1
00:00:00,210 --> 00:00:06,840
Hello, before going ahead in this session, let's have a quick overview of what type of analysis,

2
00:00:06,840 --> 00:00:12,720
what type of data preparation we still have done on a what and type of data set.

3
00:00:13,350 --> 00:00:16,140
So using this logic, we have collected data.

4
00:00:16,150 --> 00:00:20,210
Then we have access derived features using all these logics.

5
00:00:20,430 --> 00:00:26,280
What we have done then we have analyzed this problem statement in which we have basically analyzed what

6
00:00:26,280 --> 00:00:29,840
exactly that rests with this back to each and every day.

7
00:00:30,480 --> 00:00:31,880
Once after doing all these things.

8
00:00:31,890 --> 00:00:37,560
And we have this problem statement after in the previous session, we have this problem statement in

9
00:00:37,560 --> 00:00:43,980
which our problem statement was in what month your rights was maximum, that we have come up with a

10
00:00:43,980 --> 00:00:45,630
conclusion in this month.

11
00:00:45,630 --> 00:00:46,410
We have backed them.

12
00:00:46,410 --> 00:00:46,690
Right.

13
00:00:46,890 --> 00:00:50,880
So these all over through then we have in this beautiful piece.

14
00:00:51,510 --> 00:00:53,820
So in this session, we have this assignment.

15
00:00:53,820 --> 00:01:00,070
The very first problem, the statement is exactly your you have to perform your analysis of total.

16
00:01:00,090 --> 00:01:00,600
Right.

17
00:01:00,840 --> 00:01:03,060
Montalva is itemise.

18
00:01:03,060 --> 00:01:08,120
It means you have to fetch each and every month using some loops.

19
00:01:08,550 --> 00:01:15,480
Once you have that one, then you can include data that once you have that data frame, you can simply

20
00:01:15,510 --> 00:01:21,140
access the of that data and you can simply plot your histogram.

21
00:01:21,150 --> 00:01:21,660
That's it.

22
00:01:21,960 --> 00:01:28,200
So what I'm going to do for this, let's say I'm going to say let's I'm going to use my enumerate over

23
00:01:28,200 --> 00:01:33,960
here, is here I'm going to say enumerate of deals all month.

24
00:01:34,470 --> 00:01:35,820
I have to just pass this.

25
00:01:36,060 --> 00:01:43,410
And here I have to say on what I have to enumerate, it means I have to number it on a month dot unique.

26
00:01:43,500 --> 00:01:46,450
It means I just need a unique values of this month.

27
00:01:46,450 --> 00:01:46,880
That's it.

28
00:01:47,370 --> 00:01:48,640
Once I have all these things.

29
00:01:48,640 --> 00:01:53,850
So it will exactly return me to the very first one, it to return me my index and the second one, it

30
00:01:53,850 --> 00:01:55,370
will exactly return me my money.

31
00:01:55,380 --> 00:01:57,200
So I'm just going to it as this one.

32
00:01:57,660 --> 00:02:04,490
And here I have to see my index will get a start from one that simple meaning of this one.

33
00:02:04,680 --> 00:02:10,590
Once I have all this stuff, then I'm going to create a subplot using BLT Dot subplot.

34
00:02:11,190 --> 00:02:12,570
I have six months here.

35
00:02:12,570 --> 00:02:19,380
I'm going to say I have a method of three comma, two comma I it means on each and every subplot I have

36
00:02:19,380 --> 00:02:21,540
to display some piece to it.

37
00:02:21,720 --> 00:02:30,480
Then I'm going to say D.F. of month it Callicles to one whatever month I have passed from this to the

38
00:02:30,480 --> 00:02:33,120
four I come a month in this number.

39
00:02:33,120 --> 00:02:33,480
That's it.

40
00:02:33,840 --> 00:02:37,020
And here I'm going to say this is equals two months.

41
00:02:37,020 --> 00:02:39,120
This is exactly my futer.

42
00:02:39,120 --> 00:02:41,310
I have to pass this in my data frame.

43
00:02:41,850 --> 00:02:42,810
Let's say this one.

44
00:02:43,230 --> 00:02:47,900
Let's say this is exactly my data framework to dissolve comma Paoletta.

45
00:02:48,030 --> 00:02:49,590
This is my affair.

46
00:02:50,040 --> 00:02:53,460
So let's say from this data frame, you have to access your day.

47
00:02:53,760 --> 00:03:01,800
So I'm going to say d of of out of date and on this, what I'm going to do, I'm just going to call

48
00:03:01,800 --> 00:03:06,300
my histogram to be ality dot of this.

49
00:03:06,570 --> 00:03:11,430
Once you have all this stuff, let's say you have to assign some X labels.

50
00:03:11,430 --> 00:03:16,920
And while it has to for this I'm going to say p l dot x label.

51
00:03:16,920 --> 00:03:24,900
Let's see, my X level is nothing but let's say days in month, days in month.

52
00:03:24,930 --> 00:03:28,920
And after this you have to say you have to assign some value.

53
00:03:28,950 --> 00:03:36,680
But I'm going to say BLT dot y label, let's say Mylie y label is nothing but let's say total notes.

54
00:03:36,700 --> 00:03:42,060
I'm just going to say my Y label is nothing but total rights.

55
00:03:42,300 --> 00:03:45,930
Once after doing all these things, I have to just execute it.

56
00:03:46,050 --> 00:03:53,130
But before executing, let's say I have to bring this act level as like two days in month and whatever

57
00:03:53,130 --> 00:03:53,930
will be my month.

58
00:03:54,120 --> 00:04:00,180
So here I'm going to add my placeholder and for this I'm going to say that format of month.

59
00:04:00,390 --> 00:04:05,910
So this placeholder will receive value from this format function.

