1
00:00:05,950 --> 00:00:08,620
Hey everyone so welcome to this video.

2
00:00:08,650 --> 00:00:16,420
In this video we are going to have something like how you can add more rules and more columns in the.

3
00:00:17,290 --> 00:00:22,570
So we have done how to define the data frames and we have defined a very unique data field also that

4
00:00:22,570 --> 00:00:23,920
I have not removed from here.

5
00:00:24,790 --> 00:00:33,870
So to create any column in a data frame you will do something like First let me define this one in need

6
00:00:33,870 --> 00:00:38,100
of a muscle with D F and this one DFA

7
00:00:45,430 --> 00:00:46,890
and there we go.

8
00:00:47,670 --> 00:00:51,580
Now here we have the f this one.

9
00:00:52,050 --> 00:01:00,870
But if you want to add another column so you can do something like Here I have my f then you will pass

10
00:01:01,080 --> 00:01:05,920
the brackets and in brackets the column you want to add the like.

11
00:01:05,970 --> 00:01:09,800
After that we have what we get it is nothing up to it.

12
00:01:09,810 --> 00:01:14,500
So here we have something like B and then this will be equal to.

13
00:01:14,880 --> 00:01:21,610
Like if I want to add something like Here I have my

14
00:01:24,970 --> 00:01:35,980
plus and then def that element and I want to make on your column that is the sum of these two.

15
00:01:36,070 --> 00:01:40,490
That is something I said here and should be done.

16
00:01:40,760 --> 00:01:49,860
Now if you bring D if you will get an entire column that is this one and that's how you can create this

17
00:01:49,860 --> 00:01:50,610
thing.

18
00:01:50,610 --> 00:01:57,780
Now if you try something like this one that is we have this one chapter done chapter done chapter done

19
00:01:58,590 --> 00:01:59,480
and there we go.

20
00:02:00,360 --> 00:02:12,870
Now here we have done this one was there is now this one doesn't change called Sound 15 I don't know

21
00:02:12,870 --> 00:02:17,360
why maybe this deal on

22
00:02:20,930 --> 00:02:27,970
so this is a task for you try to make it a frame that consists of adding the models.

23
00:02:28,290 --> 00:02:38,790
So I have told you that how you can add the new column in a data frame that is generally the discussion

24
00:02:38,790 --> 00:02:45,530
come on and they will know how to remove any column on a needle.

25
00:02:45,980 --> 00:02:55,340
So do removes any visual we have simple matter that is the F don't drop their drops transform deleting

26
00:02:55,430 --> 00:02:57,560
detail that is data.

27
00:02:57,590 --> 00:03:04,390
We want to do it and you just need to pass the draw that is like if I want to delete this.

28
00:03:05,090 --> 00:03:13,440
Then I will pass that E and make sure you always pass that E with the courts.

29
00:03:13,780 --> 00:03:15,110
I get better.

30
00:03:15,490 --> 00:03:21,610
So this is a method I have passed back upstairs method sir.

31
00:03:21,620 --> 00:03:23,230
Define inferences.

32
00:03:23,320 --> 00:03:24,040
Now David go.

33
00:03:24,620 --> 00:03:28,870
So here if you notice we have not e here.

34
00:03:29,370 --> 00:03:31,630
That's how you can delete these rows.

35
00:03:31,800 --> 00:03:37,110
But one more thing now if you have a b here you will again get it.

36
00:03:37,380 --> 00:03:40,350
That's the thing that the thing you delete.

37
00:03:40,350 --> 00:03:42,640
There is only deleted here.

38
00:03:43,020 --> 00:03:45,500
That will not get permanently deleted.

39
00:03:45,900 --> 00:03:52,180
And if if you Breashears shift redone you will get something like this one in drop method you want.

40
00:03:52,560 --> 00:03:58,540
So here you have level and Xs and few more things like index column in place.

41
00:03:58,920 --> 00:04:05,250
So here we have this in place that is something very important one in case of deleting any.

42
00:04:06,570 --> 00:04:17,660
So to delete that permanently you also need to pass here something like in place and in place we have

43
00:04:17,660 --> 00:04:21,000
only two values that is true and false through these four.

