Air BNB in New York City data analysis

For this project, a dataset downloaded as a .csv file from Kaggle, "New York City Open Data" will be analyzed.
The known information of about 50,000 or so BNB facilities in New York City, which bououghs they are located, whether they cater to single guests or entire families, how much space they offer, and how expensive they are.

Python open datasets library will be used to download the data from Kaggle, the Pandas library will be used to clean and query the data. Matplotlib will give us visualization of the data as its analyzed.

This project is a study of data analytics, as taught in this course:

Data Analysis with Python: Zero to Pandas, and what you've learned from it.

Air BNB information for touring NYC

(from a Google search): A bed and breakfast (typically shortened to B&B or BnB) is a small lodging establishment that offers overnight accommodation and breakfast. ... Bed and breakfast is also used to describe the level of catering included in a hotel's room prices, as opposed to room only, half-board or full-board.

New York City is a heavy user of the air-bnb facility with some 50,000 available locations.
This dataset, downloaded from Kaggle, explores the resources available for staying at a bed and breakfast facility while in New York City, where in the city they are located, and how the accondations provide for individuals or families. The next few steps download that data from Kaggle for single-variant analysis.

!pip install jovian opendatasets --upgrade --quiet

# get a dataset from Kaggle, once it is downloaded we'll have the
# data in a local file
dataset_url = 'https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data'

# On the first run of this notebook the dataset was downloaded and extracted
# To repeat that operation, in case you're running this notebook in your own environment,
# uncomment and execute the following two lines
#
# NOTE: you will need an account on Kaggle as you'll need to provide your username and key to download
# the dataset. You can leave these commands commented out and just use the file in this project's folder

#import opendatasets as od
#od.download(dataset_url)