9 Interactive Python Libraries for Visualizing Data

The rapidly growing world of today is expanding technology at a rate like never before. Though people may have ways to collect data, when it comes to extracting conclusions insights with it, there still exists a strain on the head. So, data visualization charts like bar charts, scatterplots, line charts, geographical maps, etc. are extremely important. They show you information just by looking at them which normally you would get by reading text reports or spreadsheets to understand the data.

As said, the picture is better than words, various administrators and non-tech decision-makers profoundly rely on and prefer infographics and visualizations to understand the underlying business insights.

Several libraries are available today to create beautiful and complex data visualizations. Python is one of them and is the most widespread programming language for data visualization as well as data analytics. These libraries provide analysts and statisticians to design visual data models easily and efficiently as per their specifications by conveniently providing an interface and data visualization tools in one place.

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Interactive Python Libraries for Visualizing Data

Below is a list that compiles the top libraries for great data visualization. Let’s quickly get into understanding these libraries:

 1) Seaborn

Seaborn is defined as the data visualization library built on top of Matplotlib, it provides a high-level interface for drawing engaging and informative statistical graphics. Seaborn is a higher-level library- it makes it easier to create certain kinds of plots, including time series, violin plots, and heat maps. This library puts visualization at the core of understanding any data.

Unique Features:

  • Manage relationships between multiple variables (correlation)
  • Design linear regression models for dependent variables
  • Observe categorical variables for aggregate statistics
  • Offers high-level abstractions, multi-plot grids.
  • Compare and Analyze bi-variate or univariate distributions between different data subsets.


2) Plotly

Plotly is generally known as an online platform for data visualization. It is a graph plotting library for Python which can also be accessed from a Python notebook. This library allows users to easily import, copy, paste, or stream data that needs to be analyzed and visualized. You can use Plotly if you need to design and display and update figures or hover over text for details. It also has an added feature of sending data to cloud servers.

Unique Features:

  • Various kinds of Basic Charts, Statistical and Seaborn Styles, and Scientific charts and Financial Charts
  • Polar Plots, Maps, and Subplots
  • Jupyter Widgets Interaction


3) Matplotlib 

Matplotlib Python Library is the first Python data visualization library and is the most widely used library for plotting in the Python community. It is used to generate simple yet powerful visualizations and can plot a wide range of graphs – ranging from histograms to heat plots. It provides an object-oriented API for embedding plots into project files or applications.

Unique Features:

  • Matplotlib can depict a wide range of 2D visualizations.
  • Line plots, Scatter plots, Area plots, Bar charts, and Histograms
  • Matplotlib also facilitates labels, grids, legends, and some more formatting entities with Matplotlib.


4) Altair 

Altair is a Python library created for statistical visualization. It is declarative and is made on top of the powerful Vega-Lite visualization grammar with a simple API, that is friendly and consistent. Its main principle is that rather than concentrating on the code part, one should concentrate on the visualization part and write as little code as possible and still be able to design beautiful and intuitive plots.

Unique Features:

  • Quick and easy to iterate through visualizations as it uses a declarative style to create plots.
  • The source of Altair is available on GitHub
  • Easy to design effective and beautiful visualizations with minimum code.


5) D3.js

D3.js is another library for data visualization and is a powerful tool for SVG vector graphics. An advantage with SVG is that no matter how deep you zoom, the graphics will never look pixelated. D3 is a framework to load information into the browser and create reports based on data elements. It empowers a data wiz with a variety of graphics.

Unique Features:

  • Supports large datasets
  • Declarative programming
  • Animations
  • Ranking — barplot, word cloud, circular, parallel, spider, lollipop
  • Distribution charts — violin, histogram, ridgeline, density, box plot


6) Bokeh

Bokeh is a library designed to produce visualizations that are friendly on the browsers and web interface. It supports unique visualizations like Network graphs, Geospatial plots, etc. right out of the box. You will be able to convey information more intuitively through your plots because you will also notice that the visualizations generated from Bokeh are interactive.

Unique Features 

  • Quickly and easily create interactive plots, dashboards, and data applications
  • To show visualizations in a browser, there are options available to export them and you can also use it through JavaScript itself!
  • Elegant, concise construction of versatile graphics
  • Extend high-performance interactivity over large or real-time datasets


7) Pygal

Pygal is a Python module, highly customizable, low code module that is extremely simplistic. It creates SVG graphs/charts and offers interactive plots that can be embedded in a web browser. Pygal specializes in enabling users to create SVGs. SVG formatting is easily integrated with Pygal, besides the scalability of an SVG, you can edit them in any editor and print them in very high-quality resolution.

Unique Features:

  • It generates beautiful SVG (Scalable Vector Graphics)
  • Customized charts, Pie charts, Maps
  • Any graph can be styled using various styles like: DarkStyle, LightStyle, CleanStyle, RedBlueStyle.


8) Geoplotlib

Geoplotlib is an open-source Python library that is used for geospatial data visualizations. It has a simple interface and includes a wide range of geographical visualizations. Geoplotlib is primarily used for creating maps and planning geographical data that can be used to create a variety of map-types, like choropleths, heatmaps, or dot-density maps.

Unique Features:

  • Colormap — converts real values to colors.
  • Supports hardware acceleration
  • Provides performance rendering for large datasets
  • Provides map tiles for interactivity and simple animations


9) Ggplot2

ggplot2 is a strong and flexible R package for producing elegant graphics. It includes famous packages for making beautiful graphics. ggplot2 is the Python implementation of the Grammar of Graphics of R programming language to build layered, customization plots. It offers a more programmatic interface for defining what variables to plot, how they are displayed, and other visual properties.

Unique Features:

  • Only need one system to do quick-and-dirty and complex
  • Default colors and other aesthetics are nicer
  • It has a wide range of graphs that you can plot: Animation charts, Marginal charts.



There are several kinds of graphs that you can plot through Python and its various libraries. You should begin with Matplotlib if you are new to Python data visualization.

The above article describes the Top 9 Python Libraries for Data Visualization in Python, that are commonly used these days. Each of these libraries is quite popular in its benefit and reveals its strength in different scenarios. We hope these libraries inspire you and bring you the desired results.

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