Exploring Python Libraries for Data Visualization

Data visualization is vital for data science as it simplifies the presentation of complex batches of data. Today, data is more important than ever: 67 percent of all small businesses in 2020 spent over $10K for data analytics and other data measures.

Data visualization eases insight communication, storytelling, decision-making, and data exploration. If it weren’t for data visualization, extracting information from complex data and making effective business decisions would be challenging. 

Python libraries for data visualization are great because Python provides a variety of visualization tools/libraries with awesome features that are easy to implement and the results are exceptional! 

Why does Data Visualization Matter? 

With the increase in demand for data globally, businesses are in a constant race to enhance data accumulation methods and better data efficiency. 

Data visualization is an essential tool of this data revolution as it assists businesses’ growth. The information gained from the visualization is a valuable asset that decision-makers use to make better, more accurate, and more effective decisions. 

Visualization in data science is used for the analysis of complex data, to identify patterns, and to extract important insights. 

The Benefits of Using Python Libraries for Data Visualization

Data visualization can be done with any programming language but Python’s small lines of code make it an increasingly popular choice. 

The easy syntax and the reduced time for coding combined with the variety of libraries for data visualization are only some of the reasons why Python could improve data visualization processes. 

Python libraries are multidimensional and they can be used throughout industries such as data science, finance, AI, machine learning, etc.

Let’s dive into the main advantages of Python data visualization:

  • Easy learning curve 

The learning process for Python isn’t complex. This means that your developers will be ready to use it in no time. 

They will also get accustomed to the process and experience fewer issues when integrating Python into their data visualization system choice.

  • Open source 

As an open-source programming language that’s free to use and available to all, Python can be extended further depending on the desired results. 

Python developers are continuously adding new features, frameworks, and libraries that further strengthen and improve the language.

  • Connectivity 

Python can connect to almost all systems of databases on the internet. No matter the system that you’ve chosen to collect data, you can connect Python and use them together.

  • Scalability 

The amazing scalability of Python allows it to grow at the same pace as the data needs of the project. 

With this in mind, if you have a business that generates big batches of data, Python is great because it can grow and match the data generation. 

  • A variety of libraries

Python offers a wide array of awesome libraries for data visualization. 

They may not be the fastest option out there, but they’re still able to handle almost all the requirements for data visualization with great results. Plus, there are a lot of different libraries that you can explore.

The Best Python Data Visualization Libraries 

There are plenty of Python data visualization libraries that data scientists can use for a data visualization project. Let’s take a look at the top five Python data visualization libraries currently and what each of them offers:

  • Matplotlib 

At the moment, this is one of the most frequently used libraries for data visualization and data plotting with Python. It’s an open-source library with a strong community for support as well as a cross-platform ability. 

Developers use it to create animated, static, and interactive visualizations. Thanks to the extensive toolbar, every detail you want can be customized, including colors and the limits of the plot’s axis. 

  • Seaborn

Excellent for work with static visualization, this library may be based on Matplotlib, but it has its specific possibilities.

When used together with Matplotlib, they complement each other and provide almost a limitless range of data visualization options. It can be used for informative and visually pleasing content. 

There are lots of templates and themes for design to opt for. Therefore, this library reduces the need to write extensive code. 

  • Plotnine (ggplot)

This library provides an easy and simple process for graph creation as it follows the ggplot2 philosophy, i.e., you can work with graph grammar. 

It can also be used to add additional layers like text or points to present a complete final result. 

This library is best used for high-complexity purposes. 

If you don’t need full customization, you should skip this library as it’s more suitable for general visualization tasks that aren’t the project’s primary focus.

  • Bokeh 

If you need awesome interactive or publication graphs that are suitable for various purposes, Bokeh is the visualization library you need. 

You can use it to prepare various types of charts with minimal use of code and a high abstraction level. 

It also includes different interactive widgets and elements so that users can understand information better.

  • Pygal

This library is highly recommendable if you need to work on simple, interactive visualizations. With as little code as possible, it helps create attractive visualizations. 

What’s more, as the graphs with this library are based on a vector, they won’t decline in quality upon scaling. 

However, as it doesn’t provide a high customization level, this library isn’t suitable for larger projects. It also has a smaller community.

Final Thoughts

Python data visualization is used by data scientists who work with complicated and/or large data. More and more businesses are realizing the power of data visualization to gain access to unique insights like competition, market trends, consumer behavior, etc. 

Thanks to Python data visualization libraries, data scientists have powerful tools to understand complex data and present it visually to achieve effective storytelling and promote a business’s growth. Python visualization libraries and Python’s high scalability, connectivity, and open-source access make it a worthwhile data visualization tool to explore. 

If you’re excited to learn all about the benefits of Python data visualization, but you’re not sure where to start, consult our experienced developers at ArtHaus

With more than two decades of experience, 80+ clients globally, and 400+ projects in our portfolio, we are dedicated to assisting businesses discover the benefits of within-budget, effective IT solutions!