Focusing on high-value sales is a practical way for you to prioritize business opportunities and direct your attention to transactions that have the greatest impact on revenue. By isolating sales records where the revenue exceeds a certain threshold, such as $1000, you can quickly identify which products and time periods are driving the most significant results. This approach not only streamlines your analysis but also helps you allocate resources more effectively, target key customers, and make informed decisions that support business growth.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_only_high_value_records_included(self):
        import user_code
        importlib.reload(user_code)
        data = [
            {'date': '2024-01-01', 'product': 'A', 'revenue': 1500, 'region': 'West'},
            {'date': '2024-01-02', 'product': 'B', 'revenue': 1000, 'region': 'East'},
            {'date': '2024-01-03', 'product': 'C', 'revenue': 2500, 'region': 'South'},
            {'date': '2024-01-04', 'product': 'D', 'revenue': 500, 'region': 'North'}
        ]
        result = user_code.extract_high_value_sales(data)
        expected = [
            {'date': '2024-01-01', 'product': 'A', 'revenue': 1500},
            {'date': '2024-01-03', 'product': 'C', 'revenue': 2500}
        ]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Returns only records where revenue > 1000, with correct fields.",
            f"Expected {expected}, got {result}"
        )

    def test_includes_only_specified_fields(self):
        import user_code
        importlib.reload(user_code)
        data = [
            {'date': '2024-01-01', 'product': 'A', 'revenue': 1501, 'region': 'West', 'customer': 'X'},
        ]
        result = user_code.extract_high_value_sales(data)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 1 and set(result[0].keys()) == {'date', 'product', 'revenue'},
            "Each dictionary contains only 'date', 'product', and 'revenue' keys.",
            f"Returned keys: {list(result[0].keys()) if isinstance(result, list) and result else 'No result'}"
        )

    def test_excludes_revenue_equal_to_1000(self):
        import user_code
        importlib.reload(user_code)
        data = [
            {'date': '2024-01-01', 'product': 'A', 'revenue': 1000, 'region': 'West'},
            {'date': '2024-01-02', 'product': 'B', 'revenue': 999, 'region': 'East'}
        ]
        result = user_code.extract_high_value_sales(data)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 0,
            "Records with revenue <= 1000 are excluded.",
            f"Expected empty list, got {result}"
        )

    def test_empty_input_returns_empty_list(self):
        import user_code
        importlib.reload(user_code)
        data = []
        result = user_code.extract_high_value_sales(data)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 0,
            "Empty input returns empty list.",
            f"Expected empty list, got {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

Unlock the power of Python to solve real-world business analysis problems. This course guides business analysts through practical Python applications, from data wrangling and visualization to automating reports and uncovering insights. Each section blends engaging theory with hands-on challenges inspired by business scenarios.

Learn how to use Python to clean, transform, and summarize business data for analysis. This section focuses on practical data wrangling skills essential for business analysts.

Discover how to use Python to create compelling visualizations that help communicate business insights effectively.

Harness Python to automate repetitive business analysis tasks, from generating reports to extracting actionable insights.

Challenge: Extract High-Value Sales

Oplossing

Challenge: Extract High-Value Sales

Oplossing