
import unittest
import user_code
import ast
import re   
import unittest
import importlib
from unittest.mock import patch
import pandas as pd

class TestTask(unittest.TestCase):
    def setUp(self):
        self.df = pd.DataFrame({
            'Close': [100.0, 102.5, 101.0, 103.0, 104.5],
            'Volume': [2000, 2200, 2100, 2300, 2400]
        })

    def test_average_close(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.market_impact_report('FAKE')
        expected_avg = self.df['Close'].mean()
        _dynamic_test(
            self,
            abs(result['average_close'] - expected_avg) < 1e-6,
            f"Correct average_close: {expected_avg}",
            f"Expected average_close {expected_avg}, got {result['average_close']}"
        )

    def test_total_volume(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.market_impact_report('FAKE')
        expected_vol = self.df['Volume'].sum()
        _dynamic_test(
            self,
            result['total_volume'] == expected_vol,
            f"Correct total_volume: {expected_vol}",
            f"Expected total_volume {expected_vol}, got {result['total_volume']}"
        )

    def test_max_close(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.market_impact_report('FAKE')
        expected_max = self.df['Close'].max()
        _dynamic_test(
            self,
            abs(result['max_close'] - expected_max) < 1e-6,
            f"Correct max_close: {expected_max}",
            f"Expected max_close {expected_max}, got {result['max_close']}"
        )

    def test_keys_exist(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.market_impact_report('FAKE')
        keys = set(result.keys())
        expected = {'average_close', 'total_volume', 'max_close'}
        _dynamic_test(
            self,
            keys == expected,
            "All required keys are present",
            f"Expected keys {expected}, got {keys}"
        )

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
    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 not assign_nodes:
        return code
    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 course designed for accountants to leverage Python for automating, analyzing, and visualizing financial data. Learn to streamline accounting workflows, perform insightful analyses, and create compelling reports using Python and essential data libraries.

Learn how to automate common accounting tasks using Python, including data import, cleaning, and reconciliation.

Delve into financial data analysis using Python, including trend analysis, ratio calculations, and benchmarking.

Learn to retrieve financial data from online sources and create automated reports for accounting purposes.

Challenge: Automated Market Impact Report

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Challenge: Automated Market Impact Report

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