List Comprehensions

This is a tutorial on list comprehensions in Python, suitable for use in an Intro or CS0 course. We also briefly mention set comprehensions and dictionary comprehensions.

https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/list_comprehensions?viewer=share/

This OER material was produced as a result of the CS04ALL CUNY OER project

This book will teach you how to make graphical computer games in the Python programming language using the Pygame library.This book assumes you know a little bit about Python or programming in general. If you don’t know how to program, you can learn by downloading the free book "Invent Your Own Computer Games with Python" from http://inventwithpython.com. Or you can jump right into this book and mostly pick it up along the way. This book is for the intermediate programmer who has learned what variables and loops are, but now wants to know, "What do actual game programs look like?" There was a long gap after I first learned programming but didn’t really know how to use that skill to make something cool. It’s my hope that the games in this book will give you enough ideas about how programs work to provide a foundation to implement your own games.

Minecraft Pi is a free version of Minecraft that is available as a part of the Raspbian operating system. The world of Minecraft Pi can be changed using the Python programming language and this activity will introduce you to the basics.

This lesson is adapted from https://www.raspberrypi.org/learning/getting-started-with-minecraft-pi/worksheet/ under a Creative Commons license.

The Non-Programmers' Tutorial For Python is a tutorial designed to be an introduction to the Python programming language. This guide is for someone with no programming experience.

This course is broad, covering a wide range of topics that have to do with the post-pc era of computing. It is a hands-on project course that also includes some foundational subjects. Students will program iPAQ handheld computers, cell phones (series 60 phones), speech processing, vision, Cricket location systems, GPS, and more. Most of the programming will be using Python®, but Python® can be learned and mastered during the course.
This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5508 (Pervasive Computing).

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

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.

Programmation Python © 2023 by Olfa Ben Ahmed is licensed under Attribution-NonCommercial-ShareAlike 4.0 International

This class builds a bridge between the recreational world of algorithmic puzzles (puzzles that can be solved by algorithms) and the pragmatic world of computer programming, teaching students to program while solving puzzles. Python syntax and semantics required to understand the code are explained as needed for each puzzle.

The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis. Arthritis Inflammation We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in comma-separated values (CSV) format: each row holds information for a single patient, columns represent successive days. The first three rows of our first file look like this: 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0 0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1 0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1 Each number represents the number of inflammation bouts that a particular patient experienced on a given day. For example, value “6” at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study. So, we want to: Calculate the average inflammation per day across all patients. Plot the result to discuss and share with colleagues. To do all that, we’ll have to learn a little bit about programming.

Students analyze a cartoon of a Rube Goldberg machine and a Python programming language script to practice engineering analysis. In both cases, they study the examples to determine how the different systems operate and the function of each component. This exercise in juxtaposition enables students to see the parallels between a more traditional mechanical engineering design and computer programming. Students also gain practice in analyzing two very different systems to fully understand how they work, similar to how engineers analyze systems and determine how they function and how changes to the system might affect the system.

Free cc-by-sa licensed wikibook about how to learn computer game programming using python, pygame and other free & open source tools.

Working in small groups, students complete and run functioning Python codes. They begin by determining the missing commands in a sample piece of Python code that doubles all the elements of a given input and sums the resulting values. Then students modify more advanced Python code, which numerically computes the slope of a tangent line by finding the slopes of progressively closer secant lines; to this code they add explanatory comments to describe the function of each line of code. This requires students to understand the logic employed in the Python code. Finally, students make modifications to the code in order to find the slopes of tangents to a variety of functions.

New Edition! The goal of this book is to provide an Informatics-oriented introduction to programming. The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve data analysis problems common in the world of Informatics.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

The chapters in their current form have been made available to students who used Python in my Decision Science course in Fall 2019 (the course I had to prep for. Most students used R, but this helped those who choose Python). It has also been used as reference for students and project partners who use Python but have not had any training on using Python for data management.

This work is still useful for those learning Python as a data analysis platform as well as those who need to convert R code into Python due to deployment needs or to take advantage of Python resources in other domains. While it was not used as a textbook, the material was used by students in my decision models course and in senior capstone course for those who choose to use Python instead of R. While it seemed to help, the students had more difficulty than students who used R.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Advances in metagenomic sequencing have allowed for the identification of countless novel bacterial taxa in environmental samples. However, due to a lack of appropriate computational tools, the plasmids contained by many of these bacteria have received far less attention. That has restricted research into the important genetic processes plasmids are responsible for, such as horizontal gene transfer and antibiotic resistance. To address this gap, researchers recently developed the Sequence Contents-Aware Plasmid Peeler (SCAPP). An open-source Python package, SCAPP builds upon a previously developed algorithm and uses biological data to assemble plasmid sequences from metagenomic samples. SCAPP was found to outperform existing metagenomic plasmid assembly tools when tested on simulated metagenomes and real human gut microbiome samples. SCAPP could also assemble novel and clinically relevant plasmid sequences in generated samples..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

A Python Functions activity - "Scrabble game" - for CS0 students. Part of the CUNY CS04All project.

Workshop overview for the Data Carpentry Social Sciences curriculum. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. This workshop teaches data management and analysis for social science research including best practices for data organization in spreadsheets, reproducible data cleaning with OpenRefine, and data analysis and visualization in R. This curriculum is designed to be taught over two full days of instruction. Materials for teaching data analysis and visualization in Python and extraction of information from relational databases using SQL are in development. Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Social Sciences workshops.