1 00:00:06,600 --> 00:00:07,670 Hi, welcome back. 2 00:00:08,070 --> 00:00:16,410 In this lecture, we will learn how to visualize our data that we analyzed from sea is we find that 3 00:00:16,410 --> 00:00:18,790 we created and alluded to it earlier. 4 00:00:19,290 --> 00:00:28,680 So as we learn before that, we first import Banda's as BDM, run the cell to import Bizim Banda's module. 5 00:00:28,950 --> 00:00:33,780 Then we run the cell, then we will read the sea ASV file. 6 00:00:33,780 --> 00:00:36,750 Using Banda's module has the following. 7 00:00:36,750 --> 00:00:45,480 D.F. Equal Dautry underscores ESV between two brackets and two parentheses inside the two brackets. 8 00:00:45,660 --> 00:00:53,730 Airlines underscore data underscore Bertozzi as V, which is name of our file. 9 00:00:53,820 --> 00:01:02,160 So now Banda's read our C is V file and ready to accept Pisin Banda's data frame comment or methods 10 00:01:02,340 --> 00:01:06,150 to apply it on our data and CSV file. 11 00:01:07,620 --> 00:01:16,710 And we will run the common D.F., which is the name of our data frame which will hold our C is V file. 12 00:01:17,310 --> 00:01:25,200 We will run as D.F., which is the name of the data frame holding the data of our C as we file. 13 00:01:25,530 --> 00:01:29,650 After we run, it will return at Returnee's. 14 00:01:29,700 --> 00:01:36,600 The data from sheet, which contains columns and rows of our C is V file, as you see. 15 00:01:38,290 --> 00:01:46,590 Then we will run the following common D.F. Duceppe, which after running it will return the number of 16 00:01:46,590 --> 00:01:53,760 columns and rows in our cities v file, which is one million and sixteen thousand and six hundred twenty 17 00:01:53,760 --> 00:02:00,000 five rows and six columns exist in our data frame. 18 00:02:00,180 --> 00:02:03,590 Holding the C is V file data. 19 00:02:04,590 --> 00:02:14,310 Another comment that we will run, or Massood, which is the F the size as the following, which will 20 00:02:14,310 --> 00:02:24,960 return the number of values in our CSP files, which is six million ninety nine thousand seven hundred 21 00:02:24,960 --> 00:02:26,520 fifty values. 22 00:02:26,820 --> 00:02:34,770 Then we will return the rounded value of the mean of the flow data type sales column, which is one 23 00:02:34,770 --> 00:02:40,080 hundred and six thousand and one hundred eighty four point forty one. 24 00:02:40,740 --> 00:02:47,760 And before the beginning of data visualization, we will first take a sample from our data frame because 25 00:02:47,910 --> 00:02:57,910 our data frame is huge and the name of this symbol is the F one and contains 15 records. 26 00:02:57,930 --> 00:03:07,860 So the this one equals the F that sample between two brackets, 15 and the one brand 15 records, as 27 00:03:07,860 --> 00:03:08,310 you see. 28 00:03:08,850 --> 00:03:17,420 Then we will visualise the first sample, the F one and the following, but may not let that by plot 29 00:03:17,430 --> 00:03:26,210 as p l t cells equal the F one between two square brackets says between two parentheses. 30 00:03:26,460 --> 00:03:36,810 So four months will declare two variable manses, then BLT dot bar manses and sells and BLT y label. 31 00:03:36,810 --> 00:03:41,640 Our Y label will be sales and BLT dot x label. 32 00:03:41,640 --> 00:03:50,700 Our X label will be summer months and finally use so method to show our chart as the following and run 33 00:03:50,710 --> 00:03:51,210 the sale. 34 00:03:51,450 --> 00:03:57,720 We get that the sales in August more than the sales in July, more than the sales in June. 35 00:03:59,100 --> 00:04:05,340 So August is a month to month and sales of tickets in our database airlines. 36 00:04:05,700 --> 00:04:14,880 But to avoid sampling error, we will take a number of samples and visualize the data from these samba's 37 00:04:14,880 --> 00:04:19,290 to get the best results without any sampling error. 