Past Meetings • Recent Topics

Letsencrypt with Python Webapps By: Joe Jasinski
Date: May 11, 2017, 6 p.m.
In-browser encryption is more important now than ever. When building modern web-apps, encryption is a necessity. This talk will detail how you can secure your Python-based projects with Letsencrypt, a free certificate authority available to anyone. It will cover the Python-based tools available to configure Letsencrypt and an example project utilizing it.
Python for mathematical visualization: a four-dimensional case study By: David Dumas
Date: May 11, 2017, 6 p.m.
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).
Build a Game: HTML5 sockets + Phaser + flask By: Brian Ray
Date: May 11, 2017, 6 p.m.
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.
TDD with PyTest By: Sand Ip
Date: April 13, 2017, 7:45 p.m.
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.
Python Software Foundation Update + how you can be involved! By: Lorena Mesa
Date: April 13, 2017, 8:35 p.m.
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.
How a Study Group Can Help a ML Beginner Learn Deep Learning By: Apurva Naik
Date: April 13, 2017, 7:30 p.m.
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.
Grok the GIL: Write Fast And Thread-Safe Python By: A. Jesse Jiryu Davis
Date: April 13, 2017, 8 p.m.
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.
Trolling databases with Python! By: Loren Velasquez
Date: April 13, 2017, 7:35 p.m.
Slides Link
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!
Introduction to Project Magellan By: Ancy Phillip
Date: April 13, 2017, 7 p.m.
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.
How I Taught My Dog To Text Selfies By: Greg Baugues
Date: March 9, 2017, 6 p.m.
This talk is is for Python developers who would like to get started with hardware hacking but have been too intimidated in the past to do so. Also, it's for people who like dogs and/or selfies. Using a Raspberry Pi, Python, Twilio, and a big red button, I taught my dog to text selfies. In this talk, which features 25 minutes of live coding, we'll build the hardware hack from scratch. Developers will walk away knowing how to use Python to interact with Twilio and the Raspberry Pi's GPIO pins. A video of this hack was featured on Mashable and was watched over 2.5M times.
Quick prototyping with redis-helper By: Kenneth Wade
Date: March 9, 2017, 6 p.m.
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
How To Develop and Deploy Faster using Python APIs By: Paul
Date: March 9, 2017, 6 p.m.
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.
Unsupervised machine learning in engineering and neuroscience: applications of ICA By: Mark V. Albert By: Pavan Ramkumar By: Anne Zhao By: Jorge Yanar
Date: Feb. 9, 2017, 6 p.m.
This talk with be a set of four short presentations guiding everyone through three applications of unsupervised machine learning. We begin with the classic cocktail party problem - how to automatically separate mixed voices recorded by microphones - presented by Jorge Yanar. This will be followed by a brief, intuitive explanation of the algorithm used to perform the task - Independent Components Analysis (ICA) described by Professor Mark Albert. Dr. Pavan Ramkumar will demonstrate how the same technique is applied to filter unwanted noise during neural recordings using EEG, and Anne Zhao will end with a demonstration of how the same coding strategy has led to insights in how the brain encodes sensory information in the early auditory and visual systems. Her demo will allow participants to develop their own simulated neural codes for processing visual images. The brief talks will consist of a Jupyter notebook for running code and displaying results. For those who wish to run the examples during the talk, it will be necessary to install Jupyter running Python version 3 (the Anaconda Python distribution is recommended to set this up). Links and setup instructions will be given prior to the talks for people to follow along on their laptops and try the examples on their own if desired.
Mentorship Program Finals
Date: Jan. 12, 2017, 7 p.m.
The largest ever cadre of ChiPy Mentorship participants meet to present their findings after 13 weeks of study with their mentors. You'll see demos and discussion for entry level projects, web development projects and data science experiments gone mad with the power of Python. Each presenter gets five minutes to tell the story of their journey and what they produced. Plus amazing prizes and announcements on how to apply for the spring term.
Smuggling Snakes in a Box: A Docker + Python Love Story By: Hector Rios
Date: Dec. 8, 2016, 6 p.m.
Do you yearn? Have you ever tried to volunteer at a project but had an extremely difficult time trying to get everyone the right development environment? Do you ever work with a project that works in your computer but not in a server? Do you have a different dependency versions? (btw, that's kinda bad practice but don't fret, we gotchu) Yearn no more! With Docker, these things can be a thing of the past. Join in a brief, live coding session that will show you how to build a simple Bottle app and put it into a Docker container.
