Arik Chakma
274082fd77
* refactor: clean to roadmap content * refactor: move shared helpers into scripts/lib
10 lines
932 B
Markdown
10 lines
932 B
Markdown
# Customizing Plots
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Matplotlib allows extensive customization: titles with `set_title()`, axis labels with `set_xlabel()` / `set_ylabel()`, tick formatting, color palettes, line styles, font sizes, legends, and annotations. Customizing plots ensures they communicate clearly and meet the standards required for reports and presentations.
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Visit the following resources to learn more:
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- [@course@Advanced Matplotlib: Design & Customize Visualizations](https://www.coursera.org/learn/advanced-matplotlib-design-customize-visualizations)
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- [@article@Customizing Plots](https://apxml.com/courses/intermediate-python-programming-ml/chapter-4-data-visualization-matplotlib-seaborn/matplotlib-customization)
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- [@article@3 Tricks to Prettify Matplotlib Plots](https://towardsdatascience.com/3-tricks-to-prettify-matplotlib-plots-d0a73b861c09/)
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- [@video@Matplotlib customization is easy! 🎨](https://www.youtube.com/watch?v=hunq_UOdmoo) |