Three Simple Ways To (Instantly) Make Your Scatter Plots Clutter Free
Towards better data visualisation.
Scatter plots are commonly used in data visualization tasks. But when you have many data points, they often get too dense to interpret.
Here are a few techniques (and alternatives) you can use to make your data more interpretable in such cases.
One of the simplest yet effective ways could be to reduce the marker size. This, at times, can instantly offer better clarity over the default plot.
Next, as an alternative to a scatter plot, you can use a density plot, which depicts the data distribution. This makes it easier to identify regions of high and low density, which may not be evident from a scatter plot.
Lastly, another better alternative can be a hexbin plot. It bins the chart into hexagonal regions and assigns a color intensity based on the number of points in that area.
What are some other techniques that you resort to in such cases? Let me know :)
Thanks for reading Daily Dose of Data Science! Subscribe for free to learn something new about Python and Data Science every day.
👉 Read what others are saying about this post on LinkedIn.
👉 Tell me you liked this post by leaving a heart react 🤍.
👉 If you love reading this newsletter, feel free to share it with friends!
Find the code for my tips here: GitHub.
I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.
Combining a scatter plot with KDE contour lines can be especially powerful. Just make sure to select a KDE bandwidth that doesn't underfit or overfit the data (this is more art than science).