44
00:04:21,040 --> 00:04:23,330
Like if you do not want to delete that one.

45
00:04:23,540 --> 00:04:28,280
And so it defaults and 2 1 is if you want to delete that permanently.

46
00:04:28,310 --> 00:04:32,400
So now if I press you return there we go.

47
00:04:32,420 --> 00:04:35,450
And now I have the F in.

48
00:04:36,080 --> 00:04:38,680
So here we have def without any e.

49
00:04:39,170 --> 00:04:48,020
If you again print def there will be no E so there so you can delete any element in a data frame.

50
00:04:48,180 --> 00:04:51,630
And that element means the drop in our data frame.

51
00:04:51,660 --> 00:04:57,000
Now what if you want to delete the column that how you can delete these columns.

52
00:04:57,320 --> 00:05:05,950
So to delete any column if you try the same thing like the F don't drop princes and vote if I want to

53
00:05:05,950 --> 00:05:10,440
do A B and shifted then you will get at it.

54
00:05:11,580 --> 00:05:15,050
That's because this one is asking for.

55
00:05:15,060 --> 00:05:23,130
We can see that index and index we have are only the ABC B W X Y and Z are not indexes that the column

56
00:05:23,130 --> 00:05:27,320
numbers so to make them selected here.

57
00:05:27,690 --> 00:05:32,570
If you again press here shift that you will get this one axis.

58
00:05:33,420 --> 00:05:35,080
So in 2D.

59
00:05:35,100 --> 00:05:37,550
The difference in that is we are dealing here.

60
00:05:37,650 --> 00:05:39,250
We had general two axis.

61
00:05:39,300 --> 00:05:40,670
That is zero.

62
00:05:40,710 --> 00:05:43,740
This one is 0 x is that is by default here.

63
00:05:43,890 --> 00:05:48,790
This one column is one you can assume that accesses y axis.

64
00:05:48,870 --> 00:05:53,010
So access is by default 0 by used 1.

65
00:05:53,040 --> 00:05:59,590
So you also need to pass here something like X is and that will be 1.

66
00:06:00,000 --> 00:06:03,630
And if you try the thing now B is removed.

67
00:06:04,080 --> 00:06:09,410
But again the same thing if you do again that one base not permanently.

68
00:06:10,170 --> 00:06:16,960
So again you need to pass something like in place and that will be true not true.

69
00:06:17,130 --> 00:06:18,910
It's true.

70
00:06:19,020 --> 00:06:19,520
There we go.

71
00:06:20,600 --> 00:06:28,210
And now here we do not have B if you try again being permanently removed from there.

72
00:06:29,050 --> 00:06:33,000
So that's how you can remove any element.

73
00:06:33,020 --> 00:06:41,050
Sorry any column and then neutral from a data frame and you can also do this thing to Dedmon that killed

74
00:06:41,150 --> 00:06:50,320
did of him and if you want that one also I can show you here we have this one if I need to remove this

75
00:06:50,500 --> 00:07:06,610
B so I can just do the fun Don't rope here first bus B then pass X is fun and then we have in place

76
00:07:07,760 --> 00:07:15,630
is equal to true and there we go if you print now do you have fun you do not have B again if you want

77
00:07:15,630 --> 00:07:24,690
to remove the chicks but not permanently then here we have this one thing like Sir and here we do not

78
00:07:24,690 --> 00:07:32,880
have these cubes and that's how you can delete this one and if you delete something from middle like

79
00:07:33,210 --> 00:07:40,810
monkeys they will get that one removed from there and these columns can get closer to the W by one of

80
00:07:40,830 --> 00:07:45,300
gap there that's how you can delete any element.

81
00:07:45,540 --> 00:07:50,850
So we are now also done with creating and deleting the elements from data stream in the next video we

82
00:07:50,850 --> 00:07:53,130
will learn about that how you can access the elements.

83
00:07:53,460 --> 00:07:54,400
So thanks for watching.

84
00:07:54,510 --> 00:07:55,680
And soon the next video.