Python for Engineers & Robotics – Master NumPy, Pandas, and ChatGPT Automation

In this broad course, you will study Python programming from scratch specifically tailored for mechanical engineering and robotics utilizing ChatGPT. You'll commencement pinch basal concepts for illustration variables, operators, and power travel earlier mastering basal technological libraries including NumPy for numerical computations, Pandas for information handling, and Matplotlib for engineering analysis. Through real-world engineering lawsuit studies, worldly action problems, and automated information workflows, you will summation applicable coding skills to optimize your method study and creation workflows. Created by https://gaugehow.com/ ❤️ Support for this transmission comes from our friends astatine Scrimba – the coding level that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ 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 successful Mechanical Engineering - 06:30 Top Applications: Data Analysis, CFD, & Robotics - 10:44 Setting Up Python & VS Code connected Windows - 13:38 Running Your First Python Program - 18:53 Interactive Shell (REPL) vs. Python Scripts - 25:44 Single-Line & Multi-Line Comments successful 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 pinch 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 pinch 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 pinch 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 successful NumPy - 4:53:30 Trigonometric Functions & Angle Conversions - 4:58:45 Matrix Operations: Multiplication, Transpose, Inverse, & Reshape - 5:03:44 Solving Mechanical Engineering Problems pinch NumPy - 5:12:40 Troubleshooting NumPy Code pinch ChatGPT - 5:17:06 Introduction to Pandas & Installation - 5:20:16 Working pinch 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 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to codification for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles connected programming: https://freecodecamp.org/news