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# STATISTICS AND PLOTS¶

### Data¶

Here we will work with public Data from airbnb, released under License CC0: Public Domain, which comes from Kaggle

### Context¶

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in NYC, NY for 2019.

### Content¶

This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

### Inspiration¶

• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

### Acknowledgements¶

Part of this notebook is based on the work of Dgomonov, published in Kaggle under the Apache 2.0 open source license, adapted by Juan Carlos Basto Pineda. I encourage you to check out the original notebook for further details.

• Check the content, the size, and some summary statistics of the dataset

### Let's check the values of some categorical variables¶

• How many different values there are in the column neighbourhood?

### Which are the top 10 hosts, with the largest number of entries?¶

• What are the following commands doing?