--- title: "Computational Techniques: Python Basics" subtitle: A practical guide for CHL7002 students at IIT Delhi --- # Welcome This resource is designed to help you learn Python programming for the **CHL7002: Computational Techniques for Chemical Engineers** course. ```{admonition} 🧠 Explore the Mind Map :class: tip Get a visual overview of all topics with key commands at a glance. 🗺️ Open Interactive Mind Map ``` ## What You'll Learn - **Python Fundamentals**: Data types, variables, operators - **Print & Comments**: See what your code is doing, write code others can understand - **Control Flow**: Conditionals and loops - **Data Structures**: Lists, dictionaries, and more - **Functions**: Writing reusable code - **NumPy**: Numerical computing essentials - **Error Handling**: Reading and fixing common errors ## Why Learn Python? Imagine you're in the lab and you've just finished a tracer experiment. You have a CSV file with 500 data points of concentration vs. time. Your task: calculate the mean residence time and variance of the RTD. You could open Excel, manually set up columns, type formulas, drag them down, hope you didn't miss a row, and repeat this every time you run an experiment. Or you could write this: ```python import pandas as pd import numpy as np data = pd.read_csv("tracer_data.csv") t = data["time"] C = data["concentration"] E = C / np.trapz(C, t) # Normalize to get E(t) t_mean = np.trapz(t * E, t) # Mean residence time variance = np.trapz((t - t_mean)**2 * E, t) # Variance print(f"Mean residence time: {t_mean:.2f} s") print(f"Variance: {variance:.2f} s²") ``` Six lines. Run it once, and it works for any dataset, whether 500 points or 50,000. Change the experiment? Just point it to a new file. Want to plot E(t)? Add two more lines. Want to compare 10 experiments? Wrap it in a loop. This is why you learn to code: **not because it's required, but because it makes you faster, less error-prone, and capable of tackling problems that would be impractical by hand.** --- > "One student asked me: why code? What's the point?" Here's the reality: many problems in chemical engineering **don't have analytical solutions**. You can't solve them by hand. **Examples you'll encounter in this course:** | Problem | What Python Does | |---------|------------------| | Compute $e^x$ using Taylor series | Add up terms in a loop, track the error | | Find roots of equations (bisection, Newton-Raphson) | Repeat a formula until the answer is close enough | | Molar volume from van der Waals equation | Solve a nonlinear equation iteratively | | Calculate derivatives numerically | Compute $(f(x+h) - f(x))/h$ for different step sizes | | Process experimental data from a file | Read CSV, calculate statistics, plot results | These aren't abstract exercises. The first assignment asks you to compute $e^x$ from the series expansion and track the error at each step. That's a loop, some arithmetic, and print statements. By the end of this module, you'll be able to do it. **What working engineers use Python for:** Once you're comfortable with the basics, Python opens doors to real engineering work: - **Automating calculations**: Run the same analysis on 50 datasets without copy-pasting - **Plotting and visualization**: Generate publication-quality figures from your data - **Reading sensor data**: Parse log files from lab equipment - **Connecting to simulations**: Pre/post-process data for ANSYS, COMSOL, or LAMMPS - **Building simple GUIs**: Create a tool your lab group can use without coding Python is the tool that lets you solve these problems. ## Getting Started Before diving into Python syntax, read [The Parachutist Problem](parachutist_problem.md) to understand the running example we'll use throughout this tutorial. Then continue to [Data Types](datatypes.md) to learn the building blocks of Python.