1
00:00:00,210 --> 00:00:05,670
Hello, all, so let's have a quick recap of what we all have done in our all previous session.

2
00:00:06,130 --> 00:00:12,300
We're basically following lots of people, lots of great cleaning, and this is exactly analysis that

3
00:00:12,300 --> 00:00:14,410
we have both in a previous session.

4
00:00:14,640 --> 00:00:15,060
Yeah.

5
00:00:15,420 --> 00:00:19,680
Basically gaffed going from Richarlison and we have Analise.

6
00:00:19,680 --> 00:00:20,110
Yeah.

7
00:00:20,670 --> 00:00:24,600
The guests are mostly from European and Portugal.

8
00:00:24,870 --> 00:00:27,830
So this is exactly the assignment for the session.

9
00:00:27,840 --> 00:00:35,130
The very first one is how much to gased pay for a room, but might selective say.

10
00:00:35,130 --> 00:00:39,480
I'm going to say my getting a preview of Manager Frame.

11
00:00:39,480 --> 00:00:45,990
You will see this is exactly your preview, how your data frame looks like you will see over here.

12
00:00:46,000 --> 00:00:50,470
These are all the columns and all the rules in your data frame.

13
00:00:51,480 --> 00:00:59,430
So here what you have to do, let's say I'm would use box plot or I can say I'm going to use a distribution

14
00:00:59,670 --> 00:01:08,460
of exactly my this resolved in this court room on a school type room, because I have to need a distribution

15
00:01:08,460 --> 00:01:14,520
of how exactly the prices of each and every room that I thought this.

16
00:01:14,520 --> 00:01:17,820
I'm going to say I have to use this box plot.

17
00:01:18,120 --> 00:01:24,540
And if you will press shift to stab, you will get all the parameters of this box plot, what is X,

18
00:01:24,540 --> 00:01:27,220
Y you data and all these types of things.

19
00:01:27,660 --> 00:01:36,300
So on X axis, on this X axis, I have to say, I just need I just need this feature, so I have to

20
00:01:36,300 --> 00:01:37,190
just copy it.

21
00:01:37,200 --> 00:01:37,920
That's it.

22
00:01:38,250 --> 00:01:42,090
And this is exactly my x axis on Y axis.

23
00:01:42,330 --> 00:01:50,820
And I have to use something known as this, this area, which exactly says, yeah, what exactly is

24
00:01:50,820 --> 00:01:53,950
that price with respect to each and every room.

25
00:01:53,970 --> 00:01:56,880
So on y axis, I just need this one.

26
00:01:57,240 --> 00:01:59,830
Then what exactly is my little frame?

27
00:01:59,880 --> 00:02:07,950
Let's say let's say I'm going to say here I'm going to say my data of is underscore cancel is equal,

28
00:02:07,950 --> 00:02:10,710
equal to zero because I need valid bookings.

29
00:02:11,200 --> 00:02:16,830
So here I'm going to say I have to parse this in my little frame so that I will get my filtered it and

30
00:02:17,140 --> 00:02:20,250
I will say this in my nature to just execute it.

31
00:02:20,250 --> 00:02:21,780
And I have to parse this.

32
00:02:22,110 --> 00:02:25,920
Or here's here I would say data to add.

33
00:02:25,950 --> 00:02:29,620
These are all parameters and formulas.

34
00:02:29,640 --> 00:02:31,110
I have to play with something.

35
00:02:31,110 --> 00:02:39,920
Let's say I need I need us distribution with respect to my resort hotel as well as with respect material

36
00:02:39,960 --> 00:02:40,320
as well.

37
00:02:40,630 --> 00:02:47,490
For this, I have to just put this in my new parameters because Hugh is exactly used whenever you have

38
00:02:47,490 --> 00:02:55,440
to divide your distribution on the basis of some feature, let's say I need my own size, I need my

39
00:02:55,470 --> 00:02:56,100
own window.

40
00:02:56,100 --> 00:03:03,000
Say so for this, you guys can say BLT dot figure, which is exactly this one.

