There are lots of libraries in Python to do the data visualization. Out of those today we will explore and get the information about two libraries:
- The most widely used library for plotting in the Python community
- It was designed to closely resemble MATLAB, a proprietary programming language developed in the 1980s
- Because matplotlib was the first Python data visualization library, many other libraries are built on top of it or designed to work in tandem with it during analysis.
- One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends. Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish. This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib. It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.
Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package.
import matplotlib as mpl import matplotlib.pyplot as plt
- For more details refer here
Multiple subplots in one figure
And many more, please refer detailes on my GitHub
- It’s default styles and color palettes, which are designed to be more aesthetically pleasing and modern.
- Since Seaborn is built on top of matplotlib, you’ll need to know matplotlib to tweak Seaborn’s defaults.
- Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas DataFrames.
- For more details refer here.
And many more, please refer details on my GitHub
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