An great collection of Python notebooks | Python 笔记本集

Here is a really great collection of Python notebooks with lots and lots of links.  We start with some appetizers:

• matplotlib – 2D and 3D plotting in Python
• Basic Python tutorial
• Numeric Computing is Fun
• Python for Developers
• Exploratory computing with Python

But there are so many and so much more!! Okay, I am just going to copy (fork) most of this page:

Mathematics

• Linear algebra with Cython. A tutorial that styles the notebook differently to show that you can produce high-quality typography online with the Notebook. By Carl Vogel.
• Exploring how smooth-looking functions can have very surprising derivatives even at low orders, combining SymPy and matplotlib. By Javier Moreno.
• A Collection of Applied Mathematics and Machine Learning Tutorials (in Turkish). By Burak Bayramli.
• Function minimization with iminuit, an introductory companion to their hard core tutorial. By the iminuit project.
• The Discrete Cosine Transform, a brief explanation and illustration of the math behind the DCT and its role in the JPEG image format, by Jim Mahoney.
• Chebfun in Python, a demo of PyChebfun, by Olivier Verdier. PyChebfun is a pure-python implementation of the celebrated Chebfun package by Battles and Trefethen.
• The Matrix Exponential, an introduction to the matrix exponential, its applications, and a list of available software in Python and MATLAB. By Sam Relton.
• Fractals, complex numbers, and your imagination, by Caleb Fangmeier.
• A SymPy tutorial, by Andrey Grozin.
• Introduction to Mathematics with Python, a collection of notebooks aimed at Mathematicians with no/little Python knowledge. Notebooks can be selected to serve as resources for a workshop. By Vince Knight.

Mathematics, Physics, Chemistry, Biology

• A single-atom laser model. This is one of a complete set of lectures on quantum mechanics and quantum optics using QuTiP by J.R. Johansson.
• 2-d rigid-body transformations. This is part of Scientific Computing in Biomechanics and Motor Control, a complete collection of notebooks by Marcos Duarte.
• Astrophysical simulations and analysis with yt: a collection of example notebooks on using various codes that yt interfaces with: Enzo, Gadget, RAMSES, PKDGrav and Gasoline. Note: the yt site currently throws an SSL warning, they seem to have an outdated or self-signed certificate.
• Working with Reactions, part of a set of tutorials on cheminformatics and machine learning with the rdkit project, by Greg Landrum.
• CFD Python: 12 steps to Navier-Stokes. A complete set of lectures on Computational Fluid Dynamics, from 1-d linear waves to full 2-d Navier-Stokes, by Lorena Barba.
• Pytherm – Applied Thermodynamics. Lectures on applied thermodynamics using Python and the SciPy ecosystem, by ATOMS.
• AeroPython: Aerodynamics-Hydrodynamics with Python, a complete course taught at George Washington University by Lorena Barba.
• Practical Numerical Methods with Python, a collection of learning modules (each consisting of several IPython Notebooks) for a course in numerical differential equations taught at George Washington University by Lorena Barba. Also offered as a “massive, open online course” (MOOC) on the GW SEAS Open edX platform.
• pyuvvis: tools for explorative spectroscopy, spectroscopy library built for integration ipython notebooks, matplotlib and pandas.
• HyperPython: a practical introduction to the solution of hyperbolic conservation laws, a course by David Ketcheson.
• An Introduction to Applied Bioinformatics: Interactive lessons in bioinformatics, by Greg Caporaso.
• Colour science computations with colour, a Python package implementing a comprehensive number of colour theory transformations and algorithms supported by a dedicated collection of IPython Notebooks. More colour science related IPython Notebooks are available on colour-science.org.
• The notebooks from the Book Bioinformatics with Python Cookbook, covering several fields like Next-Generation Sequencing, Population Genetics, Phylogenetics, Genomics, Proteomics and Geo-referenced information.
• Learning Population Genetics in an RNA world is an interactive notebook that explains basic population genetics tools and techniques by building an in silico evolutionary model of RNA molecules.
• An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study. This notebook fully reproduces the research published in this paper. The notebook uses mostly python but includes some bash and R as well and is relevant for researchers in bioinformatics and public health.
• Lung Cancer Post-Translational Modification and Gene Expression Regulation. This Python notebook uses the Jupyter-widget Clustergrammer-Widget to visualize hierarchical clustering of gene expression and post-translational modification data from 37 lung cancer cell lines as an interactive heatmap. The notebook is part of the research project from this paper.
• Materials Science in Python using pymatgen. A series of python notebooks using the pymatgen package and materials project API for materials science.

