Lære Data Visualization for Engineering Reports | Thermodynamics and Data Analysis

When preparing engineering reports, your data visualizations must communicate information clearly and professionally. Effective engineering plots follow specific best practices to ensure accuracy, readability, and impact. Start by choosing the right plot type for your data: use bar charts for comparing discrete values, line plots for trends over time or continuous variables, and scatter plots for relationships between variables. Every plot should include descriptive axis labels, a clear title, and a legend if multiple data series are present. Units must always be indicated. Use color and style to differentiate data, but avoid unnecessary decoration. Consistency in font, size, and color palette maintains a professional appearance. Always ensure plots are large enough to read when embedded in reports, and export them in high-resolution formats for publication or presentation.


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import matplotlib.pyplot as plt
import seaborn as sns

materials = ['Aluminum', 'Copper', 'Steel', 'Brass', 'Titanium']
conductivity = [237, 401, 50, 109, 22]

sns.set(style="whitegrid")
plt.figure(figsize=(8, 5))

sns.barplot(
    x=materials,
    y=conductivity,
    hue=materials,
    palette="viridis",
    legend=False
)

plt.title("Thermal Conductivity of Common Metals")
plt.xlabel("Material")
plt.ylabel("Thermal Conductivity (W/m·K)")

for i, v in enumerate(conductivity):
    plt.text(i, v + 5, str(v), ha='center', va='bottom', fontsize=10)

plt.tight_layout()
plt.show()

The code above demonstrates how to create a comparative bar chart for material properties using matplotlib and seaborn. By calling sns.set(style="whitegrid"), you apply a clean, publication-ready background. The sns.barplot function simplifies the process of generating a bar chart and automatically applies a color palette for improved visual distinction between bars. Axis labels and a descriptive title are added for clarity, and each bar is annotated with its value to aid interpretation. This approach helps you quickly compare thermal conductivity values across different materials, making the chart easy to read and suitable for inclusion in engineering reports.


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import matplotlib.pyplot as plt

# Sample data: temperature profiles (°C) of three materials over time (minutes)
time = [0, 10, 20, 30, 40, 50, 60]
aluminum_temp = [25, 48, 65, 72, 76, 78, 79]
steel_temp = [25, 38, 52, 60, 65, 68, 70]
brass_temp = [25, 42, 58, 66, 71, 74, 76]

plt.figure(figsize=(8, 5))
plt.plot(time, aluminum_temp, marker='o', label='Aluminum')
plt.plot(time, steel_temp, marker='s', label='Steel')
plt.plot(time, brass_temp, marker='^', label='Brass')

plt.title("Temperature Profiles of Materials Over Time")
plt.xlabel("Time (minutes)")
plt.ylabel("Temperature (°C)")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

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Sektion 3. Kapitel 6

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Suggested prompts:

Can you explain why line plots are used for this type of data?

What are some tips for making multi-series line plots more readable?

How can I choose the best color palette for my plots?

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import matplotlib.pyplot as plt
import seaborn as sns

materials = ['Aluminum', 'Copper', 'Steel', 'Brass', 'Titanium']
conductivity = [237, 401, 50, 109, 22]

sns.set(style="whitegrid")
plt.figure(figsize=(8, 5))

sns.barplot(
    x=materials,
    y=conductivity,
    hue=materials,
    palette="viridis",
    legend=False
)

plt.title("Thermal Conductivity of Common Metals")
plt.xlabel("Material")
plt.ylabel("Thermal Conductivity (W/m·K)")

for i, v in enumerate(conductivity):
    plt.text(i, v + 5, str(v), ha='center', va='bottom', fontsize=10)

plt.tight_layout()
plt.show()


              1234567891011121314151617181920
            
import matplotlib.pyplot as plt

# Sample data: temperature profiles (°C) of three materials over time (minutes)
time = [0, 10, 20, 30, 40, 50, 60]
aluminum_temp = [25, 48, 65, 72, 76, 78, 79]
steel_temp = [25, 38, 52, 60, 65, 68, 70]
brass_temp = [25, 42, 58, 66, 71, 74, 76]

plt.figure(figsize=(8, 5))
plt.plot(time, aluminum_temp, marker='o', label='Aluminum')
plt.plot(time, steel_temp, marker='s', label='Steel')
plt.plot(time, brass_temp, marker='^', label='Brass')

plt.title("Temperature Profiles of Materials Over Time")
plt.xlabel("Time (minutes)")
plt.ylabel("Temperature (°C)")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

Var alt klart?

Tak for dine kommentarer!

Sektion 3. Kapitel 6

Data Visualization for Engineering Reports

1. What are the advantages of using seaborn over plain matplotlib for engineering plots?

2. Which plot types are most effective for comparing material properties?

3. How does clear labeling improve the usefulness of engineering plots?

Data Visualization for Engineering Reports

1. What are the advantages of using seaborn over plain matplotlib for engineering plots?

2. Which plot types are most effective for comparing material properties?

3. How does clear labeling improve the usefulness of engineering plots?