
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout
import pandas as pd

class TestTask(unittest.TestCase):
    def setUp(self):
        self.df = pd.DataFrame({
            "product": ["A", "B", "C", "D", "A", "B", "C", "D"],
            "sales": [100, 200, 50, 25, 150, 50, 75, 100]
        })
        self.expected_totals = {'A': 250, 'B': 250, 'C': 125, 'D': 125}

    def test_groupby_total_sales(self):
        import user_code
        # User must group by product and sum sales
        df = self.df.copy()
        try:
            grouped = df.groupby('product')['sales'].sum()
        except Exception:
            grouped = None
        # Check that grouping and sum works
        _dynamic_test(
            self,
            grouped is not None and all(grouped.get(k) == v for k, v in self.expected_totals.items()),
            "Groups and sums sales for each product correctly",
            f"Expected totals: {self.expected_totals}, got: {grouped.to_dict() if grouped is not None else None}"
        )

    def test_sorting_descending(self):
        import user_code
        # User must sort totals descending
        df = self.df.copy()
        grouped = df.groupby('product')['sales'].sum()
        sorted_sales = grouped.sort_values(ascending=False)
        # Check that order is correct
        expected_order = ['A', 'B', 'C', 'D']
        _dynamic_test(
            self,
            list(sorted_sales.index)[:2] == expected_order[:2],
            "Sorts products by total sales in descending order",
            f"Expected order: {expected_order}, got: {list(sorted_sales.index)}"
        )

    def test_prints_top_and_bottom_products(self):
        import user_code
        import importlib
        import sys
        import builtins
        import io
        import pandas as pd
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.report_top_bottom_products(self.df)
        output = normalize_text(f.getvalue())
        # Check top 2
        expected_top = normalize_text("Top 2 products by total sales:\\nA    250\\nB    250")
        expected_bottom = normalize_text("Bottom 2 products by total sales:\\nC    125\\nD    125")
        _dynamic_test(
            self,
            expected_top in output,
            "Prints correct top 2 products by total sales",
            f"Expected output with top 2: '{expected_top}' not found in output: '{output}'"
        )
        _dynamic_test(
            self,
            expected_bottom in output,
            "Prints correct bottom 2 products by total sales",
            f"Expected output with bottom 2: '{expected_bottom}' not found in output: '{output}'"
        )

    def test_handles_different_data(self):
        import user_code
        import importlib
        import io
        df2 = pd.DataFrame({
            "product": ["X", "Y", "Z", "X", "Y", "Z", "X", "Y"],
            "sales": [10, 20, 5, 40, 10, 5, 5, 10]
        })
        # Totals: X:55, Y:40, Z:10
        f = io.StringIO()
        importlib.reload(user_code)
        with redirect_stdout(f):
            user_code.report_top_bottom_products(df2)
        output = normalize_text(f.getvalue())
        expected_top = normalize_text("Top 2 products by total sales:\\nX    55\\nY    40")
        expected_bottom = normalize_text("Bottom 2 products by total sales:\\nY    40\\nZ    10")
        _dynamic_test(
            self,
            expected_top in output,
            "Handles different data and prints correct top 2",
            f"Expected top 2 output: '{expected_top}', got: '{output}'"
        )
        _dynamic_test(
            self,
            expected_bottom in output,
            "Handles different data and prints correct bottom 2",
            f"Expected bottom 2 output: '{expected_bottom}', got: '{output}'"
        )

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

Advance your Python automation skills by learning to generate analytical summaries, business reports, and visual dashboards automatically. This course guides you through practical workflows for automating data analysis, reporting, and visualization tasks using Python, pandas, matplotlib, and seaborn.

Learn to automate the generation of analytical summaries, business reports, and visual dashboards using Python. Alternate between concept introduction and hands-on automation tasks for real-world reporting scenarios.

Challenge: Identify Top and Bottom Products

Oplossing