1 00:00:00,180 --> 00:00:06,360 Halaal, before doing deep dive into this session, let's have a quick recap of what we have done in 2 00:00:06,360 --> 00:00:08,740 the previous session from the previous session. 3 00:00:08,760 --> 00:00:15,160 We are going to continue our analysis with this new data set on which we have to do a lot of preparation. 4 00:00:15,180 --> 00:00:22,290 We have basically fed these five features, these five derived attributes from my pick up date, because 5 00:00:22,290 --> 00:00:29,100 these five features will play a very major role if you have to perform your in-depth analysis on your 6 00:00:29,100 --> 00:00:29,520 data. 7 00:00:29,670 --> 00:00:31,470 That's what we have done in the last session. 8 00:00:31,780 --> 00:00:33,720 Then we have basically wine. 9 00:00:34,290 --> 00:00:39,150 What exactly the kind of tricks in New York in the first half? 10 00:00:39,180 --> 00:00:41,020 That's what we have done in the last session. 11 00:00:41,400 --> 00:00:43,710 So in this session, we have this assignment. 12 00:00:43,710 --> 00:00:50,700 The very first one is we have to analyze what exactly is our rush in New York City? 13 00:00:50,750 --> 00:00:55,840 Or you can say what exactly the rush in each and every hour in New York City. 14 00:00:56,260 --> 00:01:03,450 So for this, you guys can consider your our future for this analysis, which is exactly this one for 15 00:01:03,450 --> 00:01:03,750 this. 16 00:01:03,750 --> 00:01:07,710 I'm just going to say I'm going to use my control over here. 17 00:01:07,710 --> 00:01:12,930 So I'm going to say S.A. dot count plot and hear what I have to mention. 18 00:01:12,940 --> 00:01:18,320 Radical's, I have to mention this over 50 and here I have to mention this over. 19 00:01:18,380 --> 00:01:18,900 That's it. 20 00:01:19,200 --> 00:01:21,750 And let me assign some own figures. 21 00:01:22,100 --> 00:01:24,990 So now I'm going to say plaited or figure. 22 00:01:25,290 --> 00:01:31,710 And this time, let's see, I just need Fugo size of, let's say, 12 Colma six. 23 00:01:32,010 --> 00:01:37,050 So if you are going to execute this set, it will take a while and it will return. 24 00:01:37,050 --> 00:01:42,240 You have very beautiful visuals and you will see your beautiful visors over here. 25 00:01:42,240 --> 00:01:47,550 And this is exactly your distribution, how your rush is going in your New York City. 26 00:01:47,740 --> 00:01:54,810 And if you have to conclude from this visual, then you can definitely see or hear after the morning 27 00:01:54,810 --> 00:01:55,320 rush. 28 00:01:55,400 --> 00:01:56,400 After morning rush. 29 00:01:56,580 --> 00:02:03,900 The number of over pickups doesn't dip much throughout the rest of the morning and early afternoon as 30 00:02:03,900 --> 00:02:04,190 well. 31 00:02:04,530 --> 00:02:08,400 And there is significantly more demand in that evening. 32 00:02:08,400 --> 00:02:14,990 You will see here, here you have a more demand of over gaps in your New York City. 33 00:02:15,000 --> 00:02:17,520 So that's a type of analysis. 34 00:02:17,520 --> 00:02:21,140 That's a type of conclusion you can fetch from your data. 35 00:02:21,750 --> 00:02:28,470 So let's go out with our next statement in which we have to perform this analysis or you can say in-depth 36 00:02:28,470 --> 00:02:36,240 analysis of Rush in New York City and our advice, because here we have performed just our voice. 37 00:02:36,510 --> 00:02:41,480 But now we have to analyze this with respect to each and every day. 38 00:02:41,790 --> 00:02:43,530 So far this what we have to do. 39 00:02:43,530 --> 00:02:48,080 We have to group our day down the basis of the day for this. 40 00:02:48,090 --> 00:02:55,140 I'm just going to say over underscore 15 dot, we have to call this group by on this. 41 00:02:55,140 --> 00:02:59,970 And here very first, you have to group your data on the basis of this here. 42 00:02:59,970 --> 00:03:05,220 I going say on the basis of the ones after doing all this stuff, then I have to group my data on the 43 00:03:05,220 --> 00:03:07,560 basis of obvious ones. 44 00:03:07,560 --> 00:03:09,260 I have all these stuffs. 45 00:03:09,510 --> 00:03:14,160 What I have to do, I have to access my pick up date for this. 46 00:03:14,160 --> 00:03:19,110 I have to access these pick up data, which is exactly pick up on the school date. 47 00:03:19,350 --> 00:03:27,220 And on this I have to basically perform this count of Thaxted and if I'm going to execute it, it will 48 00:03:27,220 --> 00:03:29,790 lead into some beautiful stats over here. 49 00:03:29,790 --> 00:03:35,850 And now you can easily visualize over here with respect to Friday and with respect to that much, you 50 00:03:35,850 --> 00:03:37,300 have that much rush. 51 00:03:37,380 --> 00:03:41,130 It means you have that much number of troops going in. 52 00:03:41,140 --> 00:03:47,790 You knew, OK, I have to create its data frame so that I can easily visualize this data. 53 00:03:47,790 --> 00:03:49,380 So far, this is what I'm going do. 54 00:03:49,620 --> 00:03:57,450 I'm just going to call this reset and index over here and let me store this data somewhere else. 55 00:03:57,750 --> 00:04:00,620 Let's say this will exactly return some data frame. 