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of PILPython Scientific Lectures Notes: Tutorial material on the scientific Python ecosystem, a quick introduction to central tools and techniques.
Python for earth scientists: A two afternoons course by Andrew Walker (University of Bristol) on Python in the earth sciences.
oceanpython: Python for oceanography
PyAOS: Python for the Atmospheric and Oceanic Sciences
python4oceanographers: Learn python with examples applied to marine sciences.
Pythonic perambulations: A blog by Jake VanderPlas
A gallery of interesting IPython Notebooks "...a curated collection of IPython notebooks that are notable for some reason."
Scikit-learn tutorial: Files and other info associated with the Scipy 2013 scikit-learn tutorial developed by Gaël Varoquaux, Olivier Grisel and Jake VanderPlas.
Statistical Analysis tutorial from Chris. Fonnesbeck.
Bayesian Statistical Analysis in Python: Ipython notebooks for the Scipy 2014 tutorial on Bayesian data analysis with Python, by Chris. Fonnesbeck.
AstroML: Machine Learning and Data Mining for Astronomy: A library and tutorial by Jake VanderPlas and co-authors, accompanying the book Statistics, Data Mining, and Machine Learning in Astronomy
Data Science in Python: A series of annotated notebooks on data science (i.e. geared towards machine learning) in python
http://earthpy.org/: EarthPy is a collection of IPython notebooks with examples of Earth Science related Python code
[data science notebooks): IPython notebooks for data science, continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy.
Python for Data Analysis: From Wes McKinney (Developer of Pandas)
Think stats: Probability and statistics for programmers, from Allen Downey, pdf available for free.
Think complexity: Complexity science (graphs, cellular automata, agent-based models), from Allen Downey, pdf available for free.
Python in hydrology: A book freely available in pdf, from Sat Kumar Tomer.
Programming collective intelligence: By Toby Segaran, Good intro on (general) Machine Learning algorithms.
Machine Learning in action: By Peter Harrington.
Introduction to Python for Econometrics, Statistics and Numerical Analysis: Second Edition: By Kevin Sheppard, Oxford Uni.
A Hands-On Introduction to Using Python in the Atmospheric and Oceanic Sciences: By Johnny Lin, Professor of physics and head of the Climate Research Group at North Park University.
principles of planetary climate: by Ray Pierrehumbert. Excellent book on the physics of planetary climates, with freely downloadable python code to follow the examples given in the book.
PYMC: By Chris Fonnesbeck, Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo.
Seaborn: Statistical data visualization, by Michael Waskom. Its graphical representation of linear models is particularly interesting.
ggplot: For R users, a 'port' of the ggplot2 package to Python, see here for what's new in the latest release.
coards: A COARDS compliant time parser. See also netcdftime which is part of the NetCDF4 module
seawater: Similar to the MATLAB toolboxes SEAWATER from CSIRO and parts of OCEANS from Woods Hole Institute.
fluid: Procedures to study fluids on Python, focused for oceanography, meteorology and related sciences.
kyPyWavelet: Continuous wavelet transform module for Python ala Torrence and Compo. Some manual edits were necessary to make it work for me ...
pyresample: Resampling (reprojection) of geospatial image data in Python
Rpy2: calling R from Python
Reproducible Research in Computational Science, Roger D. Peng, Science 334, 1226 (2011).
Shining Light into Black Boxes, A. Morin et al., Science 336, 159-160 (2012).
The case for open computer programs, D.C. Ince, Nature 482, 485 (2012).
Best practices for scientific computing: Paper in PLOS Biology exposing some of the tools and methods to build better Scientific software.
In building this material, I have liberally 'borrowed' from lecture notes, online notebooks, video recording of talks, code examples, articles, etc, that are freely available online, I would like to acknowledge in particular (list not exhaustive and in no particular order):
And finally John Hunter, He was the founder and lead developer of Matplotlib, a pivotal library to make Python a viable free and open-source alternative to commercial scientific software, and very sadly passed away in 2012.
I encourage you - if you carry on using Python for science - to look up these people on google, have a look at their github repositories and the projects they contribute to, and follow them on twitter, some of them (e.g. Jake VanderPlas) have also very informative blogs. You can also donate to the John Hunter memorial fund as a way to give back to an important contributor to the Python scientific community.
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