Coaches often want to quickly identify team leaders in key statistics. Use **pandas** to automate this process.


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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.module = importlib.import_module('user_code')

    def test_max_goals_included(self):
        data = {
            'athlete': ['A', 'B', 'C'],
            'goals': [5, 10, 3],
            'assists': [1, 2, 3]
        }
        df = pd.DataFrame(data)
        result = self.module.get_team_leaders(df)
        expected = df[df['goals'] == 10]
        _dynamic_test(
            self,
            all(row['athlete'] in result['athlete'].values for _, row in expected.iterrows()),
            "All athletes with max goals are included.",
            f"Expected athletes with max goals: {expected['athlete'].tolist()} in result: {result['athlete'].tolist()}"
        )

    def test_max_assists_included(self):
        data = {
            'athlete': ['A', 'B', 'C'],
            'goals': [2, 3, 4],
            'assists': [7, 2, 7]
        }
        df = pd.DataFrame(data)
        result = self.module.get_team_leaders(df)
        expected = df[df['assists'] == 7]
        _dynamic_test(
            self,
            all(row['athlete'] in result['athlete'].values for _, row in expected.iterrows()),
            "All athletes with max assists are included.",
            f"Expected athletes with max assists: {expected['athlete'].tolist()} in result: {result['athlete'].tolist()}"
        )

    def test_no_duplicates(self):
        data = {
            'athlete': ['A', 'B'],
            'goals': [10, 10],
            'assists': [5, 5]
        }
        df = pd.DataFrame(data)
        result = self.module.get_team_leaders(df)
        _dynamic_test(
            self,
            result['athlete'].is_unique,
            "Each athlete appears only once in the result.",
            f"Duplicate athletes found: {result['athlete'].tolist()}"
        )

    def test_tied_max_values(self):
        data = {
            'athlete': ['A', 'B', 'C'],
            'goals': [8, 8, 5],
            'assists': [4, 2, 4]
        }
        df = pd.DataFrame(data)
        result = self.module.get_team_leaders(df)
        expected_athletes = ['A', 'B', 'C']  # A and C tie for assists, A and B tie for goals
        _dynamic_test(
            self,
            all(a in result['athlete'].values for a in expected_athletes),
            "All athletes tied for max goals or assists are included.",
            f"Expected athletes: {expected_athletes}, got: {result['athlete'].tolist()}"
        )

    def test_different_leaders(self):
        data = {
            'athlete': ['A', 'B', 'C'],
            'goals': [5, 8, 7],
            'assists': [3, 2, 10]
        }
        df = pd.DataFrame(data)
        result = self.module.get_team_leaders(df)
        expected_athletes = ['B', 'C']  # B has max goals, C has max assists
        _dynamic_test(
            self,
            all(a in result['athlete'].values for a in expected_athletes) and len(result) == 2,
            "Correctly selects different leaders for goals and assists.",
            f"Expected athletes: {expected_athletes}, got: {result['athlete'].tolist()}"
        )

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: Team Leaders Table

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