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Data Visualization Cheat Sheet with Seaborn and Matplotlib
Introduction
Exploratory Data Analysis — EDA is an indispensable step in data mining. To interpret various aspects of a data set like its distribution, principal or interference, it is necessary to visualize our data in different graphs or images. Fortunately, Python offers a lot of libraries to make visualization more convenient and easier than ever. Some of which are widely used today such as Matplotlib, Seaborn, Plotly or Bokeh.
Since my job concentrates on scrutinizing all angles of data, I have been exposed to many types of graphs. However, because there are way too many functions and the codes are not easy to remember, I sometimes forget the syntax and have to review or search for similar codes on the Internet. Without doubt, it has wasted a lot of my time, hence my motivation for writing this article. Hopefully, it can be a small help to anyone who has a memory of a goldfish like me.
Data Description
My dataset is downloaded from public Kaggle dataset. It is a grocery dataset, and you can easily get the data from the link below:
