In healthcare analytics, summarizing **patient demographics** is a routine yet essential task. You often need to generate reports that highlight the **average age** of patients, the distribution of **genders**, and other key statistics. Such summaries help healthcare professionals understand their patient population and tailor care or resource allocation accordingly. To accomplish this, you will use the `pandas` library, which is widely used for data manipulation and analysis in Python. You will work with a CSV file that contains anonymized patient records, each with an `age`, `gender`, and `diagnosis` column.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import user_code
import importlib
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def setUp(self):
        # Expected data for verification
        self.ages = [34, 58, 47, 29, 53]
        self.genders = ["Female", "Male", "Female", "Male", "Female"]
        self.expected_avg = sum(self.ages) / len(self.ages)
        self.expected_gender_counts = {"Female": 3, "Male": 2}

    def test_average_age(self):
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            importlib.reload(user_code)
        output = normalize_text(f.getvalue())
        expected_str = f"average age of patients: {self.expected_avg:.2f}"
        _dynamic_test(
            self,
            expected_str in output,
            f"Output includes average age: {expected_str}",
            f"Expected average age '{expected_str}' not found in output: {output}"
        )

    def test_gender_counts(self):
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            importlib.reload(user_code)
        output = normalize_text(f.getvalue())
        for gender, count in self.expected_gender_counts.items():
            expected_line = f"{gender.lower()}: {count}"
            _dynamic_test(
                self,
                expected_line in output,
                f"Output includes gender count: {expected_line}",
                f"Expected gender count '{expected_line}' not found in output: {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

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Challenge: Patient Demographics Summary

Lösung