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

class TestTask(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        # Create a test CSV file
        data = {
            'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
            'Revenue': [1000, 1200, 1100, 1300, 1250],
            'Expenses': [700, 800, 900, 850, 1000],
        }
        df = pd.DataFrame(data)
        df.to_csv('monthly_data.csv', index=False)

    @classmethod
    def tearDownClass(cls):
        os.remove('monthly_data.csv')

    def test_profit_calculation(self):
        import user_code
        importlib.reload(user_code)
        df = pd.read_csv('monthly_data.csv')
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        user_code.analyze_profit_trends('monthly_data.csv')
        plt.show = plt_show_backup
        # Check profit column
        expected_profit = df['Revenue'] - df['Expenses']
        df['Profit'] = expected_profit
        _dynamic_test(
            self,
            all(df['Profit'] == expected_profit),
            'Monthly profit is correctly calculated as Revenue - Expenses',
            f'Profit calculation incorrect. Expected {list(expected_profit)}, got {list(df["Profit"])}',
        )

    def test_moving_average(self):
        import user_code
        importlib.reload(user_code)
        df = pd.read_csv('monthly_data.csv')
        df['Profit'] = df['Revenue'] - df['Expenses']
        expected_ma = df['Profit'].rolling(window=3, min_periods=1).mean().tolist()
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        user_code.analyze_profit_trends('monthly_data.csv')
        plt.show = plt_show_backup
        # Recompute in user code
        df['Profit_MA'] = df['Profit'].rolling(window=3, min_periods=1).mean()
        _dynamic_test(
            self,
            np.allclose(df['Profit_MA'], expected_ma),
            '3-month moving average is correctly computed',
            f'Expected moving averages: {expected_ma}, got {list(df["Profit_MA"])}',
        )

    def test_plot_lines_and_labels(self):
        import user_code
        importlib.reload(user_code)
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        plt.figure()
        user_code.analyze_profit_trends('monthly_data.csv')
        # Get current axes
        ax = plt.gca()
        lines = ax.get_lines()
        labels = [line.get_label() for line in lines]
        _dynamic_test(
            self,
            'Monthly Profit' in labels and '3-Month Moving Average' in labels,
            'Both lines for profit and moving average are plotted and labeled',
            f'Expected line labels: Monthly Profit and 3-Month Moving Average. Got: {labels}',
        )
        plt.show = plt_show_backup
        plt.close('all')

    def test_x_axis_month_labels(self):
        import user_code
        importlib.reload(user_code)
        df = pd.read_csv('monthly_data.csv')
        # Patch plt.show to do nothing
        plt_show_backup = plt.show
        plt.show = lambda *a, **k: None
        plt.figure()
        user_code.analyze_profit_trends('monthly_data.csv')
        ax = plt.gca()
        x_tick_labels = [tick.get_text() for tick in ax.get_xticklabels()]
        # Remove empty labels
        x_tick_labels = [l for l in x_tick_labels if l]
        months = list(df['Month'])
        found = all(month in ''.join(x_tick_labels) for month in months)
        _dynamic_test(
            self,
            found,
            'X-axis displays month labels from the CSV file',
            f'Expected months: {months} in x-axis labels: {x_tick_labels}',
        )
        plt.show = plt_show_backup
        plt.close('all')

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 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: Analyze Profit Trends

Løsning

Challenge: Analyze Profit Trends

Løsning