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Engineering Modeling and Analysis with Python, 2E
Carl Greco, Zahra Zamanipour and Thomas Dodds · Arkansas Tech University
An open-access textbook designed to equip undergraduate engineering students with core computational and analytical skills using the Python ecosystem. It transitions from fundamental Python programming concepts, such as data structures, control flow, file I/O, and object-oriented programming into practical applications with libraries like NumPy, SciPy, Matplotlib, Pandas, and SymPy. Dynamic systems, solve linear algebra equations, and visualize engineering data.
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ATMO 321 python for atmospheric sciences
Andy Dessler · Texas A&M
In the contents: 10.01: Importing packages in python
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Introduction to Programming Concepts - Python
LibreTexts
In the contents: 1.06: Conversing with Python · 13: Python and Web Services · 13.8: Python and Web Services (Exercises)
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Python for Everybody
Chuck Severance · University of Michigan
In the contents: 1.06: Conversing with Python · 13: Python and Web Services · 13.0E: 13.E: Python and Web Services (Exercises)
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Python Programming
OpenStax · LibreTexts
In the contents: 1.08: Why Python · 2.01: The Python Shell · 2.08: Python Careers
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Engineering Modeling and Analysis with Python
Carl Greco · Arkansas Tech University
In the contents: 01: Introduction to Engineering Modeling and Analysis with Python · 11.01: NumPy - Numerical Python · 11.02: SciPy - Scientific Computing in Python
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Mathematical Computing with Python
Dennis Hall · Angelo State University
In the contents: 01: Introduction to Python · 1.02: Python Basics
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Think Python - How to Think Like a Computer Scientist (Downey)
Allen B. Downey · Olin College
In the contents: 11.01: The Python Programming Language · 13.02: Analysis of Basic Python Operations
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Think Python 2ed.
Allen B. Downey · Olin College
In the contents: 1.02: Running Python · 21.02: Analysis of basic Python operations
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Think Complexity: Exploring Complexity Science with Python
Allen B. Downey · Olin College
Complex systems include networks and graphs, cellular automatons, agent-based models and swarms, fractals and self-organizing systems, chaotic systems and cybernetic systems. This book is primarily about complexity science, but studying complexity science gives you a chance to explore topics and ideas you might not encounter otherwise, practice programming in Python, and learn about data structures and algorithms.
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Intro to Programming with Programming Fundamentals and Python for Everyone
LibreTexts
In the contents: 2.03: Conversing with Python · 6.11: Python Examples · 10.05: Python Examples
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Making Games with Python and Pygame
Al Sweigart · LibreTexts
In the contents: 02: Installing Python and Pygame · 2.02: Downloading and Installing Python · 2.06: Starting Python
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Byte of Python
Swaroop C H · LibreTexts
In the contents: 1.01: About Python
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Laboratory Manual - Computer Programming with Python, Multisim and TINA/4E
James M. Fiore · Mohawk Valley Community College
In the contents: 05: Introduction to Python
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Python for Introductory Statistics
Simon Aman · City Colleges of Chicago · 2021
The purpose of this material is to provide an overview of the free programming language, Python, and a few of its software libraries that are most useful for improving Introductory Statistics students' learning and for providing an alternative statistical software package for those who perform statistical data analysis and plotting. This material, along with the companion workbook and a free web-based Python platform, could easily replace the use of other software packages and calculators in an Introductory Statistics course. Part I, Sections 1 to 4, covers the basics of the Python programming language. Part II, Sections 5 to 13, focuses on Python for introductory statistics, and Part III, Section 14, covers selected advanced statistics topics in Python. Additionally, there are demo video links accompanying key steps for further instruction. No prior programming knowledge is required or assumed.
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Scientific Computing for Chemists with Python
Charles Weiss · Charles J. Weiss · 2025
This book serves as an introduction to coding for chemists. The tools employed in this book are the powerful and popular combination of Jupyter notebooks and the Python programming language. No background beyond first-year college chemistry and occasionally some very basic spectroscopy (for advanced chapters) is assumed for most of this book. This book starts with a brief primer on Jupyter notebooks in chapter 0 and computer programming with Python in chapters 1 and 2. If you already have a background in these tools, feel free to skip ahead. The rest of the book dives into applications of Python to solving chemical problems. Python and Jupyter were chosen for a variety of reasons, including that they are: Relatively easy to use and learn Powerful and well-suited for solving chemical problems Free, open-source software Cross-platform (e.g., runs on Windows, macOS, and Linux) Supplemented with numerous, specialized libraries for handling specific types of data or problems (e.g., machine learning) Supported by a helpful and welcoming community Learning to use a number of popular Python scientific libraries to solve chemical problems is one of the themes of this book. A Python library can be thought of as a tool pack with premade functions for performing common tasks in scientific data processing, analysis, and visualization. For example, the matplotlib library provides a variety of functions for creating a wide range of plots, while the scikit-learn library contains functions and resources for machine learning.
