The Most Visually Amazing Python Library — Seaborn
Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.
Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes 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.
Functions of Seaborn
- API Abstraction across visualizations: There is no universally best way to visualize data. Different questions are best answered by different plots. Seaborn makes it easy to switch between different visual representations by using a consistent dataset-oriented API.
- Statistical Estimation and Error Bars: Often, we are interested in the average value of one variable as a function of other variables. Many seaborn functions will automatically perform the statistical estimation that is necessary to answer these questions.
Statistical estimation in seaborn goes beyond descriptive statistics. For example, it is possible to enhance a scatterplot by including a linear regression model (and its uncertainty) using lmplot().
3. Informative Distributional Summaries: Statistical analyses require knowledge about the distribution of variables in your dataset. The seaborn function displot() supports several approaches to visualizing distributions. These include classic techniques like histograms and computationally-intensive approaches like kernel density estimation.
Seaborn also tries to promote techniques that are powerful but less familiar, such as calculating and plotting the empirical cumulative distribution function of the data.
4. Specialized plots for categorical data: Several specialized plot types in seaborn are oriented towards visualizing categorical data. They can be accessed through catplot(). These plots offer different levels of granularity. At the finest level, you may wish to see every observation by drawing a “swarm” plot: a scatter plot that adjusts the positions of the points along the categorical axis so that they don’t overlap.
5. Composite views onto multivariate datasets: Some seaborn functions combine multiple kinds of plots to quickly give informative summaries of a dataset. One, jointplot(), focuses on a single relationship. It plots the joint distribution between two variables along with each variable’s marginal distribution.
The other, pairplot(), takes a broader view: it shows joint and marginal distributions for all pairwise relationships and for each variable, respectively.
6. Classes and functions for making complex graphics: These tools work by combining axes-level plotting functions with objects that manage the layout of the figure, linking the structure of a dataset to a grid of axes. Both elements are part of the public API, and you can use them directly to create complex figures with only a few more lines of code.
7. Opinionated defaults and flexible customization: Seaborn creates complete graphics with a single function call: when possible, its functions will automatically add informative axis labels and legends that explain the semantic mappings in the plot.
In many cases, seaborn will also choose default values for its parameters based on characteristics of the data. For example, the color mappings that we have seen so far used distinct hues (blue, orange, and sometimes green) to represent different levels of the categorical variables assigned to hue. When mapping a numeric variable, some functions will switch to a continuous gradient.
There is a plethora of options you get with Seaborn as compared to matplotlib, to check out more amazing possibilities with seaborn check their website here.
In my opinion, data science is as effective as its presentation and with a library like Seaborn you can learn to master presenting statistics like never before. Seaborn is a must-have library and you can rest assured of gaining interest in data science projects with which we often tend to get slaggy.
Do tell me your opinions of Seaborn and its functions in the comments and also share more amazing thoughts about data science.
