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Python visualization in 2024

By Megan Lieu

Updated on December 15, 2023

Creating compelling visualizations in Python has become an essential skill for data scientists and analysts in 2024. This comprehensive guide will walk you through the process of creating Python visualizations, utilizing popular tools and libraries.

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Introduction to Data Visualization with Python

Data visualization in Python has evolved, offering a wide range of tools and libraries. From the basics of data viz Python to more advanced techniques, this guide covers essential concepts and practices.

Leveraging Jupyter notebook for graph visualizations

Jupyter notebook remains a popular choice for graph visualization in Python. Many Python packages allow you to create data visualizations with just a few lines of code, including the popular Matplotlib, Plotly and Seaborn libraries.

But new Python visualization tools have shaped the landscape in 2024. These tools include cloud-based notebooks that allow you to create interactive plots and visualize your data without the need for Python or any coding at all. These chart blocks are perfect for quick exploratory analysis, especially those who are unfamiliar with using data visualization libraries in Python.

You’re likely to find these no-code chart blocks in cloud-based notebooks like Deepnote, which also allows you to not just build charts manually from scratch, but also select from chart libraries based on your data, slice, dice and filter existing charts, and customize them with just a few clicks of a button.

Comparative analysis: Plotly vs Matplotlib vs Seaborn

Plotly, Matplotlib, and Seaborn are some of the most popular Python visualization libraries, each with its strengths and weaknesses.


Known for its interactive capabilities, Plotly allows users to create highly interactive and aesthetically pleasing charts. Its main advantage lies in its interactivity and web integration, but it can be less intuitive for complex customizations.

  • Pros: highly interactive, excellent for web integration, and user-friendly for creating dynamic plots. Ideal for complex, interactive visualizations and dashboards.
  • Cons: Can be less intuitive for customizations, and heavier for simple tasks.
  • Use when: You need interactive, web-based visualizations or sophisticated dashboards.


A staple in Python visualizations, Matplotlib is highly customizable and widely used for creating static, animated, and interactive visualizations. However, its syntax can be verbose, and creating interactive plots requires more effort compared to Plotly.

  • Pros: Highly customizable, great for detailed and complex plots. It's the foundation for many other libraries.
  • Cons: Can have a steep learning curve for beginners, and creating interactivity is more complex.
  • Use when: You require detailed, publication-quality static plots, or need extensive customization.


Built on top of Matplotlib, Seaborn simplifies the process of creating visually appealing and informative statistical graphics. While it's great for statistical data visualization and works well with Pandas DataFrames, its customization and interactivity capabilities are not as extensive as Plotly's.

  • Pros: Built on Matplotlib, it simplifies creating attractive statistical graphics. Great for exploratory data analysis.
  • Cons: Limited customization compared to Matplotlib and less interactive than Plotly.
  • Use when: You need to quickly create informative and attractive statistical plots, or for exploratory data analysis.

The Art of Visualizing Data in Python

Visualizing data in Python isn't just about plotting graphs; it's about storytelling with the user in mind. Below are tips and best practices for turning data into insightful visual narratives in 2024.

  1. Choose the Right Tool: Depending on your use case, select from libraries like Plotly for interactivity, Matplotlib for detailed static visuals, or Seaborn for statistical graphics.
  2. Understand Your Data: Know your dataset's characteristics to determine the most effective way to represent it visually.
  3. Understand your audience: Being mindful of your audience and their specific objectives will guide you toward creating visual narratives that actually answer your stakeholders’ questions.
  4. Simplify and Focus: Avoid clutter. Highlight key data points to make your visualizations more readable and impactful.
  5. Use Interactive Elements Wisely: In interactive plots, use elements like hover, zoom, and slider controls to enhance user engagement without overwhelming them.
  6. Aesthetics Matter: Pay attention to colors, fonts, and layout. A visually appealing chart is more likely to draw and keep attention.
  7. Make It Accessible: Ensure your visualizations are accessible to all users, including those with color vision deficiencies.
  8. Tell a Story: Your visualization should narrate the data story clearly and effectively.
  9. Iterate and Get Feedback: Continuously improve your visualization based on user feedback and testing.

Whether you're a beginner or an experienced data scientist, we hope these insights will enhance your visualization skills in the dynamic world of Python programming to be able to tell compelling stories about your data in 2024.

If you haven't tried Deepnote to visualize your data, whether that's using Python or no-code capabilities, the new year is the best time to try it for all your visual data science and data analytics needs. Get started for free today.

Megan Lieu

Data Advocate

Follow Megan on LinkedIn


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