Building on your understanding of **data visualization** and **correlation analysis** from previous chapters, you are now ready to tackle more advanced techniques for exploring engineering datasets. In engineering, visualizing the relationships between multiple material properties—such as `density`, `tensile strength`, and `thermal conductivity`—can reveal important insights for material selection and system design. **Seaborn**, a powerful Python library for data visualization, offers tools like **pair plots** and **heatmaps** that help you quickly identify patterns and correlations among variables. By combining these visualizations with **correlation matrices**, you can efficiently assess which material properties are most strongly linked, supporting better engineering decisions.


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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.material_data = pd.DataFrame({
            "Density": [2.7, 7.9, 8.9, 2.2, 4.5, 7.1, 8.0, 2.5, 3.8, 7.8],
            "Tensile_Strength": [310, 540, 620, 180, 950, 370, 480, 220, 400, 590],
            "Thermal_Conductivity": [237, 80, 401, 140, 22, 50, 58, 160, 120, 54]
        })

    def test_pairplot_generated(self):
        import user_code
        importlib.reload(user_code)
        # Patch sns.pairplot to set a flag when called
        called = {}
        orig_pairplot = user_code.sns.pairplot
        def fake_pairplot(data, *args, **kwargs):
            called['pairplot'] = True
            return orig_pairplot(data, *args, **kwargs)
        user_code.sns.pairplot = fake_pairplot
        try:
            user_code.visualize_material_properties(self.material_data)
        except Exception:
            pass
        _dynamic_test(
            self,
            called.get('pairplot', False),
            "Pair plot is generated for all columns.",
            "Pair plot was not generated by sns.pairplot."
        )
        user_code.sns.pairplot = orig_pairplot

    def test_correlation_matrix_computed(self):
        import user_code
        importlib.reload(user_code)
        corr_matrix = None
        orig_corr = pd.DataFrame.corr
        called = {'corr': False}
        def fake_corr(self_, *args, **kwargs):
            called['corr'] = True
            return orig_corr(self_, *args, **kwargs)
        pd.DataFrame.corr = fake_corr
        try:
            user_code.visualize_material_properties(self.material_data)
        except Exception:
            pass
        _dynamic_test(
            self,
            called['corr'],
            "Correlation matrix computed using DataFrame.corr().",
            "Correlation matrix was not computed."
        )
        pd.DataFrame.corr = orig_corr

    def test_heatmap_generated(self):
        import user_code
        importlib.reload(user_code)
        called = {}
        orig_heatmap = user_code.sns.heatmap
        def fake_heatmap(*args, **kwargs):
            called['heatmap'] = True
            return orig_heatmap(*args, **kwargs)
        user_code.sns.heatmap = fake_heatmap
        try:
            user_code.visualize_material_properties(self.material_data)
        except Exception:
            pass
        _dynamic_test(
            self,
            called.get('heatmap', False),
            "Heatmap of correlation matrix is displayed.",
            "Heatmap was not generated by sns.heatmap."
        )
        user_code.sns.heatmap = orig_heatmap

    def test_strongest_correlation_returned(self):
        import user_code
        importlib.reload(user_code)
        result = user_code.visualize_material_properties(self.material_data)
        _dynamic_test(
            self,
            isinstance(result, str),
            "Function returns a string.",
            "Function did not return a string."
        )
        prefix = "The strongest correlation is between "
        _dynamic_test(
            self,
            result.startswith(prefix) and "with a coefficient of" in result and result.endswith("."),
            "String format matches requirements.",
            f"Result string does not match required format: {result}"
        )
        # Check actual strongest correlation
        corr = self.material_data.corr().abs()
        for col in corr.columns:
            corr.loc[col, col] = 0
        max_corr = corr.max().max()
        max_pair = corr.stack().idxmax()
        expected = f"The strongest correlation is between {max_pair[0]} and {max_pair[1]} with a coefficient of {max_corr:.2f}."
        _dynamic_test(
            self,
            expected == result,
            "Returned string correctly describes the strongest correlation.",
            f"Expected: '{expected}', got: '{result}'"
        )

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 course designed for engineers who want to leverage Python to solve real-world engineering problems. Explore data analysis, mathematical modeling, and simulation using Python's powerful libraries and tools, all through engaging, hands-on examples and challenges.

Learn how to use Python to analyze, summarize, and visualize engineering data. This section covers essential data handling and visualization techniques tailored for engineering applications.

Dive into mathematical modeling and simulation techniques for engineering systems using Python. Learn to model physical processes, solve equations, and simulate system behavior.

Apply data science techniques to engineering problems, including classification, clustering, and predictive modeling using Python's scikit-learn and seaborn libraries.

Challenge: Visualize Correlations in Material Properties

Solution

Challenge: Visualize Correlations in Material Properties

Solution