Python for Engineers & Robotics – Master NumPy, Pandas, and ChatGPT Automation
In this comprehensive course, you will learn Python programming from scratch specifically tailored for mechanical engineering and robotics using ChatGPT. You'll start with fundamental concepts like variables, operators, and control flow before mastering essential scientific libraries including NumPy for numerical computations, Pandas for data handling, and Matplotlib for engineering analysis. Through real-world engineering case studies, material selection problems, and automated data workflows, you will gain practical coding skills to optimize your technical analysis and design workflows.Created by https://gaugehow.com/
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⭐️ Chapters ⭐️
- 00:00 Course Overview & Python Basics
- 01:14 Introduction to Python for Mechanical Engineers
- 02:43 Important Features & Execution of Python
- 05:12 Significance of Python in Mechanical Engineering
- 06:30 Top Applications: Data Analysis, CFD, & Robotics
- 10:44 Setting Up Python & VS Code on Windows
- 13:38 Running Your First Python Program
- 18:53 Interactive Shell (REPL) vs. Python Scripts
- 25:44 Single-Line & Multi-Line Comments in Python
- 31:48 Understanding Variables & Naming Rules
- 35:33 Variable Assignment Methods & Data Types
- 43:00 Python Literals Explained
- 46:50 Implicit & Explicit Type Conversion
- 55:33 Basic Input & Output (Print Formatting)
- 1:05:46 User Input & Split Method
- 1:12:46 Arithmetic & Logical Operators
- 1:20:11 Comparison, Assignment, & Identity Operators
- 1:30:30 Operator Precedence Rules & Examples
- 1:35:28 Using ChatGPT to Learn Python
- 1:41:11 Control Flow: Conditional Statements (if/elif/else)
- 1:51:14 Engineering Practical Examples for Conditional Logic
- 2:04:42 Loops: For Loops & The `range()` Function
- 2:16:53 Mechanical Engineering Applications Using For Loops
- 2:22:48 While Loops & Simulating Dynamic Processes
- 2:32:52 Loop Control Statements: `break` & `continue`
- 2:37:58 Nested Loops
- 2:42:48 Mechanical Engineering Case Studies with Loops
- 2:52:58 ChatGPT Prompts for Loops & Conditionals
- 2:57:48 Functions & Code Reusability
- 3:04:54 Function Arguments & Return Values
- 3:11:59 Arbitrary Positional (`*args`) & Keyword (`**kwargs`) Arguments
- 3:18:56 Understanding Variable Scope & LEGB Rule
- 3:24:10 Working with Global Variables
- 3:28:19 Introduction to Python Modules
- 3:33:35 Useful Built-in Modules for Engineering
- 3:38:19 Creating & Importing User-Defined Modules
- 3:41:51 Designing Functions with ChatGPT
- 3:47:00 Introduction to NumPy & Installation
- 3:53:30 Methods for Creating NumPy Arrays
- 3:59:41 Creating Multi-Dimensional (`ND`) Arrays
- 4:07:48 NumPy Data Types & Type Conversion
- 4:13:32 Essential NumPy Array Attributes
- 4:18:14 NumPy Array Indexing (1D, 2D, 3D)
- 4:27:38 Slicing & Reversing NumPy Arrays
- 4:36:46 Element-Wise Arithmetic Operations
- 4:41:12 Mathematical & Statistical Array Functions
- 4:47:40 String Operations in NumPy
- 4:53:30 Trigonometric Functions & Angle Conversions
- 4:58:45 Matrix Operations: Multiplication, Transpose, Inverse, & Reshape
- 5:03:44 Solving Mechanical Engineering Problems with NumPy
- 5:12:40 Troubleshooting NumPy Code with ChatGPT
- 5:17:06 Introduction to Pandas & Installation
- 5:20:16 Working with Pandas Series
- 5:27:56 Creating & Managing Pandas DataFrames
- 5:35:50 Default, Custom, & Range Indexing
- 5:40:38 Exploring Data: `head()`, `tail()`, & `info()`
- 5:45:07 Modifying DataFrames: Adding, Dropping, & Renaming
- 5:52:11 Advanced Selection & Slicing: `.loc` vs. `.iloc`
- 5:00:38 Filtering Rows, Boolean Indexing, & The `query()` Method
- 6:05:50 Multi-Indexing & Removing Duplicates
- 6:15:52 Reading & Writing Excel and CSV Files
- 6:25:58 Pivoting & Creating Pivot Tables
- 6:34:50 Real-World Case Study: Aircraft Material Data Analysis
- 6:44:15 Using ChatGPT for Pandas Data Cleaning & Analysis
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