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Python graphics

In Python you can find a lot of different tools for presenting visual information. In this section I will try to describe some of these visualization modules and give some examples of their functionality.

General Python graphics

Graphical libraries with almost universal functionality for general purposes.

Matplotlib - Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Seaborn - Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Pygal - Very good Python library for building SVG or PNG files from your data. It can plot main statistical charts, as well as world map.

Bokeh - Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself.

ggplot - Ggplot is a matplotlib based visualization library, derived from ggplot2 in R.

VisPy - VisPy is a high-performance interactive 2D/3D data visualization library leveraging the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets.

Mayavi - Mayavi is a general purpose, cross-platform tool for 3-D scientific data visualization.

Specific Python graphics

Graphical libraries for special tasks

Wordcloud - Word cloud is a technique for visualising frequent words in a text where the size of the words represents their frequency. Read my examples how to use wordcloud in Python

Published: 2021-09-30 03:43:58
Updated: 2022-09-26 21:16:45

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