https://github.com/mpolinowski/hotel-booking-dataset
Python Pandas Dataset Exploration with Hotel Demand Data.
https://github.com/mpolinowski/hotel-booking-dataset
data-exploration hotel-booking pandas python
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Python Pandas Dataset Exploration with Hotel Demand Data.
- Host: GitHub
- URL: https://github.com/mpolinowski/hotel-booking-dataset
- Owner: mpolinowski
- Created: 2023-04-30T13:55:35.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-04-30T13:58:39.000Z (over 2 years ago)
- Last Synced: 2025-01-28T19:17:57.296Z (9 months ago)
- Topics: data-exploration, hotel-booking, pandas, python
- Language: Jupyter Notebook
- Homepage: https://www.sciencedirect.com/science/article/pii/S2352340918315191
- Size: 1.42 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hotel Booking Demand Dataset
- [Hotel Booking Demand Dataset](#hotel-booking-demand-dataset)
- [Dataset](#dataset)
- [Size](#size)
- [Missing Data](#missing-data)
- [Exploration](#exploration)
- [Data Columns](#data-columns)
- [Top Countries](#top-countries)
- [Average Daily Rates](#average-daily-rates)
- [Average Stays](#average-stays)
- [Average Cost per Stay](#average-cost-per-stay)
- [Percentage of Returning Guest](#percentage-of-returning-guest)
- [Correlate Bookings to Day of the Week](#correlate-bookings-to-day-of-the-week)
- [Bookings within a Date Range](#bookings-within-a-date-range)
> see [Hotel booking demand datasets](https://www.sciencedirect.com/science/article/pii/S2352340918315191), [Kaggle](https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand)
```python
import numpy as np
import pandas as pd
import datetime
```
## Dataset
```python
hotel_bookings = pd.read_csv('datasets/hotel_bookings.csv')
hotel_bookings.head(5)
```
### Size
```python
# complete number of rows
print(hotel_bookings.index)
# RangeIndex(start=0, stop=119390, step=1)
# complete number of columns
print(len(hotel_bookings.columns))
# 32
```
### Missing Data
```python
# only show rows that have missing values
hotel_bookings_nan = hotel_bookings[hotel_bookings.isna().any(axis=1)]
hotel_bookings_nan
# 119173 rows × 32 columns
# only 119390 - 119173 = 217 rows don't have missing entries
```
| | hotel | is_canceled | lead_time | arrival_date_year | arrival_date_month | arrival_date_week_number | arrival_date_day_of_month | stays_in_weekend_nights | stays_in_week_nights | adults | ... | deposit_type | agent | company | days_in_waiting_list | customer_type | adr | required_car_parking_spaces | total_of_special_requests | reservation_status | reservation_status_date |
| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
| 0 | Resort Hotel | 0 | 342 | 2015 | July | 27 | 1 | 0 | 0 | 2 | ... | No Deposit | NaN | NaN | 0 | Transient | 0.00 | 0 | 0 | Check-Out | 01-07-15 |
| 1 | Resort Hotel | 0 | 737 | 2015 | July | 27 | 1 | 0 | 0 | 2 | ... | No Deposit | NaN | NaN | 0 | Transient | 0.00 | 0 | 0 | Check-Out | 01-07-15 |
| 2 | Resort Hotel | 0 | 7 | 2015 | July | 27 | 1 | 0 | 1 | 1 | ... | No Deposit | NaN | NaN | 0 | Transient | 75.00 | 0 | 0 | Check-Out | 02-07-15 |