38 00:04:19,350 --> 00:04:23,310 Second sample that we will take is as follows. 39 00:04:23,460 --> 00:04:32,400 Equal the F sample between two brackets 30 and runs this sell and visualise the second sample as following 40 00:04:32,910 --> 00:04:41,200 says equal D.F. to between two square brackets and between two parentheses cells and Monson's equal. 41 00:04:41,750 --> 00:04:52,200 They have to between two square brackets and two parentheses months column milty not born between two 42 00:04:53,460 --> 00:04:56,670 brackets, manses and sales variables. 43 00:04:57,330 --> 00:05:02,250 Belcea Dot, XRX between two brackets. 44 00:05:02,610 --> 00:05:05,280 Manses and rotation equal horizontal. 45 00:05:06,060 --> 00:05:18,690 And size equals 10, the size of our chart is 10 and BLT dot y level IT cells and X label as summer 46 00:05:18,690 --> 00:05:25,280 months and BLT dot show and run the sell, we will get about a chart from it. 47 00:05:25,620 --> 00:05:34,090 We can conclude that the July month is the most months in the sales of tickets. 48 00:05:34,120 --> 00:05:39,530 Then August Zengel the seventh Sambell is as the following year. 49 00:05:39,540 --> 00:05:45,180 F three equals the same five hundred between two brackets. 50 00:05:46,920 --> 00:05:54,990 Then we will visualize our third Sambell as the last two sambas. 51 00:05:55,560 --> 00:06:05,990 We will get the following chart, which is showing that August is the most months is in July. 52 00:06:06,000 --> 00:06:06,660 The June. 53 00:06:08,640 --> 00:06:18,170 And the sales of tickets in our database airlines, the first assemble as they have for equality after 54 00:06:18,190 --> 00:06:25,440 Sambal, one thousand between two brackets, and we will visualize the first scramble as the other three 55 00:06:25,440 --> 00:06:27,750 samples that we visualize. 56 00:06:27,750 --> 00:06:34,740 Then we will run the code for the fourth sample visualization as the following by clicking shift and 57 00:06:34,740 --> 00:06:38,490 enter after the visualization to the first sample. 58 00:06:38,970 --> 00:06:49,740 We get that the same result of the first and the third sample visualization that August, the most months 59 00:06:49,740 --> 00:06:52,260 in the sales of tickets. 60 00:06:52,290 --> 00:06:54,420 Then July and June. 61 00:06:54,630 --> 00:07:02,820 Then we will take the fifth sample as the following day at five equal D.F. dot sample between two brackets 62 00:07:02,830 --> 00:07:03,180 five. 63 00:07:03,750 --> 00:07:08,660 Then we visualize the 50 samples that we take as we learned before. 64 00:07:08,910 --> 00:07:20,040 So we get from Zabari chart the same results from the first and third and fourth samples that we visualize 65 00:07:20,040 --> 00:07:27,210 before, which is that August is the most months in the sales of tickets since July is in June. 66 00:07:27,580 --> 00:07:32,190 The last time we take is the second sample as the following day. 67 00:07:32,190 --> 00:07:36,030 F six equals the F Dawsonville between two brackets. 68 00:07:36,960 --> 00:07:37,950 Ten thousand. 69 00:07:39,550 --> 00:07:48,690 Then we visualize the thickest and last sample as the following after the chart of the last segment, 70 00:07:48,690 --> 00:07:50,270 viz. return it. 71 00:07:50,280 --> 00:07:59,430 And if it's used, we get that the same bill, same result as the first and third and fourth and fifth 72 00:07:59,430 --> 00:08:01,080 sample visualisations. 73 00:08:10,150 --> 00:08:19,330 Its results lead us to our final conclusion, which is the most months and series of tickets is August 74 00:08:19,360 --> 00:08:21,030 than July than June. 75 00:08:21,670 --> 00:08:24,730 At this point, we reached the end of this lecture. 76 00:08:24,860 --> 00:08:27,840 I hope you enjoyed this lecture and get all of that. 77 00:08:28,460 --> 00:08:29,900 Thanks for being here. 78 00:08:57,830 --> 00:08:59,030 Thanks for watching. 79 00:08:59,180 --> 00:09:00,980 See you next with you.