Remembering what we saw in 1.4. Probability Scale, do the following exercises:

In [1]:

import pandas as pd
import numpy as np

This data set contains booking information for a city hotel and a resort hotel.

In [2]:

dataset = pd.read_csv('hotel_bookings.csv')
dataset.head()

Out[2]:

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	2015-07-01
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	2015-07-01
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	NaN	NaN	Transient	75.0	0	Check-Out	2015-07-02
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	NaN	Transient	75.0	0	Check-Out	2015-07-02
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	NaN	Transient	98.0	1	Check-Out	2015-07-03

5 rows × 32 columns

We only pay attention in the month in which the tourist arrival.

In [3]:

data = dataset.iloc[:,[0,4]]
data.head()

Out[3]:

	hotel	arrival_date_month
0	Resort Hotel	July
1	Resort Hotel	July
2	Resort Hotel	July
3	Resort Hotel	July
4	Resort Hotel	July

Is more likely that the people arrive in July or in March?

In [ ]:

In [4]:

data1 = data['arrival_date_month'].value_counts().to_frame()
July = data1.loc['July'][0]/data.shape[0]
March = data1.loc['March'][0]/data.shape[0]
July, March

Out[4]:

(0.10604740765558254, 0.08203367116173883)

Now try a slightly more difficult exercise:

Decide which is the season more likely in which the tourist arrival to a City Hotel.

In [ ]:

In [6]:

data2 = data[data['hotel']== "City Hotel"]
data3 = data2['arrival_date_month'].value_counts().to_frame()
summer = data3.loc['June'][0]/data2.shape[0]+data3.loc['July'][0]/data2.shape[0]+data3.loc['August'][0]/data2.shape[0]
winter = data3.loc['December'][0]/data2.shape[0]+data3.loc['January'][0]/data2.shape[0]+data3.loc['February'][0]/data2.shape[0]
spring = data3.loc['March'][0]/data2.shape[0]+data3.loc['April'][0]/data2.shape[0]+data3.loc['May'][0]/data2.shape[0]
autumn = data3.loc['September'][0]/data2.shape[0]+data3.loc['October'][0]/data2.shape[0]+data3.loc['November'][0]/data2.shape[0]
summer, winter, spring, autumn

Out[6]:

(0.314698096558679,
 0.16176730114710702,
 0.2794655237615026,
 0.24406907853271148)

In [ ]:

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Assignment 1.4.2: Given Two Experiments Decide Which One Is More Likely