Matplotlib

Chapter 16 – Saving and Exporting Plots After creating a beautiful visualization, the next important step is to save or export it for use in reports, presentations, publications, or web.

Chapter 15 – Plotting with Pandas and NumPy In previous chapters, we learned how to create plots directly using Matplotlib’s pyplot and Object-Oriented interfaces. In this chapter, we’ll explore how.

Matplotlib isn’t limited to 2D charts — it also supports rich 3D visualizations using the mplot3d toolkit. In this chapter, you’ll learn how to create 3D line, surface, and scatter.

Chapter 13 – Styling and Themes in Matplotlib By default, Matplotlib plots look simple and functional. However, with a few styling adjustments, you can transform them into professional-quality visuals that.

Static plots are great for reports, but many data analysis scenarios demand interactivity and real-time updates. Whether you’re monitoring live data streams or exploring trends dynamically, Matplotlib has powerful tools.

Once you’ve created beautiful visualizations, the next step is to save and export them for use in reports, presentations, or websites. Matplotlib provides flexible tools for saving plots in multiple.

Chapter 10 – Adding Legends, Titles, and Labels in Matplotlib A data visualization is incomplete without proper context — and that comes from well-placed titles, labels, and legends. These elements.

📘 Matplotlib Tutorial – Chapter 9: 3D Plotting in Matplotlib Matplotlib is not limited to 2D visualizations — it can also create stunning 3D plots using the mpl_toolkits.mplot3d module. This.

📘 Matplotlib Tutorial – Chapter 8: Working with Images and Colors Matplotlib is not just about charts — it’s a versatile library that can handle image processing and color visualization.