Linguistics and Text Mining

• Workshop on text analysis by Neal Caren.
• Detecting Algorithmically Generated Domains, part of the Data Hacking collection on security-oriented data analysis with IPython & friends.
• Mining the Social Web (3rd Edition). A complete collection of notebooks accompanying Matthew Russell and Mikhail Klassen’s book by O’Reilly.

Statistics, Machine Learning and Data Science

• An introductory notebook on uncertainty quantification and sensitivity analysis developed for the Workshop On Uncertainty Quantification And Sensitivity Analysis For Cardiovascular Modeling by Leif Rune Hellevik, Vinzenz Eck and Jacob T. Sturdy.
• Python Data Science Handbook Supplemental Materials, a collection of notebooks by Jake VanderPlas to accompany the book.
• “ISP”: Introduction to Statistics with Python, a collection of notebooks accompanying the book of the same name, by Thomas Haslwanter.
• Notebooks for the exercises in Andrew Ng’s online ML course, Spark and TensorFlow, as well as extra material on other tools from the scipy stack, by John Wittenauer.
• AM207: Monte Carlo Methods, Stochastic Optimization: a complete course by Verena Kaynig-Fittkau and Pavlos Protopapas from Harvard, with all lecture materials and homework sets as notebooks.
• An introduction to Bayesian inference, this is just chapter 1 in an ongoing book titled Probabilistic Programming and Bayesian Methods for Hackers Using Python and PyMC, by Cameron Davidson-Pilon.
• Doing Bayesian Data Analysis: Python/PyMC3 code for a selection of models and figures from the book ‘Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan’, Second Edition, by John Kruschke (2015).
• Learn Data Science, an entire self-directed course by Nitin Borwankar.
• IPython Cookbook by Cyrille Rossant, a comprehensive guide to Python for Data Science. The code of the 100 recipes is available on the GitHub repository.
• An introduction to machine learning with Python and scikit-learn (repo and overview) by Hannes Schulz and Andreas Mueller.
• A progressive collection notebooks of the Machine Learning course by the University of Turin (with exercises).
• Clustering and Regression, part of the UC Berkeley 2014 Introduction to Data Science course taught by Michael Franklin.
• Neural Networks, part of a collection on machine learning by Aaron Masino.
• An introduction to Pandas, part of an 11-lesson tutorial on Pandas, by Hernán Rojas.
• Data Science and Big Data with Python by Steve Phelps.
• The Statsmodels Project has two excellent collections of examples: in their official documentation and extra ones in their wiki. Too many there to directly duplicate here, but they provide great learning materials on statistical modeling with Python.
• Machine Learning with the Shogun Toolbox. This is a complete service that includes a ready-to-run IPython instance with a collection of notebooks illustrating the use of the Shogun Toolbox. Just log in and start running the examples.
• Python for Data Analysis, an introductory collection from the CU Boulder Research Computing Group.
• The Kaggle bulldozers competition example, one of a set on tutorials on exploratory data analysis with the copper toolkit by Daniel Rodríguez/
• Understanding model reliability, part of a complete course on statistics and data analysis for psychologists by Michael Waskom.
• Graphical Representations of Linear Models, an illustration of the Seaborn statistical visualization library, that also includes Visualizing distributions of data and Representing variability in timeseries plots. By Michael Waskom.
• Desperately Seeking Silver, one of the homework sets for Harvard’s CS 109 Data Science course.
• The classic ‘An Introduction to Statistical Learning with Applications in R’ by James, Witten, Hastie, Tibshirani (2013), has not one but two collections of notebooks to accompany the book with Python (instead of the book’s default R examples). One by Jordi Warmenhoven and one by Matt Caudill.
• Python Notebooks for StatLearning Exercises, Python implementations of the R labs for the StatLearning: Statistical Learning online course from Stanford University taught by Profs Trevor Hastie and Rob Tibshirani.
• Applied Predictive Modeling with Python, Python implementations of the examples (originally written in R) from a famous introductory book, Applied Predictive Modeling, by Max Kuhn and Kjell Johnson.
• A collection of four courses in foundations of data science, algorithms and databases from multiple faculty at Columbia University’s Lede Program.
• SciPy and OpenCV as an interactive computing environment for computer vision by Thiago Santos, a tutorial presented at SIBGRAPI 2014.
• Kalman and Bayesian Filters in Python, by Roger Labbe.
• Adaboost for digit classification, by Shashwat Shukla. A complete implementation of Adaboost in Python, with code for digit recognition.
• An example machine learning notebook, by Randal. S. Olson, part of a collection in Data Analysis and Machine Learning.
• Pandas .head() to .tail(), an in-depth tutorial on Pandas by Tom Augspurger.
• Apache SINGA tutorial. A Python tutorial for deep learning with SINGA.
• Data Science Notebooks, a frequently updated collection of notebooks on statistical inference, data analysis, visualization and machine learning, by Donne Martin.
• ETL with Python, a tutorial for ETL (Extract, Transfer and Load) using python petl package, loading to MySQL and working with csv files by Dima Goldenberg.

