Seaborn Python Tutorial - Part 1

Seaborn Python Tutorial 2020. Learn how to use Seaborn to visualize and analyse your data in Python. Learn when to use which plot and why; depending what you are trying to achieve. Additionally, learn how to interpret the results you see in your plot.

Support the channel on Patreon:

Data Analytics Course Link:

In the first video of the series, I cover extensively LinePlots, BarPlots and Histograms.

Part 2 Link: TBC
Part 3 Link: TBC

Tutorial Overview:
• What is Seaborn and how/why it's used
• Trend Plots:
o Line Plots
• Summary Plots:
o Bar Plots
• Distribution of Data:
o Histogram
o Box Plots
• Relationship Plots:
o Scatter Plots
o lmplot (combo of regplot() and FacetGrid)
• Holistic views / Combo:
o Sub Plots
o Pair Plots
o Join Plots
• Correlation / Relationships:
o Heat Map

How to download and install Python through Anaconda:

Download the raw data & the Python Notebook:

Yiannis Pitsillides on Social Media:

[msctube]

You deserve a lot of credit for this video, one of the best Python tutorials I have seen in the internet. Lots of clarity and objectivity delivered with a practical and easy to follow didactic method. Thank you!

[elbezz]

Thank you for your tutorial, great the barplot: if we want to display the sum(), instead of mean(), we just add estimator=sum, , , , as follow
ax =sns.barplot(x = 'Month_ID', y ='Revenue', data=raw_data, estimator=sum)

[OmarJIBAR]

wow, your tutorial is so clear that i just want to cry. THANKS A LOT FOR THE TUTORIAL

[dhruvrathore]

This is amazing man, everything is explained crystal clear. I wonder my college lecturers are even doing, damn fools. You explained much much better. Thanks a lot !

[murilokrebskyhobus]

I usually don't comment that much on YouTube videos but this here deserves all the credits !
Awesome video, thank you so much !

Yes, I had fun watching your presentation. Thanks.

[nawilliam]

Most beautiful and clear explanation. You deserve more views.

[omkars]

Amazing tutorial, straight to the point. Thank you for the notebook

[codecreator]

this is pretty nice. thank you so much

[julianriise]

So basically I’m going to learn how to create them, different options and examples, got it! Great tutorial😁

[rishabhsharma]

Copy the code from here and paste it into algorithms.py file in your local computer.


P.S. Tutorial was brilliantly done

[Am-hsb]

Thanks a lot mate! Really like your way of teaching attributes step by step. Keep going.

[harikrishna-harrypth]

Thanks for your resourceful videos! Much appreciated! Please continue making these amazing videos!!

[ilianleandrolopezsepulveda]

thanks for the tutorial, is very usefull to start
it would be great if zoom in the jupiter to fit the screen

[miguelangeldiazoviedo]

Very good. Just what I was looking for

[attilajuhasz]

thank you for this very thorough video, clarified a lot of things for me!!!

[salbayrak]

Hey man, aside your awesome work I really loved your southern european accent which reminds me my best memories with the lads. Stay safe, cheers!

[kendallgibson]

Helpful tutorial, thank you!
tip for viewers: if you increase the quality of the video, you may be able to read the screen better.

[Praj]

Thanks a lot for this video. It was excellent and I enjoyed while doing on my system. Thanks again!

[jamalanifowoshe]

Amazing series, so many good examples!