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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.data = {
            "name": ["Alice", "Bob", "Charlie"],
            "ssn": ["123-45-6789", "987-65-4321", "555-66-7777"],
            "email": ["alice@example.com", "bob@example.com", "charlie@example.com"]
        }
        self.df = pd.DataFrame(self.data)

    def test_sensitive_columns_masked(self):
        importlib.reload(user_code)
        sensitive_columns = ["ssn", "email"]
        masked_df = user_code.mask_sensitive_data(self.df, sensitive_columns)
        mask = "***MASKED***"
        for col in sensitive_columns:
            if col in masked_df.columns:
                _dynamic_test(
                    self,
                    (masked_df[col] == mask).all(),
                    f"Column '{col}' is correctly masked with '{mask}'",
                    f"Column '{col}' is not correctly masked: {masked_df[col].tolist()}"
                )

    def test_non_sensitive_columns_unchanged(self):
        importlib.reload(user_code)
        sensitive_columns = ["ssn"]
        masked_df = user_code.mask_sensitive_data(self.df, sensitive_columns)
        _dynamic_test(
            self,
            masked_df["name"].equals(self.df["name"]),
            "Non-sensitive columns remain unchanged",
            f"Non-sensitive column 'name' was changed: {masked_df['name'].tolist()}"
        )

    def test_ignore_nonexistent_column(self):
        importlib.reload(user_code)
        sensitive_columns = ["ssn", "passport_number"]
        try:
            masked_df = user_code.mask_sensitive_data(self.df, sensitive_columns)
            _dynamic_test(
                self,
                True,
                "Function does not raise error for non-existent sensitive column",
                "Function raised error for non-existent sensitive column"
            )
        except Exception as e:
            _dynamic_test(
                self,
                False,
                "Function does not raise error for non-existent sensitive column",
                f"Function raised error: {e}"
            )

    def test_returns_copy(self):
        importlib.reload(user_code)
        sensitive_columns = ["ssn"]
        df_original = self.df.copy()
        masked_df = user_code.mask_sensitive_data(self.df, sensitive_columns)
        _dynamic_test(
            self,
            not masked_df.equals(self.df) or (masked_df is not self.df),
            "Returned DataFrame is a new object and does not modify the original",
            "Returned DataFrame is the same object as the original or modifies it in place"
        )
        # Ensure original is unchanged
        _dynamic_test(
            self,
            self.df.equals(df_original),
            "Original DataFrame remains unchanged",
            "Original DataFrame was modified"
        )

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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Learn how to handle, store, and process sensitive data securely in Python applications.

Challenge: Masking Sensitive Data in DataFrames

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Challenge: Masking Sensitive Data in DataFrames

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