1 00:00:05,830 --> 00:00:13,850 Everyone soon the last video we have plotted this graph using of our own defined values and some standard 2 00:00:13,850 --> 00:00:14,820 values. 3 00:00:14,840 --> 00:00:22,460 Now in this video we plot the original values from the CSU file on this plot so that we will be more 4 00:00:23,240 --> 00:00:24,690 familiar with that one. 5 00:00:24,710 --> 00:00:28,290 So for that of a plot this same of an axis same. 6 00:00:28,370 --> 00:00:32,270 We just need to change these two values. 7 00:00:32,270 --> 00:00:33,840 So let me begin with that one. 8 00:00:33,920 --> 00:00:35,230 That is the data. 9 00:00:35,390 --> 00:00:38,750 So your answer I agree with deadpan plot is same. 10 00:00:38,750 --> 00:00:40,230 An X is outsourcing. 11 00:00:40,370 --> 00:00:45,230 We just need to change the layout and data according to all the values available here. 12 00:00:45,230 --> 00:00:51,680 Now we will begin with dictionary vitamin romancing because we are putting corporate locations we have 13 00:00:51,740 --> 00:00:55,220 defined five location but we now need all the locations present here. 14 00:00:55,700 --> 00:01:07,490 So how can you use all the locations that entail just simply agree the name of define then by indexing 15 00:01:08,210 --> 00:01:10,090 using this code. 16 00:01:10,310 --> 00:01:11,710 So codes and code. 17 00:01:11,840 --> 00:01:17,630 Make sure they use it quotes them then locations more distilled them in some other states. 18 00:01:17,680 --> 00:01:23,660 Then the Z here it's using of a defined values that is 1 to 30 40 50. 19 00:01:23,660 --> 00:01:28,970 Now here we will use the origin values and if you notice on this figure that is representing the values 20 00:01:28,970 --> 00:01:34,430 on the particular location that is total exports because we are analyzing the total values we are not 21 00:01:34,430 --> 00:01:41,070 going to realize every single thing we will analyze all the values so here we will use agreed. 22 00:01:42,770 --> 00:01:46,650 And in that one total space exports. 23 00:01:46,700 --> 00:01:50,350 So make sure there's nothing in between them just a space. 24 00:01:50,360 --> 00:01:51,850 So total exports. 25 00:01:52,070 --> 00:01:59,510 And also here we have only 5 text but we can only find the text for every value deal but we will use 26 00:01:59,510 --> 00:02:03,520 a smart B again by using this text. 27 00:02:03,590 --> 00:02:13,030 So at Green Square brackets courts and in that one just text shifted an individual v Dettmer. 28 00:02:14,360 --> 00:02:15,640 So we are done with that one. 29 00:02:15,650 --> 00:02:18,140 Now it would change the layout. 30 00:02:18,290 --> 00:02:24,470 So if you'd run that one right now you will have this take all the values here. 31 00:02:24,470 --> 00:02:26,320 Our plot is already done. 32 00:02:26,420 --> 00:02:28,610 We just need to make few changes. 33 00:02:28,610 --> 00:02:36,380 So here if you notice Washington with value 3 8 9 4 8 1 and the tax is be 5 nine point two data all 34 00:02:36,380 --> 00:02:41,030 that things here of California that we have also defined before Arizona. 35 00:02:41,420 --> 00:02:46,100 This one Alaska up this one buggered with that particular name are penises. 36 00:02:46,100 --> 00:02:52,910 So now I hope you got the idea that how this thing is working now you can also change this color just 37 00:02:52,910 --> 00:02:59,570 by using color skin there that is color and skin you have different options there like 38 00:03:02,210 --> 00:03:11,660 Portland if he'd done this one here we have this one these colors this is widely used you can also use 39 00:03:11,660 --> 00:03:19,790 greens as we are using something like the agriculture things so you can just use greens there we have 40 00:03:19,790 --> 00:03:25,820 this one all the values in green and then went later will have the maximum value darker had the lower 41 00:03:25,820 --> 00:03:26,790 value. 42 00:03:26,840 --> 00:03:37,420 This is a positron after that here we had this thing and then you can also change the name of discipline. 43 00:03:37,490 --> 00:03:52,250 This color scale just by defining color bar and we had this one color back and make this one dictionary 44 00:03:52,250 --> 00:04:00,470 there or you can just simply pass them in this curly braces because you need to define this name so 45 00:04:00,470 --> 00:04:14,080 you can a title like gala but and also this morning coats shifted on Navy had this one color. 46 00:04:14,170 --> 00:04:16,000 So this is about changing these things. 47 00:04:16,000 --> 00:04:17,940 That's how you can change these values. 48 00:04:18,070 --> 00:04:21,520 After that if we talk about layout let me reset it. 49 00:04:22,510 --> 00:04:25,610 If we talk about layout so here we have a layout. 50 00:04:25,990 --> 00:04:27,720 You can add a few things here. 51 00:04:27,740 --> 00:04:35,500 So like first if I change this one into a dictionary because I'm not just only defining a scope here 52 00:04:35,500 --> 00:04:36,060 right now. 53 00:04:36,430 --> 00:04:40,770 So remove the quotes that equal design and display. 54 00:04:41,080 --> 00:04:51,180 Here I can define a few more characters like show legs to show all the legs present death and make this 55 00:04:51,180 --> 00:04:55,880 one true shift return shifted tone shifted. 56 00:04:56,040 --> 00:04:57,990 Here we have the lakes and they level there. 57 00:04:59,130 --> 00:05:01,430 So this is also a most basic thing. 58 00:05:01,440 --> 00:05:02,990 Many people prefer. 59 00:05:03,270 --> 00:05:05,800 You can also add a title to this one. 60 00:05:06,030 --> 00:05:08,700 So you just simply write the title. 61 00:05:09,120 --> 00:05:18,370 And this one is like every plot representing the agriculture plot shift returns should be done. 62 00:05:18,370 --> 00:05:19,630 There we have crippled. 63 00:05:20,550 --> 00:05:28,140 So that's how you can represent all the values from a CSA file upload without using much coding how 64 00:05:28,140 --> 00:05:29,580 much you have coded here. 65 00:05:29,580 --> 00:05:35,790 Just one two three four sets in four says you have analyzed all the data available here. 66 00:05:36,300 --> 00:05:38,920 So this is the power of Python and data science. 67 00:05:39,030 --> 00:05:46,010 You can even calculate the mean and the average all the values there are much more in datasets. 68 00:05:46,050 --> 00:05:52,090 So in a footnote we will continue in the next video on the global scale with GDP 5. 69 00:05:52,140 --> 00:05:53,220 So thanks for watching. 70 00:05:53,280 --> 00:05:54,330 Continuing the next video.