Summary  
This chapter explains how to use a plotting library’s procedural interface to generate and customize various graphical charts with minimal code, including options for interactivity.

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

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.

matplotlib

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