Summary  
This chapter covers how to create bar charts using a plotting library, showing how to map categorical labels to values and customize the appearance of bars via parameters like color and width.  

General domain of usage  
Data visualization

A **bar chart**, also known as a **bar graph**, is a visual representation where categorical data is displayed using rectangular bars. The height or length of these bars is directly proportional to the values they depict.

import matplotlib.pyplot as plt
programming_languages = ['Java', 'Python', 'C#', 'C++']
shares = [30, 40, 17, 13]
# Creating a bar chart with separate colors for each bar
plt.bar(programming_languages, shares, color=['blue', 'green', 'red', 'yellow'])
plt.show()

## Bar Chart Parameters

To create a bar chart, the `bar()` function from `pyplot` requires two main arguments:

* a list of labels for the **x-axis**;
* and the corresponding **heights** of the bars.

You can customize the chart using optional parameters like:

* `color` — sets the color(s) of the bars (default is blue);
* `width` — defines the width of the bars (default is `0.8`). This can be a single number or a list of values for individual bars.


import matplotlib.pyplot as plt
programming_languages = ['Python', 'Java', 'C#', 'C++']
shares = [40, 30, 17, 13]
# Specify the bars colors and widths
plt.bar(programming_languages, shares, color=['blue', 'green', 'red', 'yellow'], width=[0.9, 0.7, 0.5, 0.3])
plt.show()

The plot demonstrates a consistent decrease in bar width from left to right, aligning with the specified values.

As with other plots, you can always refer to the <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html" target="_blank"><svg width="1em" height="1em" viewBox="0 0 30 32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill="none" stroke="#098f67" style="stroke: var(--color1, #098f67)" stroke-linejoin="miter" stroke-linecap="round" stroke-miterlimit="4" stroke-width="2.4156" d="M17.289 17.305v0c-1.754 1.754-4.597 1.754-6.351 0l-7.76-7.759c-1.755-1.755-1.755-4.601 0-6.356v0c1.753-1.753 4.595-1.756 6.351-0.005l6.208 6.187"></path><path fill="none" stroke="#098f67" style="stroke: var(--color1, #098f67)" stroke-linejoin="miter" stroke-linecap="round" stroke-miterlimit="4" stroke-width="2.4156" d="M12.504 13.97v0c1.754-1.754 4.597-1.754 6.351 0l7.762 7.762c1.754 1.754 1.754 4.597 0 6.351v0c-1.754 1.754-4.597 1.754-6.351 0l-5.953-5.953"></path></svg> documentation</a> for a quick lookup of the `bar()` function syntax and all of its parameters.

Definition

import unittest
import ast

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 TestUserCode(unittest.TestCase):
    def setUp(self):
        with open("user_code.py", "r") as f:
            self.tree = ast.parse(f.read())

    def test_bar_function_used(self):
        """Check if the bar() function is used from plt."""
        found = any(
            isinstance(node, ast.Call) and
            isinstance(node.func, ast.Attribute) and
            node.func.attr == "bar"
            for node in ast.walk(self.tree)
        )
        _dynamic_test(
            self,
            found,
            "Bar chart function used correctly",
            "Expected the bar() function to be used"
        )

    def test_arguments_passed_correctly(self):
        """Check if countries and gdp_list are passed in the correct order."""
        for node in ast.walk(self.tree):
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr == "bar":
                args = node.args
                if len(args) >= 2:
                    is_valid = (
                        isinstance(args[0], ast.Name) and args[0].id == "countries" and
                        isinstance(args[1], ast.Name) and args[1].id == "gdp_list"
                    )
                    _dynamic_test(
                        self,
                        is_valid,
                        "Arguments passed in correct order",
                        "Expected countries and gdp_list as first two arguments to bar()"
                    )
                    return
        _dynamic_test(
            self,
            False,
            "",
            "No valid bar() call with expected arguments found"
        )

    def test_width_keyword_argument(self):
        """Check if width keyword is used with specific values."""
        expected_widths = [0.6, 0.45, 0.9, 0.2]
        for node in ast.walk(self.tree):
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) and node.func.attr == "bar":
                for kw in node.keywords:
                    if kw.arg == "width" and isinstance(kw.value, ast.List):
                        widths = [elt.n for elt in kw.value.elts if isinstance(elt, ast.Constant)]
                        is_correct = widths == expected_widths
                        _dynamic_test(
                            self,
                            is_correct,
                            "Width values used correctly",
                            f"Expected width={expected_widths}, but got {widths}"
                        )
                        return
        _dynamic_test(
            self,
            False,
            "",
            "Expected keyword argument width with specific values not found"
        )

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


test_bar_chart.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.

Bar Chart

Bar Chart Parameters

Oplossing