
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout
import pandas as pd

class TestTask(unittest.TestCase):
    def setUp(self):
        self.data = {
            "sales": [200, 450, 300, 150, 500],
            "conversions": [4, 9, 6, 2, 10],
            "visits": [100, 200, 150, 80, 250]
        }
        self.df = pd.DataFrame(self.data)
        self.expected_total_sales = 200 + 450 + 300 + 150 + 500
        self.expected_avg_order = (200 + 450 + 300 + 150 + 500) / 5
        self.expected_conversion_rate = ((4 + 9 + 6 + 2 + 10) / (100 + 200 + 150 + 80 + 250)) * 100

    def test_total_sales_displayed(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_metrics_dashboard(self.df)
        output = normalize_text(f.getvalue())
        expected = normalize_text(f"Total Sales: ${self.expected_total_sales:,.2f}")
        _dynamic_test(
            self,
            expected in output,
            f"Total sales correctly displayed as {expected}",
            f"Expected total sales output: '{expected}' not found in: '{output}'",
        )

    def test_avg_order_value_displayed(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_metrics_dashboard(self.df)
        output = normalize_text(f.getvalue())
        expected = normalize_text(f"Average Order Value: ${self.expected_avg_order:,.2f}")
        _dynamic_test(
            self,
            expected in output,
            f"Average order value correctly displayed as {expected}",
            f"Expected average order value output: '{expected}' not found in: '{output}'",
        )

    def test_conversion_rate_displayed(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_metrics_dashboard(self.df)
        output = normalize_text(f.getvalue())
        expected = normalize_text(f"Conversion Rate: {self.expected_conversion_rate:.2f}%")
        _dynamic_test(
            self,
            expected in output,
            f"Conversion rate correctly displayed as {expected}",
            f"Expected conversion rate output: '{expected}' not found in: '{output}'",
        )

    def test_dashboard_format(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_metrics_dashboard(self.df)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            normalize_text("=== Metrics Dashboard ===") in output,
            "Dashboard heading is present in output",
            "Dashboard heading missing in output",
        )
        # Check that all three metrics are present
        total_sales = normalize_text(f"Total Sales: ${self.expected_total_sales:,.2f}")
        avg_order = normalize_text(f"Average Order Value: ${self.expected_avg_order:,.2f}")
        conv_rate = normalize_text(f"Conversion Rate: {self.expected_conversion_rate:.2f}%")
        _dynamic_test(
            self,
            total_sales in output and avg_order in output and conv_rate in output,
            "All metrics are present in dashboard output",
            f"One or more metrics missing in output: {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

Advance your Python automation skills by learning to generate analytical summaries, business reports, and visual dashboards automatically. This course guides you through practical workflows for automating data analysis, reporting, and visualization tasks using Python, pandas, matplotlib, and seaborn.

Learn to automate the generation of analytical summaries, business reports, and visual dashboards using Python. Alternate between concept introduction and hands-on automation tasks for real-world reporting scenarios.

Challenge: Build a Simple Metrics Dashboard

Task: Business Metrics Dashboard

Lösung