60
00:04:06,220 --> 00:04:09,570
Let's say I have to sign my own window side.

61
00:04:09,810 --> 00:04:11,580
So far this is what I'm going to do.

62
00:04:11,580 --> 00:04:19,080
I'm just going to say VLT, Dot, Fagre, and here you have to sign your fixie, which is exactly this

63
00:04:19,080 --> 00:04:19,350
one.

64
00:04:19,650 --> 00:04:25,170
And efficacies, let's say 20 comma eight just executed.

65
00:04:25,170 --> 00:04:33,150
It will take some couple of seconds and it will exactly give you your validation with respect to each

66
00:04:33,150 --> 00:04:34,200
and every month.

67
00:04:34,440 --> 00:04:41,580
Now you will see over here, this is exactly your visualization with respect to each and every month.

68
00:04:41,580 --> 00:04:48,420
And from this realisation, it seems to have almost in the couple of last days, in each and every month,

69
00:04:48,420 --> 00:04:49,620
you have maximum.

70
00:04:49,620 --> 00:04:49,890
Right.

71
00:04:49,890 --> 00:04:52,530
You can easily visualize from this histogram.

72
00:04:52,530 --> 00:04:59,490
So that's a conclusion you can fetch from this all these six visas of all the six month.

73
00:05:00,000 --> 00:05:08,100
So let's go ahead in our last statement of this assignment where we have to perform analysis rush in

74
00:05:08,100 --> 00:05:15,480
our search for this, I'm basically going to use a very handy function of Seabourne, which is exactly

75
00:05:15,480 --> 00:05:21,440
my point plot for this, I'm going to say, and this dot point plot.

76
00:05:21,450 --> 00:05:27,990
And if here you will stab you will get all the documentation on this function, what those parameters

77
00:05:27,990 --> 00:05:31,070
are, what are custom parameters, what this function will do.

78
00:05:31,410 --> 00:05:38,160
So tell it, show point estimates and confidence interval using this is scatterplot Graff's.

79
00:05:38,190 --> 00:05:40,810
So it is almost similar to a line.

80
00:05:41,160 --> 00:05:47,850
But the basic difference is in this point plot, it notifies some particular point.

81
00:05:47,880 --> 00:05:52,320
So here I'm going to say on X axis, I just need my outlet.

82
00:05:52,950 --> 00:05:58,850
And on Y axis, I just need to say what exactly is my latitude?

83
00:05:59,190 --> 00:06:05,420
Once I have all this stuff, I'm going to say, let's say, what exactly is material here?

84
00:06:05,430 --> 00:06:07,790
My data frame is nothing, but let's say very fast.

85
00:06:07,800 --> 00:06:09,320
I just need some data frame.

86
00:06:09,330 --> 00:06:15,600
So this is exactly your beautiful point plot and it will definitely take some couple of minutes in its

87
00:06:15,600 --> 00:06:20,800
execution because you will observe the data is definitely very huge.

88
00:06:20,820 --> 00:06:26,410
So this is exactly your rush with respect to each an hour on different, different latitudes.

89
00:06:26,730 --> 00:06:31,790
Let's say I have to modify this this point as well.

90
00:06:32,100 --> 00:06:36,480
So for this here you have some of the parameters that you can play with as well.

91
00:06:36,930 --> 00:06:40,700
So here you have a barometer, which is exactly your parameter.

92
00:06:40,710 --> 00:06:45,630
It is on what column basis are on what parameter this is.

93
00:06:45,630 --> 00:06:47,430
You have to split your graph.

94
00:06:47,580 --> 00:06:53,360
So I'm going to say in this whole parameter, I have to pass my wigdor.

95
00:06:53,580 --> 00:06:57,720
And after what I have to do, let's say I have to assign some title as well.

96
00:06:57,720 --> 00:07:02,570
As for this, it is exactly my let's say X or you can say Azziz.

97
00:07:02,850 --> 00:07:08,130
And here I have a function which is exactly my set on this gorditas for this.

98
00:07:08,130 --> 00:07:10,410
I would say set in this code.

99
00:07:10,920 --> 00:07:13,140
And here you have to find some title as well.

100
00:07:13,650 --> 00:07:22,940
So my title is nothing more, let's say hours of the word says Lattitude of Passenger.

101
00:07:23,000 --> 00:07:27,030
So this is exactly my title just executed.

102
00:07:27,030 --> 00:07:33,750
It will definitely take some couple of minutes around two or three hours, depending upon what the specifications

103
00:07:33,750 --> 00:07:34,270
you have.

104
00:07:34,290 --> 00:07:37,030
So this is exactly a beautiful point.

105
00:07:37,430 --> 00:07:45,180
With respect to each and every weekday, you will see this this red this red wine plot with respect

106
00:07:45,180 --> 00:07:52,500
to toastie, which often refers to yeah, it is somehow on your peak, whereas this this pinkish will

107
00:07:52,500 --> 00:07:55,960
definitely this back to your Riccardi, which is exactly my sun.

108
00:07:56,460 --> 00:08:00,060
So that's the type of conclusion how you can conclude your result.

109
00:08:00,450 --> 00:08:06,120
And if you try to maximize this result, just try to assign your own window.

110
00:08:06,510 --> 00:08:10,700
That's what we all have done in all of our previous results.

111
00:08:11,220 --> 00:08:12,360
That's all about this test.

112
00:08:12,360 --> 00:08:14,250
And hope you would love the session very much.

113
00:08:14,460 --> 00:08:15,150
Thank you.

114
00:08:15,210 --> 00:08:16,230
Have a nice day.

115
00:08:16,410 --> 00:08:17,250
Keep learning.

116
00:08:17,250 --> 00:08:18,090
Keep growing.

117
00:08:18,450 --> 00:08:19,290
Keep practicing.