DarkHawk727 / Learn-Scientific-Programming-with-Python-Solutions

Repository for the questions and exercises for the 2nd Edition of Learn Scientific Programming with Python.

Learn-Scientific-Programming-with-Python

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.

Repository for the questions and exercises for the 2nd Edition of Learn Scientific Programming with Python. For more info, visit scipython.com.

Chapter 1: Introduction

• 1.1 - About this Book (No Questions or Exercises)
• 1.2 - About Python (No Questions or Exercises)
• 1.3 - Installing Python (No Questions or Exercises)
• 1.4 - The Command Line (No Questions or Exercises)

Chapter 2: The Core Python Language I

• 2.1 - The Python Shell (No Questions or Exercises)
• 2.2 - Numbers, Variables, Comparisons, and Logic
• 2.3 - Python Objects I: Strings
• 2.4 - Python Objects II - Lists, Tuples, and Loops
• 2.5 - Control Flow
• 2.6 - File Input/Output
• 2.7 - Functions

Chapter 3: Interlude: Simple Plots and Charts

• 3.1 - Basic Plotting
• 3.2 - Labels, Legends, and Customization
• 3.3 - More Advanced Plotting

Chapter 4: The Core Python Language II

• 4.1 - Errors and Exceptions
• 4.2 - Python Objects III: Dictionaries and Sets
• 4.3 - Pythonic Idioms: "Syntactic Sugar"
• 4.4 - Operating-System Services
• 4.5 - Modules and Packages (No Questions or Exercises)
• 4.6 - An introduction to Object-Oriented Programming

Chapter 5: IPython and Jupyter Notebook

• 5.1 - IPython
• 5.2 - JupyterNotebook (No Questions or Exercises)

Chapter 6: NumPy

• 6.1 - Basic Array Methods
• 6.2 - Reading and Writing an Array to a File
• 6.3 - Statistical Methods
• 6.4 - Polynomials
• 6.5 - Linear Algebra
• 6.6 - Random Sampling
• 6.7 - Discrete Fourier Transforms

Chapter 7: Matplotlib

• 7.1 - Line Plots and Scatter Plots (No Questions or Exercises)
• 7.2 - Plot Customization and Refinement (No Questions or Exercises)
• 7.3 - Bar Charts, Pie Charts, and Polar Plots (No Questions or Exercises)
• 7.4 - Annotating Plots
• 7.5 - Countour Plots and Heatmaps
• 7.6 - Three-Dimensional Plots (No Questions or Exercises))
• 7.7 - Animation

Chapter 8: SciPy

• 8.1 - Physical Constants and Special Functions
• 8.2 - Integration and Ordinary Differential Equations
• 8.3 - Interpolation (No Questions or Exercises)
• 8.4 - Optimization, Data-Fitting and Root-Finding

Chapter 9: Data Analysis with pandas

• 9.1 - Introduction to pandas (No Questions or Exercises)
• 9.2 - Reading and Writing Series and DataFrames
• 9.3 - More Advanced Indexing
• 9.4 - Data Cleaning and Exploration
• 9.5 - Data Grouping and Aggregation
• 9.6 - Examples (No Questions or Exercises)

Chapter 10: General Scientific Programing

• 10.1 - Floating-Point Arithmetic
• 10.2 - Stability and Conditioning
• 10.3 - Programming Techniques and Software Development (No Questions or Exercises)