
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

A practical Python course tailored for banking professionals, focusing on real-world financial data analysis, risk assessment, and automation tasks. Learn how Python can streamline banking operations, analyze financial data, and solve common problems faced by bankers.

Learn how to use Python to analyze, manipulate, and visualize financial data relevant to banking operations.

Apply Python to assess credit risk, analyze loan portfolios, and perform key calculations relevant to banking risk management.

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

Challenge: Flag Suspicious Transactions

Ratkaisu

Challenge: Flag Suspicious Transactions

Ratkaisu