Difference between revisions of "Python resources"
From atmoschem
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==Jupyter notebook== | ==Jupyter notebook== | ||
Jupyter notebooks are interactive web-based applications that allow users to create and share combine live codes, equations, narrative texts, figures, interactive user interface and other rich media. When combined with the Python kernel, Jupyter notebooks allow users to code in Python and display results in an interactive and convenient way. Jupyter notebooks are really wonderful tools for learning Python, data science, and coding in general. | Jupyter notebooks are interactive web-based applications that allow users to create and share combine live codes, equations, narrative texts, figures, interactive user interface and other rich media. When combined with the Python kernel, Jupyter notebooks allow users to code in Python and display results in an interactive and convenient way. Jupyter notebooks are really wonderful tools for learning Python, data science, and coding in general. | ||
| − | *We strongly recommend installing Python and Jupyter on your PC | + | *We strongly recommend installing Python and Jupyter on your PC [https://www.anaconda.com/distribution/ using the Anaconda distribution], which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science. |
*Students enrolled in courses will also be provided with an account and access to a SUSTech server running Jupyter. | *Students enrolled in courses will also be provided with an account and access to a SUSTech server running Jupyter. | ||
*[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction] | *[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction] | ||
Revision as of 21:32, 2 July 2019
Contents
Getting started
Jupyter notebook
Jupyter notebooks are interactive web-based applications that allow users to create and share combine live codes, equations, narrative texts, figures, interactive user interface and other rich media. When combined with the Python kernel, Jupyter notebooks allow users to code in Python and display results in an interactive and convenient way. Jupyter notebooks are really wonderful tools for learning Python, data science, and coding in general.
- We strongly recommend installing Python and Jupyter on your PC using the Anaconda distribution, which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.
- Students enrolled in courses will also be provided with an account and access to a SUSTech server running Jupyter.
- Jupyter notebook: an introduction
- A tutorial for Jupyter run on mybinder It is a bit slow but worth it.
Python tutorials for beginners
Python for atmospheric science
- A Hands-on Introduction to Using Python in the Atmospheric and Oceanic Sciences This is a Python textbook geared specifically for use in atmospheric and oceanic sciences. The writing is extremely clear and useful. Highly recommended. The PDFs can be downloaded for free.
- Unidata's online Python tutorial for atmospheric science.
- Dr McCray (McGill University) put together a few Python tutorials for atmospheric science.
Python for data science
- Get Data Off the Ground with Python An online MOOC course by Professor Lorena Barba at George Washington University.
- Take off with Stat in Python An online MOOC course by Professor Lorena Barba at George Washington University.
