Python resources - Revision history

Atmoschem at 06:11, 12 September 2024

2024-09-12T06:11:03Z

2022-10-06T11:39:38Z

‎Python tutorials for beginners

2022-10-06T11:38:36Z

‎Python tutorials for beginners

2021-10-29T01:33:06Z

2021-01-26T13:32:59Z

2020-09-14T02:04:48Z

2020-09-13T14:00:11Z

2020-09-13T13:37:44Z

2020-03-21T00:49:41Z

2019-11-09T10:03:40Z

← Older revision		Revision as of 06:11, 12 September 2024
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	=Xarray=		=Xarray=
	*[https://xarray.pydata.org/en/stable/ Xarray user's guide for analyzing data in NetCDF format]		*[https://xarray.pydata.org/en/stable/ Xarray user's guide for analyzing data in NetCDF format]
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		+	=Python for deep learning=
		+	Under development

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 *[https://www.learnpython.org/ A short online Python tutorial]
 *[https://jerry-git.github.io/learn-python3/ A tutorial for Python 3]
-*[https://www.runoob.com/python3/python3-tutorial.html]
+*[https://www.runoob.com/python3/python3-tutorial.html/ A tutorial for Python 3 in Chinese]
 =Python for atmospheric and oceanic science=

@@ Line 18: / Line 18: @@
 *[https://www.learnpython.org/ A short online Python tutorial]
 *[https://jerry-git.github.io/learn-python3/ A tutorial for Python 3]
+*[https://www.runoob.com/python3/python3-tutorial.html]
 =Python for atmospheric and oceanic science=

← Older revision		Revision as of 01:33, 29 October 2021
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	=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]

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 *[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction]
 *[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]

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 *[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]
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 =Advanced techniques=
 *[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.

← Older revision		Revision as of 14:00, 13 September 2020
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	*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.		*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://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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-=Python for atmospheric science=
+=Python for atmospheric and oceanic 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.
 *[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.
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 =Plotting=
 *[https://realpython.com/python-matplotlib-guide/ A guide for python plotting with Matplotlib]

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 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]
 =Plotting=
 *[https://realpython.com/python-matplotlib-guide/ A guide for python plotting with Matplotlib]