The Best Python Libraries for Data Visualization

Python has a variety of charting libraries, including Matplotlib, Seaborn, and many other data visualization tools with a variety of features for designing educational, unique, and visually appealing charts to show data in the simplest and most efficient manner.

The Best Python Libraries for Data Visualization

Why Use Python Libraries?

Python libraries help to present the data in a way that makes it simple to understand. According to the necessary requirements, they offer the interface and data visualisation capabilities in one location. Additionally, they make it simple for statisticians and analysts to visualise such data.

As visual beings, we require data visualisation charts like bar charts, scatterplots, line charts, geographical maps, and others in order to comprehend information. They convey information simply by being viewed, but spreadsheets or text reports typically require reading in order to fully understand the facts.

These 12 interdisciplinary Python data visualization libraries range in popularity from the well-known to the obscure. We hope this list will provide you some inspiration.

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The earliest Python data visualization package is called Matplotlob. As a result, Matplotlib provides the foundation for many other libraries that are created specifically to deal with the analysis. A smaller amount of code can access multiple Matplotlib methods thanks to libraries like pandas and matplotlib, which act as "wrappers" around Matplotlib.

Simple yet effective visualizations are produced using the Matplotlib Python Library. It has been around for more than a decade and is the Python community's most popular charting package. Histograms and heat maps are only two examples of the many graphs that Matplotlib can plot.

Due to its flexibility, Matplotlib supports a variety of visualization types.

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

Grids, labels, legends, and other elements may all be simply created because they are all easily adaptable.


A well-liked data visualization library based on Matplotlib is called Seaborn. Compared to Matplotlib, Seaborn's default styles and colour schemes are far more advanced. Visualization, according to Seaborn, is the key to comprehending any data. With Seaborn, you may more easily create specialised plots like violin plots, heat maps, and time series.


A Python implementation of ggplot2, the R charting engine, and ideas from The Grammar of Graphics is called plotnine. With this potent visualization tool, you may layer different elements to produce a full plot. Consider starting with axes, adding points, a line, a trendline, etc. R programmers used to ggplot2 will find Plotnine to be a functional port of that package.

When using Plotnine, it is recommended to put your data in a DataFrame because of the tight integration between Plotnine and pandas.


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.

For different user categories, Bokeh features three interfaces with varied levels of control. The highest level is used to quickly create charts. It contains techniques for creating typical charts including histograms, box plots, and bar graphs. The middle level, which has the same level of specificity as Matplotlib, enables the user to modify the fundamental components of each chart (for instance, the dots in a scatter plot). Software engineers and developers are the target audience at the bottom level. The user must create each chart element because there are no pre-defined settings.


Pygal provides interactive graphs that may be integrated in a web browser, similar to Bokeh and Plotly. Its ability to export charts as SVGs is what sets it apart most. SVGs are perfectly adequate as long as you're working with smaller datasets. Charts with tens of thousands of data points, however, will struggle to render and become slow.

Since each chart type is contained within a method and the built-in styles are attractive, making a beautiful chart with a few lines of code is simple.


You may be familiar with Plotly as an online tool for data visualization, but did you realise that a Python notebook may also use its features? Plotly's specialty, like Bokeh's, is creating interactive plots, but it also provides several charts that are uncommon in other libraries, such as contour plots, dendrograms, and 3D charts.


A toolbox for creating maps and charting geographic data is called Geoplotlib. A wide range of map types, including choropleths, heatmaps, and dot-density maps, can be produced by it. To utilise Geoplotlib, Pyglet (an object-oriented programming interface) is necessary.

By offering a collection of built-in tools for the most typical tasks, such as density visualization, spatial graphs, and shapefiles, Geoplotlib lessens the difficulty of developing visualizations.

It's beneficial to have a library devoted to maps because the majority of Python data visualization libraries don't provide maps.


Python's PyQtGraph graphics and user interface module offers features frequently needed in design and scientific applications. Its main objectives are to provide quick, interactive graphics for data visualisation (plots, video, etc.) and tools for quick application development.

Its main objectives are to provide quick, interactive graphics for data visualisation (plots, video, etc.) and tools for quick application development.

Plot windows have two primary components: the Plot Panel, which by default is at the top and contains the plotted images, and the Control Panel (by default, at the bottom). What will be plotted is configured via the Control Panel. It might just be a matter of choosing which columns to plot against one another for a straightforward scatter plot, but it can get pretty creative.


The design of Gleam was inspired by R's Shiny package. It enables users to create interactive web apps out of any analysis using just Python scripts. To do this, Gleam users don't need to be familiar with HTML, CSS, or JavaScript. Any Python data visualization library is compatible with Gleam. Users can add fields to plots they've already created to filter and organise data.


Depending on the focus of the task at hand, Python offers a wide choice of visualization tools that are incredibly diverse. This reality is reflected in the overwhelming number of libraries available. Before settling on a specific strategy, users must keep in mind the variations among the techniques and their ramifications.