Using pyodbc to execute SQL queries By: Anish Krishnan
Date: Dec. 8, 2016, 6 p.m.
How to access a database using pyodbc, and how to execute basic queries through Python.
Python in the Classroom and at Sea By: Thane Richard
Date: Dec. 8, 2016, 6 p.m.
I was Ray's mentee during the summer of 2016. My project lets a player of Minecraft Pi 3D print an object they have built in the game. I will co-present this project with Peg Keiner, the Technology Director at GEMS World Academy in Chicago where my program was used in their elementary school classroom this Fall. I will also share some neat projects from my stint this Fall as a High School Marine Science teacher aboard the schooner Roseway during Ocean Classroom (worldoceanschool.org). I introduced students to Python and had them design and code a sensor kit with Raspberry Pi's to measure an aspect of the sailboat. The code is viewable on my github profile: https://github.com/thaneofcawdor.
Migrating django application data By: Eevel Weezel
Date: Nov. 10, 2016, 6 p.m.
Discussion of common problems migrating Django application databases, particularly when switching DBMS.
Introducing Python in an after school setting By: Kenneth Wade
Date: Nov. 10, 2016, 6 p.m.
Slides Link
I've lead a couple once-a-week, 10-week apprenticeships that allow 5th-8th grade students to explore the basics of Python through an interactive shell at their elementary school. The students primarily use lab computers, but they are also exposed to general command-line concepts through the use of several customized Linux laptops. In this talk I will discuss my goals for the students, the concepts that I introduce, how I interact with the students, some of the challenges that arise (for myself and the students), and some tips that may be helpful to other volunteers.
Module the Month: Turtle By: Chris Foresman
Date: Nov. 10, 2016, 6 p.m.
In keeping with the education theme, I thought I would give a talk on the Turtle module in Python, which is more or less a clone of Logo.
Ultimate Langauge Shootout
Date: Oct. 13, 2016, 7 p.m.
Multiple Langauge competition: * JavaScript - Divya * Clojure - Cezar Jenkins * SQL - Heather White * Babbage's Analytical Engine programming cards - Phil Robare * R - Parfait * Assembly (AVR) - Nick Timkovich * Groovy - Jerry Dumblauskas * Swift - Matt Green * Julia - Andrew Webster
Using Tasks in Asyncio Web Apps By: Feihong Hsu
Date: Sept. 8, 2016, 6 p.m.
Slides Link
In this talk, I will be talking about starting, stopping, and displaying incremental data from long-running tasks in an asyncio-based web application.
Popular ORM Libraries By: Tanya Schlusser
Date: Sept. 8, 2016, 6 p.m.
Slides Link
What's the main difference between SQLAlchemy and Django's ORM? When might a person prefer Pony ORM or peewee? -- popular Object-Relational Mapping libraries in Python are compared and contrasted.
Developing with Python at Telnyx By: Alex Puglis
Date: Sept. 8, 2016, 6 p.m.
This talk will cover the development cycle, build tools, and python frameworks commonly used by Telnyx Python engineers.
Predictive Enforcement of Pollution and Hazardous Waste Violations in New York State By: Jimmy Jin By: Maria Kamenetsky By: Dean Magee
Date: Aug. 11, 2016, 6 p.m.
New York State’s Department of Environmental Conservation (NYSDEC) is the regulatory agency for environmental issues in the state. Their mission is to conserve, improve and protect New York State’s natural resources and environment and to prevent, abate and control water, land and air pollution. NYSDEC currently conducts approximately 700 inspections each year of facilities in the state that manage hazardous waste. DSSG will work on more effectively allocating inspection resources by creating predictive models that identify facilities with high likelihood of violating environmental regulations. In 2015, we worked with the federal EPA targeting hazardous waste facilities subject to the Resource Conservation and Recovery Act (RCRA). With inspection data from NYSDEC and the public RCRA dataset, we will build a similar model to identify RCRA violators specifically in the New York region, as well as further explore the possibility of applying models to other compliance inspection programs, such as the Clean Air and Clean Water Acts.
Expanding Our Early Intervention System for Adverse Police Interactions By: Sumedh Joshi By: Jonathan Keane By: Joshua Mausolf By: Lin Taylor
Date: Aug. 11, 2016, 6 p.m.