56 00:04:00,930 --> 00:04:06,640 So I'm just going to store let's say in summary, whatever name you want to assign, it's Ullapool. 57 00:04:06,690 --> 00:04:12,210 Let's say this is exactly my summary and I have to call this summary. 58 00:04:12,210 --> 00:04:19,080 Dot had as well to get a quick overview of how my data frame looks like it's just executed. 59 00:04:19,080 --> 00:04:19,980 It will take a while. 60 00:04:19,980 --> 00:04:25,560 Again, let's say I have to do some modification in this, let's say, rather than this pick. 61 00:04:25,710 --> 00:04:30,510 Did I have to consider this column as number of count number of trips? 62 00:04:30,510 --> 00:04:33,030 Or you can say count whatever name you want. 63 00:04:33,870 --> 00:04:40,860 You swear I'm going to say summary dot columns or you can use your reading function as well. 64 00:04:40,860 --> 00:04:41,610 It's all up to you. 65 00:04:41,880 --> 00:04:47,400 So my very first column name, let's say that is exactly what it is earlier. 66 00:04:47,610 --> 00:04:51,070 And the second one is exactly your, let's say hour. 67 00:04:51,360 --> 00:04:55,530 And the third one is, let's say exactly your let's say count. 68 00:04:55,980 --> 00:04:59,790 And if I'm going to execute it and if again, we. 69 00:04:59,850 --> 00:05:06,600 To call ahead so that to crosscheck whether this modification has done or not, and you can easily with 70 00:05:06,960 --> 00:05:13,290 all these things, has happened successfully now what you have to do, you have to simply visualize 71 00:05:13,290 --> 00:05:16,250 this data because you can easily see or hear. 72 00:05:16,540 --> 00:05:19,200 Here you have a huge chunk of data. 73 00:05:19,200 --> 00:05:24,450 And whenever you have this data and if some data frame, it is how to control that data. 74 00:05:24,720 --> 00:05:31,890 So at the time your data visualization comes into picture selected, I'm going to use our point plot 75 00:05:31,920 --> 00:05:32,910 over here for this. 76 00:05:32,910 --> 00:05:38,840 I'm going to say S.A.S. dot one plot that what we have in our succession. 77 00:05:39,150 --> 00:05:45,570 So if I'm going to shift tab, you will get all the custom parameters, documentation, all of these 78 00:05:45,570 --> 00:05:46,000 stuffs. 79 00:05:46,380 --> 00:05:53,790 So if on this access, let's say I just need my hour, which is exactly this one, let's say on y axis, 80 00:05:53,790 --> 00:05:56,970 I just need my cowens, which is exactly this one. 81 00:05:56,970 --> 00:06:04,170 Let's say I have to split my point on the basis of each and every day is where I have to pass this day 82 00:06:04,230 --> 00:06:05,450 in this parameter. 83 00:06:05,580 --> 00:06:11,610 What's happening, all the stuff I have to say, what exactly minded FEMA is, which is exactly this 84 00:06:11,970 --> 00:06:12,470 summary. 85 00:06:12,480 --> 00:06:19,930 So I have to say this summary is exactly my data from let's say I have to customize my point as well 86 00:06:19,990 --> 00:06:24,820 as I'm going to say figure or figger, just assign some fixie. 87 00:06:24,840 --> 00:06:26,880 So I'm going to say Fixie does nothing. 88 00:06:26,880 --> 00:06:32,460 But let's say to compare it to it's all up to you just executed. 89 00:06:32,460 --> 00:06:37,750 It will take a while, but it will return you some beautiful visualizations over here. 90 00:06:38,220 --> 00:06:41,720 Now you can easily conclude from this visual. 91 00:06:41,910 --> 00:06:51,230 Yeah, definitely this this red line, you can easily see this red, which definitely first to you, 92 00:06:51,240 --> 00:06:55,980 we can it means on Sunday, you have this pattern, you have this pattern. 93 00:06:56,190 --> 00:07:03,930 Whereas with respect to this green line, which exactly stays on Saturday, you have a higher structure 94 00:07:04,080 --> 00:07:05,970 rather than all these Veekuhane. 95 00:07:05,970 --> 00:07:07,740 Let's say you have some money. 96 00:07:07,740 --> 00:07:11,570 Let's say you have this pinkish, which is exactly your rasti. 97 00:07:11,730 --> 00:07:19,050 So basically in your weekdays, you have Ardeche in your mornings, but in your weekend you don't have 98 00:07:19,050 --> 00:07:19,700 that much. 99 00:07:19,950 --> 00:07:25,980 It means because people who are going to office in their early morning or children's choir, going to 100 00:07:25,980 --> 00:07:29,760 their schools have a holiday on that day in a similar way. 101 00:07:29,760 --> 00:07:37,530 You can conclude as much as you can, because here in front of you here, you have your Fabin find blood. 102 00:07:37,530 --> 00:07:44,290 And from each and every point block, you can definitely find some kind of conclusion from this data. 103 00:07:44,820 --> 00:07:48,290 So that's all about the session of the session very much. 104 00:07:48,570 --> 00:07:49,190 Thank you. 105 00:07:49,200 --> 00:07:50,100 Have a nice day. 106 00:07:50,140 --> 00:07:51,000 Keep learning. 107 00:07:51,000 --> 00:07:51,780 Keep growing. 108 00:07:52,110 --> 00:07:52,950 Keep practicing.