Which is a Better Python Library for Data Visualization: Apache Superset or Matplotlib? 

What is a Python Library?

Python has been around for more than three decades and remains one of the most popular programming languages in the world. It’s easy for novice programmers to learn, and has a variety of applications in fields like machine learning, AI, and deep learning.

In computer programming, a library refers to a bundle of code consisting of dozens or even hundreds of modules that offer a range of functionality. Each library contains a set of pre-combined codes whose use reduces the time necessary to code. Libraries are especially useful for accessing pre-written codes that are repeatedly used, which saves users the time of having to write them from scratch every time.

Python has more than 137,000 libraries. Within Python, each library, or module, has a different purpose. Some of these modules play an important role in fields like data science, data manipulation, machine learning, and data visualization.

This article will explore two Python libraries, Apache Superset and Matplotlib, to see which is best suited for data visualization purposes.

What is Apache Superset?

Apache Superset is a free, open-source business intelligence web application and data exploration platform. It was designed to help users complete data analytics at the speed of thought, and to simplify the process of transforming data insights into visualizations. Even those who don’t come from a technical background can use Apache Superset to analyze, organize, and visualize data. This intuitive model works quickly and has a plethora of tools that can be used to create simple visualizations like line charts, as well as detailed geospatial charts. This platform is currently being used by many companies for their data visualization needs, such as Tesla and Airbnb.

Some of the most helpful Apache Superset features for data visualization are:

• A robust metadata browser.
• A visualization picker that allows users to click on a specific type of visualization, then switch to another visualization type with just one click.
• A SQL IDE that can aid with data preparation.
• An interface that allows users to intuitively visualize datasets and create their own interactive dashboards.
• A semantic layer that helps Data Scientists and Data Analysts to customize metrics and define dimensions.
• An API designed for customization.
• The capacity to add visualization plugins.

What is Matplotlib?

Matplotlib is a two-dimensional Python data visualization and plotting library. It was written by John Hunter in 2002. In Hunter’s words, “Matplotlib tries to make easy things easy and hard things possible.” This multi-platform library was created on NumPy arrays and was intended to work with the SciPy stack. It is used in Python and IPython shells, as well as web application servers and Jupyter Notebook.

Matplotlib allows users to write a single script that can be used for flexible data parsing and plotting. This free, open-source library supports many output types, which allows it to be used on any operating system. In addition, it’s helpful for modeling machine learning technologies.

Matplotlib is particularly suited to working with numerical information that needs to be visually conveyed. It is able to create publishable, high-quality graphs with much less effort than other data visualization tools. Matplotlib is used by Data Analysts around the world to design engaging and stunning figures, charts, and graphs. This extensive library can change even the most minute details of a figure to enhance the subsequent visualization. Many companies and businesses use Matplotlib for their data visualization needs, such as Nordstrom, WellsFargo, and Cigna.

Matplotlib has a wide range of uses for Data Analysts who are looking to create visualizations based on their data findings. Here are a few of the benefits of working with Matplotlib for visualization purposes:

• Matplotlib comes with several plot options. These allow users to identify patterns and trends, and to make correlations. Because most Matplotlib plots are created by following the same steps, they can easily be generated using this library.
• This library includes an object-oriented API, which is useful for embedding plots into various applications.
• Matplotlib is useful for those who wish to create bar graphs to compare and contrast data in different categories or track changes during a given period of time.
• When working with Matplotlib’s scatter plots, it’s easy to spot outliers.
• In situations where numerical proportion must be communicated, Data Analysts can create pie charts using Matplotlib. These charts depict the proportions of a part to the whole.
• Matplotlib is a powerful tool for designing histograms, which are essential for counting the variables in a plot.
• Those who wish to monitor changes over time for multiple related groups can use Matplotlib to create area plots.

Which Comes Out Ahead for Data Visualization?

Both Apache Superset and Matplotlib are great options for data visualization. However, choosing the right library for your specific visualization needs can depend on some factors, such as the type of data being collected and visualized, as well as the scope of the project. Here are some factors to be aware of when deciding which library is best for you:

• Both Apache Superset and Matplotlib are free and open-source, so in terms of cost, they are both great options.
• Matplotlib was not designed to handle exploratory data analysis, so for projects that require extensive exploratory analysis, Apache Superset comes out ahead.
• When more than one dataset is involved, Matplotlib can be unwieldy.
• For collaborative endeavors, Apache Superset provides dashboards and charts that can be easily shared. This allows users to seamlessly share the results of data exploration. So, for collaborative projects, using Apache Superset is a good option.
• For those who are working with time series data, Apache Superset comes out ahead of Matplotlib.
• Matplotlib requires users to write more code when generating visualizations. Therefore, for those who are not trained programmers, Apache Superset provides an easier-to-navigate interface for creating visualizations.

Ultimately, when selecting which Python library is best for your data visualization needs, both Apache Superset and Matplotlib offer helpful features and tools that help users to transform large datasets into actionable insights and beautiful visualizations.

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