1 00:00:05,660 --> 00:00:06,570 Hey everyone. 2 00:00:07,200 --> 00:00:09,860 So just a few more places to go. 3 00:00:09,920 --> 00:00:14,600 Now the next problem we have is how many different projects type sexist. 4 00:00:14,620 --> 00:00:17,650 What is the total donation amount for each of them. 5 00:00:17,650 --> 00:00:21,430 So here we need to get that the how many project types out there. 6 00:00:21,430 --> 00:00:24,890 Then what is the total amount of donation for them. 7 00:00:25,270 --> 00:00:37,390 So first of all here I have data for the head and being just 2 there and such for column name as project 8 00:00:37,390 --> 00:00:38,320 types. 9 00:00:38,320 --> 00:00:40,600 There is not here. 10 00:00:40,990 --> 00:00:43,340 Let me show you that one in the columns name. 11 00:00:43,360 --> 00:00:47,350 So here we have a column that is project type here. 12 00:00:47,350 --> 00:00:53,800 This one not represented in here at this one but that one is better and. 13 00:00:54,110 --> 00:00:59,070 You can check in the data types so see the difference. 14 00:00:59,170 --> 00:01:02,650 So there is something containing that project type that how many people. 15 00:01:02,650 --> 00:01:03,780 Which category. 16 00:01:03,940 --> 00:01:07,090 All we can say that which type of project that particular is. 17 00:01:07,090 --> 00:01:13,430 So we need that column and we need to count that how many particular project types out there. 18 00:01:13,780 --> 00:01:19,430 Then after getting the project types we get this some of the donations for these but project types. 19 00:01:19,780 --> 00:01:29,910 So here we need to very well first one six that is data for and then we pass the column project type 20 00:01:33,050 --> 00:01:42,460 there we have this one and then we just count the values there that how many values out there. 21 00:01:42,460 --> 00:01:46,020 And if you've been six you will get this one. 22 00:01:46,360 --> 00:01:53,640 These three and then they have particular values like this one and this one for the three thousand forty 23 00:01:53,640 --> 00:01:55,700 five flag 32000. 24 00:01:55,770 --> 00:02:02,740 So here we have only three types of categories that is teacher led student led and professional development. 25 00:02:03,070 --> 00:02:05,330 So only triple digit types out there. 26 00:02:05,370 --> 00:02:06,800 Now what will we do. 27 00:02:06,930 --> 00:02:14,130 We will first find this some of the donations with each of these Category Four that we have a variable 28 00:02:14,270 --> 00:02:21,690 seven and how we will do that one we will first group by all the elements according to project type 29 00:02:22,540 --> 00:02:25,290 fit this one you will also understand it how group by works. 30 00:02:25,710 --> 00:02:28,740 So here we have project for group by. 31 00:02:30,360 --> 00:02:32,820 And then we have project type. 32 00:02:32,820 --> 00:02:38,420 So first I will group the data according to the project type. 33 00:02:38,520 --> 00:02:45,210 It means that this thing that first it will group all the projects according to their project type that 34 00:02:45,210 --> 00:02:49,290 how many ties are there then it will group all the projects according to that plan. 35 00:02:49,290 --> 00:02:51,240 So in that case we have only three groups. 36 00:02:51,240 --> 00:02:59,610 These three which containing these numbers of values and now I will access this column of donations. 37 00:02:59,740 --> 00:03:03,250 Like here we have only the number then base their donation amount. 38 00:03:03,520 --> 00:03:06,550 So there must be a donation amount also. 39 00:03:06,700 --> 00:03:08,860 That is the this column donation amount. 40 00:03:09,070 --> 00:03:11,180 I will accept that donation amount. 41 00:03:11,740 --> 00:03:14,970 He is simply donation amount. 42 00:03:16,300 --> 00:03:19,420 And then I will make this sum of all the values there. 43 00:03:19,840 --> 00:03:26,530 But if you pass this thing this is okay but if you pass this fund you will find the sum in exponential 44 00:03:26,530 --> 00:03:28,900 forms the 80s. 45 00:03:28,900 --> 00:03:36,370 This is like the one that is when we have applauded the values like here this one then we have described 46 00:03:36,370 --> 00:03:39,700 this in form of explanation values. 47 00:03:39,700 --> 00:03:44,120 So what to do now because this is something non understandable. 