Many police departments in the United States use “early intervention systems” to identify officers who may benefit from additional training, resources, or counseling. These systems attempt to determine behavioral patterns that predict a higher risk of future adverse incidents, ranging from excessive use of force and citizen complaints to on-duty accidents and personal injury. Detecting these risk factors enables departments to develop targeted interventions and make operational changes to protect officer safety and improve police/community interactions. Last summer, DSSG worked with the Charlotte-Mecklenburg Police Department on building a better early intervention system, applying data analysis to provide insights on individual and situational risk factors for adverse interactions. This year, we will partner with additional police departments, including the Metro Nashville Police Department, to test and expand this work in new municipalities, improving both the overall model and local performance. Like last year, we will use anonymized police data and contextual data about local crime and demographics to detect the factors most indicative of future issues, so that departments can provide additional support to their officers.
Intro to Deploying Django with Ansible By: Joe Jasinski
Date: July 14, 2016, 6 p.m.
Deploying Django is a breeze when using Ansible. Learn a bit about the power that Ansible provides and how easy it is to get started using it!
Getting meaningful results from unit tests By: Manu Phatak
Date: July 14, 2016, 6 p.m.
Slides Link
We count on unittests failure messages to give us reasonable feedback on how to proceed with a failing test. When you're working with builtin data structures and objects, unit test feedback is usually pretty good. It helps you identify and solve the problem. In contrast, the default results you get from custom objects can be practically useless. https://gist.github.com/bionikspoon/2e434a2c193a06b0996cc98c6a604de9
Computer Vision in Python: How to build a basic face detection system By: Jeremy Watt
Date: July 14, 2016, 6 p.m.
In this short talk Jeremy will describe the universal pipeline for performing object detection (that is, the automatic detection of objects in digital images) in Python. This will include a discussion of various classification schemes, feature extraction methods, and their fusion in the form of deep neural networks. Demo code illustrating these concepts will be shown using the IPython notebook environment.
An overview of python projects of OS X administration By: Ryan Manly
Date: June 9, 2016, 6 p.m.
In this talk Ryan will give a brief overview of several python projects used by many OS X admins to provide cached update services, imaging, software deployment, and configuration management. Looking at the code in these tools can provide insight into Apple's preferences frameworks and if you are in a "DevOps" type role some of the projects discussed may help you immensely.
PyCon 2016 recap By: Jerry Dumblauskas
Date: June 9, 2016, 6 p.m.
Let's give an overview of Pycon
JIRA + Python By: Jonathan Pietkiewicz
Date: June 9, 2016, 6 p.m.
JIRA is a popular issue tracking and project management software. In this talk you will learn about JIRA and how to interact with the tool using the jira-python library. Primary topics covered will include an overview of the API, creating and modifying issues, linking issues, and searching from Python.
Python Hype? By: Brian Ray
Date: June 9, 2016, 6 p.m.
Brian will reveal the survey results that will help shed some light on the current and future projectile of the Python programming language. Has Python reached a peak? Will it popularity continues to rise? What are the users of Python at different levels saying about the state of Python Programming Language?
pyStan: Bayesian Inference for Fun and Profit By: Stephen Hoover
Date: May 12, 2016, 6 p.m.
Slides Link
Probabilistic programming languages offer a flexible and expressive way to model data by treating random variables as first-class objects. Stan is a popular and well-supported library which allows users to write models in the Stan programming language and use MCMC methods to perform Bayesian inference. Stan itself is written in C++, and has a Python interface through the PyStan package. In this talk, I'll show off some of the capabilities of PyStan and go through a simple practical example of Bayesian inference in Python.
Python, Startups, Tech Debt, and You By: Matt Erickson
Date: May 12, 2016, 6 p.m.
There's a lot of people newish to Python and either interested or already in a startup environment (either within a larger corporation or an actual startup). Python makes a *great* tool for that, however while there’s ways to use it to carry your work along to great success, there’s ways to wind up with such spaghetti you’re tempted to throw your hands in the air and go back to Java. The focus on the talk is how to use Python and the tools it provides to avoid the unmaintainable mess while still being able to “cut corners” to get something out the door to make your boss/investors/customers happy.
The 'collections' module By: Phil Robare
Date: May 12, 2016, 6 p.m.
A quick overview of the collections module and its five data structures. The talk will be aimed at the intermediate level python user who has the basic syntax down but has not yet delved into the wealth of programming tools in the standard library.
Multiple System Failure: A case study in debugging By: Adam Forsyth
Date: April 14, 2016, 7 p.m.
Recently, the Braintree Python library wasn't working on Google App Engine. Braintree, GAE, requests, and urllib3 all had problems and I tracked down each one. I'll walk you through debugging with only basic tools -- editing the code to observe state and using git to find the responsible commit. This talk expects a basic understanding of web programming, git, and Python.
