Difference between revisions of "Python resources"
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| − | =Getting started= | + | =Getting started with Python= |
==Jupyter notebook== | ==Jupyter notebook== | ||
| − | Jupyter notebooks are interactive web-based applications that allow users to create and share | + | Jupyter notebooks are interactive web-based applications that allow users to create and share documents containing live codes, equations, narrative texts, figures, interactive user interfaces, 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 [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. However, this server can only be accessed from within the SUSTech network. You can use a SUSTech VPN from off-campus locations. Details will be given during the first lecture. | ||
| + | |||
*[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction] | *[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction] | ||
| + | *[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial] | ||
| + | *[https://gke.mybinder.org/v2/gh/ipython/ipython-in-depth/master?filepath=binder/Index.ipynb A tutorial for Jupyter run on mybinder] It is a bit slow but worth it. | ||
| + | *[https://www.itcodemonkey.com/article/6025.html 一个中文tutorial] | ||
| + | *[https://www.edureka.co/blog/wp-content/uploads/2018/10/Jupyter_Notebook_CheatSheet_Edureka.pdf Jupyter notebook cheat sheet] | ||
==Python tutorials for beginners== | ==Python tutorials for beginners== | ||
| − | *[https://www.learnpython.org/ | + | There are many, many good tutorials for Python. We encourage you to try out a few yourself. A few examples are listed here, but there are many more on the web. After learning the basics, you should seek out examples particular to your application on your own. |
| + | *[https://www.learnpython.org/ A short online Python tutorial] | ||
*[https://jerry-git.github.io/learn-python3/ A tutorial for Python 3] | *[https://jerry-git.github.io/learn-python3/ A tutorial for Python 3] | ||
| + | *[https://www.runoob.com/python3/python3-tutorial.html/ A tutorial for Python 3 in Chinese] | ||
| − | + | =Python for atmospheric and oceanic science= | |
| − | =Python for atmospheric science= | + | |
*[http://www.johnny-lin.com/pyintro/ 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. | *[http://www.johnny-lin.com/pyintro/ 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. | ||
| + | *[https://rabernat.github.io/research_computing_2018/ Research Computing in Earth Sciences] is a Columbia University course designed to introduce incoming LDEO graduate students to modern computing software, programming tools and best practices that are broadly applicable to carrying out research in the Earth sciences. All materials are available online. Highly recommended. | ||
*[https://unidata.github.io/online-python-training/ Unidata's online Python tutorial for atmospheric science.] | *[https://unidata.github.io/online-python-training/ Unidata's online Python tutorial for atmospheric science.] | ||
*[http://www.meteo.mcgill.ca/~cmccray/python.html Dr McCray (McGill University) put together a few Python tutorials for atmospheric science.] | *[http://www.meteo.mcgill.ca/~cmccray/python.html Dr McCray (McGill University) put together a few Python tutorials for atmospheric science.] | ||
| + | *This course [https://currents.soest.hawaii.edu/ocn_data_analysis/index.html "Oceanographic Data Analysis With Open Source Tools"] at University of Hawaii is a great resource. In particular, I highly recommend [https://currents.soest.hawaii.edu/ocn_data_analysis/analysis_methods.html these Jupyter notebooks] on climate data analyses. | ||
| + | *[https://climlab.readthedocs.io/en/latest/intro.html climlab] is a python package for process-oriented climate modeling developed by Brian Rose (SUNY Albany) | ||
| + | *[https://pyoceans.github.io/sea-py/ A collection of python tools for oceanography studies] | ||
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*[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp1+2018/about Get Data Off the Ground with Python] An online MOOC course by Professor Lorena Barba at George Washington University. | *[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp1+2018/about Get Data Off the Ground with Python] An online MOOC course by Professor Lorena Barba at George Washington University. | ||
*[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp2+2018/about Take off with Stat in Python] An online MOOC course by Professor Lorena Barba at George Washington University. | *[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp2+2018/about Take off with Stat in Python] An online MOOC course by Professor Lorena Barba at George Washington University. | ||
