Summary  
Demonstrates how to leverage a high-level plotting library to quickly implement statistical plots with default styles and seamless DataFrame integration.

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.

First, we will discuss plotting applications, Matplotlib library, and its advantages. Afterwards you will create your first simple plot using Matplotlib.

Now you are ready to create various commonly used in practice plots. Without further ado, let's dive in right now!

Plotting and visualization is not only about creating a plot or several plots, it is also about making them clear, understandable and pleasant to look at. Time to learn plots customization in maplotlib, let's do it!

Statistics is essential in working with data, hence why it is extremely important to know how to create various statistical plots. Now it's time for us to explore the most common ones.

Seaborn is another powerful plotting library in Python built on top of matplotlib. Now that you are familiar with matplotlib, it's time to dive into visualization with seaborn.

Seaborn Introduction

Solution

Awesome!

Seaborn Introduction

Solution

Awesome!