Essential Python Programming Resources: Basics, Libraries, and Modeling Tools

1. Python Programming Basics

Why Choose Python Over Matlab as a Programming Language

Python Basics 01: Installation and Simple Usage

Python Basics 03: Lists, Tuples, Dictionaries, Sets

Python Basics 04: Conditional and Loop Statements

Python Basics 05: Defining Functions

Python Data Types: Strings and Numeric Types

2. Common Python Libraries for Mathematical Modeling

2.1 Numpy – Data Processing, Functions, Linear Algebra, Statistics

Numpy Overview

Numpy – Underlying Mechanisms of Common Functions

2.2 Matplotlib – Plotting

Matplotlib – Common Plots

Matplotlib – Setting Plot Styles and Chinese Support

Matplotlib – Displaying Chinese Correctly

Matplotlib – 3D Curves, Scatter, Surface Plots

Matplotlib – Creating Publication‑Quality Figures

2.3 Pandas – Data Processing and Visualization

Pandas – Basic Operations

Pandas – Visualizing Multidimensional Data

2.4 Scipy – Fitting, Optimization, Clustering, Image Processing

Scipy – Custom Function Fitting

Scipy – Function Interpolation

Scipy – Linear Algebra

Scipy – Clustering

Scipy – Optimization and Root Finding

Scipy – Sparse Graphs

2.5 PuLP – Linear and Integer Programming

PuLP – Solving Simple Linear Programming Problems

PuLP – Solving 0‑1 Programming Problems

2.6 NetworkX – Graph Theory and Complex Networks

NetworkX – Basic Operations

NetworkX – Minimum Spanning Tree Example

2.7 Geatpy – Genetic Algorithms

Geatpy – Introduction and Using Genetic Algorithms for Optimization

2.8 Numba and Taichi – Accelerating Python Execution

Numba – Speeding Up Python

Taichi – Using Taichi Library to Accelerate Python