Python Data Visualization Libraries:

Python is one of the most innovative and popular tools for data visualization, the good news is that it doesn’t take much to create a Python visualization, since this language has been around for over twenty years and has accumulated exclusive libraries. Analyzing and predicting events with data is a valuable skill, but it is also able to convey these findings to others who do not have familiarity with the data sets.

The most common type of display is a simple bar chart, this is a popular and commonly used type of display to make a comparison between values ​​and variety of categories. But we can use this simple graph up to Excel, but what if we want to add more value to the data, for this we have several Python libraries to create detailed graphs that represent what we want to explain, then there are the most used libraries:

Matplotlib:

Despite having more than a decade, it is still the most used library for viewing in the Python community. Because it was Python’s first data visualization library, many other libraries were built on top or designed to work together with it during the analysis, some of them are Pandas and Seaborn.

While matplotlib is good for getting an idea of ​​the data, it is not very useful for creating graphics with quality of publication quickly and easily, it has even been criticized for its predetermined styles, which has a distinctive 90’s feel.

Seaborn:

Seaborn integrates very well with Pandas and is another open source software library for data analysis and visualization. It is a popular library to make attractive graphs of statistical data in Python.

Take advantage of the power of matplotlib to create beautiful graphics in a few lines of code. The key difference is Seaborn’s default styles and color palettes, which are designed to be more aesthetic and modern, among the features they offer are:

  • Several built-in themes that improve matplotlib default aesthetic.
  • Tools to choose color palettes to make beautiful patterns that reveal patterns in your data.
  • Functions to visualize distributions or to compare them between subsets of data.
  • Tools that adjust and visualize linear regression models for different types of independent and dependent variables.
  • Functions that visualize data matrices and use clustering algorithms to discover the structure in those matrices.

Bokeh:

Bokeh is an open source library and free to use for any type of project, it is versatile and integrates very well with javascript. This is an interactive library that was created for modern web browsers to visualize highly interactive plots and data applications. With Bokeh you can create any type of graphic diagram, including dashboards and variety of graphics. Therefore, its strength lies in the ability to create interactive graphics ready for the web, which makes it different from the two libraries mentioned above.

Bokeh provides three interfaces with different levels of control to adapt to different types of users:

The highest level is to create charts quickly, it includes methods to create common charts such as bar charts, box diagrams and histograms.

Pygal:

It is a part of the Python library that exports vector graphics in different shapes and styles, is free open source and has been widely used due to its high customization and simplicity options at the same time. The options to create visualizations are very open and include pie charts, bar charts, histograms, maps, among others, it all depends on the aspect and the required sensations of the chart.

Its main differentiator is the ability to generate SVG graphics or scalable vector graphics. While working with small data sets, SVG will do well, but if you are creating graphs with hundreds of thousands of data points, you will have trouble rendering and they will become slow.

Plotly:

This library is also known as Plot.ly due to its online platform. It is an online visualization tool that is used for data analysis, scientific graphs and other visualizations, there are many interactive visualizations of professional quality online that were created with this module.

It is different from the other Python libraries because it is an interactive online tool that creates representations, therefore, what is being created with it is published on the web. The graphics created are highly interactive with tool tips and a variety of other options, such as zoom effect, panning, auto scale selection, motion, reset, among others.

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