Earth Science and Geo-Spatial data

• EarthPy, a collection of IPython notebooks with a focus on Earth Sciences, from whale tracks to the flow of the Amazon.
• Python for Geosciences, a tutorial series aimed at the Earth Sciences community, by Nikolay Koldunov.
• Find graffiti close to NY subway entrances, one of a rich collection of notebooks on large-scale data analysis, by Roy Hyunjin Han.
• Logistic models of well switching in Bangladesh, part of the “Will it Python” blog series (repo) on Machine Learning and data analysis in Python. By Carl Vogel.
• Estimated likelihood of observing a large earthquake on a continental low‐angle normal fault and implications for low‐angle normal fault activity, an executable version of a paper by Richard Styron and Eric Hetland published in Geophysical Research Letters, on earthquake probabilities.
• python4oceanographers, a blog demonstrating analyses in physical oceanography from resource-demanding numerical computations with functions in compiled languages to specialized tidal analysis to visualization of various geo data using fancy things like interactive maps.
• Machinalis has a public repo with material support for geospatial-data processing related blog posts. It includes notebooks about Object Based Image Analysis and irrigation circles detection.
• seismo-live is a collection of live Jupyter notebooks for seismology. It includes a fairly large number of notebooks on how to solve the acoustic and elastic wave equation with various different numerical methods. Additionally it contains notebooks with an extensive introduction to data handling and signal processing in seismology, and notebooks tackling ambient seismic noise, rotational and glacial seismology, and more.
• Geo-Python is an introduction to programming in Python for Bachelors and Masters students in geo-fields (geology, geophysics, geography) taught by members of the Department of Geosciences and Geography at University of Helsinki, Finland. Course lessons and exercises are based on Jupyter notebooks and open for use by any interested person.

Signal Processing

• Sound Analysis with the Fourier Transform. A set of IPython Notebooks by Caleb Madrigal to explain what the Fourier Transform is and how to use it for basic audio processing applications.
• An introduction to Compressed Sensing, part of Python for Signal Processing: an entire book (and blog) on the subject by Jose Unpingco.
• Kalman and Bayesian Filters in Python. A textbook and accompanying filtering library on the topic of Kalman filtering and other related Bayesian filtering techniques.
• Classify human movements using Dynamic Time Warping & K Nearest Neighbors: Signals from a smart phone gyroscope and accelerometer are used to classify if the person is running, walking, sitting standing etc. This IPython notebook contains a python implementation of DTW and KNN algorithms along with explanations and a practical application.
• Digital Signal Processing A collection of notebooks that accompanies a masters course on the topic.
• An introduction to openCV An introduction course into using openCV for computer vision in python

Engineering Education

• Introduction to Chemical Engineering Analysis by Jeff Kantor. A collection of IPython notebooks illustrating topics in introductory chemical engineering analysis, including stoichiometry, generation-consumption analysis, mass and energy balances.
• Sensors and Actuators by Andres Marrugo. A collection of Jupyter notebooks in the form of lecture notes and engineering calculations for the course IMTR 1713 Sensors and Actuators taught at the Universidad Tecnológica de Bolívar.

