Smith JamesSeaborn is a library for creating statistical graphs in Python. It is built on matplotlib and integrates tightly with pandas data structures. Seaborn helps you explore and understand data. Its plotting functions work with datasets and perform all the necessary transformations to create informative graphs.
Its dataset-oriented syntax allows you to focus on the graphs rather than the details of plotting them.
Bar Plot grouping in Seaborn
Often, you want to group bars in your charts based on a single characteristic. Let’s say you want to compare some common data, passenger survival rates, and group them by a given criteria.
We may want to visualize the number of surviving passengers, depending on the class (first, second and third), but also take into account the city they came from.
All this information can be easily displayed in a histogram.
Seaborn is easy to use, but it is quite difficult to navigate. The library comes with many built-in features and extensive documentation. It can be hard to figure out which arguments to use if you don’t need all the possible trickery.
If you need to compare many variables to each other, such as average life expectancy along with happiness scores and economic levels, there is no need to build a 3D chart.
While there are 2D charts that allow you to visualize the relationship between sets of variables, not all of them are easy to use.
Unfortunately, Seaborn lacks its own 3D engine. Being only an add-on to Matplotlib, it relies on the graphical capabilities of the main library. Nevertheless, we can still apply the Seaborn style to a 3D diagram.
With Seaborn, you can easily customize various elements of the diagrams you create. For example, it is possible to change the color and size of each point on the chart.
Seaborn automatically assigns different colors to different variables so that they can be easily visually distinguished. You can change the color of the charts by providing your own list of colors.
Violin Plot or Violin Charts are used to visualize the distribution of data by displaying the range of the data, the median, and the area of the data distribution.
Such charts, like mustache boxes, show summary statistics. Additionally, they include distribution density plots, which is what determines the shape/distribution of the data when visualized.
A point distribution plot can be overlaid on a violin diagram to see the placement of the points that make up that distribution. To do this, you simply create one drawing area and then create two diagrams sequentially in it.