This is a Python introduction guide target to scientific applications. The idea is to provide a guide tutorial divided by subjects to aid my workmates @ ICFO to learn Python by put hands on.
These guides notebooks are not meant to be a lecture but a collection of illustrative short piece of commented codes, which one can run and modify locally. Details will be provided by recommended further readings on each notebook.
Inside folder docs you'll find all guide notebooks, as follow:
docs
├── 0-Installation.ipynb
├── 1-Basics.ipynb
├── 2-Plot.ipynb
└── 3-Fitting.ipynb-
Install Anacoda - 0-Installation
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Download this repository:
Clone or downloadgreen button on top-right of this page >Download ZIP -
Extract repository ZIP file
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On Terminal (Anaconda prompt for Windows) command line, inside the extracted py4sci-master folder
jupyter notebook
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Inside docs explore and run the guide notebooks
Please don't leave alone! I would like to this to be a collaborative guide. We have different interest application to Python (plot graphs, fitting data, data mining, image analysis, ...) so please help me to complete this puzzle. Write a guide notebook contribution and pull it, or just send me via email.
This guide will continue as long you have interest. Please, write me for questions, comments, contributions, complains, ...
Telegram Channel: t.me/py4sci 📢
Email: alex.duarte@icfo.eu 📧