Mathematics: A Simple Tool for Geologists, Second Edition
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Ortiz About the instructor jortiz kent. Thus research from older notebooks can be replicated, and updated using the most current data. For example, this notebook forecasts likely Fed policy for setting the Fed Funds rate , but market sentiment across major asset classes is observable from the CFTC Commitment of Traders Report. Major economics indicators are renormalized: for example, various measures of inflation , optionally with the forward-looking break-even rates derived from U. Treasury bonds. Other notebooks examine international markets: especially, gold and foreign exchange. This embeds a slideshow and a Web Spinning Globe Cesium in the notebook.
By Massimo Di Stefano. Geo-Spatial Data with IPython.
Tutorial by Kelsey Jordahl from SciPy Plotting pitfalls : common problems when plotting large datasets, and how to avoid them. By James A. Data and visualization integration via web based resources. Visualizing complex-valued functions with Matplotlib and Mayavi , by Emilia Petrisor. Checkout the pythonic recreation of Hans Rosling's Wealth of Nations.
Exploring how smooth-looking functions can have very surprising derivatives even at low orders , combining SymPy and matplotlib. By Burak Bayramli.
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Function minimization with iminuit , an introductory companion to their hard core tutorial. By the iminuit project. PyChebfun is a pure-python implementation of the celebrated Chebfun package by Battles and Trefethen. By Sam Relton. Fractals, complex numbers, and your imagination , by Caleb Fangmeier. A SymPy tutorial , by Andrey Grozin. Notebooks can be selected to serve as resources for a workshop. By Vince Knight. PyOracle: Automatic analysis of musical structure , by Greg Surges.
Note that in the 'collections' section above there are also pandas-related links, such as the one for an lesson tutorial. Python Pandas DataFrame Basics , this notebook explains the basic concepts of a pandas data frame from scratch for beginners with examples, by Tanu Nanda Prabhu. A minute whirlwind tour of pandas , this is the notebook accompanying a video presentation by Wes McKinney, author of Pandas and the Python for Data Analysis book.
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Manipulating the data with Pandas using Python , this notebook explains various operations and methods of Pandas library from the scratch with the help of an example, by Tanu Nanda Prabhu. Time-series analysis with Pandas. Financial data analysis with Pandas. Clustering of smartphone sensor data for human activity detection using pandas and scipy , part of Coursera data analysis course, done in Python repo. Analyzing and visualizing sun spot data with Pandas , by Josh Hemann. An enlightening discussion of how naive plotting choices subtly influence our interpretation of data.
Advanced analysis of Apache logs , by Nikolay Koldunov. Companion videos 1 , 2 , 3 , 4. Python Strings from Scratch!!!
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Python Tuples from Scratch!!! Python Dictionary from Scratch!!! Python Lists from Scratch!!! Learning to code with Python , part of an introduction to Python from the Waterloo Python users group. Python Descriptors Demystified , an in-depth discussion of the descriptor protocol in Python, by Chris Beaumont. A collection of not so obvious Python stuff you should know! Key differences between Python 2. These are notebooks that use [one of the IPython kernels for other languages] IPython kernels for other languages :.
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The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language. The Julia team has created IJulia , and these are some Julia notebooks:.
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Fractals 3 ways , by Jeff Bezanson. A tutorial on making interactive graphs with Plotly and Julia. Functional Geometry by Shashi Gowda. JuliaOpt notebooks , a collection of optimization-related notebooks. Similar to the Julia kernel there exists also a Ruby kernel for IPython.