| + | *[https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/ A very simple tutorial] of using python for data science. | ||
| + | *[https://www.datacamp.com/community/tutorials/python-statistics-data-science#hypothesistesting 40+ Python Statistics For Data Science Resources] | ||
| + | A list of Python resources for the eight statistics topics that you need to know to excel in data science | ||
| + | *[https://www.statsmodels.org/stable/index.html Documentation for the python package StatsModels] | ||
| + | ==Python pandas tutorial== | ||
| + | The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. | ||
| + | *[https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/ Python Pandas Tutorial: A Complete Introduction for Beginners] | ||
=Plotting= | =Plotting= | ||
*[https://realpython.com/python-matplotlib-guide/ A guide for python plotting with Matplotlib] | *[https://realpython.com/python-matplotlib-guide/ A guide for python plotting with Matplotlib] | ||
| + | |||
| + | |||
| + | =Xarray= | ||
| + | *[https://xarray.pydata.org/en/stable/ Xarray user's guide for analyzing data in NetCDF format] | ||
| + | |||
| + | |||
| + | =Python for deep learning= | ||
| + | Under development | ||
| + | |||
| + | |||
| + | =Advanced techniques= | ||
| + | ==Bokeh== | ||
| + | *[https://bokeh.org/ Bokeh] is a set of tools that allow you to make interactive visualizations in the browser. Some of the interactive demos in this course were made using this fun tool. | ||
| + | *[https://rebeccabilbro.github.io/interactive-viz-bokeh/ Making interactive visualizations with Python using Bokeh]. This tutorial shows how to reproduce the famous Hans Rosling's ''The Health and Wealth of Nations'' plot using Bokeh. | ||
| + | |||
| + | |||
| + | ==LaTeX== | ||
| + | *[http://tug.ctan.org/info/latex-refsheet/LaTeX_RefSheet.pdf LaTeX cheat sheet] | ||
Latest revision as of 14:11, 12 September 2024
Contents
Getting started with Python
Jupyter notebook
Jupyter notebooks are interactive web-based applications that allow users to create and share documents containing live codes, equations, narrative texts, figures, interactive user interfaces, 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. However, this server can only be accessed from within the SUSTech network. You can use a SUSTech VPN from off-campus locations. Details will be given during the first lecture.
- Jupyter notebook: an introduction
- How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial
- A tutorial for Jupyter run on mybinder It is a bit slow but worth it.
- 一个中文tutorial
- Jupyter notebook cheat sheet
Python tutorials for beginners
There are many, many good tutorials for Python. We encourage you to try out a few yourself. A few examples are listed here, but there are many more on the web. After learning the basics, you should seek out examples particular to your application on your own.
Python for atmospheric and oceanic 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.
- Research Computing in Earth Sciences is a Columbia University course designed to introduce incoming LDEO graduate students to modern computing software, programming tools and best practices that are broadly applicable to carrying out research in the Earth sciences. All materials are available online. Highly recommended.
- Unidata's online Python tutorial for atmospheric science.
- Dr McCray (McGill University) put together a few Python tutorials for atmospheric science.
- This course "Oceanographic Data Analysis With Open Source Tools" at University of Hawaii is a great resource. In particular, I highly recommend these Jupyter notebooks on climate data analyses.
- climlab is a python package for process-oriented climate modeling developed by Brian Rose (SUNY Albany)
- A collection of python tools for oceanography studies
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.
- A very simple tutorial of using python for data science.
- 40+ Python Statistics For Data Science Resources
A list of Python resources for the eight statistics topics that you need to know to excel in data science
Python pandas tutorial
The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today.
Plotting
Xarray
Python for deep learning
Under development
Advanced techniques
Bokeh
- Bokeh is a set of tools that allow you to make interactive visualizations in the browser. Some of the interactive demos in this course were made using this fun tool.
- Making interactive visualizations with Python using Bokeh. This tutorial shows how to reproduce the famous Hans Rosling's The Health and Wealth of Nations plot using Bokeh.
LaTeX
- This page was last modified on 12 September 2024, at 14:11.
- This page has been accessed 12,539 times.
