Flagging slow-loading pages is a crucial aspect of technical SEO audits. Pages with high load times can negatively impact **user experience** and **search engine rankings**. In this exercise, you will work with a dictionary that contains page URLs as keys and their corresponding load times (in seconds) as values. Your task is to identify and return the URLs for pages that take longer than `2.5` seconds to load.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_returns_only_slow_urls(self):
        import user_code
        importlib.reload(user_code)
        test_data = {
            "https://a.com": 1.0,
            "https://b.com": 2.6,
            "https://c.com": 3.1,
            "https://d.com": 2.5
        }
        result = user_code.flag_slow_pages(test_data)
        expected = ["https://b.com", "https://c.com"]
        _dynamic_test(
            self,
            isinstance(result, list) and set(result) == set(expected),
            "Returns only URLs with load times greater than 2.5 seconds",
            f"Expected {expected}, got {result}",
        )

    def test_returns_empty_when_none_slow(self):
        import user_code
        importlib.reload(user_code)
        test_data = {
            "https://a.com": 0.9,
            "https://b.com": 2.4,
            "https://c.com": 1.3
        }
        result = user_code.flag_slow_pages(test_data)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 0,
            "Returns empty list when no URLs exceed 2.5 seconds",
            f"Expected empty list, got {result}",
        )

    def test_returns_empty_on_empty_dict(self):
        import user_code
        importlib.reload(user_code)
        test_data = {}
        result = user_code.flag_slow_pages(test_data)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 0,
            "Returns empty list for empty dictionary",
            f"Expected empty list, got {result}",
        )

    def test_all_urls_are_slow(self):
        import user_code
        importlib.reload(user_code)
        test_data = {
            "https://a.com": 5.0,
            "https://b.com": 4.0,
            "https://c.com": 2.51
        }
        result = user_code.flag_slow_pages(test_data)
        expected = list(test_data.keys())
        _dynamic_test(
            self,
            isinstance(result, list) and set(result) == set(expected),
            "Returns all URLs if all exceed 2.5 seconds",
            f"Expected {expected}, got {result}",
        )

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

Master the use of Python to automate, analyze, and optimize SEO workflows. This course guides SEO professionals through practical Python applications for keyword research, on-page analysis, and technical SEO, using real-world scenarios and hands-on coding tasks.

Learn how Python can automate repetitive SEO tasks, from scraping search results to generating meta tags, making your SEO workflow more efficient.

Explore how Python can be used to analyze keyword data, identify opportunities, and visualize keyword trends for SEO campaigns.

Apply Python to analyze on-page SEO elements and perform technical audits, including content checks, tag analysis, and site structure evaluation.

Challenge: Flag Slow Pages

Oplossing

Challenge: Flag Slow Pages

Oplossing