Scientific computing and data analysis with the SciPy Stack

General topics in scientific computing

• Algorithms in IPython notebooks, by Sebastian Raschka
• Comparing the performance of Python compilers – Cython vs. Numba vs. Parakeet, by Sebastian Raschka
• A Crash Course in Python for Scientists, by Sandia’s Rick Muller.
• A gentle introduction to scientific programming in Python, biased towards biologists, by Mickey Atwal, Cold Spring Harbor Laboratory.
• Python for Data Science, a self-contained mini-course with exercises, by Joe McCarthy.
• First few lectures of the UW/Coursera course on Data Analysis. (Repo) by Chris Fonnesbeck.
• CythonGSL: a Cython interface for the GNU Scientific Library (GSL) (Project repo, by Thomas Wiecki.
• Introduction to numerical computing with numpy by Steve Phelps.
• Using Numba to speed up numerical codes. And another Numba example: self-organizing maps.
• Numpy performance tricks, and blog post, by Cyrille Rossant.
• IPython Parallel Push/Execute/Pull Demo by Justin Riley.
• Understanding the design of the R “formula” objects by Matthew Brett.
• Comparing different approaches to evolutionary simulations. Also available here to better visualization. The notebook was converted to a HTML presentation using an old nbconvert with the first developing implementation of reveal converter. By Yoav Ram.
• The Traveling Salesperson Problem by Peter Norvig.
• A git tutorial targeted at scientists by Fernando Perez.
• Running MATLAB in an IPython Notebook, using pymatbridge.
• Interactive Curve-Fitting The lmfit package provides a widget-based interface to the curve-fitting algorithms in SciPy.
• A visual guide to the Python Spark API for distributed computing by Jeff Thompson
• A tutorial on Map-Reduce programming with Apache Spark and Python by Steve Phelps.
• CodeCombat gridmancer solver by Arn-O. This notebook explains how to improve a recursive tree search with an heuristic function and to find the minimum solution to the gridmancer.

Social data

• Survival Analysis, an illustration of the lifelines library, by Cam Davidson Pilon.
• A reconstruction of Nate Silver’s 538 model for the 2012 US Presidential Election, by Skipper Seabold (complete repo).
• Data about the Sandy Hook massacre in Newtown, Conneticut, which accompanies a more detailed blog post on the subject. Here are the notebook and accompanying data. By Brian Keegan.
• More on gun violence analysis with Wikipedia data.
• An analysis of the Gaza-Israel 2012 crisis.
• Ranking NFL Teams. The full repo also includes an explanatory slideshow. By Sean Taylor.
• Automated processing of news media and generation of associated imagery.
• An analysis of national school standardized test data in Colombia using Pandas (in Spanish). By Javier Moreno.
• Getting started with GDELT, by David Masad. GDELT is a dataset containing more than 200-million geolocated events with global coverage for 1979 to the present. Another GDELT example from David, that nicely integrates mapping visualizations.
• Titanic passengers, coal mining disasters, and vessel speed changes, by Christopher Fonnesbeck
• A geographic analysis of Indonesian conflicts in 2012 with GDELT, by herrfz.
• Bioinformatic Approaches to the Computation of Poetic Meter, by A. Sean Pue, C. Titus Brown and Tracy Teal.
• Analyzing the Vélib dataset from Paris, by Cyrille Rossant (Vélib is Paris’ bicycle-sharing program).
• Using Python to see how the Times writes about men and women, by Neal Caren.
• Exploring graph properties of the Twitter stream with twython and NetworkX, by F. Perez (complete gist repo with utilities here.)
• Kaggle Competition: Titanic Machine Learning from Disaster. By Andrew Conti.
• How clean are San Francisco’s restaurants?, a data science tutorial that accompanies a blog post from Zipfian Academy.
• NYT gender wage gap and US crime by state.
• Predicting usage of the subway system in NYC, a final project for the Udacity Intro to Data Science Course, by Asim Ihsan.
• An exploratory statistical analysis of the 2014 World Cup Final, by Ricardo Tavares. Part of a notebook collection on football (aka soccer) analysis.
• San Francisco’s Drug Geography, a GIS analysis of public crime data in SF, by Lance Martin.
• Geographic Data Science is an entire course by Dani Arribas-Bel to learn to access, munge, and analyse spatial data on social phenomena.
• Analysis and visualization of a public OKCupid profile dataset using Python and Pandas by Alessandro Giusti includes many colorful data visualizations.

