Altair is a declarative statistical visualization library for Python, based on Vega-Lite.

With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.

This notebook was originally prepared for the workshop Advanced Text Analysis with SpaCy and Scikit-Learn, presented as part of NYCDH Week 2017. Here, we try out features of the SpaCy library for natural language processing. We also do some statistical analysis using the scikit-learn library.

The nfft package is a lightweight implementation of the non-equispaced fast Fourier transform (NFFT), implemented via numpy and scipy and released under the MIT license. For information about the NFFT algorithm, see the paper Using NFFT 3 – a software library for various nonequispaced fast Fourier transforms.

The nfft package achieves comparable performance to the C package described in that paper, without any customized compiled code. Rather, it makes use of the computational building blocks available in NumPy and SciPy. For a discussion of the algorithm and this implementation, see the Implementation Walkthrough notebook.

Although spatial interaction modeling is a fundamental technique to many geographic disciplines, relatively little software exists for spatial interaction modeling and for the analysis of flow data. This applies particularly to the realm of free and open source software. As a result, this primer introduces the recently developed spatial interaction modeling (SpInt) module of the python spatial analysis library (PySAL). The underlying conceptual framework of the module is first highlighted, followed by an overview of the main functionality, which will be illustrated using migration data. Finally, some future additions are discussed.

An introduction to quantum computing using the pyQuil Python library. This tutorial covers such topics as qubit operations, Pauli operators, multi-qubit operations, the Quantum Abstract Machine, classical/quantum interaction, the probabilistic halting problem, and more, complete with pyQuil code examples.

Scikit-Fuzzy is a collection of fuzzy logic algorithms intended for use in the SciPy Stack, written in the Python computing language.

This SciKit is developed by the SciPy community. Contributions are welcome!

In this post we will first discuss how to set up Spark to start easily performing analytics, either simply on your local machine or in a cluster on EC2. We then will explore Spark at an introductory level, moving towards an understanding of what Spark is and how it works (hopefully motivating further exploration). In the last two sections we will start to interact with Spark on the command line and then demo how to write a Spark application in Python and submit it to the cluster as a Spark job.

xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.

xtensor provides
- an extensible expression system enabling lazy broadcasting,
- an API following the idioms of the C++ standard library, and
- tools to manipulate array expressions and build upon xtensor.

Containers of xtensor are inspired by NumPy, the Python array programming library. Adaptors for existing data structures to be plugged into our expression system can easily be written. In fact, xtensor can be used to process numpy data structures inplace using Python’s buffer protocol. For more details on the numpy bindings, check out the xtensor-python project.

This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

As a follow-up of my previous post on reliability diagrams, I have worked jointly with Alexandre Gramfort, Mathieu Blondel and Balazs Kegl (with reviews by the whole team, in particular Olivier Grisel) on adding probability calibration and reliability diagrams to scikit-learn. Those have been added in the recent 0.16 release of scikit-learn as CalibratedClassifierCV and calibration_curve.

This post contains an interactive version of the documentation in the form of an IPython notebook; parts of the text/code are thus due to my coauthors.

Code reuse is a very common need. It saves you time for writing the same code multiple times, enables leveraging other smart people’s work to make new things happen. Even just for one project, it helps organize code in a modular way so you can maintain each part separately. When it comes to python, it means format your project so it can be easily packaged. This is a simple instruction on how to go from nothing to a package that you can proudly put it in your portfolio to be used by other people.

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming.

Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

Scikit-plot is an intuitive library to add plotting functionality to scikit-learn objects.

Scikit-plot is the result of an unartistic data scientist's dreadful realization that visualization is one of the most crucial components in the data science process, not just a mere afterthought.

Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a single-line dump of numbers enclosed in brackets. Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel.

That said, there are a number of visualizations that frequently pop up in machine learning. Scikit-plot is a humble attempt to provide aesthetically-challenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible.

We speed up the image generation algorithm of PixelCNN++ by avoiding redundant computation through caching. Naive generation discards computation that can be re-used and performs additional computation that will not be used to generate a particular pixel. Naive generation can take up to 11 minutes to generate 16 32-by-32 images on a Tesla K40 GPU. By re-using previous computation and only performing the minimum amount of computation required, we achieve up to a 183 times speedup over the naive generation algorithm. We have tested our code with Python 3 and TensorFlow 1.0. You may need to make small changes for other versions of Python or TensorFlow.

James McCaffrey’s SciPy Programming Succinctly offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this e-book to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming.

This Python notebook implements the toy example from Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks by Lars Mescheder, Sebastian Nowozin, Andreas Geiger.

Jeff Knupp's blog on Python programming.

An article and tutorial on using linters to manage large, enterprise-sized Python projects.

A gallery comparing colorschemes from stylesheets defined in Python Matplotlib with source code available on github..

Jupyter (formerly known as IPython) notebooks are great – but have you ever accidentally deleted a cell that contained a really important function that you want to keep? Well, this post might help you get it back.