The Python Package Index contains libraries for almost every data visualization requirement, from Pastalog for real-time visualizations of neural network training to Gaze Parser for eye movement research. Some of these libraries are used in a variety of fields. Despite this, many of them are intensely focused on completing a specific task.

Here’s a rundown of 11 interdisciplinary Python data visualization libraries that you’ll learn about in this post, from most popular to least popular. Continue reading to learn how to use Data Visualization Tool and Python data visualization libraries to create data visualization charts.


Matplotlib Python Library is used to generate simple yet powerful visualizations. It is more than a decade old and the most widely used library for plotting in the Python community. Matplotlib can plot a wide range of graphs – from histograms to heat plots.

Matplotlob is the first Python data visualization library. Therefore, many other libraries are built on top of Matplotlib and designed to work with the analysis. Libraries like pandas and matplotlib are “wrappers” over Matplotlib, allowing access to several Matplotlib methods with less code.

Matplotlib’s versatility allows for visualization types such as:

  • Scatter plots
  • Bar charts and Histograms
  • Line plots
  • Pie charts
  • Stem plots
  • Contour plots
  • Quiver plots
  • Spectrograms

You can create grids, labels, legends, etc., with ease since everything is easily customizable.


Seaborn is a popular data visualization library built on top of Matplotlib. Seaborn’s default styles and color palettes are much more sophisticated than Matplotlib. Seaborn puts visualization at the core of understanding any data. Seaborn is a higher-level library- it’s easier to generate specific plots, including heat maps, time series, and violin plots.


ggplot is a Python visualization library based on R’s ggplot2 and the Grammar of Graphics. You can construct plots using high-level grammar without worrying about the implementation details. ggplot operates differently compared to Matplotlib: it lets users layer components to create a complete plot. For example, the user can start with axes and add points, a line, a trend line, etc. The Grammar of Graphics has been hailed as an “intuitive” method for plotting. However, seasoned Matplotlib users might need time to adjust to this new mindset.


Bokeh, native to Python, is also based on The Grammar of Graphics like ggplot. It also supports streaming and real-time data. The unique selling proposition is its ability to create interactive, web-ready plots, accessible output as JSON objects, HTML documents, or interactive web applications.

Bokeh has three interfaces with varying degrees of control to accommodate different types of users. The topmost level is for creating charts quickly. It includes methods for building common charts such as bar plots, box plots, and histograms. The middle level allows the user to control the basic building blocks of each chart (for example, the dots in a scatter plot) and has the same specificity as Matplotlib. The bottom level is geared toward developers and software engineers. It has no pre-set defaults and requires the user to define every element of the chart.


While Plotly is widely known as an online platform for data visualization, very few people know it is accessible from a Python notebook. Like Bokeh, Plotly’s strength lies in making interactive plots, and it offers contour plots, which are not found in most libraries.


Pygal, like Plotly and Bokeh, offers interactive plots that you can embed in a web browser. The ability to output charts as SVGs is its prime differentiator. For work involving smaller datasets, SVGs will do just fine. However, charts with hundreds of thousands of data points become sluggish and have trouble rendering.

It’s easy to create a nice-looking chart with just a few lines of code since each chart type is packaged into a method and the built-in styles are great.


Altair is a declarative statistical visualization Python library based on Vega-Lite. You only need to mention the links between data columns to the encoding channels, such as x-axis, y-axis, color, etc. The rest of the plotting details are handled automatically. This fact makes Altair simple, friendly, and consistent. It is easy to design compelling and beautiful visualizations with a minimal amount of code using Altair.


Geoplotlib is a toolbox used for plotting geographical data and map creation. It can create a variety of map types, like choropleths, heatmaps, and dot-density maps. Pyglet (an object-oriented programming interface) is required to use Geoplotlib.

Geoplotlib reduces the complexity of designing visualizations by providing a set of in-built tools for the most common tasks such as density visualization, spatial graphs, and shapefiles.

Since most Python data visualization libraries don’t offer maps, it’s good to have a library dedicated to them.


R’s Shiny package is the inspiration behind Gleam. It allows the user to turn any analysis into interactive web apps using only Python scripts. Gleam users don’t need to know HTML, CSS, or JavaScript to do this. Gleam works with any Python data visualization library. Once users have created a plot, they can build fields on top of it to filter and sort data.


Dealing with missing data is cumbersome. Missingno can quickly gauge the completeness of a dataset rather than painstakingly searching through a table. The user can filter and sort data based on completion or spot correlations with a heat map or a dendrogram.


Leather is designed to work with all data types and produces charts such as SVGs. It is scalable without losing image quality. Leather’s creator, Christopher Groskopf, puts it best: “Leather is the Python charting library for those who need charts now and don’t care if they’re perfect.” 

Since this library is relatively new, some of the documentation is still in progress. The charts are pretty basic—but that’s the intention.

There is a wide range of visualization tools, with huge diversity, depending on the focus of the task at hand available for Python. The sheer number of libraries available reflects this fact. It is imperative for the users to bear in mind the differences between the approaches and their implications before zeroing in on a particular approach.

Would you add any other Python data visualization libraries to this list? Please share your favorites in a comment below.

Guest Author – Quincy is part of the team at Springboard and is passionate about online learning and strong coffee.

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4 responses on “The Best Python Data Visualization Libraries

  1. Worthful Python tutorial. Appreciate a lot for taking up the pain to write such a quality content on Python course. Just now I watched this similar Python tutorial and I think this will enhance the knowledge of other visitors for sure. Thanks anyway.

  2. Всем привет!Я здесь новенький.
    Помогите,посоветуйте что делать?
    У нас давно 15 соток в Белгороде но мы както не чего там
    некогда не делали. Сейчас летом просела крыша кирпичного сарая,
    т.е. просела прям по всему периметру, крыша старая из нержавейки
    Возможно ли её както просто отремонтировать
    или нужно делать новую?
    Если делать новую, то что Сейчас актуальнее черепица или шифер?
    Очень надеюсь на ответ. Заранtе спасибо!

  3. Thanks for this overview! I think it’s helpful, but there are several ways it could be improved to steer people in the right direction:

    – ggplot -> plotnine: The link to the “ggplot” project above goes to, which hasn’t been updated since 2016. I agree with : people interested in a ggplot2 API for python should use plotnine (, which is actively maintained and much closer to ggplot2’s functionality.

    – Bokeh.charts -> HoloViews: Up until 2017 bokeh provided the three interfaces mentioned in this article: bokeh.models (low level), bokeh.plotting (mid level), and bokeh.charts (high level). But bokeh.charts was retired more than a year ago, because HoloViews ( provides much more extensive high-level charting, built on bokeh.models and bokeh.plotting.

    – Plotly for 3D: Plotly’s description should probably mention that it provides much more extensive 3D plotting options than Matplotlib and Bokeh do; that’s a very clear reason to choose Plotly.

    – geoplotlib -> GeoViews: The description of geoplotlib makes it sounds like there is only one Python library dedicated to maps, and the one that’s mentioned (geoplotlib) hasn’t had any code updates since 2016. Others like our GeoViews library ( are much more active projects.

    – Leather hasn’t been updated since 2016, and does not appear to be a current project. Personally, I’d recommend our actively maintained hvPlot library (, which provides simple starting points like leather does but then isn’t a dead end, because the resulting objects can be customized or combined with other plots when needed.

  4. I’m really surprised that PyQtGraph didn’t make this list. Not only does it offer powerful, easy-to-use functionality, but for plotting real-time data at high data rates with animated plots PyQtGraph really stands out. Anyone who’s evaluating their python data visualization options should really give PyQtGraph a test drive.

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