1 00:00:05,750 --> 00:00:06,410 Here we go. 2 00:00:06,790 --> 00:00:08,980 So first one is the point plot. 3 00:00:09,440 --> 00:00:12,020 And let me move to the Jupiter notebooks. 4 00:00:12,020 --> 00:00:14,360 So here I have the Jupiter notebooks. 5 00:00:14,360 --> 00:00:17,280 This one is the page containing the dataset. 6 00:00:17,570 --> 00:00:19,750 And here's the example entry. 7 00:00:19,790 --> 00:00:25,700 So first of all before doing anything we need to import Siebel and we can do by just import. 8 00:00:26,330 --> 00:00:32,310 And then Siebel Coleman Verdi's S.A. but you can use any one you want. 9 00:00:33,280 --> 00:00:35,990 So here this one imported successfully. 10 00:00:36,260 --> 00:00:43,960 After that also include here name B and banned us. 11 00:00:44,080 --> 00:00:47,760 They are not going to name them but still. 12 00:00:48,050 --> 00:00:53,240 So if we require that we do not require to again upload applauded them. 13 00:00:53,720 --> 00:01:02,650 After that here first we will consider having the data like out of these that we go for Diamond stored 14 00:01:02,670 --> 00:01:08,030 CSP so to have the data here first. 15 00:01:08,080 --> 00:01:16,460 I have a variable that is diamond and that will be equal to the library we are using in this data is 16 00:01:16,460 --> 00:01:19,320 that is S.A. Stansell Siebel. 17 00:01:19,630 --> 00:01:21,630 Just like the BLT used in MetroCard. 18 00:01:22,300 --> 00:01:31,590 So Ascend is lower underscored the asset because this one is a data set and then the name of the data 19 00:01:31,590 --> 00:01:34,870 set that is diamonds. 20 00:01:35,470 --> 00:01:37,110 And I think this one. 21 00:01:37,300 --> 00:01:46,760 Yes and you do not require to add CSP just the diamonds should return or shift tent on Windows. 22 00:01:47,010 --> 00:01:49,050 You will get that one done successfully. 23 00:01:49,050 --> 00:01:53,840 After that if you try to pin that one they will get they were digitize. 24 00:01:54,000 --> 00:01:58,290 So I hope you got the idea now that these are the data sheets here. 25 00:01:58,290 --> 00:02:04,050 So here we have 5 3 9 4 0 rolls into 10 columns. 26 00:02:04,050 --> 00:02:08,070 These data these three dots here they are hidden there. 27 00:02:08,640 --> 00:02:10,960 So I hope you get that. 28 00:02:10,950 --> 00:02:18,870 We have this much of data that is 5 2 9 4 0 into 10 columns each containing these titles. 29 00:02:18,990 --> 00:02:27,600 So nearly this one five like thirty nine thousand four hundred elements. 30 00:02:27,670 --> 00:02:37,060 Now to check whether the data is successfully uploaded here we use Diamond Head and then panties to 31 00:02:37,060 --> 00:02:42,150 get the first of nearly four or five day does like here we have five it does. 32 00:02:42,220 --> 00:02:47,980 So we will get the idea of columns because if you make this one like that one. 33 00:02:48,040 --> 00:02:54,800 So in that case you need to again and again go for this one column name and then again go to that one. 34 00:02:54,820 --> 00:02:56,520 So that will be a little typical. 35 00:02:56,530 --> 00:03:06,880 So just print here the head so Dot had fences will easily able to get the columns after that. 36 00:03:06,880 --> 00:03:08,470 Now we will move to the plots. 37 00:03:09,640 --> 00:03:17,020 So in case of the point plot we are going to consider here some common one that it's just plot bloat 38 00:03:17,320 --> 00:03:19,280 and Katie EPO. 39 00:03:19,330 --> 00:03:25,810 That is one which we are also looking there but we did not find and I think this not. 40 00:03:26,650 --> 00:03:27,660 So leave that one. 41 00:03:27,760 --> 00:03:34,340 Here we have the DEA plot so soon debts of any plot here is just first the sentence. 