Python Libraries for Data Visualization

Introduction to the most important Python Libraries for Data Visualization.

Data visualization is the field of representing data and information in graphical form. Data visualization makes it easier for us to understand data and as a result, finding patterns, trends and correlations in big data becomes much easier. In this article, I’ll introduce you to some important Python libraries for data visualization.

Python has some great data visualization libraries for creating interactive charts, charts and everything in between. But not knowing the benefits and basic functionality of these libraries, it would be difficult for someone to choose from these libraries.

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Therefore, in this article, I’m going to introduce you to some essential data visualization libraries with their uses so that you can select the best data visualization library for your favourite task.

Essential Python Libraries for Data Visualization

Matplotlib:

Matplotlib is one of the oldest and most widely used data visualization libraries in Python. It is used to create static, animated and interactive 2D data visualizations in Python and can also be highly customized to create advanced visualizations such as 3D plots.

Matplotlib can be used in Python shells, Jupyter notebooks, web applications and is also supported by various Python GUI toolkits.

The biggest advantage of Matplotlib is the freedom it offers to customize almost anything and create beautiful, advanced data visualizations, but these visualizations can be easily achieved with other libraries.

You can learn more about the practical Matplotlib implementation for data visualization using Python from here.

Seaborn:

Seaborn is built on Matplotlib and supports pandas and NumPy data structures. By using Seaborn beautiful and high-quality visualizations can be achieved with just a few lines of code.

Seaborn can even take data frames and tables that contain the dataset and plot them while handling all semantic mapping and statistical aggregation in the plots.

Seabron should be the perfect choice for anyone looking to create beautiful static diagrams. For interactive web visualizations, there are better libraries than Seaborn.

You can learn more about Seaborn’s practical implementation for data visualization using Python from here.

Plotly:

Plotly is built on Plotly.Js and also offers webpage integration. Plotly offers excellent interactivity and over 40 different types of charts.

Plotly can be used to create stunning 3D visualizations and also to create standalone HTML visualizations. It takes a little more effort, but the result is a sleek and highly interactive visualization.

Plotly will be the choice of those looking to create highly interactive visualizations. You can learn more about the practical Plotly implementation for data visualization with Python from here.

How to Choose From Python Libraries for Data Visualization?

So all these essential Python libraries for data visualization; Matplotlib, Seaborn and Plotly are unique in their way. So choosing one from them comes down to your personal needs and preferences.

To wrap up the uses of these libraries for data visualization, I can just summarize that you can use Matplotlib and Seaborn for static visualizations and Plotly for highly interactive web embedded visualizations.

So these were the most essential Python libraries for data visualization that you should know. Please feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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