Summary  
This chapter introduces Matplotlib’s `pyplot` submodule as an easy-to-use, open-source Python library for creating a variety of static and interactive plots with just a few lines of code.

General domain of usage  
data visualization

**Matplotlib** is a powerful and widely used Python library for creating static, animated, and interactive visualizations. It provides a flexible and customizable way to generate a wide range of plots and charts, including line plots, bar charts, histograms, scatter plots, and more.

Definition

Although it offers comprehensive functionality to create a variety of plot types, it remains surprisingly **easy to use**. On top of that, `matplotlib` is **open source**, which means it is freely available and continuously improved by a large community.

`matplotlib` is an excellent starting point for learning data visualization in Python, as many other libraries are built on top of it.

Before diving into `matplotlib` let's have a look at an example of the plot created with its help:

This plot can be created with only a few lines of code. Furthermore, it is possible to make it **interactive**, showcasing the capabilities of `matplotlib`.  

The majority of `matplotlib` utilities are located in the `pyplot` submodule, which is used throughout the course.


import unittest
import ast
import inspect
import user_code

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 TestMatplotlibImports(unittest.TestCase):
    def setUp(self):
        self.source = inspect.getsource(user_code)
        self.tree = ast.parse(self.source)

    def test_import_without_from(self):
        found = any(
            isinstance(node, ast.Import) and
            any(alias.name == "matplotlib.pyplot" and alias.asname == "plt" for alias in node.names)
            for node in self.tree.body
        )
        _dynamic_test(
            self,
            found,
            "Import without 'from' keyword is correct",
            "Missing or incorrect import without 'from' keyword"
        )

    def test_import_with_from(self):
        found = any(
            isinstance(node, ast.ImportFrom) and
            node.module == "matplotlib" and
            any(alias.name == "pyplot" and alias.asname == "plt" for alias in node.names)
            for node in self.tree.body
        )
        _dynamic_test(
            self,
            found,
            "Import with 'from' keyword is correct",
            "Missing or incorrect import with 'from' keyword"
        )

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


test_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.

Why Matplotlib Matters?

Solution

Awesome!

Why Matplotlib Matters?

Solution