Summary  
This chapter introduces a high-level plotting library that simplifies creation of statistical graphics through a concise interface, built-in styling, and seamless integration with tabular data.

General domain of usage  
Statistical 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

Learn how to explore and communicate data through visual analysis. The course covers creating common plots, customizing visualizations, and using statistical charts to examine data distributions, relationships, and patterns. Exploratory data analysis techniques are applied to identify trends, anomalies, and insights in real-world datasets.

seaborn

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