Hacking Bokeh By: Brian Ray
Date: April 14, 2016, 6 p.m.
I brief introduction into Bokeh http://bokeh.pydata.org/en/latest/ And a bit on how to build interactive graphs in jupyter notebooks or stand alone.
module of the month - usaddress By: Cathy Deng
Date: April 14, 2016, 6 p.m.
usaddress is a python library that uses NLP methods to parse address strings into structured components (e.g. street name, city, zip). it is trained on real-world addresses with real-world data quirks - as a result, it's robust in handling messy data. usaddress was built by DataMade, a local civic technology company. TL;DR usaddress helps you avoid regex for address data, which is a terrible rabbit hole.
Job Market By: Jerry Dumblauskas
Date: March 10, 2016, 12:11 p.m.
Let's see what's happening in the Python Job market in Chicago!
ChiPy Python Mentorship By: Tathagata
Date: March 10, 2016, 12:10 p.m.
Slides Link
This April we will the start the fourth round of ChiPy's mentorship program. We have worked with more than 70 developers till now, and some of them have landed exciting jobs by showcasing their mentorship projects. I'll give a quick view of the program and what are we looking for in a mentor and a mentee. FAQ: http://www.chipy.org/pages/sigs/mentorship/
Python-based data science to understand knowledge discovery and expertise: A science perspective By: Daniel E. Acuna
Date: March 10, 2016, 12:10 p.m.
All kinds of businesses are using data science and machine learning to understand themselves, lowering costs, engineering better products, and improving customer experiences. Similarly, we use data science to improve science itself, understanding how scientific topics are discovered and modeling institutional expertise. In our work, we use a combination of Python-powered big data analytics and web-based tools to achieve this goal, including pyspark (http://spark.apache.org), scikit-learn (http://http://scikit-learn.org), Django (https://www.djangoproject.com/), Celery (http://www.celeryproject.org/), and or-tools (https://developers.google.com/optimization). First, we will present the infrastructure behind Scholarfy, a recommender system for massive scientific conferences (http://www.scholarfy.net). We will also present a machine learning approach to automatically match expert scientific reviewers to research proposals (http://pr.scienceofscience.org). Finally, we will present the work behind our award-winning visualization, World’s Science Map (http://map.scienceofscience.org), where we modeled the institutional expertise, collaboration network, and funding of all institutions in the world. At the end of our talk, we will argue that Python-powered data science can improve not only businesses but also science, making it more agile and accurate.
The wonder and the horror of the mock module By: Stephen Hoover
Date: March 10, 2016, 4:47 p.m.
The "mock" module is a powerful (and fun!) tool for unit testing, and it comes built in to the the Python standard library. I'll give an overview of some of the more useful features of the module, and finish with a warning about the dangers of too much mockery.
Python at Credit Suisse By: David Matsumura
Date: Feb. 11, 2016, 7 p.m.
How Credit Suisse uses Python.
How SpotHero uses Python By: Cezar Jenkins
Date: Feb. 11, 2016, 7 p.m.
SpotHero is one of the leading online parking reservation companies in the country. Come see how were using Python to make that happen.
Python at OptionsAway By: Tim Saylor
Date: Feb. 11, 2016, 7 p.m.
How OptionsAway, an airfare lock startup, uses Python.
Python at Cofactor By: Hector Rios
Date: Feb. 11, 2016, 7 p.m.
We are going to talk about our history -- a high level why we switched from .net to python. Topics include .NET pain points and the reason why we chose Python. We looked at Ruby, and chose against Ruby. We will also talk about how we use Python, which include Bottle + MongoDB to build our APIs.
Python at Datascope Analytics By: Brian Lange
Date: Feb. 11, 2016, 7 p.m.
How Datascope Analytics uses Python to improve business and society through science and design.
Python-Powered Data Science at Civis By: Stephen Hoover
Date: Feb. 11, 2016, 7 p.m.
Civis Analytics uses Python to develop the machine learning software which powers our products, and we run our Python software in production. I'll give a (very) brief overview of what Civis Analytics does and where Python and the open source community fit into the picture.
Python at ShiftGig By: Brian Eagan By: Tyler Hendrickson
Date: Feb. 11, 2016, 7 p.m.
How ShiftGig, which connects the right people with the right job, uses Python.
Python at Modest By: Emily Anderson By: Mark Ashton
Date: Feb. 11, 2016, 7 p.m.
We will describe how Modest, recently acquired by Braintree (PayPal) uses Python.
Python at Vokal By: Chris Foresman By: Adam Bain
Date: Feb. 11, 2016, 7 p.m.