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Introduction to Computer Programming with Python
Harris Wang · Athabasca University Press · 2023
This introduction to computer programming with Python begins with some of the basics of computing and programming before diving into the fundamental elements and building blocks of computer programs in Python language. From the installation of Python, Python interactive programming, and integrated development environments to raising and handling exceptions, using compound data types to solve problems, and implement divide-and-conquer processes using functions, classes and modules, this textbook will set students up for success in programming and computing study and practice. The included exercises and projects are designed to hone students’ skills.
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An Introduction to Python Jupyter Notebooks for College Math Teachers
Paul Isihara; Claire Wagner; Peter Jantsch; Thomas VanDrunen · Paul Isihara, Claire Wagner, Peter Jantsch, and Thomas VanDrunen · 2024
This book is an introduction to the use of Python Jupyter Notebooks (JNBs) for college math teachers and their students. The book is an evolving work in progress, but we hope this 1st Edition may already be quite useful to a college math teacher who is interested to use Jupyter Notebooks in their courses. Each section of the book has been written in the form of a Jupyter Notebook and can be downloaded using the download button at the top menu bar. In some cases, additional data files or image files may need to be downloaded as well. All the files are available in the src folder of the github repository https://github.com/timothyprojectGiG/JB_Math_Textbook Each chapter contains a JNB lab with solutions. Experienced teachers can modify these labs and create new labs tailored to their courses. The chapters were written by different authors/authorship teams, and as such, vary in style. Each chapter essentially can be read independently of the others as long as one has the pre-requisite mathematical knowledge. Some effort has been made to standardize the chapter formats, and the index may help the reader find specific topics of interest. There is virtually no limit to what can be done with JNBs, and we hope this work serves as a useful starting point for teachers and students to enrich and enliven the study of mathematics using this tool. The JNBs used to create this book contain special markdown code which are needed to produce features such as indexing, referencing, and highlight boxes in the Jupyter Book. Some editing of a downloaded JNB may may make it more suitable for classroom use.
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Introduction to Data Science Using Python
Afrand Agah · The Pennsylvania Alliance for Design of Open Textbooks (PA-ADOPT) · 2024
This book contains two parts, the first is designed to be used in an introductory programming course for students looking to learn Python, without having any prior experience with programming. Basic programming concepts are discussed, explained, and illustrated with a Python program. Ample programming questions are provided for practice. The second part of the book utilizes machine-learning concepts and statistics to accomplish data-driven resolutions. Python programs are provided to apply scientific computing to conclude statistically driven results.
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Prealgebra via Python Programming: First Steps to Perform Large Scale Computational Tasks in the Sciences and Engineerings
Sergio Rojas · Sergio Rojas · 2018
This book was written for students and instructors who want to learn how to use a computer for other than the most common uses, such as web browsing, document creation, or paying bills online. This book is for anyone who wants to perform computational tasks that they design. In other words, if you wish to learn how to program a computer, this book is for you. Because prealgebra is a subject that practically everyone is supposed to learn in grade school, it provides a platform to introduce basic computer programming concepts. Consequently, this book should also be of interest to students in middle or high school who want to learn how to program, and who are willing to invest the time and effort in learning a programming language that they could continue using throughout their schooling and in their professional life. Similarly, this book could also be of interest to pre-service and in-service mathematics teachers wishing to have at their disposal a complementary tool to assist in fostering understanding, competency, and interest in mathematics among their students. This book can be integrated with the teachers’ curriculum as way to tackle non-traditional math problems using an inexpensive modern computer language. By the end of the book, a reader will have learned enough to be able to write a preliminary, step-by-step one variable equation solver that can be expanded in the future to use with more complex equations. In other words, by the end of the book, you will be able to write code that programs their machines to solve equations. This code is foundational and readers are ecouraged to learn on their own how to build on it to suit their mathematics learning needs.