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

A practical Python course tailored for healthcare professionals, focusing on real-world data analysis, visualization, and automation scenarios in the medical field. Learn to harness Python's power to streamline healthcare workflows, analyze patient data, and visualize medical trends.

Explore the essentials of working with healthcare data in Python, including data structures, importing datasets, and basic data cleaning tailored to medical records.

Master the art of visualizing healthcare data to uncover insights and communicate findings effectively using Python's plotting libraries.

Automate repetitive healthcare tasks and generate insightful reports using Python, streamlining workflows and improving efficiency.

Challenge: Patient Demographics Summary

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