This is a talk about creating pictures of a mathematical object---specifically, a 4-dimensional fractal "dust" that has been the subject of mathematical research in hyperbolic geometry since the 1980s. In the end this is accomplished using a little algebra, a little geometry, and a healthy dose of Python. That is, I will present a case study of using Python in several aspects of a mathematical visualization project, from the computation itself, to transforming and converting data, and finally for scripting the process of generating the images. Along the way I'll explain how Python's convenient idioms and containers (e.g. sets and set comprehensions) are a good fit for some of the algebraic and geometric questions that come up, how Scipy and Numpy enable fast numerical calculations, and how Python's strength as a language for scripting and automation allows easy orchestration of rendering of still images and frames of animations. The mathematical visualization project we describe is a collaboration with François Guéritaud (Université de Lille).

Brian will show how to use flask and Python to power a browser based HTML5 game over sockets. Events can be pushed to the browser or pushed to flask from the browser. Great starter for those who are interested in event driven programming.

Day by day, the world is becoming more data driven, making data science extremely popular. Data Wrangling , Data Analysis form the two important stages in any Data Science problem and Entity Matching(EM) is extremely critical in the latter phase. EM has been a long-standing challenge in data management. Most current EM works focus only on developing matching algorithms. A solution to this, Magellan, is a new kind of EM systems, open sourced on top of the PyData eco-system. Magellan is novel in four important aspects. (1) It provides how-to guides that tell users what to do in each EM scenario, step by step. (2) It provides tools to help users do these steps; the tools seek to cover the entire EM pipeline, not just match- ing and blocking as current EM systems do. (3) Tools are built on top of the data analysis and Big Data stacks in Python, allowing Magellan to borrow a rich set of capabil- ities in data cleaning, IE, visualization, learning, etc. (4) Magellan provides a powerful scripting environment to fa- cilitate interactive experimentation and quick “patching” of the system. Magellan is used at Walmart Labs, Johnson Controls, Marshfield Clinic and as a teaching tool in UWM classes.

You are the data troll who allows what data can be pushed up. All data requests are in your hands but first you need to become an official data troll by getting your information in the data troll table (you need to be legit in the database or else it didn't happen). This is a super simple example of how Python can be friends with database, today we’ll look at Postgres!

PyTest helps Python developers with test-driven development, continuous integration, and quality engineering. In this talk we’ll cover setup, data fixtures, case types, and results interpretation by walking through a PyTest demo.

What's happening at the Python Software Foundation? Look no further Python Software Foundation Director Lorena Mesa will run through an update! Information about elections, a new PyCon organizers manual, the PSF Code of Conduct Committee will be briefly covered.

Deep learning has never been accessible to people with limited ML experience. All over the internet, beginners only come across discouragement, exclusion and elitism when they express an interest in doing deep learning. A recently released MOOC, fast.ai is specifically designed for those with some coding experience. The MOOC's creators use a hands-on approach of teaching that focuses on coding first and understanding later. I will talk about the balancing act between work, family and passion projects, how my study buddies help me stay on track, and what we're doing to help others learn.

This is a sneak preview of a talk accepted to PyCon 2017, this June in Portland. A. Jesse Jiryu Davis is a prominent open source developer who has spoken at the last three PyCons, so this talk promises to be thorough, technical, and fun. He describes the talk thus: "I wrote Python for years while holding mistaken notions about the Global Interpreter Lock, and I've met others in the same boat. The GIL's effect is simply this: only one thread can execute Python code at a time, while N other threads sleep or await network I/O. Let's read CPython interpreter source and try some examples to grok the GIL, and learn to write fast and thread-safe Python." Jesse is a Staff Engineer at MongoDB in New York City specializing in C, Python, and async. Lead developer of the MongoDB C Driver libraries libbson and libmongoc. Author of Motor, an async MongoDB driver for Tornado and asyncio. Contributor to Python, PyMongo, MongoDB, Tornado, and asyncio. Co-author with Guido van Rossum of "A Web Crawler With asyncio Coroutines", a chapter in the "500 Lines or Less" book in the Architecture of Open Source Applications series.

In this talk, I will demonstrate some uses of https://pypi.python.org/pypi/redis-helper and how you can easily store, index, and modify Python dicts in Redis. Some asciinema demos are available at https://asciinema.org/~kenjyco

Building and deploying applications has never been easier, especially with the proliferation of APIs. In this talk, I will share the 4 concepts that will allow Python developers to quickly learn and use any Python-based API. The target audience for this talk are intermediate newbies who have a couple of projects under their belt.