?? Day 31 of 365: Introduction to Seaborn for Advanced Visualizations ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, visualizers!
Welcome to Day 31 of our #365DaysOfDataScience journey! ??
For Day 31 of our Data Science adventure, we’re moving into the world of Seaborn—a library that takes our visualizations to the next level. Seaborn makes it super easy to create stunning, informative visualizations with less effort. Today, we’ll dive into some advanced plotting techniques to better understand our data. Ready to get started together? Let’s go!
?? What We’ll Be Doing Today:
- What is Seaborn, and how does it differ from Matplotlib?
- Learn how to create statistical visualizations like histograms, box plots, and pair plots, all with a few lines of code.
?? Learning Resources:
- Read: The introductory section of the Seaborn documentation. It’s short and sweet!
??- [Seaborn Documentation: Getting Started](https://seaborn.pydata.org/)
- Watch: A Seaborn tutorial on YouTube to walk through some examples.
??- [YouTube: Seaborn for Data Visualization](https://www.youtube.com/results?search_query=seaborn+tutorial)
?? Today’s Task:
- Let’s create some beautiful visualizations using Seaborn:
??1. Histogram: Start by visualizing the distribution of a dataset.
??2. Box Plot: Get a sense of the spread and outliers in the data.
??3. Pair Plot: Create a pair plot to visualize relationships between multiple variables.
Collaborate & Share!
Once you’ve created your visualizations, share your plots with everyone! Let’s compare notes on how different datasets look with Seaborn, and share any tips we discover along the way. ??
You're doing amazing! Keep building those data viz skills.
Happy Learning and See You Soon!
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