
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.data = {
            'transaction_id': [1, 2, 3, 4, 5],
            'amount': [100, 120, 105, 1000, 115]
        }
        self.df = pd.DataFrame(self.data)

    def test_suspicious_column_added(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.flag_suspicious_transactions(self.df.copy())
        _dynamic_test(
            self,
            'suspicious' in result.columns,
            "The 'suspicious' column was correctly added to the DataFrame.",
            "The 'suspicious' column was not added to the DataFrame.",
        )

    def test_suspicious_flag_logic(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.flag_suspicious_transactions(self.df.copy())
        mean = self.df['amount'].mean()
        std = self.df['amount'].std()
        threshold = 2 * std
        expected_flags = [abs(x - mean) > threshold for x in self.df['amount']]
        actual_flags = list(result['suspicious']) if 'suspicious' in result.columns else None
        _dynamic_test(
            self,
            actual_flags is not None and actual_flags == expected_flags,
            "The 'suspicious' column flags are correct.",
            f"Expected flags: {expected_flags}, got: {actual_flags}.",
        )

    def test_non_suspicious_flag(self):
        import user_code
        importlib.reload(user_code)
        df = pd.DataFrame({'transaction_id': [1, 2, 3], 'amount': [100, 102, 98]})
        result = user_code.flag_suspicious_transactions(df.copy())
        expected_flags = [False, False, False]
        actual_flags = list(result['suspicious']) if 'suspicious' in result.columns else None
        _dynamic_test(
            self,
            actual_flags is not None and actual_flags == expected_flags,
            "All transactions correctly flagged as not suspicious when amounts are close to the mean.",
            f"Expected all False flags, got: {actual_flags}.",
        )

    def test_returns_dataframe(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.flag_suspicious_transactions(self.df.copy())
        _dynamic_test(
            self,
            isinstance(result, pd.DataFrame),
            "The function returns a DataFrame.",
            f"Expected a DataFrame, got {type(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

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Explore how Python can automate banking tasks and help detect fraudulent activities using data analysis techniques.

Challenge: Flag Suspicious Transactions

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Challenge: Flag Suspicious Transactions

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