respect to USC. 124 00:08:44,350 --> 00:08:47,710 This is your Brasi, this is your India, this is Russia. 125 00:08:48,250 --> 00:08:55,030 And you can easily figure out over here this USC has a highest number of total number of cases. 126 00:08:55,030 --> 00:09:00,070 After that, we have this Brazil, then we have India, then we have Russia. 127 00:09:00,070 --> 00:09:02,370 And this is exactly that in that. 128 00:09:02,380 --> 00:09:07,180 HATALSKY So this is amazing inference from this visa. 129 00:09:07,240 --> 00:09:10,750 So that's all about the session of the session very much. 130 00:09:11,050 --> 00:09:11,680 Thank you. 131 00:09:11,980 --> 00:09:12,630 Nice day. 132 00:09:12,640 --> 00:09:13,410 Keep learning. 133 00:09:13,420 --> 00:09:14,200 Keep growing. 134 00:09:14,350 --> 00:09:14,620 Keep.