Python Visualization Help Sheet
For most beginners, the first Python data visualization library that they use is, naturally, Matplotlib. It is a Python 2D plotting library that enables users to make publication-quality figures. It is quite an extensive library where a cheat sheet will definitely come in handy when you’re learning, but when you manage to use this library effectively, you’ll also be able to get insights and work better with other packages, such as Pandas, that intend to build more plotting integration with Matplotlib as time goes on.
Another package that you’ll be able to tackle easily is Seaborn, the statistical data visualization library of Python.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative 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.
Python Seaborn Help Sheet:
Visualization using Matplotlib:
Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line.
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.
Recent Matplotlib versions make it relatively easy to set new global plotting styles and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs — for example, Seaborn ggpy, holoviews altairs, and even Pandas itself can be used as wrappers around Matplotlib’s API.
Matplotlib Help sheet:
