1 00:00:00,150 --> 00:00:07,170 Halaal, before going ahead in this session, let's have a quick recap of what we have done below in 2 00:00:07,170 --> 00:00:15,230 this water project since the very first session, we have collect this data using this baby logic. 3 00:00:15,240 --> 00:00:21,900 After what we have done, we have had some derived attributes from data because once we have that much 4 00:00:22,110 --> 00:00:24,690 that much data, which is exactly this one. 5 00:00:24,940 --> 00:00:30,930 So from this data, you can come up with some different different insights with respect like these days 6 00:00:31,080 --> 00:00:36,020 with respect to some minerals month out and lots of analysis, you can buff on the data. 7 00:00:36,360 --> 00:00:37,500 That's what I have. 8 00:00:37,500 --> 00:00:40,900 First, some kind of did I have to attribute some of this data? 9 00:00:40,920 --> 00:00:43,140 So in this session, we have this assignment. 10 00:00:43,140 --> 00:00:48,480 The very first one is we have to perform analysis of journey by week. 11 00:00:48,480 --> 00:00:54,520 This you will visualize over here you have a feature, which is exactly what we did. 12 00:00:54,570 --> 00:00:57,240 You have to perform analysis on this feature. 13 00:00:57,300 --> 00:01:01,890 So for this what I am going to do very close, I have to exit this video. 14 00:01:02,010 --> 00:01:09,300 So I'm going to say for V.T. and on this, if I'm going to call this value underscore counselor, now 15 00:01:09,300 --> 00:01:14,100 you will see with respect to each and every day you have some value. 16 00:01:14,110 --> 00:01:17,210 It means on Thursday you have that much trust. 17 00:01:17,460 --> 00:01:21,410 You on Friday, you have that much with respect to each and every day. 18 00:01:21,420 --> 00:01:23,460 You have some value assigned to it. 19 00:01:23,490 --> 00:01:25,000 Now, what do you have to do? 20 00:01:25,020 --> 00:01:27,410 You have to simply plot this data. 21 00:01:27,630 --> 00:01:29,570 So far, this is what I'm going to do. 22 00:01:29,610 --> 00:01:34,950 I'm basically going to use some advance validation library, which is exactly plotline. 23 00:01:35,160 --> 00:01:42,730 And if you are not very much aware about plot, the plot is basically that advanced level with the additional 24 00:01:42,780 --> 00:01:44,910 library that is highly used. 25 00:01:44,910 --> 00:01:49,060 Whenever you have to create some deployment level visual for this. 26 00:01:49,080 --> 00:01:55,980 What I'm going to do, I'm just going to say encode plot, lead, dot express, which is exactly a submodular 27 00:01:55,980 --> 00:01:56,340 plot. 28 00:01:56,790 --> 00:02:02,270 And if you haven't installed floridly, you can install using PIP install this plot. 29 00:02:02,470 --> 00:02:07,290 So I'm just going to install using this one and I have already installed, so I'm not going to install 30 00:02:07,290 --> 00:02:07,440 it. 31 00:02:07,680 --> 00:02:11,580 So using this you can simply import this plot. 32 00:02:11,730 --> 00:02:20,190 So just executed after what I have to do in this plot, I have something which is exactly which is exactly 33 00:02:20,190 --> 00:02:24,180 my backload because BA plot over here will definitely help us a lot. 34 00:02:24,430 --> 00:02:31,200 So I'm going to say dorkbot and if I will just step, you will get the documentation of this and that 35 00:02:31,200 --> 00:02:35,400 function on X what you need, or Y-axis, what is it. 36 00:02:35,400 --> 00:02:41,010 Color and all these different different types of custom parameters that you can play with that let's 37 00:02:41,010 --> 00:02:42,060 say on Access's. 38 00:02:42,060 --> 00:02:43,980 I just need the index of this. 39 00:02:44,190 --> 00:02:46,820 So if I'm going to call this index over here. 40 00:02:47,010 --> 00:02:50,240 So it will exactly return me my this index. 