Man must explore, and exploring airbnb data might just save you cash during your city break

Accomomdation might just come up cheap thanks to Python

Posted by baroude ntsiba on November 01, 2017

Few days before my weekend escape to Paris, I find myself looking for the best possible accommodation in the city of light. I recall reading Twitter feeds about possible deals being offered through the Airbnb platform. So, I decided to have a look at airbnb.fr in the hope to find a good deal in one of most trendy area of Paris.

One way of finding out the best location at the descent price was to compare the price online. However, not such service is offered on the airbnb website. Since, Airbnb doesn’t really offer such services, I decided to come up with the solution for myself and maybe for you too since you are reading about this.

To do so I am going to use data made available by inside Airbnb site. The data provided here is sourced from publicly available information from the Airbnb site. The two datasets to consider for this data analysis are the listings and calendar

Let's start by importing the various modules that will help our basic analysis. Data science is open to exploration. These modules are free to download from the internet. Thank to the magic of the open source community

Let’s have a look at the dataset and get the statistics to draw some basic understanding (We are only going to look at four variables to start with)

Looking at the price variable, we can learn that the average price in euros is 96. The maximum price for a stay is 7790 euros. Paying 8000 euros for a night is quiet much for a night I don’t know about you but I won’t be considering this accommodation.

On average people rent their accommodation 146 days per year which is approximately 5 months.

Let's verify these findings using pandas

On average people stay for 4 days in a typical Airbnb accommodation in the Paris area. This is indeed two days more than I am actually planning to stay

Let's visualise our findings

Now let’s look at the type of accommodation on offer and their associated price.

Finding a decent and affordable accommodation is essential to remembering our trip forever

The best way to enjoy a city isn't by visiting its most exclusive area? well let's find out in which exclusive area of Paris we will be able to stay for a fair price.

Sometimes we must enjoy life without ruining ourselves. The Opera area is full of life and certainly a very nice area when it comes to shopping. Thanks to data science I will be heading there instead of the Elysee "quartier"

Conclusion

Python is a great tool for analysing data. Applying data science to solve real life problems from our daily life isn't only exciting but, can also help us save money.

I'll be posting the notebook on my github repository

Keep posted if you want to find out more on how you can solve business or personal problems. Plesase contact me here.


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