https://seaborn.pydata.org/introduction.html
https://python-graph-gallery.com/seaborn/

Seaborn Documentation

The experience of using seaborn can be shared here, including problems, summary, solutions and etc.

Seaborn useful functions

Seaborn is another Python data visualization library. Seaborn is built on Matplotlib, meaning that it uses matplotlib to create visualizations. This library also works closely with Pandas and NumPy. With Seaborn, we can easily visualize statistical relationships and distributions of data.

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Data visualization tools, such as seaborn, Bokeh, and matplotlib, are essential for manually inspecting datasets. By plotting the data, practitioners can visually identify underlying data quality issues—like extreme outliers or recording errors—and develop an intuition for the type of problems they may need to address before feeding data into a model.

Data Visualization Libraries

Dive into Deep Learning

Matplotlib is a Python library for "creating static, animated, and interactive visualizations." Some examples of these visualizations are histograms, bar charts, scatter plots, and image plots.

Matplotlib

https://www.geeksforgeeks.org/difference-between-matplotlib-vs-seaborn/

Difference Between Matplotlib and Seaborn

Seaborn

The plotly Python library is an interactive, open-source plotting library that supports over unique chart types covering a wide range of statistical, financial, geographic, and scientific graphs. Built on top of the Plotly JavaScript library, plotly enables python users to create interactive web-based visualizations. This library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library. 

Plotly

Bokeh is a data visualization library that helps practitioners to manually inspect datasets. Visualizing data with tools like Bokeh allows developers to identify data quality issues and develop intuitions about the types of problems they need to address before feeding the data into a machine learning model.

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