You have already learned how customer segmentation helps startups understand their audience and personalize strategies. By dividing customers into groups based on shared traits—such as age, business type, or spending—startups can tailor offerings, marketing, and support for greater impact. Visualizing spending patterns across these segments lets you quickly spot which groups are most valuable, where new opportunities might lie, and how to allocate resources effectively. A well-crafted visualization can reveal differences in purchasing behavior between, say, startup and enterprise clients, guiding smarter business decisions.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import sys
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def test_boxplot_and_labels(self):
        import user_code
        import matplotlib
        matplotlib.use('Agg')  # Use non-interactive backend for testing
        importlib.reload(user_code)
        # Patch plt.show to prevent GUI blocking
        import matplotlib.pyplot as plt
        shown = {'called': False}
        orig_show = plt.show
        def fake_show(*args, **kwargs):
            shown['called'] = True
        plt.show = fake_show
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.visualize_customer_segments()
        plt.show = orig_show
        # Check that the plot was shown
        _dynamic_test(
            self,
            shown['called'],
            "Plot was displayed using plt.show().",
            "Plot was not displayed with plt.show()."
        )
        # Check that the plot has a title and axis labels
        fig = plt.gcf()
        ax = fig.axes[0] if fig.axes else None
        has_title = hasattr(ax, 'get_title') and ax.get_title() != ''
        has_xlabel = hasattr(ax, 'get_xlabel') and ax.get_xlabel() != ''
        has_ylabel = hasattr(ax, 'get_ylabel') and ax.get_ylabel() != ''
        _dynamic_test(
            self,
            has_title and has_xlabel and has_ylabel,
            "Plot includes title and axis labels.",
            f"Plot missing title or axis labels. Title: {getattr(ax, 'get_title', lambda: None)()}, X: {getattr(ax, 'get_xlabel', lambda: None)()}, Y: {getattr(ax, 'get_ylabel', lambda: None)()}"
        )

    def test_insight_output(self):
        import user_code
        import matplotlib
        matplotlib.use('Agg')
        importlib.reload(user_code)
        import matplotlib.pyplot as plt
        plt.show = lambda *a, **k: None  # Patch show
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.visualize_customer_segments()
        output = normalize_text(f.getvalue())
        # Check for presence of 'enterprise' and 'startup' and 'spend' in output
        insight_words = ['enterprise', 'startup', 'spend']
        found = all(w in output for w in insight_words)
        _dynamic_test(
            self,
            found,
            "Insight summary mentions both segments and spending.",
            f"Insight summary missing required information: {output}"
        )

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

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Harness the power of Python for data analysis and visualization to make informed decisions that drive startup growth.

Apply Python to solve growth, marketing, and customer insight challenges faced by startup founders.

Challenge: Visualize Customer Segments

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Challenge: Visualize Customer Segments

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