41 00:02:50,400 --> 00:02:52,830 So I'm just going to copy this entire code. 42 00:02:53,010 --> 00:03:00,720 And on this X I'm basically going to paste over here once I will do all the stuff on Y axis. 43 00:03:00,720 --> 00:03:03,540 I just need value with respect to this. 44 00:03:03,540 --> 00:03:08,500 So if I'm going to remove this and again, I'm going to execute it, it will return me my value. 45 00:03:08,520 --> 00:03:14,550 So here I'm going to say on Y X is I have to simply pass this data. 46 00:03:14,550 --> 00:03:18,230 So on Y axis, I just need this one just to copy paste. 47 00:03:18,240 --> 00:03:25,230 And if I'm going to execute it, it will take some couple of seconds, but it will be done in some beautiful 48 00:03:25,230 --> 00:03:25,980 results. 49 00:03:26,250 --> 00:03:30,300 Now you will see over here how much user friendly this visual is. 50 00:03:30,330 --> 00:03:36,300 If you are going to hover your mouse on this bar chart, you will see with respect to 30, you have 51 00:03:36,300 --> 00:03:37,160 that much of those. 52 00:03:37,170 --> 00:03:42,450 With respect to Friday, you have that Munster's similarly with respect to each and every day. 53 00:03:42,450 --> 00:03:44,090 You have some values assigned to it. 54 00:03:44,310 --> 00:03:52,080 So if you have to conclude this visualization, then you can see a rush is definitely highest on Tosti. 55 00:03:52,200 --> 00:03:56,410 So that's the type of conclusion you can fetch from this visit. 56 00:03:56,670 --> 00:04:00,840 So just go ahead with our next statement, which is exactly. 57 00:04:01,040 --> 00:04:04,950 We have to perform analysis of Jurnee by hour. 58 00:04:05,100 --> 00:04:13,740 So if let's say if on this D.F. of our if I'm going to create its histogram basically using this histo 59 00:04:13,740 --> 00:04:14,270 function. 60 00:04:14,520 --> 00:04:20,370 So here, if I'm going to pass mine this hour, which is exactly this one, and if I'm going to execute 61 00:04:20,370 --> 00:04:23,280 it, it will be done with this beautiful histogram. 62 00:04:23,400 --> 00:04:31,920 If I have to conclude from this histogram that I can see, it definitely peaks during evening time when 63 00:04:31,920 --> 00:04:35,070 people are logging off from the work. 64 00:04:35,220 --> 00:04:41,800 Let's say I have to perform this analysis with respect to each and every month. 65 00:04:42,090 --> 00:04:47,360 So for this, it means I need I need each and every month very first. 66 00:04:47,520 --> 00:04:54,410 So if on this let's say if on DFAS month, if I'm going to call this unique or. 67 00:04:55,020 --> 00:04:58,440 So it will exactly ridgen my each and every month. 68 00:04:58,680 --> 00:04:59,550 And if. 69 00:04:59,930 --> 00:05:08,030 On this month, I'm going to say for I in this department, not unique, it means I have to fetch each 70 00:05:08,030 --> 00:05:09,260 and every month here. 71 00:05:09,260 --> 00:05:13,450 I'm just going to paste and just I have to accept each and every month. 72 00:05:13,790 --> 00:05:15,680 So far, this is what I'm going to do. 73 00:05:15,680 --> 00:05:23,960 Let's say on each and every subclause I'm going to visualize my visitation with respect to each and 74 00:05:23,960 --> 00:05:24,530 every month. 75 00:05:24,680 --> 00:05:28,600 Four days I'm going to say BLT Dot subplot. 76 00:05:28,610 --> 00:05:35,600 And here let's say I need a matrix of three, comma two, because I have six months and here I'm going 77 00:05:35,600 --> 00:05:41,160 to say it is nothing but I plus one because by default I will be zero. 78 00:05:41,450 --> 00:05:44,260 So here, let me show you what I think over here. 79 00:05:44,310 --> 00:05:44,630 Yeah. 80 00:05:44,960 --> 00:05:51,650 So if I am going to say we're here for let me copy this entire thing. 