Building on your previous experience with function-based dosage calculations and handling patient data, you are now ready to tackle a more complex scenario: calculating medication dosages for multiple patients at once. In real pharmacy practice, you often need to process a list of patients, each with individual characteristics such as **name**, **weight**, and **prescribed dose per kilogram**. Efficiently iterating through this data and producing accurate results is essential for **patient safety** and effective workflow management.


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

class TestTask(unittest.TestCase):
    def test_returns_correct_list(self):
        import user_code
        importlib.reload(user_code)
        patients = [
            {'name': 'Alice', 'weight_kg': 70, 'dose_per_kg': 2},
            {'name': 'Bob', 'weight_kg': 80, 'dose_per_kg': 1.5},
            {'name': 'Charlie', 'weight_kg': 60, 'dose_per_kg': 2.5}
        ]
        expected = [
            {'name': 'Alice', 'calculated_dose': 140},
            {'name': 'Bob', 'calculated_dose': 120.0},
            {'name': 'Charlie', 'calculated_dose': 150.0}
        ]
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.calculate_patient_doses(patients)
        _dynamic_test(
            self,
            isinstance(result, list) and all(isinstance(item, dict) for item in result),
            "Function returns a list of dictionaries.",
            f"Expected a list of dictionaries, got: {result}",
        )
        # Check values
        for r, e in zip(result, expected):
            _dynamic_test(
                self,
                r['name'] == e['name'] and abs(r['calculated_dose'] - e['calculated_dose']) < 1e-6,
                f"Correct calculated dose for {e['name']}.",
                f"Expected {e}, got {r}.",
            )

    def test_prints_highest_dose_patient(self):
        import user_code
        importlib.reload(user_code)
        patients = [
            {'name': 'Alice', 'weight_kg': 70, 'dose_per_kg': 2},
            {'name': 'Bob', 'weight_kg': 80, 'dose_per_kg': 1.5},
            {'name': 'Charlie', 'weight_kg': 60, 'dose_per_kg': 2.5}
        ]
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.calculate_patient_doses(patients)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "charlie" in output,
            "Correctly prints patient with highest calculated dose (Charlie).",
            f"Expected output to include 'Charlie', got: {output}",
        )

    def test_empty_patient_list(self):
        import user_code
        importlib.reload(user_code)
        patients = []
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.calculate_patient_doses(patients)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == 0,
            "Returns empty list for empty input.",
            f"Expected empty list, got: {result}",
        )
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            output.strip() == '',
            "No output printed for empty input.",
            f"Expected no output, got: {output}",
        )

    def test_single_patient(self):
        import user_code
        importlib.reload(user_code)
        patients = [
            {'name': 'Dana', 'weight_kg': 65, 'dose_per_kg': 1.2}
        ]
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.calculate_patient_doses(patients)
        expected = [{'name': 'Dana', 'calculated_dose': 78.0}]
        _dynamic_test(
            self,
            isinstance(result, list) and result == expected,
            "Correct result for single patient.",
            f"Expected {expected}, got: {result}",
        )
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "dana" in output,
            "Correctly prints single patient name.",
            f"Expected output to include 'Dana', 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

A practical Python course tailored for pharmacists, focusing on real-world pharmaceutical data analysis, medication management, and healthcare problem-solving using Python. Designed for learners with basic Python knowledge, this course bridges pharmacy and programming through engaging theory and hands-on challenges.

Learn how to represent, manipulate, and analyze pharmaceutical data using Python and pandas. This section introduces core data structures and techniques for handling medication and patient information.

Apply Python to calculate medication dosages, check for drug interactions, and support safe patient care. This section focuses on mathematical operations and logic relevant to pharmacy practice.

Use Python to analyze and visualize clinical data relevant to pharmacy, such as lab results, adverse event rates, and medication adherence. This section emphasizes data-driven decision making.

Challenge: Dosage Calculator for Multiple Patients

Ratkaisu

Challenge: Dosage Calculator for Multiple Patients

Ratkaisu