10 Python Libraries for Data Visualization You Need to Know
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10 Python Libraries for Data Visualization You Need to Know

11 mins read

Data visualization with Python has never been easier. This post covers some of the best Python libraries that are designed specifically to make it easy to create data-driven visualizations, dashboards, and reports to help you gain insights from your data, without having to use other tools like R or Tableau.

Many of these libraries are open source, so they’re free to use, and others offer paid versions with extra features and support if you need them. Try out these 10 Python libraries for data visualization today!

What is Data Visualisation?

10 Python Libraries for Data Visualization You Need to Know

Data visualization is the representation of data using common graphics such as charts, diagrams, infographics, and even animations. The visual representation of this information can communicate complex data relationships and data-driven insights in an easy-to-understand manner.

What is Library?

A library is a pre-assembled set of code used to reduce the time required for programming. They are especially useful for accessing previously written, commonly used code rather than scribbling it from scratch over and over again. Like physical libraries, these are collections of reusable resources.

Matplotlib

Matplotlib in Python: Getting Started

Matplotlib is the most popular python library for data visualization. It allows you to create a wide range of different types of plots and charts with ease.

Plus, it is very user-friendly and easy to get started with. If you are looking for a powerful and versatile data visualization tool, then matplotlib is the library for you.

One major advantage of matplotlib is that you can use all sorts of graphical formats including SVG, EPS, PDF or PNG. Additionally, it has good support for animation. For example, if your chart includes some dynamic components like 3D plotting or zooming in/out on specific sections of your chart, then matplotlib can do that with no problem.

Seaborn

Seaborn is a statistical data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn is well suited for exploratory data analysis and for making complex visualizations.

Seaborn has several features that make it particularly useful for working with data frames. First, Seaborn can automatically detect column names and structure from Pandas data frames.

Second, Seaborn can easily take multiple columns of data from a data frame and create visualizations that show the relationships between those columns. Finally, Seaborn comes with a number of pre-built themes that make your visualizations look professional and stylish.

Bokeh

Bokeh is a powerful library that allows you to create stunning visualizations. It’s particularly well suited for large, complex datasets.

Plus, it’s easy to use and has excellent documentation. Here are some examples of what you can do with Bokeh Bokeh offers a huge number of options for visualization. For example, you can choose from scatter plots, bar charts, histograms, or choropleths.

Furthermore, Bokeh offers options like brushed surfaces, polar graphs, bubble charts, 3D maps and more! One of the best features about Bokeh is its interactivity.

With Bokeh, you can select and manipulate data points on your graph by clicking on them.

What makes this so cool is that as you change the dataset, the graph updates automatically (in real time). What I really love about this interactive feature is how smooth it feels as I’m changing my selections. The transitions are natural, not jarring or choppy in any way.

If you’re looking for an easy-to-use yet powerful data visualization tool for your next project, then check out Bokeh!

Plotly

Plotly is a powerful Python library that can be used for creating interactive data visualizations. It is built on top of the popular JavaScript library D3.js and can be used from within Jupyter Notebooks, as well as from the command line.

Plotly’s primary goal is to make it easy for developers to create beautiful, interactive data visualizations.

To that end, it has a number of features that make it easy to get started, including built-in plot styling themes, drag-and-drop widgets for building plots without code, pre-built plot types like scatterplots and heatmaps, preconfigured dashboards with example data sets.

If you’re using Jupyter notebooks with Plotly’s R package installed (or if you have access to Plotly’s IPython kernel), you can use Plotly inline in your notebooks.

Anaconda Dash

Anaconda Dash is a great tool for data visualization. It’s easy to use and has a lot of features that make it a great choice for data scientists.

Plus, it’s free and open source! With Dash you can create rich, interactive visualizations using the Jupyter Notebook environment with the help of built-in libraries like Plotly, Seaborn, Matplotlib, and Bokeh. The library also provides pre-built graphs in JSON format, making dashboards or custom graphs quick and easy to build.

Chartpy

Chartpy is a powerful data visualization library that allows you to create a wide variety of charts and graphs.

With Chartpy, you can easily create beautiful, interactive, and informative visualizations that can help you better understand your data. Whether you are looking for a scatter plot, histogram, line chart, or even 3D charts, this library has it all.

Chartpy is designed to work with different python frameworks like matplotlib and Seaborn. In addition to the core features available in this module are modules with extra functionality such as: contour plots (3D), regression plots (3D), heatmaps (2D), sunburst plots (2D) and more!

Pygal

Pygal is a great Python library for data visualization. It’s simple to use and easy to install, and it produces high-quality charts. Pygal is also very flexible, so you can create a wide variety of charts. Pygal supports bar charts, pie charts, line graphs, scatter plots, histograms, heat maps and more. In addition to its excellent features for visualization output production, Pygal also provides rich documentation on how to customize the output.

Ggplot

Ggplot is a Python library that enables you to create beautiful data visualizations. It’s based on the grammar of graphics, which means you can create any type of plot you want.

Plus, it’s easy to use and has a lot of features. For example, with Ggplot you can: create scatter plots, histograms, bar charts; embed images in plots; produce geographical maps; easily change colors and style in plots; add different shapes such as circles or squares; set limits on y-axis ranges.

Superset

Superset is an open source project that was created by Airbnb. It’s a data visualization tool that allows you to create interactive dashboards. Superset is written in Python and uses the React JavaScript library for its frontend.

One of the best things about Superset is that it’s very easy to use. Even if you’re not a developer, you can still create beautiful visualizations with Superset. You can drag-and-drop your datasets into the sidebar, or import from a spreadsheet. The latter option makes it easy to manipulate your dataset before passing them into Superset.

Altair

Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. It’s easy to use and produces beautiful visualizations.

Plus, it’s open-source and free! One of the best things about Altair is that you can interact with your data without writing any code. Simply upload your data file and drag-and-drop variables from the left side panel into the canvas on the right, then select your variable in the top left panel.

Another really cool feature of Altair is that you can choose from one of three different graph types: scatter plots, line graphs or bar charts.

Once you have made your selection, click Generate Plot in the top right corner of the screen to see your chart rendered live!

Verdict

Python has become the go-to language for data science and analytics, and it’s no surprise that it comes with a robust set of libraries for data visualization. In this blog post, we’ll take a look at 10 of the best Python libraries for data visualization.

We start with matplotlib. Matplotlib is a 2D plotting library which provides many different plots in both 2D and 3D.

It also includes subplots which allow you to divide the screen into smaller sections which can be used as individual plots or combined into one plot if desired. There are over 100 different types of plots available in matplotlib including scatter plots, histograms, bar charts, error charts, pie charts, etc. 

The second library on our list is Bokeh which is designed specifically for interactive visualizations in modern web browsers using HTML5 Canvas element rendering.

It supports various user interface controls like sliders and input boxes to modify values and interactively visualize data without writing any code whatsoever!

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