Coaches often want to reward or recognize athletes with high attendance. Automating the identification of consistent performers helps ensure fair recognition and motivates athletes to maintain their commitment. In this challenge, you will work with attendance records to find out which athletes have attended at least 90% of their sessions.


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

class TestTask(unittest.TestCase):
    def test_returns_names_with_at_least_90_percent_attendance(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {'name': 'Alex', 'attendance': [True, True, False, True, True, True, True, True, True, True]}, # 9/10
            {'name': 'Jamie', 'attendance': [True, False, False, True, False, True, False, True, False, False]}, # 4/10
            {'name': 'Morgan', 'attendance': [True, True, True, True, True, True, True, True, True, False]}, # 9/10
            {'name': 'Taylor', 'attendance': [True, True, True, True, True, True, True, True, True, True]}, # 10/10
            {'name': 'Casey', 'attendance': []}
        ]
        result = user_code.consistent_performers(records)
        expected = ['Alex', 'Morgan', 'Taylor']
        _dynamic_test(
            self,
            isinstance(result, list) and set(result) == set(expected),
            "Function returns correct names for >=90% attendance",
            f"Expected {expected}, got {result}",
        )

    def test_excludes_athletes_below_90_percent(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {'name': 'Jamie', 'attendance': [True, False, False, True, False, True, False, True, False, False]}, # 4/10
            {'name': 'Sam', 'attendance': [False, False, False]}, # 0/3
        ]
        result = user_code.consistent_performers(records)
        expected = []
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function excludes athletes below 90% attendance",
            f"Expected {expected}, got {result}",
        )

    def test_includes_athletes_with_100_percent_attendance(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {'name': 'Chris', 'attendance': [True, True, True]}, # 3/3
            {'name': 'Alex', 'attendance': [True, True, False]}, # 2/3
        ]
        result = user_code.consistent_performers(records)
        expected = ['Chris']
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function includes 100% attendance athletes",
            f"Expected {expected}, got {result}",
        )

    def test_includes_athletes_with_exactly_90_percent(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {'name': 'Jamie', 'attendance': [True, True, True, True, True, True, True, True, True, False]}, # 9/10
            {'name': 'Morgan', 'attendance': [True, True, True, True, True, True, True, True, True, True]}, # 10/10
            {'name': 'Taylor', 'attendance': [True, True, False, True, True, True, True, True, True, True]}, # 9/10
        ]
        result = user_code.consistent_performers(records)
        expected = ['Jamie', 'Morgan', 'Taylor']
        _dynamic_test(
            self,
            isinstance(result, list) and set(result) == set(expected),
            "Function includes athletes with exactly 90% attendance",
            f"Expected {expected}, got {result}",
        )

    def test_excludes_athletes_with_empty_attendance(self):
        import user_code
        importlib.reload(user_code)
        records = [
            {'name': 'Casey', 'attendance': []},
            {'name': 'Alex', 'attendance': [True, True, True, True, False, True, True, True, True, True]},
        ]
        result = user_code.consistent_performers(records)
        expected = ['Alex']
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Function excludes athletes with empty attendance list",
            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

A practical Python course designed specifically for coaches, trainers, and sports professionals. Learn how to leverage Python to analyze performance, track progress, and make data-driven decisions to enhance coaching outcomes.

Learn how to use Python to monitor, analyze, and visualize individual athlete progress and performance metrics.

Use Python to analyze and compare team-level data, uncover trends, and support strategic coaching decisions.

Explore how Python can automate repetitive coaching tasks, generate reports, and provide actionable insights.

Challenge: Identify Consistent Performers

Ratkaisu

Challenge: Identify Consistent Performers

Ratkaisu