Now that you know how to create and customize scatter plots using plt.scatter(), you’re ready to start practicing with your own datasets and examples. This versatile function gives you the ability to explore your data and present your findings in a clear way.

In this course, you’ve learned how to:

Create a scatter plot using plt.scatter()
Use the required and optional input parameters
Customize scatter plots for basic and more advanced plots
Represent more than two dimensions with plt.scatter()

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Sample Code (.zip)

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Course Slides (.pdf)

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Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.

00:00 Visualizing data in Python: summary.

00:05 Well done! You’ve made it to the end of the course. Now that you know how to create and customize scatter plots using plt.scatter(), you’re ready to start practicing with your own datasets and examples.

00:17 This versatile function gives you the ability to explore your data and present your findings in a clear way. In this course, you’ve learned how to create a scatter plot using plt.scatter(), use the required and optional input parameters, customize scatter plots for basic and more advanced plots, and represent more than two dimensions with plt.scatter().

00:42 You can get the most out of visualization using plt.scatter() by learning more about all the features in Matplotlib and dealing with data using NumPy.

00:51 We hope you found this course useful, and we’ll see you again soon at realpython.com.

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Using plt.scatter() to Visualize Data in Python (Summary)