Visualizing trends in business data is essential for anticipating demand and planning resources effectively. By transforming raw numbers into clear, interpretable line charts, you can quickly spot patterns such as seasonal peaks, gradual growth, or sudden drops. These insights help you forecast inventory needs, schedule staff, and allocate budgets more efficiently. Understanding the story your data tells through visualization empowers you to make informed decisions and stay ahead in a competitive market.


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 test_chart_has_correct_number_of_points(self):
        importlib.reload(user_code)
        sales = [100, 200, 300, 400]
        plt.close('all')
        user_code.plot_monthly_sales(sales)
        found = False
        for ax in plt.get_fignums():
            fig = plt.figure(ax)
            axes = fig.get_axes()
            if axes:
                lines = axes[0].get_lines()
                if lines:
                    xdata = lines[0].get_xdata()
                    ydata = lines[0].get_ydata()
                    condition = len(ydata) == len(sales)
                    _dynamic_test(
                        self,
                        condition,
                        "Chart has correct number of data points.",
                        f"Expected {len(sales)} points, got {len(ydata)}."
                    )
                    found = True
        _dynamic_test(
            self,
            found,
            "Chart was generated.",
            "No chart was generated by the function."
        )

    def test_x_axis_labels_are_months(self):
        importlib.reload(user_code)
        sales = [100, 200, 300, 400, 500]
        expected_labels = ["Jan", "Feb", "Mar", "Apr", "May"]
        plt.close('all')
        user_code.plot_monthly_sales(sales)
        found = False
        for ax in plt.get_fignums():
            fig = plt.figure(ax)
            axes = fig.get_axes()
            if axes:
                ticklabels = [label.get_text() for label in axes[0].get_xticklabels()]
                # Remove empty labels
                ticklabels = [l for l in ticklabels if l]
                match = all(month in ticklabels for month in expected_labels)
                _dynamic_test(
                    self,
                    match,
                    "X-axis labels correspond to correct months.",
                    f"Expected months: {expected_labels}, got: {ticklabels}"
                )
                found = True
        _dynamic_test(
            self,
            found,
            "Chart was generated.",
            "No chart was generated by the function."
        )

    def test_chart_has_title(self):
        importlib.reload(user_code)
        sales = [100, 200, 300]
        plt.close('all')
        user_code.plot_monthly_sales(sales)
        found = False
        for ax in plt.get_fignums():
            fig = plt.figure(ax)
            axes = fig.get_axes()
            if axes:
                title = axes[0].get_title()
                _dynamic_test(
                    self,
                    title != '',
                    "Chart includes a title.",
                    "No title found on the chart."
                )
                found = True
        _dynamic_test(
            self,
            found,
            "Chart was generated.",
            "No chart was generated by the function."
        )

    def test_data_points_are_marked(self):
        importlib.reload(user_code)
        sales = [5, 10, 15, 20]
        plt.close('all')
        user_code.plot_monthly_sales(sales)
        found = False
        for ax in plt.get_fignums():
            fig = plt.figure(ax)
            axes = fig.get_axes()
            if axes:
                lines = axes[0].get_lines()
                if lines:
                    marker = lines[0].get_marker()
                    _dynamic_test(
                        self,
                        marker is not None and marker != '' and marker != 'None',
                        "Each data point is marked on the line.",
                        "No marker found for data points."
                    )
                    found = True
        _dynamic_test(
            self,
            found,
            "Chart was generated.",
            "No chart was generated by the function."
        )

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 solve real-world business analysis problems. This course guides business analysts through practical Python applications, from data wrangling and visualization to automating reports and uncovering insights. Each section blends engaging theory with hands-on challenges inspired by business scenarios.

Learn how to use Python to clean, transform, and summarize business data for analysis. This section focuses on practical data wrangling skills essential for business analysts.

Discover how to use Python to create compelling visualizations that help communicate business insights effectively.

Harness Python to automate repetitive business analysis tasks, from generating reports to extracting actionable insights.

Challenge: Plot Monthly Sales Trend

Ratkaisu

Challenge: Plot Monthly Sales Trend

Ratkaisu