Summary  
This chapter introduces a high-level plotting library that simplifies creating statistical visualizations through concise function calls, built-in styling, and seamless integration with tabular data.  

General domain of usage  
Data visualization

**Seaborn** is a high-level plotting library based on `matplotlib`. Let's have a look at its advantages in comparison to `matplotlib`:
* simpler interface;
* a wide range of default styles and color palettes; 
* works well with `pandas` DataFrame;
* a wide range of built-in statistical functions.

Here is an example of a plot created using `seaborn` with just one line of code:

Apparently, everything you can accomplish using `seaborn` can be also done with `matplotlib`, yet it may often take more time and effort. 

Note

However, `matplotlib` being a rather **low-level** library in comparison, offers more **flexibility** for your plots, so if you need to create a rather unique plot with a lot of customizations, `matplotlib` is still the best choice.

import unittest
import inspect
import ast
import user_code  # The user's script with `import seaborn as sns`

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestSeabornImport(unittest.TestCase):
    
    @classmethod
    def setUpClass(cls):
        source = inspect.getsource(user_code)
        cls.tree = ast.parse(source)

        cls.imports = [
            node for node in ast.walk(cls.tree)
            if isinstance(node, ast.Import)
        ]

        cls.seaborn_import = next(
            (imp for imp in cls.imports
             for alias in imp.names
             if alias.name == 'seaborn' and alias.asname == 'sns'),
            None
        )

    def test_seaborn_is_imported_with_alias(self):
        _dynamic_test(
            self,
            self.seaborn_import is not None,
            "seaborn is imported with alias sns",
            "seaborn is not imported with alias sns"
        )

if __name__ == '__main__':
    unittest.main()


test_seaborn_import.py

Data is everywhere around us, and making sense of it is extremely important. Visualization helps you deal with data by finding certain patterns and insights in it. You will develop a solid foundation of data visualization using Python and its libraries, such as matplotlib and seaborn, to get as much information from data as possible in a neat and concise way.

Discover the essentials of data visualization with Matplotlib. Learn its core concepts, explore its advantages, and create your first simple plot using this fundamental plotting library.

Master how to visualize data through the most popular plot types. Learn to build line, scatter, and bar charts to clearly communicate insights from your data.

Learn to make your plots more informative and visually appealing. Add titles, legends, colors, and grids, and discover how to arrange multiple subplots effectively.

Explore statistical visualizations that help analyze data distributions and patterns. Create histograms, box plots, and pie charts to uncover deeper statistical insights.

Take your visualization skills to the next level with Seaborn. Create advanced plots like countplots, KDEs, pair plots, and heatmaps while mastering Seaborn’s elegant style and customization options.

Seaborn Introduction

Solution