42 00:03:34,370 --> 00:03:41,470 It's like the BLT then hey you need to add the name of your whatever deployed you are using just enter 43 00:03:41,470 --> 00:03:43,230 that thing one more thing. 44 00:03:43,270 --> 00:03:48,980 There is no capital in name of any plot like if I need to have this plot then just this. 45 00:03:49,420 --> 00:03:54,040 That is a central distribution then plots it stand for distribution. 46 00:03:54,040 --> 00:04:01,270 But I am considering this one in point plots then they plot this one is same like the Altidore plot 47 00:04:01,330 --> 00:04:04,290 but here we have S.A. and then we have the name of the plot. 48 00:04:05,650 --> 00:04:16,080 And after that here we will pass the parameter that is data and the data is this diamond particular 49 00:04:16,080 --> 00:04:16,620 column. 50 00:04:16,750 --> 00:04:25,870 Like if I need to have the price so I will just do something like Diamond then this backwards and in 51 00:04:25,870 --> 00:04:29,410 brackets pass the price but you will get added. 52 00:04:29,410 --> 00:04:36,180 In this case if you tried to run that one you will get at it and I hope you got that vibe you get. 53 00:04:36,610 --> 00:04:42,940 Still if you do not get then you have that name at it name price is not defined. 54 00:04:43,000 --> 00:04:43,440 Why. 55 00:04:44,200 --> 00:04:51,520 Because as I have told you these are just strings the column names we are just sitting there in these 56 00:04:51,520 --> 00:04:54,910 one indexes but that actually strings. 57 00:04:54,910 --> 00:05:01,220 So here we need to Bostic corpse so make sure you are passing the course and then you will get that 58 00:05:01,240 --> 00:05:07,540 but this one is the line you will get first then after processing you will get deployed. 59 00:05:07,630 --> 00:05:10,560 So here this one is the best block. 60 00:05:10,570 --> 00:05:18,850 Hey if you notice zoom that one you will get these his two drum types of charts here. 61 00:05:19,240 --> 00:05:27,070 Prices on X axes and the actual for them alone by Xs. 62 00:05:27,270 --> 00:05:31,680 So this is how you can load the distribution plots. 63 00:05:31,800 --> 00:05:33,780 After that we have second one. 64 00:05:33,810 --> 00:05:36,150 Now this is all you need to do here. 65 00:05:36,180 --> 00:05:39,680 Now you just gave this graph to anyone who analyzing the data. 66 00:05:39,690 --> 00:05:41,440 Then they will do their own work. 67 00:05:41,460 --> 00:05:48,340 And if you are analyzing data by yourself then just load in the plot in any way you like. 68 00:05:48,370 --> 00:05:52,430 Like I left any other note that I will tell you that what that was. 69 00:05:52,450 --> 00:05:53,670 Which one that was. 70 00:05:53,670 --> 00:05:57,380 And you can pay for any plot and analyze the data by that. 71 00:05:58,010 --> 00:06:03,300 Now the second one that I am going to teach you here is this rub block. 72 00:06:03,390 --> 00:06:10,150 That is just a sentence dot ideology rug and plot. 73 00:06:10,230 --> 00:06:16,920 Then again pass this one just come on C and come on V here. 74 00:06:16,920 --> 00:06:22,320 But now this time that we have something else like X.. 75 00:06:22,320 --> 00:06:27,090 So here if you run that one shoot it down you will get the line and then you will get this one. 76 00:06:27,600 --> 00:06:30,830 So this one is like something QR code or barcode. 77 00:06:30,850 --> 00:06:32,220 We can see. 78 00:06:32,220 --> 00:06:36,060 And if you notice this one you will have a continuous line. 79 00:06:36,330 --> 00:06:41,470 And if you'll focus here on this point we also have a break here at this point. 