In digital marketing, summarizing campaign performance is crucial for understanding the effectiveness of your strategies and communicating results to stakeholders. The most important metrics to include in a campaign performance summary are **total impressions**, which show how many times your ads were displayed; **average click-through rate (CTR)**, which measures the percentage of impressions that resulted in clicks and reflects audience engagement; and the **conversion rate**, which indicates the proportion of clicks that led to a desired action such as a purchase or signup. Identifying the campaign with the highest conversion rate helps you recognize what resonates best with your audience and informs future optimizations.


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 sys
import pandas as pd

class TestTask(unittest.TestCase):
    def setUp(self):
        self.data = {
            'campaign': ['Spring Sale', 'Summer Splash', 'Fall Fest', 'Winter Warmup'],
            'impressions': [10000, 15000, 8000, 12000],
            'clicks': [500, 600, 320, 480],
            'conversions': [50, 90, 40, 60]
        }
        self.df = pd.DataFrame(self.data)
        self.expected_total_impressions = 10000 + 15000 + 8000 + 12000  # 45000
        ctrs = [500/10000, 600/15000, 320/8000, 480/12000]
        self.expected_average_ctr = sum(ctrs) / 4
        conversion_rates = [50/500, 90/600, 40/320, 60/480]
        self.expected_top_campaign = self.data['campaign'][conversion_rates.index(max(conversion_rates))]

    def _run_function_and_capture(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.summarize_campaign_performance(self.df)
        output = f.getvalue().strip().split('\\n')
        return output

    def test_total_impressions(self):
        output = self._run_function_and_capture()
        _dynamic_test(
            self,
            any(str(self.expected_total_impressions) == line.strip() for line in output),
            f"Correct total impressions ({self.expected_total_impressions}) printed",
            f"Expected total impressions {self.expected_total_impressions} in output, got: {output}",
        )

    def test_average_ctr(self):
        output = self._run_function_and_capture()
        found = False
        for line in output:
            try:
                val = float(line)
                # Acceptable error threshold for float comparison
                if abs(val - self.expected_average_ctr) < 1e-6:
                    found = True
                    break
            except Exception:
                continue
        _dynamic_test(
            self,
            found,
            f"Correct average CTR ({self.expected_average_ctr}) printed",
            f"Expected average CTR {self.expected_average_ctr} in output, got: {output}",
        )

    def test_top_campaign(self):
        output = self._run_function_and_capture()
        _dynamic_test(
            self,
            any(self.expected_top_campaign == line.strip() for line in output),
            f"Correct top campaign ('{self.expected_top_campaign}') printed",
            f"Expected top campaign '{self.expected_top_campaign}' in output, got: {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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Learn how to use Python for analyzing campaign data, extracting insights, and making data-driven decisions in digital agencies.

Master the art of generating clear, insightful reports and visualizations for clients using Python.

Challenge: Campaign Performance Summary

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Challenge: Campaign Performance Summary

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