#056 - The Minimum Viable Python for Structural Engineers

A fast, focused guide to the core Python skills for handling data, calculations, and plots.

How much Python do you really need?

This article is an overview of core Python syntax, essential tools, and practical workflows that you can start using immediately.

There’s always more to learn, but most of it isn’t necessary at the start.

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1. Setting Up Your Workspace

📌 Use Jupyter Notebooks.

It’s the best environment for engineers. You can write code, add notes, and visualize results—all in one document.

Start on Google Colab if you want to skip installations. Once you’re comfortable, move to JupyterLab or VS Code for more control. I use VS Code.

For a deeper look at setup check out:

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2. Basic Syntax: The Essentials

You don’t need to know everything. Here’s what will cover 90% of your tasks:

✅ Variables

length = 5.5       # Float
material = "Steel" # String

✅ Lists (Arrays)

beams = ["Beam A", "Beam B", "Beam C"]

✅ Dictionaries (Key-Value Pairs)

properties = {"Beam A": {"Length": 6.0, "Material": "Steel"}}

✅ If Statements

if length > 5:
    print("Beam is long")
else:
    print("Beam is short")

✅ Loops

for beam in beams:
    print(f"Inspecting {beam}")

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3. Functions

A function is like a reusable tool in your workflow.

It takes inputs, does some calculation, and gives you a result.

Define them once, and call them anytime.

Example: Calculate the area of a rectangle using length and width:

# Function to calculate area
def rectangle_area(length, width):
    return length * width

# Usage
L = 10  # Length in meters
W = 5   # Width in meters
area = rectangle_area(L, W)
print(f"Area: {area} m²")

Output:

Area: 50 m²

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4. Data Handling with Pandas

📊 pandas is an excellent library for handling data.

For handling large datasets, I’ve been using Polars lately. It’s faster for big data, but pandas is more than enough to get started. Their syntax is very similar.

Understanding dataframes and how they work is important. I’ll come back to this in another post.

This is a great overview for now: https://www.w3schools.com/python/pandas/pandas_dataframes.asp

Load a CSV File:

A very common use case. You can also load .xlsx files.

import pandas as pd

df = pd.read_csv("beam_data.csv")
print(df.head())

Filter Data:

steel_beams = df[df["Material"] == "Steel"]

Calculate Statistics:

mean_length = df["Length"].mean()
print(f"Average Length: {mean_length} meters")

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5. Plotting with Matplotlib

Use matplotlib to visualize results. It’s quick, and your plots can be exported directly into reports.

I like Seaborn which is built on matplotlib, simply because I prefer the visual styles.

Seaborn Examples: https://seaborn.pydata.org/examples/index.html

Plotly is also a great option.

Plot Beam Lengths:

Here’s a table of beam data that we’ll use for calculations and plotting:

Here’s a quick example of how to plot simple bending moments.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create the DataFrame
data = {
    "Beam": ["Beam A", "Beam B", "Beam C", "Beam D"],
    "Length": [6.0, 7.5, 5.0, 8.0],
    "UDL": [-5.0, -4.8, -4.2, -5.5],  # Load in kN/m
    "Material": ["Steel", "Concrete", "Timber", "Steel"]
}

df = pd.DataFrame(data)

# Calculate bending moments for UDL (wl^2/8)
df["Moment"] = (df["UDL"] * df["Length"]**2) / 8

# Set up the plot
plt.figure(figsize=(8, 4.5))

# Colors for each beam
colors = ["blue", "green", "orange", "red"]

# Plot BMD for each beam
for i, row in df.iterrows():
    length = row["Length"]
    udl = row["UDL"]
    x = np.linspace(0, length, 100)  # Divide the beam length into 100 points
    bmd = (udl * x * (length - x)) / 2  # Parabolic BMD equation for UDL

    plt.plot(x, bmd, label=row["Beam"], color=colors[i])

# Add plot details
plt.title("Bending Moment Diagrams (BMD) for UDL Beams")
plt.xlabel("Beam Length (m)")
plt.ylabel("Bending Moment (kNm)")
plt.axhline(0, color="black", linewidth=0.8, linestyle="--")
plt.legend()
plt.grid(True)

# Show the plot
plt.show()

Output:

What’s Happening?

1. DataFrame: We create a self-contained table of beam data, including UDL values in kN/m.

2. UDL Moment Calculation: We calculate the UDL bending moment for each beam.

   \(M(x) = w * x * (L - x) / 2\)

3. Beam Plot: Plots each beam's parabolic BMD as a negative curve to display the typical representation for downward UDLs.

4. Format: Titles, axes labels, a dashed zero line, and a legend to identify each beam.

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6. Key Takeaways

1. Jupyter Notebooks for interactive design.

2. pandas handles your data.

3. matplotlib helps you visualize results.

4. Write functions to automate repetitive calculations.

This is all you need to get started. Build small scripts, automate tasks, and improve your workflows gradually.

Python is a tool. Use it to make your life easier. Don’t overcomplicate it.

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Check out this article for my ongoing list of Python for Engineering packages.

#023 - Python Libraries for Civil/Structural Engineering

See you in the next one.

James 🌊