48 00:03:44,180 --> 00:03:53,120 So for that just pass him as type that in this type you need to find the sum in then you will find the 49 00:03:53,120 --> 00:03:55,530 donation amount in it. 50 00:03:55,730 --> 00:03:57,300 Here we have this one. 51 00:03:57,350 --> 00:04:04,100 So this is how you can have this project types and the amount of donations related to these project 52 00:04:04,100 --> 00:04:04,970 types. 53 00:04:04,970 --> 00:04:13,040 Now we will simply float us by Jack with this one and this one plus 20 showing that how many projects 54 00:04:13,760 --> 00:04:19,460 are there and how many numbers of project type t to do that but then we will blow it up by a judge for 55 00:04:19,460 --> 00:04:21,020 their donation. 56 00:04:21,620 --> 00:04:24,650 So we will have BLT and value supplement. 57 00:04:25,760 --> 00:04:31,410 And if you remember in support we have us the number of frauds number of columns and the number of one 58 00:04:31,570 --> 00:04:34,650 that day number at which we are currently working. 59 00:04:34,730 --> 00:04:41,390 So hey I'm going to use two rows one column so that I have to buy charts I'm not using one two and two 60 00:04:41,390 --> 00:04:49,700 column because that will be a little mess there because this victim's little smaller no blowed debated 61 00:04:49,850 --> 00:04:59,840 just BLT by and they passed the value that is close to an Essex and and a parameter that is start angle 62 00:05:01,040 --> 00:05:14,900 this one just 90 after that when we have a second thought BLT dot subplot this one two one two and simply 63 00:05:14,900 --> 00:05:22,850 BLT or by their past seven comma and this trading. 64 00:05:23,420 --> 00:05:24,950 Come on see that one. 65 00:05:24,950 --> 00:05:26,860 Come on V here. 66 00:05:26,930 --> 00:05:28,140 Here we have this one. 67 00:05:28,310 --> 00:05:31,730 If you shifted on that one you will have a small pie charts. 68 00:05:31,730 --> 00:05:36,410 So let me change their sizes so that they can be visualize easily. 69 00:05:36,410 --> 00:05:46,220 So here we will first away a tight label so BLT tight underscore layout also provide margins so that 70 00:05:47,450 --> 00:05:50,630 it will be mode good looking. 71 00:05:50,630 --> 00:05:56,830 So here we have zero point zero five and then about this size. 72 00:05:56,830 --> 00:06:03,730 So here we will first create a fake variable that is BLT and use the metric GCF for size declaration 73 00:06:04,450 --> 00:06:08,140 and then we will use this matter to set the size. 74 00:06:08,170 --> 00:06:14,470 So fig don't set underscores size and in which amount we needed inches. 75 00:06:14,500 --> 00:06:23,050 So these are the tools you cannot get everyone at now but whenever you are working just get to the problem 76 00:06:23,050 --> 00:06:24,570 that what is your problem. 77 00:06:24,640 --> 00:06:28,500 You need to change the size and Google out deck that shifted on there. 78 00:06:28,510 --> 00:06:29,290 We have this one. 79 00:06:30,670 --> 00:06:35,140 This one is the teacher led I believe and these two are these student led and daredevil because if you 80 00:06:35,140 --> 00:06:45,810 notice this is in LAX and this one is only 1 percent of that flat you can also provide the D denoting 81 00:06:45,840 --> 00:06:54,180 colors that what they are denoting just by simply adding for this one BLT note legend 82 00:06:56,990 --> 00:07:08,060 and provide this one a location like this that we go for a boat lift shifted on there we have this one 83 00:07:08,160 --> 00:07:10,700 baseball but not visible. 84 00:07:10,700 --> 00:07:17,890 You can also do that one that's for you a homework again to add diligence in both these graphs. 85 00:07:18,200 --> 00:07:20,750 So we are not done with debt financing. 86 00:07:21,110 --> 00:07:27,080 We have not analyzed that how many projects types in that particular data frame is and what is the donation 87 00:07:27,080 --> 00:07:29,480 amount for these types. 88 00:07:29,480 --> 00:07:36,200 And here I am just doing these things like I have already says the columns but whenever you are working 89 00:07:36,380 --> 00:07:41,630 you need to find the columns there so you first need to completely analyze the columns that what they 90 00:07:41,630 --> 00:07:48,450 are denoting their donation ideas amount school I.D. teacher I.D. and then you need to work on that 91 00:07:48,450 --> 00:07:48,880 one. 92 00:07:49,280 --> 00:07:53,300 So don't be stupid like me because I just need to teach you here. 93 00:07:53,720 --> 00:08:01,000 I am not performing my project so this is about this one in the next video we will work on subcategories. 94 00:08:01,460 --> 00:08:02,480 So thanks for watching. 95 00:08:02,480 --> 00:08:03,550 See in the next video.