41
00:03:03,330 --> 00:03:07,170
And here you have some parameter, which is exactly a thick side.

42
00:03:07,410 --> 00:03:13,960
So I'm going to say my fixed side is nothing but like so I fix this to Algoma it that's my windows have

43
00:03:13,980 --> 00:03:21,450
this to allow it and after it you can play with some other parameters like say what exactly.

44
00:03:21,450 --> 00:03:32,940
The title, let's say my title is the thing, but what is my price of room times per night and per person.

45
00:03:32,940 --> 00:03:36,180
Or you can say person whatever you want, it's all up to you.

46
00:03:36,450 --> 00:03:38,580
And let's say you have to do some modification.

47
00:03:38,580 --> 00:03:39,250
Your calculator.

48
00:03:39,300 --> 00:03:45,030
I want some own custom font size which is exactly of sixteen after it.

49
00:03:45,030 --> 00:03:50,160
What we have to do, we have to set some own label which is exactly BLT X label.

50
00:03:50,460 --> 00:03:56,340
And on this X label you have to say room underscore that it's all up to you.

51
00:03:56,340 --> 00:04:04,440
Whatever you want to assign, let's say on Y label, I'm going to say it is nothing but let's say price

52
00:04:04,680 --> 00:04:11,940
in terms of some currency, let's say price in euro and after it, what we have to do now you will think

53
00:04:11,940 --> 00:04:17,880
why I'm going to consider euro, because in the in this analysis you will observe more subtle guests

54
00:04:17,880 --> 00:04:20,760
are exactly four from European country.

55
00:04:20,760 --> 00:04:24,000
That's what I would say, price in euro currency.

56
00:04:24,270 --> 00:04:31,920
And after what I have to do, I have to just call this show over there to showcase whatever my vision

57
00:04:31,920 --> 00:04:33,330
will be just executed.

58
00:04:33,330 --> 00:04:40,560
This is exactly that beautiful box thought that you need you will see over here with respect to this

59
00:04:40,740 --> 00:04:45,150
Ekert agree it has its highest price approx.

60
00:04:45,360 --> 00:04:46,770
Five hundred euro.

61
00:04:46,950 --> 00:04:53,550
Whereas with respect to this this gigantic room where my hotel is today, you will see this.

62
00:04:53,550 --> 00:04:55,200
Exactly the distribution.

63
00:04:55,200 --> 00:04:59,810
And definitely these are those rooms that are much, much costlier.

64
00:04:59,890 --> 00:05:04,960
And all of the rooms you will observe over here, there is all these data points that you will see,

65
00:05:04,960 --> 00:05:10,620
these are exact outliers with respect to all the rules in this particular room type.

66
00:05:11,110 --> 00:05:14,500
So this is exactly that analysis that you have performed over here.

67
00:05:15,100 --> 00:05:18,580
So let's say let's let's go ahead with our second analysis.

68
00:05:18,730 --> 00:05:20,110
Second part of the statement.

69
00:05:20,410 --> 00:05:24,010
The second one is how does that price per night?

70
00:05:24,150 --> 00:05:25,300
Were I over here?

71
00:05:25,510 --> 00:05:27,150
It means you need to confirm.

72
00:05:27,370 --> 00:05:29,850
It means you need to column the very first one.

73
00:05:29,950 --> 00:05:31,240
What exactly do you want?

74
00:05:31,240 --> 00:05:36,760
And on the basis of what exactly is the price, let's say I have to do this analysis with respect to

75
00:05:36,760 --> 00:05:39,730
my result and with respect to my city hotel.

76
00:05:39,820 --> 00:05:42,220
For this, I would say this is nothing.

77
00:05:42,220 --> 00:05:45,780
But let's say very first I have to access my result of him.

78
00:05:45,790 --> 00:05:48,670
And on this I have to access my is in the score cancer.

79
00:05:48,910 --> 00:05:55,360
And here I have to say is it's nothing worth it is zero because I need my valid bookings and I have

80
00:05:55,360 --> 00:06:00,830
to just pass this filter in my data frame so that I will have my filter him next.