| 3 | Resort Hotel | 0 | 13 | 2015 | July | 27 | 1 | 0 | 1 | 1 | ... | No Deposit | 304.0 | NaN | 0 | Transient | 75.00 | 0 | 0 | Check-Out | 02-07-15 |
| 4 | Resort Hotel | 0 | 14 | 2015 | July | 27 | 1 | 0 | 2 | 2 | ... | No Deposit | 240.0 | NaN | 0 | Transient | 98.00 | 0 | 1 | Check-Out | 03-07-15 |
| ... |
| 119385 | City Hotel | 0 | 23 | 2017 | August | 35 | 30 | 2 | 5 | 2 | ... | No Deposit | 394.0 | NaN | 0 | Transient | 96.14 | 0 | 0 | Check-Out | 06-09-17 |
| 119386 | City Hotel | 0 | 102 | 2017 | August | 35 | 31 | 2 | 5 | 3 | ... | No Deposit | 9.0 | NaN | 0 | Transient | 225.43 | 0 | 2 | Check-Out | 07-09-17 |
| 119387 | City Hotel | 0 | 34 | 2017 | August | 35 | 31 | 2 | 5 | 2 | ... | No Deposit | 9.0 | NaN | 0 | Transient | 157.71 | 0 | 4 | Check-Out | 07-09-17 |
| 119388 | City Hotel | 0 | 109 | 2017 | August | 35 | 31 | 2 | 5 | 2 | ... | No Deposit | 89.0 | NaN | 0 | Transient | 104.40 | 0 | 0 | Check-Out | 07-09-17 |
| 119389 | City Hotel | 0 | 205 | 2017 | August | 35 | 29 | 2 | 7 | 2 | ... | No Deposit | 9.0 | NaN | 0 | Transient | 151.20 | 0 | 2 | Check-Out | 07-09-17 |
```python
# which columns have the most missing entries
hotel_bookings.isna().sum()
# the columns company, agent and country have the most missing data:
```
| | |
| -- | -- |
| hotel | 0 |
| is_canceled | 0 |
| lead_time | 0 |
| arrival_date_year | 0 |
| arrival_date_month | 0 |
| arrival_date_week_number | 0 |
| arrival_date_day_of_month | 0 |
| stays_in_weekend_nights | 0 |
| stays_in_week_nights | 0 |
| adults | 0 |
| children | 4 |
| babies | 0 |
| meal | 0 |
| country | 488 |
| market_segment | 0 |
| distribution_channel | 0 |
| is_repeated_guest | 0 |
| previous_cancellations | 0 |
| previous_bookings_not_canceled | 0 |
| reserved_room_type | 0 |
| assigned_room_type | 0 |
| booking_changes | 0 |
| deposit_type | 0 |
| agent | 16340 |
| company | 112593 |
| days_in_waiting_list | 0 |
| customer_type | 0 |
| adr | 0 |
| required_car_parking_spaces | 0 |
| total_of_special_requests | 0 |
| reservation_status | 0 |
| reservation_status_date | 0 |
_dtype: int64_
```python
# drop columns with missing data
hotel_bookings_dropped_nan = hotel_bookings.drop(['company', 'agent'], axis=1)
hotel_bookings_dropped_nan.head(2)
```
```python
hotel_bookings_dropped_nan[hotel_bookings_dropped_nan.isna().any(axis=1)]
# 4 rows × 29 columns
# only the 4 rows with missing data in the children column and 488 country column remain
```
### Exploration
#### Data Columns
```python
# what columns do we have
pd.Series(hotel_bookings.columns)
```
| | |
| -- | -- |
| 0 | hotel |
| 1 | is_canceled |
| 2 | lead_time |
| 3 | arrival_date_year |
| 4 | arrival_date_month |
| 5 | arrival_date_week_number |
| 6 | arrival_date_day_of_month |
| 7 | stays_in_weekend_nights |
| 8 | stays_in_week_nights |
| 9 | adults |
| 10 | children |
| 11 | babies |
| 12 | meal |
| 13 | country |
| 14 | market_segment |
| 15 | distribution_channel |
| 16 | is_repeated_guest |
| 17 | previous_cancellations |
| 18 | previous_bookings_not_canceled |
| 19 | reserved_room_type |
| 20 | assigned_room_type |
| 21 | booking_changes |
| 22 | deposit_type |
| 23 | agent |
| 24 | company |
| 25 | days_in_waiting_list |
| 26 | customer_type |
| 27 | adr |
