Building on your skills in representing medication data and using **pandas** for pharmacy applications, you can now tackle real-world inventory problems by filtering and visualizing key information. In pharmacy practice, identifying medications that are running low is crucial for patient care and inventory management. By filtering a DataFrame to find drugs with low stock and creating a clear horizontal bar chart, you make it easy to spot which medications need to be reordered. This approach uses **pandas** for efficient data selection and **matplotlib** for effective visualization, ensuring that your results are both accurate and actionable.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import importlib
import io
import sys

class TestTask(unittest.TestCase):
    def setUp(self):
        self.data = {
            'Drug': ['Aspirin', 'Lisinopril', 'Metformin', 'Simvastatin', 'Amoxicillin'],
            'Stock': [12, 25, 8, 30, 14]
        }
        self.df = pd.DataFrame(self.data)
        import user_code
        importlib.reload(user_code)
        self.user_code = user_code

    def test_filter_low_stock(self):
        df = self.df.copy()
        # Patch plt.show to prevent actual rendering
        plt.close('all')
        orig_show = plt.show
        plt.show = lambda *a, **k: None
        try:
            self.user_code.plot_low_stock_medications(df)
            ax = plt.gca()
            bars = ax.patches
            drugs_shown = [t.get_text() for t in ax.get_yticklabels() if t.get_text() != '']
            expected_drugs = ['Aspirin', 'Metformin', 'Amoxicillin']
            # Check that only low stock drugs are shown
            _dynamic_test(
                self,
                set(drugs_shown) == set(expected_drugs),
                f"Chart displays correct drugs: {expected_drugs}",
                f"Expected only {expected_drugs} in chart, got {drugs_shown}",
            )
        finally:
            plt.show = orig_show
            plt.close('all')

    def test_bar_values(self):
        df = self.df.copy()
        plt.close('all')
        orig_show = plt.show
        plt.show = lambda *a, **k: None
        try:
            self.user_code.plot_low_stock_medications(df)
            ax = plt.gca()
            bar_lengths = [bar.get_width() for bar in ax.patches]
            expected_stocks = [12, 8, 14]
            _dynamic_test(
                self,
                sorted(bar_lengths) == sorted(expected_stocks),
                "Bar chart shows correct stock levels for low-stock drugs",
                f"Expected stocks {expected_stocks}, got {bar_lengths}",
            )
        finally:
            plt.show = orig_show
            plt.close('all')

    def test_axis_labels_and_title(self):
        df = self.df.copy()
        plt.close('all')
        orig_show = plt.show
        plt.show = lambda *a, **k: None
        try:
            self.user_code.plot_low_stock_medications(df)
            ax = plt.gca()
            x_label = ax.get_xlabel()
            y_label = ax.get_ylabel()
            title = ax.get_title()
            _dynamic_test(
                self,
                x_label == 'Stock Level',
                "X-axis label is correct",
                f"Expected x-axis label 'Stock Level', got '{x_label}'",
            )
            _dynamic_test(
                self,
                y_label == 'Drug Name',
                "Y-axis label is correct",
                f"Expected y-axis label 'Drug Name', got '{y_label}'",
            )
            _dynamic_test(
                self,
                title == 'Medications with Low Stock (<15 units)',
                "Chart title is correct",
                f"Expected title 'Medications with Low Stock (<15 units)', got '{title}'",
            )
        finally:
            plt.show = orig_show
            plt.close('all')

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: Visualize Low Stock Medications

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Challenge: Visualize Low Stock Medications

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