Python Data Visualization
Python libraries like Matplotlib, Seaborn, and Plotly help you create compelling visualizations that communicate insights from your data. Build charts, graphs, and interactive dashboards that tell stories and reveal patterns. Learn which visualization types work best for different data relationships and audiences.
Join Now: Click here to join the Real Python Newsletter and you’ll never miss another Python tutorial, course, or news update.
Specialized tools like Bokeh for interactive web plots, Altair for declarative visualizations, and Plotly Dash for full dashboard applications expand your visualization toolkit. Master customization techniques, color schemes, and design principles that make your visualizations clear and professional. Apply these skills to data science projects, reports, and presentations that drive decisions.
Matplotlib is the foundational library and most flexible. Seaborn builds on Matplotlib with beautiful statistical plots. Plotly creates interactive charts for web dashboards. Choose based on your needs: Matplotlib for control, Seaborn for statistics, Plotly for interactivity.
Use Plotly for interactive charts with hover tooltips and zoom. Build dashboards with Plotly Dash or Streamlit. Bokeh offers interactive plots for web browsers. All support Python and generate JavaScript-based visualizations you can embed in web pages.
Use bar charts for comparisons, line charts for trends over time, and scatter plots for relationships between variables. Histograms show distributions, heatmaps reveal correlations, and box plots display statistical summaries. Choose charts that make patterns obvious to your audience.
Set figure size with plt.figure(figsize=(width, height)), customize colors with the color parameter, and add labels with xlabel(), ylabel(), and title(). Use plt.style.use() for themes, adjust legends, and control line styles, markers, and fonts.
Matplotlib provides low-level control over every plot element. Seaborn is built on Matplotlib and offers high-level functions for statistical plots with attractive default styles. Use Seaborn for quick exploratory plots and Matplotlib when you need precise customization.