81
00:06:00,920 --> 00:06:07,360
I have to store it in somewhere else, which is exactly my data and a score resort and just executed

82
00:06:07,360 --> 00:06:08,820
after what we have to do.

83
00:06:09,310 --> 00:06:12,140
Similar operation I have to do for my city.

84
00:06:12,160 --> 00:06:14,410
So just to copy, just paste over here.

85
00:06:14,420 --> 00:06:16,780
This time I have to see my data.

86
00:06:16,780 --> 00:06:19,300
Family name and city similarly were here.

87
00:06:19,300 --> 00:06:27,130
My little fame name is next to City and this time my this one data from let's say data underscore city

88
00:06:27,160 --> 00:06:28,630
data on the school city.

89
00:06:28,750 --> 00:06:29,910
Just executor's.

90
00:06:29,950 --> 00:06:33,730
This is exactly the data frame with respect to your city hotels.

91
00:06:34,210 --> 00:06:36,100
Now let's say what I'm going to do.

92
00:06:36,100 --> 00:06:42,250
Let's say I need overview here, how my data looks like just executed.

93
00:06:42,280 --> 00:06:46,870
You will see this is the data frame with respect to city hotels.

94
00:06:47,830 --> 00:06:55,170
Similarly, in case of your this this data result, it will look something like something like this.

95
00:06:55,180 --> 00:07:03,550
Or let me let me show you let me show you some data underscore resort dot had you will see this is a

96
00:07:03,550 --> 00:07:05,580
data frame with respect to a resort hotel.

97
00:07:05,920 --> 00:07:10,880
Now, what you have to do, you need basically two features.

98
00:07:10,930 --> 00:07:12,690
What is your arrival date month?

99
00:07:12,700 --> 00:07:16,330
And on the basis of month, what exactly do you mean price?

100
00:07:16,690 --> 00:07:23,440
So I'm going to say it means it means you have to group your data on the basis of on the basis of this

101
00:07:23,440 --> 00:07:23,980
column.

102
00:07:24,550 --> 00:07:30,220
So here I'm going to say let's say very first I have to access my data and as a result, data frame.

103
00:07:30,220 --> 00:07:33,320
And on this, I have to basically call my group back.

104
00:07:33,610 --> 00:07:37,420
And here I have to group my data on the basis of this column.

105
00:07:37,900 --> 00:07:43,420
And after it, what I have to do, I have to access my ADR, which is exactly my price feature.

106
00:07:43,780 --> 00:07:50,920
And on this, if I'm going to call my meeting or just executed, this is that this is that is stuff

107
00:07:51,070 --> 00:07:52,120
that you need.

108
00:07:52,610 --> 00:07:55,810
Let's say I have to convert it into some data frame for this.

109
00:07:55,810 --> 00:07:58,660
I have to just call this reset and index.

110
00:07:59,050 --> 00:08:00,800
Let's I have to store it somewhere.

111
00:08:01,330 --> 00:08:06,820
I would say I have to store it in, let's say resort, underscore hotel, just execute it.

112
00:08:07,030 --> 00:08:15,450
And after it, what I have to do, let's say I have to perform the similar operations for my city hotel

113
00:08:15,460 --> 00:08:15,960
as well.

114
00:08:15,970 --> 00:08:23,350
So here I'm going to say data underscore city and similarly all these operations.

115
00:08:23,530 --> 00:08:28,510
Similarly on here, my data family name a city and a score to just execute it.

116
00:08:28,510 --> 00:08:33,490
And if I'm going to visualize my city and it's got a hotel data frame, you will see this is the data

117
00:08:33,490 --> 00:08:35,980
frame with respect to City Hotel.

118
00:08:36,770 --> 00:08:42,550
You will see in both the data frame, in both the data frame, you have a common column.

119
00:08:42,550 --> 00:08:47,020
It means you can merge this both digital frame for this.

120
00:08:47,030 --> 00:08:53,860
What I'm going to do, I'm going to use my Banda's much function, like I'm going to say the very first

121
00:08:53,860 --> 00:08:58,030
Netafim, which is exactly my resort hotel, not much.