Psychology and Neuroscience

• Cue Combination with Neural Populations by Will Adler. Intuition and simulation for the theory (Ma et al., 2006) that through probabilistic population codes, neurons can perform optimal cue combination with simple linear operations. Demonstrates that variance in cortical activity, rather than impairing sensory systems, is an adaptive mechanism to encode uncertainty in sensory measurements.
• Modeling psychophysical data with non-linear functions by Ariel Rokem.
• Visualizing mathematical models of brain cell connections. The effect of convolution of different receptive field functions and natural images is examined.
• Python for Vision Research. A three-day crash course for vision researchers in programming with Python, building experiments with PsychoPy and psychopy_ext, learning the fMRI multi-voxel pattern analysis with PyMVPA, and understading image processing in Python.
• Loading and visualizing fMRI data, part of the Functional connectivity with NiLearn course by Gaël Varoquaux.

Machine Learning, Statistics and Probability

• A tutorial introduction to machine learning with sklearn, an IPython-based slide deck by Andreas Mueller.
• Introduction to Machine Learning in Python with scikit-learn by Cyrille Rossant, a free recipe from the IPython Cookbook, a comprehensive guide to Python for Data Science.
• An introduction to Predictive Modeling in Python, by Olivier Grisel.
• Face Recognition on a subset of the Labeled Faces in the Wild dataset, by Olivier Grisel.
• An Introduction to Bayesian Methods for Multilevel Modeling, by Chris Fonnesbeck.
• Introduction to Bayesian Networks by Kui Tang
• Bayesian data analysis with PyMC3 by Thomas Wiecki.
• A collection of examples for solving pattern classification problems, by Sebastian Raschka.
• Introduction to Linear Regression using Python by Kevin Markham
• Machine learning in Python, a series based on Andrew Ng’s Coursera class on machine learning. Part of a larger collection of data science notebooks by John Wittenauer.
• Probability, Paradox, and the Reasonable Person Principle, by Peter Norvig.
• How Likely Would You Give A Five-Star Review on Yelp? — Getting Your Hands Dirty with scikit-learn, by Xun Tang. Complimentary slides.
• Geodemographic Segmentation Model, by Filipa Rodrigues

Physics, Chemistry and Biology

• Writing A Genome Assembler with blasr and (I)Python, by [Jason Chin](Jason Chin).
• Multibody dynamics and control with Python and the notebook file by Jason K. Moore.
• Manipulation and display of chemical structures, by Greg Landrum, using rdkit.
• The sound of Hydrogen, visualizing and listening to the quantum-mechanical spectrum of Hydrogen. By Matthias Bussonnier.
• Particle physics at the Large Hadron Collider (LHC): using ROOT in an LHCb masterclass: Notebook 1 and Notebook 2 notebooks by Alexander Mazurov and Andrey Ustyuzhanin at CERN.
• A Reaction-Diffusion Equation Solver in Python with Numpy, a demonstration of how IPython notebooks can be used to discuss both the theory and implementation of numerical algorithms on one page, by Georg Walther.
• Comparing different approaches to evolutionary simulations. Also available here to better visualization. The notebook was converted to a HTML presentation using an old nbconvert with the first developing implementation of reveal converter. By Yoav Ram.

Economics and Finance

• Replication of the highly-contentious analysis of economic growth by Reinhart and Rogoff, by Vincent Arel-Bundock, full repo here. This is based on the widely-publicized critique of the original analysis done by Herndon, Ash, and Pollin.
• fecon235 for Financial Economics series of notebooks which examines time-series data for economics and finance. Easy API to freely access data from the Federal Reserve, SEC, CFTC, stock and futures exchanges. 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.S. Treasury bonds. Other notebooks examine international markets: especially, gold and foreign exchange.
• Fixed Income: A Structured Bond- Interactive scenarios , Sequential repayment of a bond using interactive widgets and Python in Jupyter, by Mats Gustavsson.

Earth science and geo-spatial data

• Exploring seafloor habitats: geographic analysis using IPython Notebook with GRASS & R. 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 SciPy2013.

