Understanding your audience is crucial for creating content that resonates and drives engagement. Visualizing demographic distributions, such as age groups, provides a clear picture of who is watching your content. This insight allows you to tailor your messaging, select relevant topics, and optimize your content strategy for maximum impact. By representing age data visually, you can quickly identify which groups are most engaged and where there may be opportunities to reach new viewers.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import user_code

class TestTask(unittest.TestCase):
    def setUp(self):
        importlib.reload(user_code)

    def test_grouping_counts(self):
        test_ages = [10, 17, 18, 23, 24, 25, 30, 34, 35, 40, 44, 45, 50, 54, 55, 60, 80]
        expected = [2, 3, 3, 3, 3, 3]
        # Patch plt.show to prevent actual chart from displaying
        plt_show_original = plt.show
        plt.show = lambda: None
        try:
            fig = plt.figure()
            user_code.visualize_age_distribution(test_ages)
            ax = plt.gca()
            bars = ax.patches
            actual = [int(bar.get_height()) for bar in bars]
            _dynamic_test(
                self,
                actual == expected,
                f"Grouping and counting is correct: {actual}",
                f"Expected group counts {expected}, got {actual}"
            )
        finally:
            plt.show = plt_show_original
            plt.close('all')

    def test_number_of_bars(self):
        test_ages = [10, 20, 30, 40, 50, 60]
        plt_show_original = plt.show
        plt.show = lambda: None
        try:
            fig = plt.figure()
            user_code.visualize_age_distribution(test_ages)
            ax = plt.gca()
            bars = ax.patches
            _dynamic_test(
                self,
                len(bars) == 6,
                "Number of bars matches the number of age groups",
                f"Expected 6 bars, got {len(bars)}"
            )
        finally:
            plt.show = plt_show_original
            plt.close('all')

    def test_x_axis_labels(self):
        test_ages = [10, 20, 30, 40, 50, 60]
        expected_labels = ['<18', '18-24', '25-34', '35-44', '45-54', '55+']
        plt_show_original = plt.show
        plt.show = lambda: None
        try:
            fig = plt.figure()
            user_code.visualize_age_distribution(test_ages)
            ax = plt.gca()
            labels = [tick.get_text() for tick in ax.get_xticklabels()]
            # Matplotlib may not set tick labels if not enough space; check if all labels are present
            found_labels = set(labels)
            _dynamic_test(
                self,
                all(label in found_labels for label in expected_labels),
                "All x-axis labels are correct",
                f"Expected labels {expected_labels}, got {labels}"
            )
        finally:
            plt.show = plt_show_original
            plt.close('all')

    def test_y_axis_counts(self):
        test_ages = [10, 17, 18, 23, 24, 25, 30, 34, 35, 40, 44, 45, 50, 54, 55, 60, 80]
        expected = [2, 3, 3, 3, 3, 3]
        plt_show_original = plt.show
        plt.show = lambda: None
        try:
            fig = plt.figure()
            user_code.visualize_age_distribution(test_ages)
            ax = plt.gca()
            bars = ax.patches
            actual = [int(bar.get_height()) for bar in bars]
            _dynamic_test(
                self,
                actual == expected,
                "Y-axis counts match expected group counts",
                f"Expected counts {expected}, got {actual}"
            )
        finally:
            plt.show = plt_show_original
            plt.close('all')

    def test_chart_title(self):
        test_ages = [10, 20, 30, 40, 50, 60]
        plt_show_original = plt.show
        plt.show = lambda: None
        try:
            fig = plt.figure()
            user_code.visualize_age_distribution(test_ages)
            ax = plt.gca()
            title = ax.get_title()
            _dynamic_test(
                self,
                title == 'Audience Age Distribution',
                "Chart title is correct",
                f"Expected title 'Audience Age Distribution', got '{title}'"
            )
        finally:
            plt.show = plt_show_original
            plt.close('all')

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)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

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


test_main.py

Unlock the power of Python to automate, analyze, and enhance your content creation workflow. This course is designed for content creators who want to leverage Python for tasks such as automating repetitive processes, analyzing audience data, and generating creative content. With hands-on challenges and engaging theory, you'll learn to solve real-world problems and boost your productivity.

Learn how to use Python to automate repetitive tasks in content creation, such as renaming files, organizing assets, and generating batch content.

Use Python to analyze and visualize audience engagement data, helping content creators make data-driven decisions.

Explore how Python can assist in generating creative content, such as captions, summaries, and batch content for various platforms.

Challenge: Visualize Audience Age Distribution

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