| 28 | required_car_parking_spaces |
| 29 | total_of_special_requests |
| 30 | reservation_status |
| 31 | reservation_status_date |
_dtype: object_
#### Top Countries
```python
# top5 country codes
hotel_bookings_dropped_nan['country'].value_counts().head(5)
```
| | |
| -- | -- |
| PRT | 48590 |
| GBR | 12129 |
| FRA | 10415 |
| ESP | 8568 |
| DEU | 7287 |
_Name: country, dtype: int64_
```python
hotel_bookings_dropped_nan['country'].value_counts().head(15).plot.bar(figsize=(12,4),rot=65)
```
#### Average Daily Rates
```python
plot = hotel_bookings_dropped_nan.plot.scatter(
figsize=(12,8),
x='adr',
y='hotel')
# there are only 2 hotels and all adr's are within 0-500$ with one outlier above 5000$
```
```python
# find outlier
hotel_bookings_dropped_nan.sort_values('adr', ascending=False).iloc[0]
```
| | |
| -- | -- |
| hotel | City Hotel |
| is_canceled | 1 |
| lead_time | 35 |
| arrival_date_year | 2016 |
| arrival_date_month | March |
| arrival_date_week_number | 13 |
| arrival_date_day_of_month | 25 |
| stays_in_weekend_nights | 0 |
| stays_in_week_nights | 1 |
| adults | 2 |
| children | 0.0 |
| babies | 0 |
| meal | BB |
| country | PRT |
| market_segment | Offline TA/TO |
| distribution_channel | TA/TO |
| is_repeated_guest | 0 |
| previous_cancellations | 0 |
| previous_bookings_not_canceled | 0 |
| reserved_room_type | A |
| assigned_room_type | A |
| booking_changes | 1 |
| deposit_type | Non Refund |
| days_in_waiting_list | 0 |
| customer_type | Transient |
| adr | 5400.0 |
| required_car_parking_spaces | 0 |
| total_of_special_requests | 0 |
| reservation_status | Canceled |
| reservation_status_date | 19-02-16 |
_Name: 48515, dtype: object_
```python
plot = hotel_bookings_dropped_nan.plot.hist(
column=["adr"],
by="hotel",
bins=100,
figsize=(10, 8)
)
# the outlier squeezes the first histogram and makes it hard to compare them
```
```python
# let's find the outlier iloc and drop the row
hotel_bookings_dropped_nan['adr'].idxmax()
# 48515
```
```python
hotel_bookings_dropped_outlier = hotel_bookings_dropped_nan.drop(48515, axis=0)
plot = hotel_bookings_dropped_outlier.plot.hist(
column=["adr"],
by="hotel",
bins=100,
figsize=(10, 8)
)
# nice :)
```
```python
# calculate the average daily rate `adr` for a guest staying at each hotel
adr_by_hotel = hotel_bookings_dropped_nan.groupby('hotel').mean(numeric_only=True)['adr']
adr_by_hotel
```
__Average Daily Rate__
| | |
| -- | -- |
| hotel | |
| City Hotel | 105.304465 |
| Resort Hotel | 94.952930 |
_Name: adr, dtype: float64_
#### Average Stays
```python
# how long do guest stay on average
hotel_bookings_dropped_nan['total_days'] = hotel_bookings_dropped_nan['stays_in_weekend_nights'] + hotel_bookings_dropped_nan['stays_in_week_nights']
hotel_bookings_dropped_nan['total_days'].head(5)
```
| | |
| -- | -- |
| 0 | 0 |
| 1 | 0 |
| 2 | 1 |
| 3 | 1 |
| 4 | 2 |
_Name: total\_days, dtype: int64_
```python
average_stays = hotel_bookings_dropped_nan.groupby('hotel').mean(numeric_only=True).round(1)['total_days']
average_stays
# the average staying time is 3 and 4.3 days, respectively
```
| hotel | |
| -- | -- |
| City Hotel | 3.0 |
| Resort Hotel | 4.3 |