How Vokal uses Python with Adam Bain and Chris Foresman
Python at Deloitte By: Brian Ray
Date: Feb. 11, 2016, 7 p.m.
How Deloitte uses Python within the Enterprise Science Team.
Web App for Caregivers By: Shannon Cochran
Date: Jan. 14, 2016, 7 p.m.
This presentation will cover the Django project I completed with my mentor, Adam Bain. The idea for this project came from my former work as a caregiver for a child with Autism. As a caregiver, there were many times behavioral issues came up and I often wondered what other possible interventions people may have tried. The child I worked with was nonverbal which made discipline and finding out the source of a behavior much trickier. Every case of Autism is different but there are still some behaviors which are more common, especially as a result of the inability to communicate. For example, self-injurious behaviors are common and usually associated with the frustration of not being able to communicate needs. My idea is to create an app where caregivers are able to share their solutions to behavior problems and search for other caregiver’s solutions as well. The app will have a space for people to share both problem behaviors they want to decrease in their client or child and positive behaviors they want to encourage. This project allows caregivers to search for problem behaviors as well as positive behaviors and find out how other caregivers addressed the behavior and whether those interventions were successful or not.
Constructing a risk metric from google query data By: Michael Tamillow
Date: Jan. 14, 2016, 7 p.m.
We have created a dataset from the search queries provided by Google and matched it up with some market data. We will attempt to produce a some metric or predictive model given the limited, slightly correlated data.
Dustin Shapiro's Python 101 Menteeship! By: Dustin Shapiro
Date: Jan. 14, 2016, 7 p.m.
This is a brief overview depicting where I started before this mentorship, through the various projects me and Ray worked on, and where I plan to take it moving forward!
Using Python for Kaggle competitions By: Hana Lee
Date: Jan. 14, 2016, 7 p.m.
(Lightning talk as part of ChiPy mentorship) I'll be talking about using Python to develop a classifier for a Kaggle competition looking at crime data in San Francisco
Building a BusTracker Tracker By: Ellie Anderson
Date: Jan. 14, 2016, 7 p.m.
First, I’ll discuss a data-gathering pipeline that uses AWS Lambda functions written in Python to scrape CTA’s BusTracker prediction service and interpolate actual arrival times. Then I’ll detail an API written in Django REST Framework to select and analyze a range of data. Finally, a simple JavaScript-based front-end visualizes the data provided by the API.
*half time special* imposter syndrome. By: David Beazley
Date: Jan. 14, 2016, 7 p.m.
Seeing as this winter marks 20 years of my using Python, I might be inclined to say a few short words about imposter syndrome.
Shuang Qiu By: Shuang Qiu
Date: Jan. 14, 2016, 7 p.m.
Project Goal: Create an interactive dashboard using Django, featuring data table and chart which take customized user filtering and sorting and return the filtered result. Progress: 1. Data normalization 2. Data Importer 3. url patterns 4. Django form - HTTP get/post request 5. Created chart view with C3.js 6. Bootstrap for error warning and numeric stepper 7. Manipulate data within shell
SQLAlchemy: Beyond ORM By: Will Engler
Date: Dec. 10, 2015, 7 p.m.
Before I started my new job, I thought of SQLAlchemy as "that ORM people use with Flask." Well, it is that - and more! With this talk, I want to give the audience a taste of SQLAlchemy's philosophy and capability. Outline: 1) Picking the right abstraction: SQLAlchemy's ORM and Core layers. 2) Transaction management: The Unit of Work pattern (SQLAlchemy) vs. the Active Record pattern (Django models, Rails ActiveRecord). 3) In the wild: code samples plus practical concerns like migrations.
Meet the micro:bit By: Naomi Ceder
Date: Dec. 10, 2015, 7 p.m.
You may have heard of the BBC micro:bit - a tiny (2" x 2.5") ARM based single board computer that every 11 year old in Britain will be receiving in a few months. (And if you haven't, well, as for everything else, start with Wikipedia.) Even better, the micro:bit runs Python 3 (MicroPython, to be exact). The Python Software Foundation is a partner in the project. (see http://ntoll.org/article/story-micropython-on-microbit for more) The micro:bit will be released in the UK some time around February, and should be available commercially shortly after that. Even though the micro:bit has't been officially released yet, a few have made their way out the door. So I happen to have one these precious few devices in the wild. I'd be happy to give a 30-45 minute talk about the background of the micro:bit and getting Python on it, about the teaching implications, the development done so far, and what's needed for the future, as well as the world tour that several of the devices are on. There would also be a live demo of the device.
