Clear visualizations are essential for helping stakeholders quickly understand business performance. By transforming raw data into well-designed charts, you enable decision-makers to instantly spot trends, compare product results, and identify opportunities or issues. A good chart can reveal insights at a glance, making complex information accessible and actionable for everyone in your organization.


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 sys

class TestTask(unittest.TestCase):
    def setUp(self):
        # Patch plt.show to prevent window popup
        self._original_show = plt.show
        plt.show = lambda *a, **kw: None

    def tearDown(self):
        plt.show = self._original_show
        plt.close('all')

    def test_bars_and_labels(self):
        import user_code
        importlib.reload(user_code)
        summary_dict = {"Widget": 15000, "Gadget": 12000, "Doohickey": 9000}

        plt.close('all')
        user_code.plot_sales_performance(summary_dict)
        fig = plt.gcf()
        ax = fig.axes[0] if fig.axes else None
        _dynamic_test(
            self,
            ax is not None,
            "Figure and axes created.",
            "No axes found in figure.",
        )
        bars = [rect for rect in ax.patches if rect.get_width() > 0]
        _dynamic_test(
            self,
            len(bars) == len(summary_dict),
            f"Chart displays {len(summary_dict)} bars.",
            f"Expected {len(summary_dict)} bars, found {len(bars)}.",
        )
        xlabels = [tick.get_text() for tick in ax.get_xticklabels()]
        for prod in summary_dict:
            _dynamic_test(
                self,
                prod in xlabels,
                f"Product '{prod}' is shown on x-axis.",
                f"Product '{prod}' not found in x-axis labels: {xlabels}",
            )
        ylabel = ax.get_ylabel()
        _dynamic_test(
            self,
            ylabel and ("revenue" in ylabel.lower()),
            f"Y-axis labeled for revenue.",
            f"Y-axis label missing or incorrect: '{ylabel}'",
        )
        title = ax.get_title()
        _dynamic_test(
            self,
            title and ("sales" in title.lower()),
            f"Chart includes a title.",
            f"Chart title missing or incorrect: '{title}'",
        )
        # Check for value labels above bars
        found_labels = [t.get_text() for t in ax.texts if t.get_text().isdigit()]
        for revenue in summary_dict.values():
            _dynamic_test(
                self,
                str(revenue) in found_labels,
                f"Revenue value label '{revenue}' displayed above bar.",
                f"Revenue value label '{revenue}' not found among: {found_labels}",
            )

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

Explore the essentials of data analysis for business intelligence using Python. This course emphasizes hands-on data cleaning, exploration, and visualization, using real-world business datasets and interactive exercises. Automation and advanced ETL topics are excluded, keeping the course streamlined for foundational skills.

Challenge: Visualize Sales Performance

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