_Name: total|_days, dtype: float64_
#### Average Cost per Stay
```python
# given the # of days and average daily adr we can calculate the average total cost per stay
hotel_bookings_dropped_nan['total_cost'] = hotel_bookings_dropped_nan['total_days'] * hotel_bookings_dropped_nan['adr']
hotel_bookings_dropped_nan['total_cost'].head(5)
```
| | |
| -- | -- |
| 0 | 0.0 |
| 1 | 0.0 |
| 2 | 75.0 |
| 3 | 75.0 |
| 4 | 196.0 |
_Name: total\_days, dtype: float64_
```python
average_total_cost = hotel_bookings_dropped_nan.groupby('hotel').mean(numeric_only=True).round(2)['total_cost']
average_total_cost
```
| hotel | |
| -- | -- |
| City Hotel | 318.66 |
| Resort Hotel | 435.45 |
_Name: total_days, dtype: float64_
#### Percentage of Returning Guest
```python
# total number of bookings per hotel
hotel_bookings_dropped_nan.value_counts('hotel')
```
| hotel | |
| -- | -- |
| City Hotel | 79330 |
| Resort Hotel | 40060 |
_dtype: int64_
```python
# select only city hotel
city_hotel_bookings = hotel_bookings_dropped_nan[hotel_bookings_dropped_nan['hotel'] == 'City Hotel']
city_hotel_bookings['hotel'].head(5)
```
| | |
| -- | -- |
| 40060 | City Hotel |
| 40061 | City Hotel |
| 40062 | City Hotel |
| 40063 | City Hotel |
| 40064 | City Hotel |
_Name: hotel, dtype: object_
```python
# select only resort hotel
resort_hotel_bookings = hotel_bookings_dropped_nan[hotel_bookings_dropped_nan['hotel'] == 'Resort Hotel']
resort_hotel_bookings['hotel'].head(5)
```
| | |
| -- | -- |
| 0 | Resort Hotel |
| 1 | Resort Hotel |
| 2 | Resort Hotel |
| 3 | Resort Hotel |
| 4 | Resort Hotel |
_Name: hotel, dtype: object_
```python
returning_customer_city_hotel = sum(city_hotel_bookings['is_repeated_guest'] == 1)
returning_customer_city_hotel
# 2032
```
```python
total_customer_city_hotel = hotel_bookings_dropped_nan.value_counts('hotel')['City Hotel']
total_customer_city_hotel
# 79330
```
```python
percentage_returning_customer_city_hotel = (
returning_customer_city_hotel * 100 / total_customer_city_hotel
)
percentage_returning_customer_city_hotel.round(2)
# 2.56%
```
```python
returning_customer_resort_hotel = sum(resort_hotel_bookings['is_repeated_guest'] == 1)
total_customer_resort_hotel = hotel_bookings_dropped_nan.value_counts('hotel')['Resort Hotel']
percentage_returning_customer_resort_hotel = (
returning_customer_resort_hotel * 100 / total_customer_resort_hotel
)
percentage_returning_customer_resort_hotel.round(2)
# 4.44%
```
```python
# visualize
hotel_index = ['City Hotel', 'Resort Hotel']
booking_columns = ['Total Bookings', 'Returning Customer', 'Percentage']
data_array = [
(
total_customer_city_hotel,
returning_customer_city_hotel,
percentage_returning_customer_city_hotel.round(2)
),
(
total_customer_resort_hotel,
returning_customer_resort_hotel,
percentage_returning_customer_resort_hotel.round(2)
)
]
return_customer_df = pd.DataFrame(data_array, hotel_index, booking_columns)
return_customer_df
```
| Hotel | Total Bookings | Returning Customer | Percentage|
| -- | -- | -- | -- |
| City Hotel | 79330 | 2032 | 2.56 |
| Resort Hotel | 40060 | 1778 | 5.07 |
```python
plot = return_customer_df[
['Total Bookings', 'Returning Customer']
].plot.bar(figsize=(12,8), rot=0)
