In this chapter, you will deepen your skills in working with structured data by focusing on extracting information from a list of JSON-like dictionaries. This is a common task when handling data received from APIs or files, where each dictionary represents a record with specific fields. Your challenge is to identify and retrieve the name associated with the highest score in a given list of such dictionaries. This exercise will help you practice key techniques in data extraction and transformation, building on your understanding of working with JSON data.


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

class TestTask(unittest.TestCase):
    def test_unique_highest_score(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {"name": "Alice", "score": 88},
            {"name": "Bob", "score": 95},
            {"name": "Charlie", "score": 90}
        ]
        result = user_code.get_top_scorer(records)
        _dynamic_test(
            self,
            result == "Bob",
            "Returns correct name when highest score is unique.",
            f"Expected 'Bob', got '{result}' for unique highest score.",
        )

    def test_tied_highest_score(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {"name": "Alice", "score": 95},
            {"name": "Bob", "score": 95},
            {"name": "Charlie", "score": 90}
        ]
        result = user_code.get_top_scorer(records)
        _dynamic_test(
            self,
            result == "Alice",
            "Returns name from first dictionary with highest score when there are ties.",
            f"Expected 'Alice', got '{result}' for tied highest score.",
        )

    def test_empty_list(self):
        import user_code
        importlib.reload(user_code)
        records = []
        result = user_code.get_top_scorer(records)
        _dynamic_test(
            self,
            result is None,
            "Returns None when the input list is empty.",
            f"Expected None, got '{result}' when input list is empty.",
        )

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()
    for i, node in enumerate(tree.body):
        if isinstance(node, ast.Assign):
            for target in node.targets:
                if isinstance(target, ast.Name) and target.id == var_name:
                    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] = []
                    
                    return '\\n'.join(lines)
    return code

if __name__ == "__main__":
    unittest.main()


test_main.py

A beginner-friendly course focused on mastering string manipulation and handling various data formats in Python. Learn essential techniques for processing, formatting, and converting data, with hands-on practice and quizzes to reinforce your understanding.

Learn the core techniques for working with strings in Python, including slicing, searching, and modifying text.

Develop skills for formatting strings and converting between data types for effective data presentation and processing.

Gain practical experience handling structured data formats such as CSV and JSON using Python's built-in capabilities.

Challenge: JSON Data Extraction

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Challenge: JSON Data Extraction

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