Hands-On Predictive Analytics with Python

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Seaborn

This is a high-level visualization library that specializes in producing statistical plots commonly used in data analysis. The advantage of using Seaborn is that with very few lines of code it can produce highly complex multi-variable visualizations, which are, by the way, very pretty and professional-looking.

The Seaborn library helps us in creating attractive and informative statistical graphics in Python. It is built on top of matplotlib, with a tight PyData stack integration. It supports NumPy and pandas data structures and statistical routines from SciPy and statsmodels.

Some of the features that Seaborn offers are built-in themes for styling matplotlib graphics tools for choosing color palettes to make beautiful plots that reveal patterns in your data, functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data, tools that fit and visualize linear regression models for different kinds of independent and dependent variables, functions that visualize matrices of data and use clustering algorithms to discover structure in those matrices, a function to plot statistical time series data with flexible estimation and representation of uncertainty around the estimate, and high-level abstractions for structuring grids of plots that let you easily build complex visualizations

Seaborn aims to make visualization a central part of exploring and understanding data. The plotting functions operate on DataFrames and arrays containing a complete dataset, which is why it is easier to work with Seaborn when doing data analysis.

We will use Seaborn through the book; we will introduce a lot of useful visualizations, especially in Chapter 3, Dataset Understanding – Exploratory Data Analysis.

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