Leaderboards are a powerful tool in digital agencies for fostering healthy competition and recognizing high-performing content. By clearly displaying which campaigns, posts, or articles are generating the most engagement, you can motivate teams to strive for better results and make informed decisions about future strategy. A well-designed leaderboard highlights trends in audience preferences and helps you identify which content types or topics resonate most with your target market. This approach not only rewards success but also provides actionable insights for optimizing your content strategy.


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 = {
            "Title": [
                "How to Optimize Facebook Ads",
                "Instagram Stories: Best Practices",
                "Email Marketing 101",
                "SEO Checklist for 2024",
                "Creative TikTok Campaigns",
                "LinkedIn for B2B Growth",
                "YouTube Shorts: Quick Wins",
                "Twitter Polls: Engagement Hacks",
                "Pinterest Boards for Brands",
                "The Power of Influencer Marketing"
            ],
            "Engagement Score": [1200, 950, 1100, 1300, 1600, 800, 1150, 700, 600, 1400]
        }
        self.df = pd.DataFrame(self.data)

    def test_sort_descending(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_top_content_leaderboard(self.df)
        output = f.getvalue()
        expected_titles = [
            "Creative TikTok Campaigns",
            "The Power of Influencer Marketing",
            "SEO Checklist for 2024",
            "How to Optimize Facebook Ads",
            "YouTube Shorts: Quick Wins"
        ]
        # Check that the first 5 titles in output are in the correct descending order
        output_lines = [line for line in output.splitlines() if any(title in line for title in expected_titles)]
        found_titles = [title for line in output_lines for title in expected_titles if title in line]
        _dynamic_test(
            self,
            found_titles == expected_titles,
            "Leaderboard is sorted by engagement score in descending order.",
            f"Expected order: {expected_titles}, got: {found_titles}",
        )

    def test_top_5_entries(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_top_content_leaderboard(self.df)
        output = f.getvalue()
        # Count number of content entries printed (ignore header)
        lines = [line for line in output.splitlines() if line.strip() and not line.startswith("   Title") and not line.startswith("Title")]
        # Only lines containing a known title should be counted
        content_lines = [line for line in lines if any(title in line for title in self.data["Title"])]
        _dynamic_test(
            self,
            len(content_lines) == 5,
            "Function selects and prints only the top 5 entries.",
            f"Expected 5 entries, got {len(content_lines)}: {content_lines}",
        )

    def test_prints_only_title_and_score(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.print_top_content_leaderboard(self.df)
        output = f.getvalue()
        # The header must only contain 'Title' and 'Engagement Score'
        header_line = next((line for line in output.splitlines() if "Title" in line and "Engagement Score" in line), None)
        has_only_two_columns = False
        if header_line:
            # Use regex to robustly extract header columns including those with spaces
            # The header should be exactly 'Title  Engagement Score' (with two or more spaces)
            columns = re.split(r'\\s{2,}', header_line.strip())
            has_only_two_columns = (len(columns) == 2 and columns[0] == "Title" and columns[1] == "Engagement Score")
        _dynamic_test(
            self,
            has_only_two_columns,
            "Output prints only 'Title' and 'Engagement Score' columns.",
            f"Header columns found: {header_line}",
        )

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: Content Leaderboard

Solución

Challenge: Content Leaderboard

Solución