80 00:06:41,490 --> 00:06:45,750 This one small then we have brakes here also in which they lose law. 81 00:06:46,320 --> 00:06:53,000 So these are just high where we have more data and law where we do not have any data. 82 00:06:53,040 --> 00:07:05,670 And if you try to load here price like price here she Britain and let it run you will get something 83 00:07:05,670 --> 00:07:08,040 like this a continuous line. 84 00:07:08,050 --> 00:07:12,990 That's why I have not brought it to the price because I have noticed here that all the values are close 85 00:07:12,990 --> 00:07:16,330 to each other 326 326 327. 86 00:07:16,440 --> 00:07:23,060 Then we are going to have a line there but in X we have a little variable like three point ninety nine 87 00:07:23,070 --> 00:07:24,640 four point two zero. 88 00:07:24,690 --> 00:07:31,290 There is a gap of like point to fight and if we consider these value then point to five is much greater 89 00:07:31,290 --> 00:07:35,610 in case of like here only 1 2 5 10. 90 00:07:35,610 --> 00:07:37,380 So this is the plot. 91 00:07:37,590 --> 00:07:44,640 And this one is something people do not prefer because you do not get the idea about the what's the 92 00:07:44,670 --> 00:07:49,980 happening what these things are happening here and how the data is considered and distributed at different 93 00:07:49,980 --> 00:07:51,540 points. 94 00:07:51,660 --> 00:07:56,330 After that we have the most simple one that is known as the key to implode. 95 00:07:56,430 --> 00:07:58,060 That is just a sentence. 96 00:07:58,270 --> 00:08:07,810 Don't give the E blow then pass defenses and in that just pass the parameter you need to shift return 97 00:08:07,820 --> 00:08:09,100 you will get a line there. 98 00:08:09,620 --> 00:08:11,520 This is one easy more simple chart. 99 00:08:11,780 --> 00:08:14,950 Here you have just won a legend that is price. 100 00:08:15,110 --> 00:08:15,790 This blue line. 101 00:08:16,670 --> 00:08:25,600 And if you notice KDE bloat and bloat then you'll see that this line is seem like it's maximum point 102 00:08:25,600 --> 00:08:27,530 is this 1 3 0. 103 00:08:27,550 --> 00:08:30,260 And here we have this three this one maximum. 104 00:08:30,490 --> 00:08:37,770 And you'd get to finish on twenty thousand and then we have twenty thousand this one one here. 105 00:08:37,780 --> 00:08:39,360 This one also one. 106 00:08:39,400 --> 00:08:49,420 So these are same line and not just idea that policing and this line is not that this protest plot is 107 00:08:49,420 --> 00:08:51,020 the one that is shown in back. 108 00:08:51,040 --> 00:08:53,510 This is a giddy line in this plot. 109 00:08:53,620 --> 00:09:00,160 You can also remove that one just by doing here KDE e that stand for this scary line and then just pass 110 00:09:00,160 --> 00:09:03,700 there false whatever department you do not require. 111 00:09:03,850 --> 00:09:05,380 Just make that font false. 112 00:09:05,650 --> 00:09:07,870 So you will not get that get a line here. 113 00:09:08,470 --> 00:09:11,740 So if you notice we do not have this kiddy line here. 114 00:09:11,740 --> 00:09:14,240 So that's how you can remove that line. 115 00:09:14,650 --> 00:09:18,210 And these are the few basic example I go this one on this table. 116 00:09:18,220 --> 00:09:21,250 You can have much more if you search for them. 117 00:09:21,430 --> 00:09:30,160 That vigil noting the plots so then about the point plot and I hope you get the idea that how easy these 118 00:09:30,270 --> 00:09:34,850 things are here you just need to have the data and plot according to the plot you need to have. 119 00:09:35,800 --> 00:09:42,680 And this one is only when you need to plot only one parameter and that is like that table price. 120 00:09:42,820 --> 00:09:48,530 And that will be variable according to any other parameter according to the data. 121 00:09:48,550 --> 00:09:49,740 So thanks for watching. 122 00:09:49,750 --> 00:09:52,560 We will continue with the distribution plots in the next video.