Data Visualization is a fun part of data analytics, where we draw different types of plots and charts to make the data visually drawn on the screen. Data visualization software is available to help you with data visualization. There are excel and other software that can help you draw the data and visualize it. But Python programming is the most widely accepted option for data visualization.
You can customize the visualization of the data with the python programming language. You have millions of options to visualize your data with python.
Python programming language provides Libraries for data visualization, that make your life easier. by just importing these libraries you can use the function and visualize your data the way you wanted.
In this article, I will introduce you to the Top 13 best Python Libraries for data visualization, which you can use in your project. If you are looking for tutorials for these Python libraries, The best option for you is the official documentation of these python libraries.
- Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent, and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair is developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab.
- BokehBokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
- bqplotbqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar of Graphics. In bqplot, every component of a plot is an interactive widget. This allows the user to integrate visualizations with other Jupyter interactive widgets to create integrated GUIs with a few lines of Python code. it provides a unified framework for 2-D visualizations with a pythonic API, Provide a sensible API for adding user interactions.
- CartopyCartopy is a Python package designed to make drawing maps for data analysis and visualization easy. The main features of Cartopy are, object-oriented projection definitions, point, line, polygon, and image transformations between projections, integration to expose advanced mapping in Matplotlib with a simple and intuitive interface, powerful vector data handling by integrating shapefile reading with Shapely capabilities.
- DashDash is a productive Python framework for building web applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It’s particularly suited for anyone who works with data in Python.
- DiagramsDiagrams let you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently support main major providers including AWS, Azure, GCP, Kubernetes, Alibaba Cloud, Oracle Cloud, etc. It also supports On-Premise nodes, SaaS and major Programming frameworks, and languages.
- MatplotlibMatplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib will help you Develop publication-quality plots with just a few lines of code, Use interactive figures that can zoom, pan, update.
- plotnineplotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot. Plotting with grammar is powerful, it makes custom (and otherwise complex) plots easy to think about and then create, while the simple plots remain simple.
- pygalpygal is a dynamic SVG charting library written in python. It is a well-documented Python library that you can learn very easily. Among them all, I like the documentation of pygal. You can do fun things to the data with the help of this python library.
- PyGraphvizPyGraphviz is a Python interface to the Graphviz graph layout and visualization package. With PyGraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. PyGraphviz provides a similar programming interface to NetworkX
- PyQtGraphPyQtGraph is a pure-python graphics and GUI library built on PyQt / PySide and NumPy. It is intended for use in mathematics / scientific / engineering applications. Despite being written entirely in python, the library is very fast due to its heavy leverage of NumPy for number crunching and Qt’s GraphicsView framework for fast display. PyQtGraph is distributed under the MIT open-source license.
- SeabornSeaborn is a very widely used python library for data visualization. You can learn it very easily by going to the official documentation of the seaborn. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on data frames and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.
- VisPyVisPy is a high-performance interactive 2D/3D data visualization library. VisPy leverages the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets. Applications of VisPy can be High-quality interactive scientific plots with millions of points, Direct visualization of real-time data. Fast interactive visualization of 3D models (meshes, volume rendering), OpenGL visualization demos, Scientific GUIs with fast, scalable visualization widgets (Qt or IPython notebook with WebGL).
Summary and Conclusion:-
So this was all about the data visualization library for the python programming language. Please write down your questions in the comment section if you have any. If you are interested in other python tutorials please visit my youtube channel Code with Ali.