Best Python Visualization Tools: Awesome, Interactive, and 3D

It is not a surprise that today you can find a long list of awesome, interactive and even 3D graph Python visualization tools that can contribute greatly to your data science or machine learning projects.




With Python code visualization and graphing libraries you can create a line graph, bar chart, pie chart, 3D scatter plot, histograms, 3D graphs, map, network, interactive scientific or financial charts, and many other graphics of small or big data sets.

On this page:

  • An overview of the best Python data visualization tools, libraries, and software solutions.

 

1. Matplotlib

Matplotlib Python visualization screen shot

Matplotlib is one of the most popular and oldest data visualization tools using Python. It is a quite powerful but also a complex visualization tool.

Matplotlib is a Python 2D plotting library that provides publication quality figures in a variety of hardcopy formats and interactive environments across many platforms.

Matplotlib is used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Although it is a complex tool and it takes a lot of time to do the job, Matplotlib is a very powerful solution in doing a broad range of tasks. In addition, many famous plotting libraries are coordinated to work in conjunction with Matplotlib.

Key features and benefits:

  • You can generate and use plots, bar charts, pie charts, 3D plotting, error charts, histograms, power spectra, scatter plot, etc.
  • Provides a MATLAB-like interface
  • An object-oriented interface and a set of functions familiar to MATLAB to control your line styles, axes properties, font properties, etc.
  • A great examples gallery and a list of plotting commands to help you learn how to do a particular kind of plot.
  • Ships with many add-on toolkits, including 3d plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist.
  • An open source Python visualization tool with an active developer community and contributions.

Website: https://matplotlib.org

 

2. Seaborn 

Seaborn -one of the best Python Visualization Tools - print screen

Seaborn is also one of the very popular Python visualization tools and is based on Matplotlib. Seaborn is thin wrappers over Matplotlib.

It is a good software program for those who want a high-level interface for creating beautiful, attractive, and informative statistical types of graphs and charts.

In other words, Seaborn is able to build default data visualizations in a more visually appealing way. One of the best Seaborn’s benefits is that it can make complex and complicated plots simpler to build.

Key features and benefits:

  • A higher-level Python visualization library based on the Matplotlib library.
  • A great range of settings for processing graphs and charts.
  • Very rich gallery of visualizations and some of them are complicated types such as time series, and violin plots.  Also, it has a great range of more common charts and examples such as box and whisker plot examples, line plots, scatter plots, FacetGrid, bar plots, cat plot and etc.
  • Sophisticated styles and color palettes.
  • Easily displays distributions and data relationships.
  • Can show information from matrices and DataFrames.

Website: https://seaborn.pydata.org/

 

3. Plotly

Plotly Python library print screen

When it comes to interactive Python visualization tools, Plotly has a top place here. Plotly’s Python free and open source graphing library help you create interactive, publication-quality graphs easily online.

Plotly has it all – 3D data visualization, line plots, bar charts, error bars, scatter plots, area charts, box plots, multiple-axes, histograms, heatmaps, subplots, polar charts, and bubble charts.

You can use Plotly online or offline mode, or in jupyter notebooks.

Key features and benefits:

  • A comprehensive range of rich and interactive graphs.
  • The interactive plots allow you to seamlessly explore the data by panning, selecting, zooming on the graphing surface.
  • Ternary plots and 3D charts.
  • A great range of basic charts, statistical and Seaborn-style charts, scientific graphs, financial charts, 3d scatter plot, maps, 3D graphs, multiple Axes, subplots, insets, and transformations.
  • Supports for “multiple linked views” and animation.
  • Plotly is able to graph and visualize almost all sorts of data.

Website: https://plot.ly/

4. Bokeh

Bokeh Python library - print screen

Bokeh also is an interactive Python visualization library tool that provides elegant and versatile graphics. It is able to extend the capability with high-performance interactivity and scalability over very big data sets.

Bokeh allows you to easily build interactive plots, dashboards or data applications.




The library provides a comprehensive collection of charts, styling options, adding widgets, and many interesting features.

Bokeh also allows you to create network graph visualizations and geographical data such as Google Maps, GeoJSON, Tile Rendering.

Key features and benefits:

  • Able to combine multiple plots and widgets.
  • Handles categorical data with different techniques such as bar charts and categorical heatmaps.
  • Creates network graph visualizations.
  • Working with geographical data.
  • Uses interactive tools such as pan, zoom, and select, on your plots.
  • You can easily customize every visual aspect of the plots- axes, labels, glyphs, and more.
  • You can also create very sophisticated interactions including widgets or linked planning.
  • Displays interactive plots inside Jupyter notebooks.
  • You can add new features to Bokeh with custom user extensions.

