oldoc63
commented
1 year ago The Python visualization landscape is complex and we only touched the surface. Pyviz.org lists all Python viz libraries. In this tutorial, we only focused on Matplotlib and two Matplotlib-based libraries. There are also javascript-based libraries with extensive interactivity features, and other libraries with their own advantages.
Additional Resources:
- Read [hvPlot](https://hvplot.holoviz.org/) documentation (interactive plots using the same .plot() API you learned here!)
- Try the [HoloViz](https://holoviz.org/) tutorial (builds on the material covered here for more advanced plotting)
- Read [The Easiest Way to Create an Interactive Dashboard blog post](https://anaconda.cloud/easiest-interactive-dashboard) on Nucleus
- Read [Python Dashboarding Landscape blog post](https://anaconda.cloud/dashboardlandscape) on Nucleus
- Visit [PyViz.org](https://pyviz.org/) for links and overviews to other Python viz librariesCongratulations! You've completed the "Introduction to Data Visualization with Python" course. Please click "Next" to share your feedback in the course survey and receive your completion badge.
Let's build an end-to-end data visualization project, where you'll be asked to locate a dataset, develop questions about the data, clean the data, explore the data to gain insights, and visualize the data using the skills and packages you've learned in this course. You'll complete this final project in a Jupyter Notebook using the UFO sightings dataset on Kaggle.
To begin, open up a new Jupyter Notebook to practice in a new browser window, or open the Jupyter Notebook provided below and follow along.