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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.df = pd.DataFrame({
            'event_timestamp': pd.to_datetime([
                '2024-06-01 08:00:00',
                '2024-06-01 08:10:00',
                '2024-06-01 08:20:00',
                '2024-06-01 09:00:00'
            ]),
            'processing_duration': [30, 45, 50, 40]
        })

    def test_average_processing_time(self):
        import user_code
        importlib.reload(user_code)
        # Patch print to capture output
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.report_operational_metrics(self.df)
        output = normalize_text(f.getvalue())
        expected_avg = round(sum([30, 45, 50, 40]) / 4, 2)
        _dynamic_test(
            self,
            f"average processing time: {expected_avg:.2f} seconds" in output,
            f"Prints correct average processing time rounded to two decimals: {expected_avg}",
            f"Average processing time not correct in output: {output}"
        )

    def test_time_range_and_throughput(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.report_operational_metrics(self.df)
        output = normalize_text(f.getvalue())
        # The time range in hours:
        start = pd.to_datetime('2024-06-01 08:00:00')
        end = pd.to_datetime('2024-06-01 09:00:00')
        total_seconds = (end - start).total_seconds()
        total_hours = total_seconds / 3600 if total_seconds > 0 else 1
        expected_throughput = round(4 / total_hours, 2)
        _dynamic_test(
            self,
            f"throughput: {expected_throughput:.2f} events per hour" in output,
            f"Prints correct throughput rounded to two decimals: {expected_throughput}",
            f"Throughput not correct in output: {output}"
        )

    def test_report_format(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.report_operational_metrics(self.df)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "average processing time:" in output and "throughput:" in output,
            "Output contains both average processing time and throughput labels",
            f"Output missing required labels: {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: Report Key Operational Metrics

Solución