
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

class TestTask(unittest.TestCase):
    def setUp(self):
        # Prepare a sample DataFrame for all tests
        self.data = {
            'Date': pd.to_datetime([
                '2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05',
                '2024-01-06', '2024-01-07', '2024-01-08', '2024-01-09', '2024-01-10'
            ]),
            'Amount': [100, 120, 110, 115, 105, 300, 95, 102, 99, 500],
            'CustomerID': ['A', 'A', 'B', 'B', 'C', 'C', 'D', 'D', 'E', 'E']
        }
        self.transactions = pd.DataFrame(self.data)

    def test_iqr_outlier_detection(self):
        import user_code
        importlib.reload(user_code)
        q1 = self.transactions['Amount'].quantile(0.25)
        q3 = self.transactions['Amount'].quantile(0.75)
        iqr = q3 - q1
        lower = q1 - 1.5 * iqr
        upper = q3 + 1.5 * iqr
        expected_outliers = self.transactions[(self.transactions['Amount'] < lower) | (self.transactions['Amount'] > upper)]
        # Patch plt.show to prevent plot display
        plt_show_backup = plt.show
        plt.show = lambda: None
        try:
            # Capture outlier mask from user function
            # We'll monkeypatch plt.scatter to capture colors
            scatter_calls = []
            orig_scatter = plt.Axes.scatter
            def fake_scatter(self, x, y, *args, **kwargs):
                scatter_calls.append({'x': list(x), 'y': list(y), 'color': kwargs.get('color', None)})
                return orig_scatter(self, x, y, *args, **kwargs)
            plt.Axes.scatter = fake_scatter
            user_code.visualize_and_highlight_outliers(self.transactions)
            plt.Axes.scatter = orig_scatter
        finally:
            plt.show = plt_show_backup
        # Find scatter calls with color 'red' (outliers)
        outlier_points = []
        for call in scatter_calls:
            if call['color'] == 'red':
                outlier_points.extend(zip(call['x'], call['y']))
        expected_points = list(zip(expected_outliers['Date'], expected_outliers['Amount']))
        _dynamic_test(
            self,
            set(outlier_points) == set(expected_points),
            "Outlier points are correctly detected and highlighted in red.",
            f"Expected outlier points: {expected_points}, got: {outlier_points}"
        )

    def test_scatter_plot_created(self):
        import user_code
        importlib.reload(user_code)
        plt_show_backup = plt.show
        plt.show = lambda: None
        scatter_call_count = {'count': 0}
        orig_scatter = plt.Axes.scatter
        def fake_scatter(self, x, y, *args, **kwargs):
            scatter_call_count['count'] += 1
            return orig_scatter(self, x, y, *args, **kwargs)
        plt.Axes.scatter = fake_scatter
        try:
            user_code.visualize_and_highlight_outliers(self.transactions)
        finally:
            plt.Axes.scatter = orig_scatter
            plt.show = plt_show_backup
        _dynamic_test(
            self,
            scatter_call_count['count'] >= 2,
            "Scatter plot is created for both normal and outlier points.",
            f"Expected at least 2 scatter calls, got {scatter_call_count['count']}"
        )

    def test_boxplot_by_customer(self):
        import user_code
        importlib.reload(user_code)
        plt_show_backup = plt.show
        plt.show = lambda: None
        boxplot_called = {'called': False}
        orig_boxplot = pd.DataFrame.boxplot
        def fake_boxplot(self, *args, **kwargs):
            if 'by' in kwargs and kwargs['by'] == 'CustomerID':
                boxplot_called['called'] = True
            return orig_boxplot(self, *args, **kwargs)
        pd.DataFrame.boxplot = fake_boxplot
        try:
            user_code.visualize_and_highlight_outliers(self.transactions)
        finally:
            pd.DataFrame.boxplot = orig_boxplot
            plt.show = plt_show_backup
        _dynamic_test(
            self,
            boxplot_called['called'],
            "Boxplot is generated grouped by CustomerID.",
            "Boxplot by CustomerID was not generated."
        )

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: Visualize and Highlight Outliers

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