122
00:08:58,030 --> 00:09:03,160
And what I have to model, I have to merge it to my city and a score hotel.

123
00:09:03,160 --> 00:09:08,560
And if you will shift plus tab, you will get all the documentation, all the custom parameter of this

124
00:09:08,560 --> 00:09:09,070
function.

125
00:09:09,070 --> 00:09:11,440
This is a very handy function opener's.

126
00:09:12,130 --> 00:09:15,790
And here you have something on parameter.

127
00:09:15,880 --> 00:09:17,050
On what parameter?

128
00:09:17,050 --> 00:09:18,280
You have to merge it.

129
00:09:18,430 --> 00:09:20,920
So I have to merge it basically on this parameter.

130
00:09:21,340 --> 00:09:23,890
So I have to just paste over there after it.

131
00:09:24,160 --> 00:09:27,100
What I have to do, say I'm just going to execute it.

132
00:09:27,100 --> 00:09:30,280
You will see this is exactly the data that you need.

133
00:09:30,520 --> 00:09:33,940
But still you have to do some modifications in this data.

134
00:09:34,720 --> 00:09:35,110
Let's say.

135
00:09:35,110 --> 00:09:38,610
I'm going to say I have to store it somewhere else, which is exactly final.

136
00:09:38,620 --> 00:09:47,680
And after it, I have to say I'm going to say final column in this column, the very first column name.

137
00:09:47,680 --> 00:09:53,350
I have to assign it exactly my month, the second column that I have to assign, which is exactly my

138
00:09:53,350 --> 00:09:56,110
price for resort.

139
00:09:56,380 --> 00:09:59,500
And after it, what I have to do, I have to say my.

140
00:09:59,700 --> 00:10:08,820
Price for City Hotel, which is exactly this one, just like you ordered, and if I again going to call

141
00:10:08,820 --> 00:10:14,160
my hat over there, you will see this is the data frame that you need.

142
00:10:14,310 --> 00:10:20,850
But but here, there, the hack or whatever, because you will see my this month go along isn't in a

143
00:10:20,850 --> 00:10:22,320
proper hierarchy.

144
00:10:22,320 --> 00:10:27,250
And if I am going to visualize this data, you will get some improper conclusion.

145
00:10:27,270 --> 00:10:31,750
It means you have to you have to make this data in a proper hierarchy.

146
00:10:31,860 --> 00:10:39,180
Either you can use your own logic or you can use some handy modules or python, but using your own logic

147
00:10:39,180 --> 00:10:41,310
will be definitely very complex.

148
00:10:41,970 --> 00:10:45,340
So let me use some some handy modules of Python.

149
00:10:45,840 --> 00:10:49,540
So if you haven't installed it, you guys can install using this paper.

150
00:10:49,570 --> 00:11:03,300
So I'm going to say pip install short data, frame it frame by month or week, month or week.

151
00:11:03,600 --> 00:11:07,740
And this this library has definitely some dependency.

152
00:11:08,070 --> 00:11:15,090
So it means if you haven't installed this dependency as well, you can install using PIP install shorted

153
00:11:15,630 --> 00:11:19,420
month, month, week days.

154
00:11:19,440 --> 00:11:25,390
So this is exactly the dependency that you have to install for I have already installed in my environment,

155
00:11:25,390 --> 00:11:26,730
so I'm not going to install it.

156
00:11:27,030 --> 00:11:34,380
So I'm just going to import this one, which is exactly this one to just copy, just paste to just do

157
00:11:34,380 --> 00:11:40,260
some modification as short a data frame on this score, this one month a week.

158
00:11:40,260 --> 00:11:46,470
And I had to create a Eliade, let's say Steet just executed and and after eight, what will you do

159
00:11:46,710 --> 00:11:54,000
from this S.T. Alyosha's I have to call a function which is exactly short and screw the frame by month

160
00:11:54,240 --> 00:11:59,700
and if you will, by Chispa step, it needs a data frame and month column name.

