基于炎症数据分析的实践教程
Python-novice-inflammation项目是一个面向非程序员的Python入门教程,通过分析模拟炎症数据实践编程技能。课程涵盖Python基础、数据处理、可视化、列表操作、循环、条件语句和函数创建等主题。教程强调语言无关的编程原则,如循环自动化和函数封装,旨在提升学员的数据分析能力和科学计算技能。项目采用12个模拟炎症数据文件作为教学素材,展示如何使用函数实现自动化分析。教程分为12个章节,每个章节聚焦特定编程概念,如数据类型、文件处理、数据可视化等。课程内容基于软件开发最佳实践,适合希望提升科学计算能力的研究人员和学生。
An introduction to Python for non-programmers using inflammation data.
This lesson teaches novice programmers to write modular code to perform data analysis using Python. The emphasis, however, is on teaching language-agnostic principles of programming such as automation with loops and encapsulation with functions, see Best Practices for Scientific Computing and Good enough practices in scientific computing to learn more.
The example used in this lesson analyses a set of 12 files with simulated inflammation data collected from a trial for a new treatment for arthritis. Learners are shown how it is better to automate analysis using functions instead of repeating analysis steps manually.
The rendered version of the lesson is available at: https://swcarpentry.github.io/python-novice-inflammation/.
This lesson is also available in R and MATLAB.
# | Episode | Time | Question(s) |
---|---|---|---|
1 | Python Fundamentals | 30 | What basic data types can I work with in Python?<br>How can I create a new variable in Python?<br>Can I change the value associated with a variable after I create it? |
2 | Analyzing Patient Data | 60 | How can I process tabular data files in Python? |
3 | Visualizing Tabular Data | 50 | How can I visualize tabular data in Python?<br>How can I group several plots together? |
4 | Storing Multiple Values in Lists | 30 | How can I store many values together? |
5 | Repeating Actions with Loops | 30 | How can I do the same operations on many different values? |
6 | Analyzing Data from Multiple Files | 20 | How can I do the same operations on many different files? |
7 | Making Choices | 30 | How can my programs do different things based on data values? |
8 | Creating Functions | 30 | How can I define new functions?<br>What's the difference between defining and calling a function?<br>What happens when I call a function? |
9 | Errors and Exceptions | 30 | How does Python report errors?<br>How can I handle errors in Python programs? |
10 | Defensive Programming | 30 | How can I make my programs more reliable? |
11 | Debugging | 30 | How can I debug my program? |
12 | Command-Line Programs | 30 | How can I write Python programs that will work like Unix command-line tools? |
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes!
Lesson maintainers are Toan Phung and Indraneel Chakraborty.
A list of contributors to the lesson can be found in AUTHORS.
Instructional material from this lesson is made available under the Creative Commons Attribution (CC BY 4.0) license. Except where otherwise noted, example programs and software included as part of this lesson are made available under the MIT license. For more information, see LICENSE.md.
To cite this lesson, please consult with CITATION.
Software Carpentry is a volunteer project that teaches basic computing skills to researchers since 1998. More information about Software Carpentry can be found here.
The Carpentries is a fiscally sponsored project of Community Initiatives, a registered 501(c)3 non-profit organisation based in California, USA. We are a global community teaching foundational computational and data science skills to researchers in academia, industry and government. More information can be found here.
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