An Introduction to the Portable Format for Analytics (PFA) and to Python-based Titus Scoring Engine By: Robert Grossman
Date: Dec. 10, 2015, 7 p.m.
The Portable Format for Analytics (PFA) (www.dmg.org) is an emerging standard for predictive analytics that addresses some of the limitations of the Predictive Model Markup Language (PMML) and was designed for today’s big data environments, including Hadoop, Storm and Spark. In this talk, I give an introduction to PFA, model deployment, and Titus: Open Data's Python toolkit for building, inspecting, and modifying PFA scoring engines. Robert Grossman is the Founder and a Partner at Open Data Group, which has building predictive models over big data for its clients since 2002. He is also a Professor in the Division of Biological Sciences at the University of Chicago, where he leads a research group in bioinformatics with a focus in managing and analyzing large genomic datasets for advancing the understanding of human disease.
Python at Nokia (by MacGregor Felix) By:
Date: Nov. 12, 2015, 7 p.m.
Python is known to be a multi-purpose and multi-paradigm programming language. Come see how the Reality Capture & Processing (RCP) group of Nokia HERE is making use of Python’s versatility. We will show you how HERE RCP uses Python’s Object Oriented constructs to represent business models in production systems. You will see how Python’s functional lambdas are used to elegantly facilitate the handling of big data. We will discuss the use of Python not only in production code but also in test code. We not only use Python for production purposes but also to build utilities. We hope to show you how we utilize Python's versatility and closeness to the operating system to build sophisticated tools for development and operational productivity. You’ll see our Test Driven development effort while building Python products and how we use Python in Behavior Driven Development to code language-agnostic acceptance tests for the evolution of software and services. We will also give you a pick at our Python packaging and distribution.
Python-fu in the GIMP By: Tanya Schlusser
Date: Nov. 12, 2015, 7 p.m.
Slides Link
GIMP (the GNU Image Manipulation Program) is great all by itself but is even better with Python-fu. This talk demonstrates a little Python-fu to manipulate images in GIMP, with a little (slightly ugly) hacking to add external libraries.
Python-fu in the GIMP By: Tanya Schlusser
Date: Oct. 8, 2015, 7 p.m.
GIMP (the GNU Image Manipulation Program) is great all by itself but is even better with Python-fu. This talk demonstrates a little Python-fu to manipulate images in GIMP.
Fancy genetics and simple scripts: Manipulating DNA data and becoming more proficient with Python By: Mark Mandel
Date: Oct. 8, 2015, 7 p.m.
Our ability to read the genetic code of organisms and to use DNA sequencing to learn new biology has benefited tremendously from technological advances in the past ten years. My lab looks at how animals get colonized with specific bacteria. As we have been generating more data it has become clear that we are underutilizing the information. We are beginning to build resources to be more efficient and clever at data processing and data mining from biological samples. I'll talk a little about the science in the lab and show one of our Python projects that is functional but in its early stages. I am eager for feedback, and I think the talk will have resonance for a new motivated Python user in any field.
Factor analysis: simplifying high dimensional data sets for visualization and machine learning By: Mark Albert
Date: Oct. 8, 2015, 7 p.m.
For many machine learning problems, there are far more dimensions to our data than there need to be for efficient learning. Often a first step is dimensionality reduction to remove both redundancy and noise. In addition to more efficient automated learning, factor analysis allows us to visualize high dimensional data sets in our standard human-limited 2 or 3 dimensions. For demonstration, we will apply PCA on a set of questions asked of the audience to map everyone onto a 2D "personality" map - allowing us to visualize the underlying personality factors of those present. Beyond fun visualizations, these techniques are the basis of more efficient generalization in many machine learning problems.
Why You Can't Sit With Us - Understanding Network Analysis in Python With Mean Girls By: Richard Harris
Date: Sept. 10, 2015, 7 p.m.
Network analysis is a handy tool used to understand group dynamics, provide product recommendations, and prevent homicides (and other things). This talk will introduce the theory behind network analysis and showcase the flexibility of Python's NetworkX library. No knowledge of network analysis (or Mean Girls) is needed, but basic knowledge of Python and the iPython Notebook, will be helpful. I gave this talk last month in Columbus OH at PyOhio 2015.
ChiPy Mentorship Oct-Dec 2015 By: Tathagata
Date: Sept. 10, 2015, 7 p.m.
Slides Link
The wait is over! ChiPy's Mentorship program returns for the third time. We learned a lot from the previous two mentorship program and will do things a bit differently this time. This will be a quick overview how we are going to conduct the ChiPy's Python mentorship program.
