1 00:00:05,760 --> 00:00:06,400 Hey everyone. 2 00:00:06,920 --> 00:00:12,020 So this video is about creating the data for pandas crystallization and you're gonna clear it up and 3 00:00:12,020 --> 00:00:12,880 you are working on that. 4 00:00:12,890 --> 00:00:17,660 But here this is a separate video in which I'm going to show you that how you can create some of the 5 00:00:17,660 --> 00:00:22,900 data frames and we will use these data frames to realize by using pandas. 6 00:00:22,940 --> 00:00:26,020 So here I have the Jupiter notebook. 7 00:00:26,090 --> 00:00:41,480 First of all import pandas as baby also import seaboard as a S.A. and NIMBY as and b that is it for 8 00:00:41,480 --> 00:00:44,090 importing seaborne is not where we are going to seaborne. 9 00:00:44,120 --> 00:00:46,210 It's just we need the data from that. 10 00:00:46,430 --> 00:00:49,530 Also at this length that is met bloat lab 11 00:00:52,850 --> 00:00:55,240 space in line and difficult. 12 00:00:55,890 --> 00:01:03,100 Now if you remember this dataset that is the data set that I have downloaded randomly This is 2 0 wondered 13 00:01:03,160 --> 00:01:09,500 easy to CSP in the previous videos when we have done this one in by loading the dataset in pandas. 14 00:01:09,500 --> 00:01:11,330 You have the name ECD or CSP. 15 00:01:11,360 --> 00:01:18,530 Now you will get to 0 1 not easy to CSB so be sure about the name and again if you forget where to save 16 00:01:18,560 --> 00:01:28,220 this file that you have downloaded go to computer data the hard drive users Dave whatever your location 17 00:01:28,220 --> 00:01:32,800 is and you can get the location just by writing the WD. 18 00:01:33,440 --> 00:01:36,050 So this is for those who forgot this thing. 19 00:01:36,050 --> 00:01:41,240 Now just copy that one and paste it here that is here. 20 00:01:41,240 --> 00:01:43,300 This is too to wonder wondered easy NTSB. 21 00:01:43,550 --> 00:01:50,480 I have already posted this one so that the one type of data here. 22 00:01:50,540 --> 00:01:53,080 Now let me create few details. 23 00:01:53,090 --> 00:02:00,500 That is first we have the effort and this is going to be simple seaborne dataset that is S.A. don't 24 00:02:01,340 --> 00:02:09,070 load underscore data set and load any one of these like tips. 25 00:02:09,170 --> 00:02:11,710 The most common one that we have used. 26 00:02:12,050 --> 00:02:14,740 After that we have the F2. 27 00:02:14,930 --> 00:02:24,180 This one is going to be simple be read by the CSC matter and define file name that is disparate. 28 00:02:25,070 --> 00:02:34,040 So you need to pass here to 0 1 doped ECB note CSP so extension is also required when you read five 29 00:02:34,190 --> 00:02:35,900 files from your hard drive. 30 00:02:36,830 --> 00:02:40,150 So David go with that one life of print one. 31 00:02:40,250 --> 00:02:47,630 You have this one and if you print the F too you will have despite the standard data set. 32 00:02:48,380 --> 00:02:56,630 And let me know remote not those more separate data types so rated assets that are random datasets that 33 00:02:56,650 --> 00:02:57,920 we are going to create. 34 00:02:57,920 --> 00:03:07,580 First one is x1 and this one is going to be just and B don't random note Randi make sure you are not 35 00:03:07,580 --> 00:03:11,540 writing already and here and I will show you why. 36 00:03:12,290 --> 00:03:16,000 But before that just ran with hundred draws. 37 00:03:16,010 --> 00:03:21,390 And let we have five columns then we have another dataset sorry. 38 00:03:21,820 --> 00:03:34,760 And in the random variable that is and B do random not read sorry friend may we have only 10 draws and 39 00:03:34,760 --> 00:03:36,630 five columns. 40 00:03:37,070 --> 00:03:45,020 Now create these two intra day difference that is DLF three now and then we have D4. 41 00:03:45,080 --> 00:03:48,400 So first day of three and that will be just BD. 42 00:03:48,410 --> 00:03:50,670 Don't get a frame. 43 00:03:50,690 --> 00:03:53,500 I'm going a little fast because you are all expert in these things. 44 00:03:53,500 --> 00:03:56,880 Now just so that you have the data now. 45 00:03:56,900 --> 00:04:06,590 So here we have X1 first and then defined the columns and the columns would be something like we have 46 00:04:06,710 --> 00:04:07,760 a. 47 00:04:07,760 --> 00:04:21,020 And let we have small letters so that it's easy to use them b c b and we have five columns so to be 48 00:04:21,410 --> 00:04:23,180 otherwise you will get data. 49 00:04:23,210 --> 00:04:29,130 Now if you've been DFT you will have dispatched a data frame of hundred rows. 50 00:04:29,150 --> 00:04:38,220 This one is 99 because this is beginning from 0 and five columns and if you did not provide him five 51 00:04:38,220 --> 00:04:39,870 values you will get there Adam. 52 00:04:39,930 --> 00:04:45,550 So make sure you are writing the exact number of columns wheels. 53 00:04:45,570 --> 00:04:49,470 This one is the most prone to these errors. 54 00:04:49,470 --> 00:04:56,690 Now we have the fourth dataset that is these seem like this one data frame. 55 00:04:56,910 --> 00:05:00,830 There we have just x 2 instead of X1. 56 00:05:01,140 --> 00:05:06,490 Then we have columns and we will provide the same columns. 57 00:05:06,720 --> 00:05:09,250 This in command c come on. 58 00:05:09,570 --> 00:05:11,110 There you go. 59 00:05:11,110 --> 00:05:15,460 Now if you print the fourth we will have this kind of data. 60 00:05:15,480 --> 00:05:18,090 Now you can do all the things with only one kind of data. 61 00:05:18,300 --> 00:05:23,550 But I'm showing you here all the kinds of data so that whatever the type you will get in your project 62 00:05:23,590 --> 00:05:25,120 you will easily able to do that. 63 00:05:25,950 --> 00:05:26,680 So thanks for watching. 64 00:05:26,680 --> 00:05:29,930 This is only about creating the data as from the next video. 65 00:05:29,940 --> 00:05:34,080 We will look at how we can you blow these data so see in the next video.