Data visualization and plotting

• Plotting pitfalls: common problems when plotting large datasets, and how to avoid them. By James A. Bednar.
• US Census data and NYC Taxi data visualized using datashader.
• A Notebook with an interactive Hans Rosling Gapminder bubble chart from Plotly.
• Data and visualization integration via web based resources. Using NetCDF, Matplotlib, IPython Parallel and ffmpeg to generate video animation from time series of gridded data. By Massimo Di Stefano.
• 21 Interactive, D3 Plots from matplotlib, ggplot for Python, prettyplotlib, Stack Overflow, and seaborn.
• Visualizing complex-valued functions with Matplotlib and Mayavi, by Emilia Petrisor.
• bqplot is a d3-based interactive visualization library built entirely on top of that ipywidgets infrastructure. Checkout the pythonic recreation of Hans Rosling’s Wealth of Nations.
• A D3 Viewer for Matplotlib Visualizations, different from above by not depending on Plot.ly account.
• Bokeh is an interactive web visualization library for Python (and other languages). It provides d3-like novel graphics, over large datasets, all without requiring any knowledge of Javascript. It also has a Matplotlib compatibility layer.
• HoloViews lets you construct visualizations very concisely in the notebook.
• Winner of the 2014 E. Tufte Slope Graphs contest, by Pascal Schetelat. The original contest info on Tufte’s site.
• matta, d3.js-based visualizations in the IPython Notebook, by Eduardo Graells-Garrido.
• Clustergrammer Interactive Heatmap and DataFrame Viewer This Python notebook shows a simple example of how to visualize a matrix file and Pandas DataFrame as an interactive heatmap (built using D3.js) using the Jupyter Widget Clustergrammer (see paper).

Signal and Sound Processing

• Simulation of Delta Sigma modulators in Python with deltasigma, Python port of of Richard Schreier’s excellent MATLAB Delta Sigma Toolbox, by Giuseppe Venturini. Several demonstrative notebooks on the package README.
• PyOracle: Automatic analysis of musical structure, by Greg Surges.
• A Gallery of SciPy’s Window Functions for quick visual inspection and comparison by Jaidev Deshpande
• Poisson Image Editing | Seamless Cloning by Dhruv Ilesh Shah is a notebook that achieves Seamless Image Cloning by employing the Poisson Solver in the iterative form.
• Blind Source Separation | Cocktail Party Problem by Dhruv Ilesh Shah & Shashwat Shukla is a notebook that achieves blind source separation, on audio signals in an attempt to approach the Cocktail Party Prblem. The problem has been tackled in two different methods – the FOBI and fastICA.

Natural Language Processing

• Python Programming for the Humanities by Folgert Karsdorp & Maarten van Gompel.
• News Categorization using Multinomial Naive Bayes by Andres Soto Villaverde.
• Using random cross-validation for news categorization by Andres Soto Villaverde.

Pandas for data analysis

Note that in the ‘collections’ section above there are also pandas-related links, such as the one for an 11-lesson tutorial.

• A 10-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.
• 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).
• Log analysis with Pandas, part of a group presented at PyConCa 2012 by Taavi Burns.
• 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.
• Statistical Data Analysis in Python, by Christopher Fonnesbeck, SciPy 2013. Companion videos 1, 2, 3, 4

General Python Programming

• Learning to code with Python, part of an introduction to Python from the Waterloo Python users group.
• Introduction to Python for Data Scientists by Steve Phelps (part of a larger collection on Data Science and Big Data).
• 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!, by Sebastian Raschka.
• Key differences between Python 2.7.x and Python 3.x, by Sebastian Raschka.
• A beginner’s guide to Python’s namespaces, scope resolution, and the LEGB rule, by Sebastian Raschka.
• Sorting CSV files using the Python csv module, by Sebastian Raschka.
• Python 3 OOP series by Leonardo Giordani: Part 1: Objects and types, Part 2: Classes and members, Part 3: Delegation – composition and inheritance, Part 4: Polymorphism, Part 5: Metaclasses, Part 6: Abstract Base Classes
• How to Aggregate Subscriber’s Interest using the 3 methods: (1) Python Dictionary, (2) Apache PySpark – GroupBy Transformation, and (3) Apache PySpark – ReduceBy Transformation by Abbas Taher.