Exploratory Data Analysis: Playing Around With Seaborn

Hello everyone!

One of the things that I enjoy the most about Python is using the power of Seaborn.

In case you are not familiar with what Seaborn is, it’s a powerful data visualization library to make your data look amazing; plus, it gives you capability of customizing your graphs as you see fit.

Going back to the NYC AirBnB exploratory data analysis, I want to show you something cool that you can do with Seaborn.

If we take a look at the DataFrame, we’ll see that it includes the coordinates for the AirBnB listings in NYC:

I’m going to use Seaborn’s Scatterplot to map out the AirBnB listings by borough:

I’ll start by sizing it appropriately:
plt.figure(figsize=(20,20))

Now, I’ll go ahead and set everything up:
sns.scatterplot(data=nyc, x='longitude', y='latitude', hue='neighbourhood_group')
sns.set_style('whitegrid')

Here’s what it looks like:

If I were to do it by listing type, I simply change the hue to hue='room_type' and this is what New York City looks like:

Now, let’s take a look at the boroughs with the most AirBnB listings:

Manhattan by listing type:

And Brooklyn by listing type:

That’s nice, right?! There’s so many other things that you can do with Seaborn!