Website: https://bokeh.pydata.org/

 

5. Pygal

Pygal Python visualization library - print screen

Pygal, as Bokeh and Plotly is also one of the top Python visualization tools that provide interactive plots, good-looking visualizations and support additional features.

The big difference is that Pygal concentrate on allowing you to create SVGs. SVG formatting is integrated greatly with Django and Flask.

Also, you can easily create a wide variety of graph and charts such as line graph, bar chart, histogram, XY, pie charts, box plot, radar, funnel, SolidGauge, pyramid, treemap, country charts, and maps such as World map.

Pygal is ideal for smaller datasets although it can handle big data sets too.

Key features and benefits:

  • Three ways to style the charts – built-in styles, parametric styles, and custom styles.
  • Provides good-looking interactive data visualizations.
  • A lot of options for charts configuration as sizing, titles, labels, legend, axis, interpolations, tooltip, rendering, data, misc, and other specific options.
  • Serie and value configuration.
  • Provides a simple way to get beautiful sparklines.
  • Pygal also supports an HTML table export.
  • Can generate multiple output formats such as SVG, file, PNG, Etree, Browser, PyQuery, and Flask App.

Website: http://pygal.org/en

 

6. Dash

Dash Python visualization library - print screen

Dash is a Python framework for building web applications. It is perfect for creating data visualization apps with highly custom user interfaces in Python.

Dash is written on Flask, Plotly.js, and React.js. Dash apps are rendered in the web browser and also mobile-ready.

If you are someone who uses Python for data analysis methods, visualization, modeling, and reporting, you will be surprised how useful Dash can be.

Practically, Dash is an open source library. Plotly develops Dash and also offers a platform for deploying Dash in an enterprise environment with premium pricing plans.

Dash makes it very easy to create compound apps that have a variety of interactive elements.

Key features and benefits:

  • Interactive graphing and cross-filtering.
  • Dash apps are made interactive through Dash Callbacks.
  • Highly customizable and flexible tool with a wide variety of options such as sizing, positioning, fonts, colors, and etc.
  • Provides modern UI elements like sliders, dropdowns, and graphs to your analytical Python code.
  • Dash apps are built and published on the Web, so you have the full power of CSS.
  • Open source – you can run the awesome Dash on your desktop for free.
  • Handles expensive data processing tasks and process big data.
  • A broad range of examples in different areas such as finance, energy, transportation, life sciences, government & public health, machine learning, and many others.

Website: https://plot.ly/products/dash/

 

7. Altair 

Altair Python visualization library - print screen

Altair is one of the good statistical Python visualization tools, based on Vega and Vega-Lite.

Altair allows you to create a comprehensive gamma of statistical visualizations easily thanks to its powerful and concise visualization grammar.




Altair is a very simple and friendly declarative tool. Declarative tool means that when you are declaring links between data columns and visual encoding channels ( such as the x-axis, y-axis, color, etc), the rest of the plot details are handled automatically.

Key features and benefits:

  • An excellent range of simple to sophisticated plots and visualizations can be created.
  • Altair plots can be made interactive. You can create many linked interactive chart types.
  • Transforms or filter data in the process of visualizing.
  • Top-level chart configuration.
  • A number of compound plot types that can be used to create stacked, layered, faceted, and repeated charts.
  • Automatically choose useful plot settings and configurations so that you can focus on the data rather than the mechanics of plotting.
  • A selection of charts such as simple charts, bar and line graphs, area charts, scatter plots, histograms, maps, interactive charts, case studies, and many others.

Website: https://altair-viz.github.io

Need more Python visualization tools? 

Here is a list of other popular solutions:

  • Pydot
  • Ggplot
  • Pandas
  • Geoplotlib
  • Missingno
  • Gleam
  • Leather

Conclusion:

Python has a top position when it comes to key data scientist skills for solving different data science tasks and challenges.

Many of the above Python data visualization tools are alternatives to each other and solve the same data problems.

But still, they differ in the options they provide to visualize data – from basic plotting to comprehensive and complicated interactive charts.

Nevertheless, the above tools offer various benefits and high productivity in a way that data scientists and other analytics professionals can rely on them on a daily basis.

Which are your favorite Python visualization tools that you use with pleasure? Share your thoughts on the field below.

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