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8 Best visualization libraries for Jupyter notebooks

By Nick Barth

Updated on March 6, 2024

Data visualization is a pivotal aspect of data analysis and interpretation. Jupyter Notebooks, a widely-used tool among data scientists, offers support for numerous visualization libraries to enhance the presentation of data insights. Here are eight of the best visualization libraries that can integrate seamlessly with Jupyter Notebooks, including Deepnote, an emerging favorite.

  1. Matplotlib: A veteran in the field of data visualization, Matplotlib is a flexible library that enables the creation of static, interactive, and animated visualizations in Python. Its extensive array of functions and customization options make it the go-to choice for many analysts and researchers.
  2. Seaborn: Built on top of Matplotlib, Seaborn simplifies the creation of more complex visualizations. It specializes in statistical graphics and provides a high-level interface that can generate eye-catching and informative charts with ease.
  3. Plotly: For those who require interactive plots, Plotly excels with its user-friendly syntax and wide array of chart types. It supports complex visualizations that are interactive out of the box and works well when sharing results with non-technical stakeholders.
  4. Bokeh: Bokeh is another interactive visualization library that targets modern web browsers for presentation. Its ability to generate real-time streamplots makes it a splendid choice for visualizations that require showing changing data over time.
  5. Altair: This declarative statistical visualization library is designed for Python. Altair offers a simple and intuitive API that compiles to Vega and Vega-Lite, which are powerful visualization grammars. This makes it ideal for quick exploratory data analysis with clear and effective visual representation.
  6. ggplot: Based on the concept of The Grammar of Graphics, ggplot (known as ggplot2 in the R community) has a Python port that allows for creating complex multi-plot layouts, tailored to the structured visualization of data.
  7. Holoviews: For analysts wanting to focus on their data rather than their code, Holoviews is the library of choice. It automates much of the visualization process, making it possible to create complex dashboards with minimal code.
  8. Deepnote: A relatively new entrant, Deepnote is not just a visualization library but an entire collaborative data science platform designed for Jupyter notebooks. It gives teams the ability to work in real-time, much like Google Docs, and includes powerful visualization tools that integrate seamlessly with standard libraries.

In the rapidly evolving landscape of data science, these visualization libraries and platforms, such as Deepnote, provide vital tools in the data scientist's arsenal, enhancing their ability to communicate complex data stories in a clear and engaging manner.

Nick Barth

Product Engineer

Nick has been interested in data science ever since he recorded all his poops in spreadsheet, and found that on average, he pooped 1.41 times per day. When he isn't coding, or writing content, he spends his time enjoying various leisurely pursuits.

Follow Nick on LinkedIn and GitHub

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