1 00:00:00,670 --> 00:00:05,270 Later on you learn how to extract portions of a data frame. 2 00:00:05,370 --> 00:00:09,840 Let's say you want these three values here. 3 00:00:09,840 --> 00:00:12,540 These three and these three in here. 4 00:00:12,780 --> 00:00:16,610 So that's portion or maybe you just want one single value. 5 00:00:16,620 --> 00:00:21,930 In that case you need to find a way to refer to that single value by coordinates. 6 00:00:22,050 --> 00:00:27,990 When I say my coordinates I mean like by column name. 7 00:00:28,000 --> 00:00:35,070 So you'd be referring to name because you want this value of this column and also you'd have 8 00:00:35,070 --> 00:00:41,190 to refer to the row, and for that we have the index concept. 9 00:00:41,190 --> 00:00:48,180 In this case you see in bold here that the index has been assigned by default by pandas. 10 00:00:48,180 --> 00:00:51,930 So it starts from 0 always, the default index from 0 and so on. 11 00:00:51,930 --> 00:00:58,530 If you want, if you have your own index such as in this case I would like to to assign ID as an index 12 00:00:58,770 --> 00:00:59,390 of these data. 13 00:00:59,400 --> 00:01:08,910 And later on when I show you how to refer to certain values you would be using that ID to refer to 14 00:01:08,910 --> 00:01:10,200 those values. 15 00:01:10,200 --> 00:01:21,030 So how to assign an index a new index to your data? Well, you can do that with df8.set_index. 16 00:01:21,030 --> 00:01:28,920 And then you assign the name of the column that has those values that you want to set as an index. 17 00:01:28,950 --> 00:01:34,750 So ID in that case, in my case and this is what happens. 18 00:01:34,950 --> 00:01:45,300 Now here you have to be very careful because the set index operation is not an in-place operation meaning 19 00:01:45,300 --> 00:01:47,960 that when you apply that method 20 00:01:47,970 --> 00:01:56,050 It produces a new data frame but just on the fly, it doesn't actually modify the existing data frame. 21 00:01:56,100 --> 00:01:59,520 So df8, the object remains the same as you see in here. 22 00:02:02,630 --> 00:02:10,100 Therefore if you want to have the changes you may want to create another variable where to store the new 23 00:02:12,650 --> 00:02:13,250 data frame. 24 00:02:13,580 --> 00:02:25,430 So this is actually equal to a new data frame with that index set in, and df9 now will be a permanent 25 00:02:25,610 --> 00:02:31,700 object, a permanent data frame with ID s index. There is also another way if you like. 26 00:02:35,890 --> 00:02:41,880 You can still do the same thing and say inplace equals to true. 27 00:02:42,010 --> 00:02:48,610 In this case the df8 is modified permanently as you can see in here. 28 00:02:49,510 --> 00:02:55,990 However here you have to be very very cautious because what's happening here is that at the moment 29 00:02:55,990 --> 00:02:59,720 df8 has as an index column ID. 30 00:02:59,920 --> 00:03:07,630 Now what happens if you set as index the address column instead? 31 00:03:13,400 --> 00:03:21,160 We're talking about df8. The address column is assigned successfully as an index. 32 00:03:21,200 --> 00:03:22,280 That's good. 33 00:03:22,280 --> 00:03:26,660 However if you look for the ID column, it has disappeared. 34 00:03:26,660 --> 00:03:34,790 So what happens is that the set index method it sets a new index, but the old index is not assigned 35 00:03:34,820 --> 00:03:42,840 as a column so ID is not to reassigned as a column of a data frame, it just drops, it is deleted. 36 00:03:43,130 --> 00:03:45,870 However there is a way to avoid that. 37 00:03:46,100 --> 00:03:54,150 What you can do is let me repeat a similar operation, this time I want to set as ID 38 00:03:54,230 --> 00:04:01,570 the name, inplace equals to true and the existing index is address. 39 00:04:01,700 --> 00:04:02,510 So we are setting 40 00:04:02,510 --> 00:04:08,450 name, the existing address and you have to say drop equals to false. 41 00:04:11,300 --> 00:04:18,450 So what's happened here is that name remained as a column also. 42 00:04:19,140 --> 00:04:23,700 So it's an index but it's also a column, however address was deleted 43 00:04:23,700 --> 00:04:25,110 again as you can see. 44 00:04:25,690 --> 00:04:32,320 So this actually drop equals to false tells Python not to drop as a column the column 45 00:04:32,320 --> 00:04:35,130 that you are setting as a new index. 46 00:04:35,130 --> 00:04:40,590 So please keep that in mind or just use the other method where you create a new variable. 47 00:04:40,590 --> 00:04:42,740 It depends on a scenario but yeah. 48 00:04:42,780 --> 00:04:44,400 Please keep those things in mind!