Integrating Matplotlib with Pandas for Data Visualization

Integrating Matplotlib with Pandas for Data Visualization

Matplotlib offers extensive customization options for enhancing plots. Key features include adding titles and labels, modifying line styles and colors, and customizing legends. Create subplots for complex visualizations, improve readability with ticks and grid lines, and save plots using the savefig() method. Refine visualizations to meet specific needs.
Creating Stacked Bar Charts with matplotlib.pyplot.bar

Creating Stacked Bar Charts with matplotlib.pyplot.bar

Customization in Matplotlib charts enhances clarity and accessibility. Assign specific colors for differentiation, utilize colorblind-safe palettes, and add data labels for better readability. Adjust legend placement to avoid clutter and rotate x-axis labels for improved legibility. Consider interactive libraries like Plotly for larger datasets.