In the world of analytics, the best way to get insights is by visualizing the data. Data can be visualized by representing it as plots which are easy to understand, explore and grasp.Such data helps in drawing the attention towards key elements. Just as Matplotlib is a widely used 2D plotting library to analyse a set of data using Python, the popular enhanced visualization library used is Seaborn. It is built on top of Matplotlib.
Seaborn vs Matplotlib
It is generally accepted that Matplotlib “makes easy tasks easier”, while Seaborn “makes even a well-defined set of hard tasks easy”. Seaborn also helps resolve two major problems faced by Matplotlib, namely default Matplotlib parameters and working with dataframes. As Seaborn compliments and extends Matplotlib, the learning curve is quite gradual. Simply put, Seaborn brings serious power to the table that other tools can’t. Developers familiar with Matplotlib are almost half-way through Seaborn.
Seaborn is built on top of Python’s core visualization library Matplotlib. It is meant to serve as a complement, and not a replacement. However, Seaborn comes with some very important features. Here are a few of them.
● Built in themes for styling Matplotlib graphics
● Visualizing univariate and bivariate data
● Fitting in and visualizing linear regression models
● Plotting statistical time series data
● Seaborn works well with NumPy and Pandas data structures
● It comes with built in themes for styling Matplotlib graphics
Dataset based interface
Seaborn offers high-level dataset based interface to make amazing statistical graphics. Data visualization and storytelling are both important for machine learning projects, as they often require exploratory analysis of datasets to decide on the type of machine learning algorithm to apply. With Seaborn’s ML library, it is simple to create certain types of plots like time series, heat maps, and violin plots. Its features allow performing statistical estimation at the time of combining data across observations, plotting and visualizing the suitability of statistical models to strengthen dataset patterns. However, developers in most cases continue to use Matplotlib for simple plotting, and its knowledge is recommended to tweak Seaborn’s default plots.