Analyzing the performance of your social media campaigns is essential for optimizing marketing spend and improving results. By automating the calculation of key metrics such as **click-through rate (CTR)**, **conversion rate**, and **cost per acquisition (CPA)**, you can quickly identify which platforms and campaigns are delivering the best value. This not only saves time but also ensures that your decisions are based on accurate, up-to-date data. Automating these calculations with Python allows you to scale your analysis as your campaigns grow.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import pandas as pd
import importlib

class TestTask(unittest.TestCase):
    def setUp(self):
        self.data = {
            "Platform": ["Facebook", "Instagram", "Twitter", "LinkedIn"],
            "Impressions": [10000, 8000, 5000, 3000],
            "Clicks": [300, 240, 80, 90],
            "Conversions": [30, 18, 4, 5],
            "Spend": [500, 400, 120, 150]
        }
        self.df = pd.DataFrame(self.data)

    def test_output_columns(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.calculate_metrics(self.df)
        expected_cols = ["Platform", "Impressions", "Clicks", "Conversions", "Spend", "CTR", "Conversion Rate", "CPA"]
        _dynamic_test(
            self,
            isinstance(result, pd.DataFrame) and all([col in result.columns for col in expected_cols]),
            "Output DataFrame contains all original and new metric columns.",
            f"Missing columns in output. Expected columns: {expected_cols}, got: {list(result.columns)}",
        )

    def test_ctr_values(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.calculate_metrics(self.df)
        for idx, row in result.iterrows():
            expected_ctr = row["Clicks"] / row["Impressions"]
            _dynamic_test(
                self,
                abs(row["CTR"] - expected_ctr) < 1e-8,
                f"Row {idx} CTR correct.",
                f"Row {idx} CTR incorrect. Expected {expected_ctr}, got {row['CTR']}.",
            )

    def test_conversion_rate_values(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.calculate_metrics(self.df)
        for idx, row in result.iterrows():
            expected_conv = row["Conversions"] / row["Clicks"]
            _dynamic_test(
                self,
                abs(row["Conversion Rate"] - expected_conv) < 1e-8,
                f"Row {idx} Conversion Rate correct.",
                f"Row {idx} Conversion Rate incorrect. Expected {expected_conv}, got {row['Conversion Rate']}.",
            )

    def test_cpa_values(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.calculate_metrics(self.df)
        for idx, row in result.iterrows():
            expected_cpa = row["Spend"] / row["Conversions"]
            _dynamic_test(
                self,
                abs(row["CPA"] - expected_cpa) < 1e-8,
                f"Row {idx} CPA correct.",
                f"Row {idx} CPA incorrect. Expected {expected_cpa}, got {row['CPA']}.",
            )

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 marketing problems. This course guides marketers through practical Python applications, from analyzing campaign data to visualizing trends and automating repetitive tasks. Each section blends engaging theory with hands-on challenges tailored to marketing scenarios.

Learn to harness Python for analyzing marketing data, uncovering trends, and making data-driven decisions.

Explore how Python can automate repetitive marketing tasks, saving time and reducing errors.

Delve into advanced analytics techniques to uncover deeper insights and optimize marketing strategies.

Challenge: Calculate Key Metrics for a Social Media Campaign

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