```
```python
arrival_date_year 2016
arrival_date_month March
arrival_date_day_of_month 25
Aug 27, 1989
pd.to_datetime(date_series)
```
```python
city_hotel_bookings[[
'arrival_date_year',
'arrival_date_month',
'arrival_date_day_of_month'
]].head(5)
```
| | arrival_date_year | arrival_date_month | arrival_date_day_of_month |
| -- | -- | -- | -- |
| 40060 | 2015 | July | 1 |
| 40061 | 2015 | July | 1 |
| 40062 | 2015 | July | 1 |
| 40063 | 2015 | July | 1 |
| 40064 | 2015 | July | 2 |
#### Correlate Bookings to Day of the Week
```python
city_hotel_bookings['datetime'] = (
pd.to_datetime(
city_hotel_bookings['arrival_date_month'] + ' ' + city_hotel_bookings['arrival_date_day_of_month'].astype(str) + ' , ' + city_hotel_bookings['arrival_date_year'].astype(str)
)
)
city_hotel_bookings['datetime']
```
| | |
| -- | -- |
| 40060 | 2015-07-01 |
| 40061 | 2015-07-01 |
| 40062 | 2015-07-01 |
| 40063 | 2015-07-01 |
| 40064 | 2015-07-02 |
| ... |
| 119385 | 2017-08-30 |
| 119386 | 2017-08-31 |
| 119387 | 2017-08-31 |
| 119388 | 2017-08-31 |
| 119389 | 2017-08-29 |
_Name: datetime, Length: 79330, dtype: datetime64[ns]_
```python
# get weekday out of datetime object
city_hotel_bookings['datetime'].loc[40064].weekday()
# 3 == Thursday
```
```python
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
def weekday(day_of_the_week):
return days[day_of_the_week]
```
```python
city_hotel_bookings['day_of_the_week'] = np.vectorize(weekday)(
city_hotel_bookings['datetime'].dt.weekday
)
city_hotel_bookings['day_of_the_week'].tail(5)
```
| | |
| -- | -- |
| 119385 | Wednesday |
| 119386 | Thursday |
| 119387 | Thursday |
| 119388 | Thursday |
| 119389 | Tuesday |
_Name: day\_of\_the\_week, dtype: object_
```python
bookings_by_weekday_city_hotel = city_hotel_bookings.value_counts('day_of_the_week')
bookings_by_weekday_city_hotel
```
| day_of_the_week | |
| -- | -- |
| Friday | 13955 |
| Thursday | 13009 |
| Monday | 11823 |
| Wednesday | 11229 |
| Saturday | 10993 |
| Sunday | 9194 |
| Tuesday | 9127 |
_dtype: int64_
```python
bookings_by_weekday_city_hotel.plot.bar(figsize=(12,8), rot=0)
```
```python
resort_hotel_bookings['datetime'] = (
pd.to_datetime(
resort_hotel_bookings['arrival_date_month'] + ' ' + resort_hotel_bookings['arrival_date_day_of_month'].astype(str) + ' , ' + resort_hotel_bookings['arrival_date_year'].astype(str)
)
)
resort_hotel_bookings['day_of_the_week'] = np.vectorize(weekday)(
resort_hotel_bookings['datetime'].dt.weekday
)
bookings_by_weekday_resort_hotel = resort_hotel_bookings.value_counts('day_of_the_week')
bookings_by_weekday_resort_hotel
```
| day_of_the_week | |
| -- | -- |
| Saturday | 7062 |
| Monday | 6348 |
| Thursday | 6245 |
| Friday | 5676 |
| Sunday | 4947 |
| Wednesday | 4910 |
| Tuesday | 4872 |
_dtype: int64_
```python
bookings_by_weekday_resort_hotel.plot.bar(figsize=(12,8), rot=0)
```
#### Bookings within a Date Range
```python
first_15 = hotel_bookings_dropped_nan['arrival_date_day_of_month'].apply(lambda day: day in range(1,16)).sum()
first_15
# 58152
```
```python
last_15 = hotel_bookings_dropped_nan['arrival_date_day_of_month'].apply(lambda day: day in range(15,32)).sum()
last_15
# 65434
```