161
00:11:59,880 --> 00:12:09,510
So my data is a but my final and my my column name on which I have to make this data and a proper hierarchy

162
00:12:09,510 --> 00:12:11,460
is exactly my money.

163
00:12:11,520 --> 00:12:12,920
Just a sign over there.

164
00:12:12,930 --> 00:12:18,040
Let's say this is exactly my final to date frame that it will return me just executed.

165
00:12:18,060 --> 00:12:23,640
Now what I have to do, let's say I'm going to realize my data frame just executed this.

166
00:12:23,640 --> 00:12:28,490
Is that the frame that you have to consider for your visual exit?

167
00:12:28,530 --> 00:12:32,100
I'm going to use I would use my landlord over here.

168
00:12:32,110 --> 00:12:38,760
Yeah, I can definitely go ahead with my landlord because this data frame is in proper hierarchy, Gen

169
00:12:38,760 --> 00:12:43,350
Fab, March, April, May and all these types of different environment.

170
00:12:43,710 --> 00:12:47,880
So I can definitely go ahead with my life chart, if you will.

171
00:12:48,360 --> 00:12:53,820
Plus tab, you will get all the documentation, all the different different custom parameters for him.

172
00:12:54,300 --> 00:12:55,080
The very first.

173
00:12:55,090 --> 00:13:02,970
What is exactly your data frame, which is my final two then what I want on X-axis, which is exactly

174
00:13:02,970 --> 00:13:12,840
my month, then what exactly I want on Y-axis for the very first one is exactly my price for resort.

175
00:13:12,840 --> 00:13:15,570
Just to copy, just paste.

176
00:13:15,970 --> 00:13:20,960
Just, you can just copy from here and you have to just paste over here.

177
00:13:20,970 --> 00:13:24,570
That said, that's like a piece of cake after it.

178
00:13:24,600 --> 00:13:27,510
What you have to do, let's add it and subtitles as well.

179
00:13:27,520 --> 00:13:38,190
So here I am going to say my title is nothing but let's say room price for night over the over the year.

180
00:13:38,310 --> 00:13:39,090
It's all up to you.

181
00:13:39,090 --> 00:13:44,370
Whatever you want to assign now, what you have to do, you have to just execute the self.

182
00:13:44,970 --> 00:13:49,740
So this is exactly you're beautiful with the additions that you need.

183
00:13:49,740 --> 00:13:54,100
And if you are thinking, yeah, I can definitely go out with my previous claim.

184
00:13:54,130 --> 00:13:55,680
So let me show you what.

185
00:13:55,680 --> 00:14:01,980
If you have your previous data, let me show you you will see this is that in proper controls and this

186
00:14:01,980 --> 00:14:06,570
is as improper with that you have finally work here that we just remove it.

187
00:14:06,570 --> 00:14:08,370
And this is that exactly the frame?

188
00:14:08,370 --> 00:14:10,620
This is the exact visual that you need.

189
00:14:10,920 --> 00:14:17,220
Now, see, I have to conclude this this Veysel, you will see this clearly shows that this this resort

190
00:14:17,220 --> 00:14:24,720
hotel prices are much, much higher during the summer, whereas the prices of a city hotel doesn't get

191
00:14:24,860 --> 00:14:29,550
that much and it is most expensive during the summer.

192
00:14:29,760 --> 00:14:34,830
Or I can see during the summer, during spring and autumn, autumn season.

193
00:14:35,310 --> 00:14:37,230
So it's all about the session.

194
00:14:37,350 --> 00:14:40,440
It's all about our analysis for this particular session.

195
00:14:40,830 --> 00:14:41,880
You will love it very much.

196
00:14:42,030 --> 00:14:42,770
Thank you.

197
00:14:42,780 --> 00:14:43,670
Have a nice day.

198
00:14:43,800 --> 00:14:44,640
Keep learning.

199
00:14:44,760 --> 00:14:45,570
Keep going.

200
00:14:46,020 --> 00:14:46,830
Keep practicing.