Summary  
This chapter explains how to construct bar charts by calling a plotting library’s bar function with categorical labels and corresponding numerical values, and how to customize bar colors and widths.

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

Master the foundational skills of data visualization using the object-oriented approach in Matplotlib. Learn to control every aspect of your figures, from axes and legends to colors and styles. Create professional-quality static charts, handle time-series data, and export high-resolution graphics for reports and publications.

Constructing Bar Charts

Bar Chart Parameters

解答