Exploring uWSGI By: Chris Sinchok
Date: Sept. 10, 2015, 7 p.m.
uWSGI is a very popular software package, but most Python programmers just connect it to nginx, and leave it at that. I'll be exploring some of the more advanced features of uWSGI, and how they can make your life easier.
Setting Up Machine learning with anaconda By: Joshua Herman
Date: Sept. 10, 2015, 7 p.m.
5 min What is anaconda and how do i use it 5 min What is ipython 10 min Why machine learning is fun and how to do easy classification tasks
Data Games in Python By: C. S. Schroeder
Date: Aug. 13, 2015, 7 p.m.
Slides Link
There has been recent work on the taxonomy of games which are based, one way or another, on real world data. Typically these games help people learn that data or how to cope with it. The traditional examples are simulation games (flight, driving, etc.), while other games incorporate data in such a way that it is beneficial to learn the real world data in the game play (trivia). These types of data-games commonly have a domain specific focus. We intend to explore the possibility of interactive games which help people to learn data analysis, in general, implementing some such games in python using web2py and Scipy.
Keep calm and conda install By: Jonathan J. Helmus
Date: Aug. 13, 2015, 7 p.m.
Conda is a cross platform, package management system widely used in the scientific and data science Python communities. Although designed for Python packages, conda can be used to package and distribute software written in any language. This talk will cover how to use conda to install and manage scientific packages as well as how conda can be used to create isolated Python environment similar to virtualenv. Conda’s use within the Anaconda and Miniconda Python distributions will be discussed as an easy method for obtaining a full featured SciPy stack. Instructions on building packages with conda and hosting them on Anaconda.org will be covered briefly.
Automating a fishtank with python and IoT sensors By: Benjamin Chodroff
Date: Aug. 13, 2015, 7 p.m.
Fish tanks are simple enough that even a child can maintain them. I don't have children yet to maintain my tank, but luckily my very patient wife has allowed me to explore over engineering a solution. In this talk we'll explore how python scripts running on a Raspberry Pi can be used to measure and control many aspects of maintaining a fish tank or any number of IOT applications. A demo of the hardware connectivity will be shown which includes an Atlas Scientific pH meter, digital submerged temperature probe, liquid flow meter, liquid level sensor, video camera, and an eight channel relay controlling 12V DC water and 120V AC CO2 gas solenoids, peristaltic dosing pumps, and lighting. A live python coding demo with sample scripts will show how to connect to the serial devices and control the analog and digital hardware. We will broadcast the measured data and hardware states using the Eclipse Paho MQTT python client with the IBM IoT Foundation on BlueMix or IBM MessageSight to create a dashboard using a Javascript MQTT client and Freeboard.io. Finally, we'll create a linux script which allows the attached RaspiCam to live stream a HD video to Google's Youtube Live so the whole world can see.
Why learning process matters to student dev's By: Lane Campbell
Date: July 9, 2015, 7 p.m.
I took up learning Python and Web Development early this year. I started attending Django lessons held by folks in the community. After the lessons students had trouble finding help learning together. To help everyone organize I founded the Django Study Group. I've been learning for the last six months but I am still a student. I joined the Chipy mentorship program to learn from a local professional Python developer. While enrolled in that I took the opportunity to join a student team led by Brian Ray for more experience learning to code. It was working alongside Brian that I learned the importance of how you build software.
Quantopian Trading By: Sean Ware
Date: July 9, 2015, 7 p.m.
Brief introduction to the Quantopian api which is used for trading financial assest with python.
Building a Temperature Control Program for Monitoring Aquaculture Tanks Using Raspberry Pi and Python By: Thao Nguyen
Date: July 9, 2015, 7 p.m.
