When preparing a monthly client report, you want to focus on the metrics that matter most to your client’s business objectives. Essential elements typically include a breakdown of campaign performance by name, total ad spend, and the number of conversions achieved. Summarizing these figures for the entire month helps your client quickly understand the overall impact of their campaigns and supports data-driven decision making for future strategies. A clear, concise summary of these results not only highlights successes but also builds trust and transparency in your agency’s work.


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 = {
            'Campaign': ['Brand Awareness', 'Lead Generation', 'Retargeting'],
            'Spend': [1200.50, 2300.00, 800.75],
            'Conversions': [95, 210, 60]
        }
        self.df = pd.DataFrame(self.data)
        self.expected_total_spend = sum(self.data['Spend'])
        self.expected_total_conversions = sum(self.data['Conversions'])

    def test_total_spend_correct(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.generate_monthly_report(self.df)
        output = f.getvalue()
        _dynamic_test(
            self,
            f"${self.expected_total_spend:.2f}" in output,
            "Correct total spend is included in output.",
            f"Total spend ${self.expected_total_spend:.2f} not found in output: {output}"
        )

    def test_total_conversions_correct(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.generate_monthly_report(self.df)
        output = f.getvalue()
        _dynamic_test(
            self,
            str(self.expected_total_conversions) in output,
            "Correct total conversions is included in output.",
            f"Total conversions {self.expected_total_conversions} not found in output: {output}"
        )

    def test_campaign_breakdown_included(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.generate_monthly_report(self.df)
        output = f.getvalue()
        # Check that each campaign name is present in the output
        for campaign in self.data['Campaign']:
            _dynamic_test(
                self,
                campaign in output,
                f"Campaign '{campaign}' is included in the summary.",
                f"Campaign '{campaign}' not found in output: {output}"
            )

    def test_totals_labels_included(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.generate_monthly_report(self.df)
        output = f.getvalue()
        _dynamic_test(
            self,
            "Total Spend" in output and "Total Conversions" in output,
            "Labels for total spend and total conversions are included in the output.",
            f"Expected labels for totals not found 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

Learn how to leverage Python to solve real-world problems faced by digital agencies. This course covers automation, data analysis, and reporting techniques tailored for marketing, content, and analytics teams. Through engaging theory and hands-on challenges, you'll gain practical skills to streamline workflows, analyze campaign data, and generate actionable insights.

Discover how Python can automate repetitive tasks in digital agencies, from campaign scheduling to content management and client communication.

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: Monthly Report Generator

Ratkaisu

Challenge: Monthly Report Generator

Ratkaisu