1 00:00:02,550 --> 00:00:03,240 Hello, everyone. 2 00:00:03,810 --> 00:00:07,560 So let's just load this data into Python. 3 00:00:09,220 --> 00:00:13,120 For that, we first have to learn to bite the notebook. 4 00:00:14,470 --> 00:00:18,220 You can go to an iPhone to navigate that from your to start menu. 5 00:00:22,420 --> 00:00:23,570 Sequel in that OpenAir. 6 00:00:27,540 --> 00:00:29,650 Licona get known shogan. 7 00:00:31,930 --> 00:00:35,740 Now in the lighter notebook by logbooks, click on launch. 8 00:00:41,670 --> 00:00:46,530 So the home page you are seeing is your default, what kinetic three? 9 00:00:51,090 --> 00:00:56,240 You can open any existing notebook which are stored in this work industry. 10 00:00:57,210 --> 00:01:01,770 For example, if I want to open lecture one dot, I bite a notebook. 11 00:01:01,830 --> 00:01:05,210 I can just click on it and it will open that notebook. 12 00:01:07,790 --> 00:01:12,240 But since we want to turn your notebook will go to home and click. 13 00:01:13,720 --> 00:01:14,760 On this new look. 14 00:01:15,920 --> 00:01:18,670 And then the drop down menu, I would click one by country. 15 00:01:23,320 --> 00:01:27,970 And this video will lend out the import of what house price data? 16 00:01:28,630 --> 00:01:30,590 And we will also look at the duty. 17 00:01:32,400 --> 00:01:37,230 So before starting, we will first look at of the new year, what kinetically? 18 00:01:38,490 --> 00:01:40,310 So, you know, you're working directory. 19 00:01:40,770 --> 00:01:42,580 You have to write BWB. 20 00:01:44,100 --> 00:01:45,820 Just different bet. 21 00:01:46,800 --> 00:01:48,330 So this is you got to work indirectly. 22 00:01:48,510 --> 00:01:54,980 We will use this working directory later on in our courts to copy BETYE and do this sort of kinetically 23 00:01:55,000 --> 00:01:56,130 or to save files. 24 00:01:56,160 --> 00:01:57,480 And this what, kinetic three. 25 00:01:58,470 --> 00:02:02,460 And this is the directory which you are seeing in your home bits. 26 00:02:02,670 --> 00:02:03,750 If you click on home. 27 00:02:05,980 --> 00:02:08,570 Does the content of that working directory. 28 00:02:09,020 --> 00:02:13,690 So all this for years and all this files are present at the folder. 29 00:02:13,890 --> 00:02:18,080 Mention the import of the CSP file off our whole space data. 30 00:02:18,380 --> 00:02:25,820 Download the yes we find from the resource section of this lecture and copy that file anywhere in this 31 00:02:25,820 --> 00:02:29,510 working directory before importing. 32 00:02:30,670 --> 00:02:33,010 Let's just import all the libraries. 33 00:02:33,430 --> 00:02:35,080 So we will first import. 34 00:02:35,100 --> 00:02:36,680 No, I will write. 35 00:02:36,910 --> 00:02:39,070 Import them by as Empy. 36 00:02:42,130 --> 00:02:43,850 Will import ban does. 37 00:02:44,650 --> 00:02:44,980 Right. 38 00:02:45,040 --> 00:02:50,330 Import Ban does SPDM then for Groff's. 39 00:02:50,530 --> 00:02:54,670 We will import Seabourne like Seabourne as S.A.S.. 40 00:02:57,630 --> 00:02:57,960 And run. 41 00:02:59,640 --> 00:03:07,800 You can see here and sort of when I'm getting a stick sign that I mean, this statement is running in 42 00:03:07,800 --> 00:03:08,320 our garden. 43 00:03:09,570 --> 00:03:16,040 So whenever you see this a strict sign, that means that cell is executing in the background. 44 00:03:17,780 --> 00:03:25,100 Now, the important thing is, if I will first define a variable for our data frame that is D.F., then 45 00:03:25,190 --> 00:03:25,850 equal to. 46 00:03:27,180 --> 00:03:30,400 We will use the reach CSP function of pandas. 47 00:03:30,890 --> 00:03:31,290 All right. 48 00:03:31,370 --> 00:03:39,180 Beadie Daughtry's, ESV reads, E.S.P is a function defined in Pandas library and we are important pandas 49 00:03:39,560 --> 00:03:40,260 as speedy. 50 00:03:40,380 --> 00:03:43,110 That's why I will write Beauty Dot Route CSP. 51 00:03:45,460 --> 00:03:49,990 The first argument of read CSP is the location of your CSC fine. 52 00:03:51,920 --> 00:03:54,800 So I have just copy paste the location. 53 00:03:55,370 --> 00:04:01,960 If you are using windows, you may get backslash in sort of this forward slash. 54 00:04:05,930 --> 00:04:11,970 So if you are just copping the address from the address, but off windows, you will be getting address 55 00:04:12,050 --> 00:04:16,160 in the form of Beck's leches instead of forward slathers. 56 00:04:17,470 --> 00:04:22,520 So convert those back slashes and do forward slashes before running this, come on. 57 00:04:23,620 --> 00:04:26,170 And the second argument, I will write. 58 00:04:27,350 --> 00:04:28,780 Does equate to zero. 59 00:04:29,560 --> 00:04:36,030 Since my CSU file contains high debt and the location of higher debts is at the first draw. 60 00:04:36,370 --> 00:04:38,350 That is the zero, all of the file. 61 00:04:38,680 --> 00:04:40,930 That's why I let that make those zero. 62 00:04:43,660 --> 00:04:45,760 I run this command by using a director. 63 00:04:46,160 --> 00:04:49,530 If you remember, either under executes the come on. 64 00:04:50,300 --> 00:04:53,240 And also ended up blank sell below. 65 00:04:57,370 --> 00:05:02,470 Now we'll look at our data frame, which be B.F. Dopehead. 66 00:05:05,780 --> 00:05:07,650 If you remember, Head will give me. 67 00:05:08,720 --> 00:05:10,910 The first five rows of my data. 68 00:05:13,660 --> 00:05:14,030 Then there. 69 00:05:15,070 --> 00:05:19,720 As you can see, we have all the data in our variable EDF. 70 00:05:23,100 --> 00:05:29,090 We have already discussed the meanings and definitions of all this variable in the word theory lecture. 71 00:05:30,300 --> 00:05:36,720 So let's just look at the number of rules and number of columns of this dataset. 72 00:05:37,000 --> 00:05:39,800 Will that be a dot ship? 73 00:05:42,880 --> 00:05:46,660 You can see the first argument is the number of rows. 74 00:05:47,680 --> 00:05:50,730 So we have total 506 observations. 75 00:05:52,570 --> 00:05:55,280 And total number of variables are 19. 76 00:05:55,690 --> 00:05:56,170 So. 77 00:05:57,590 --> 00:06:02,320 We have 19 columns and five zero six rows in our data set.