81 00:05:51,900 --> 00:05:53,670 Let me again paste over here. 82 00:05:53,930 --> 00:05:54,260 Yeah. 83 00:05:54,470 --> 00:05:58,340 And on this, if I'm going to call this enumerate. 84 00:05:58,910 --> 00:06:01,950 So I'm just going to call enumerate over here. 85 00:06:02,150 --> 00:06:06,820 So if I'm going to call this enum rate, it will exactly return me some index. 86 00:06:07,040 --> 00:06:15,620 So if I have to access this, I'm going to say for I come a month in, so I will basically that close 87 00:06:15,620 --> 00:06:23,060 to my index and month that I'm going to access from this one for I come a month in this one. 88 00:06:23,210 --> 00:06:32,150 And if I'm going to print, let's say I and UpToDate if I'm going to print my month so you will see 89 00:06:32,150 --> 00:06:34,420 over here, it will exactly that. 90 00:06:34,440 --> 00:06:39,650 And this is debt zero, which is exactly my index nine, which is exactly my month. 91 00:06:39,830 --> 00:06:43,310 First is exactly my index five is exactly my month. 92 00:06:43,670 --> 00:06:47,620 So here I'm just going to copy this entire code. 93 00:06:47,630 --> 00:06:52,230 Let me copy this code and let me just paste over here. 94 00:06:52,490 --> 00:06:58,940 So once you have all these stuff, you will access each and every eye, which is exactly my index. 95 00:06:59,120 --> 00:07:05,120 Once you have each and every index, then you have each and every month and whatever month you have 96 00:07:05,390 --> 00:07:06,180 very close. 97 00:07:06,200 --> 00:07:08,840 You need a separate data frame of that month. 98 00:07:09,080 --> 00:07:16,850 So here I am basically going to define a filter as deals all month if it goes to month. 99 00:07:16,870 --> 00:07:18,310 So this is exact my filter. 100 00:07:18,410 --> 00:07:25,400 I have to just pass this filter in my dear data stream and what our data stream I have on this data 101 00:07:25,400 --> 00:07:29,300 frame I have to access this hour, which is exactly this tool. 102 00:07:29,570 --> 00:07:33,770 And once I have this hour, I'm just going to create its histogram. 103 00:07:33,770 --> 00:07:34,490 That's it. 104 00:07:34,640 --> 00:07:41,960 Let's say I have to create my own will decide on which I have to display this visual for this. 105 00:07:41,960 --> 00:07:47,530 I'm just going to say BLT, Dot, Fagre, and here I have to mention my own site. 106 00:07:47,810 --> 00:07:54,890 Let's say I want a window of forty cross twenty trollope's, whatever you want, so just executed. 107 00:07:54,890 --> 00:08:00,260 It will take some couple of seconds but it will exactly return me my six. 108 00:08:00,260 --> 00:08:06,100 ViSalus with respect to each and every month with respect to rush of each and every month. 109 00:08:06,350 --> 00:08:13,160 Now you will realize over here this is exactly your rush with respect to your nine month, with respect 110 00:08:13,160 --> 00:08:15,500 to a five month and all these things. 111 00:08:15,740 --> 00:08:21,710 So if you have to conclude from this vision, you will see almost in each and every month you have your 112 00:08:21,710 --> 00:08:24,550 peak time in your evening hours. 113 00:08:24,560 --> 00:08:26,630 So that's all about the session. 114 00:08:26,630 --> 00:08:31,190 Hopefully you will love my way of explaining this complex thing in the easiest way. 115 00:08:31,340 --> 00:08:33,020 So I hope you will love a lot. 116 00:08:33,020 --> 00:08:33,710 Thank you. 117 00:08:33,710 --> 00:08:34,780 Have a nice day. 118 00:08:34,790 --> 00:08:35,660 Keep learning. 119 00:08:35,660 --> 00:08:36,560 Keep growing. 120 00:08:36,560 --> 00:08:37,400 Keep practicing.