Growth of the Mentee as a Pythonista I have turned from totally no experience with Python to gaining a good amount of knowledge in this language. I have learned from the very basic syntaxes to writing functions, then writing functions for different types of data (list, string, integer, decimal, float, epoch, threshold…) to serve various purposes; I know how to install redis, bokeh and flask for data acquisition, storage and performance; I also learned how to send an email alert from the Raspberry Pi with Python, thanks to the hackathon midterm meetup and my mentor. And because our project covers a wide range of activities, I have learned a lot of the fundamental elements of Python as well as programming in general. Above all, the best thing I have learned about Python through this Mentorship program is being confident and feeling more comfortable with it. Before this project, I wasn’t really sure about Python. Is it what I want or I might be better off with other languages? But after finished the project, I can say it was fun, and it serves well what I want to do. So I decided to move forward with it. And even though this is my very first programming language, but the dynamic from its strong supportive community, rich wonderful open sources and inspiring opportunities like this Mentorship program, all makes me feel that Python is a good choice. The Mentor's role When I asked my mentor for his advices on learning programming, he told me that to him, the best way to learn is doing projects, just like what we are doing. And that is so true. Sometimes I feel like the best way of learning how to swim is just jumping into the water, like doing a project; it can be scary, uncertain, and possibly failed, but it can also be very exciting and thrilling. Of course, one should only jump with a life preserver if she never knows how to swim before. And our mentors are life preservers. For a novice, it could be very confused at first of where to go, what direction to take, or how to get there; and easy be overwhelmed by too much information. The life saver may not be able to tell you what direction to take either, but at least, it will help you have some time to think and to practice before you decide your next moves. Obviously, a mentor is much better than a life saver, because no life saver can talk nor answer questions; and the best part is, they have a lot of experiences in their hands and are willing to share them with you. Thao Nguyen
Formula One Data Visualization and Interpretation: adventures in mentorship By: Seth Difley
Date: July 9, 2015, 7 p.m.
We participated in the Chipy mentorship program. Our plan for the mentorship was to use Python to visualize and interpret Formula One racing data. Join us to hear about the triumphs and obstacles we encountered along the way.
Machine Learning with Python By: Alexander Flyax
Date: July 9, 2015, 7 p.m.
I will briefly describe my journey into applied machine learning using Python packages like scikit-learn and statsmodels.
DePy 2015 Review By: Joe Jasinski
Date: June 11, 2015, 7 p.m.
A quick recap of the Chicago DePy conference that occurred this month.
PyCon 2015 Review By: Jason Wirth
Date: June 11, 2015, 7 p.m.
Introduction to PySpark By: Nusreth Baig
Date: June 11, 2015, 7 p.m.
Big Shoulders Data Camp presents an “Introduction to PySpark”. One of our top instructors and data scientists, Adam McElhinney, will be giving a talk on working with PySpark, and presenting a use case. Audience is encouraged to come prepared to take notes, ask questions, and get a high-level understanding on one of Python's many analytical libraries.
QML vs. Python By: Patrick K. O'Brien
Date: May 14, 2015, 7 p.m.
If you think Python is Pythonic, wait until you see QML from the point of view of an experienced Python developer. QML is the Qt Meta Language or Qt Modeling Language. http://en.wikipedia.org/wiki/QML
Postscript. Yes, it's a programming language By: Ken Schutte
Date: May 14, 2015, 7 p.m.
I'll describe Postscript - a interpreted, stack-based "page description language" used to produce vector graphics and documents.
R and Python for regression By: Jerry Dumblauskas
Date: May 14, 2015, 9:48 a.m.
Let's compare our favorite language to an 'upstart' highly focused statistical language.
Conway's Game of Life: Programming in a non-language By:
Date: May 14, 2015, 7 p.m.
The Game of Life is Turing Complete. That means it can (theoretically) calculate anything that any computer can calculate. What does this mean in practice and how can you program a calculation when the total syntax is just flipping cells in a 2D bit field?
Swift By: Feihong Hsu
Date: May 14, 2015, 7 p.m.
as former C# developer the lessons I learned to become pythonic By: JC LatinoTV
Date: May 14, 2015, 7 p.m.
language comparison in 5 minutes
Go: Concurrency is Built In By: Chris Foresman
Date: May 14, 2015, 7 p.m.
Discussing the pros and cons of Golang from a Python user's perspective, particularly focusing on its built-in support for concurrency and the advantages over asyncio.
Erlang By: Garrett Smith
Date: May 14, 2015, 7 p.m.
ULS Erlang entry
Is True true? : A mini-venture into Python & Ruby truth testing By: Lorena Nicole
Date: May 14, 2015, 7 p.m.
Review of truth testing in Python and Ruby. If "Explicit is better than Implicit" then why does Python decide that values like empty sequences are "falsey"? How is it that Ruby only defines false and nil as false values, isn't this more explicit? Highlight how languages embed their own philosophies of what is correct and true with surprising overlaps and at times odd contradictions.
From Code to Coffee Table with Blender By: Matt Meshulam
Date: March 12, 2015, 8 p.m.
I've been developing a Python library for turning 3D models into CNC-machinable parts. I will demonstrate the basics of the library and how I used it to build a wood coffee table.
A Talk on Giving a Pythonic Talk By: Xan Vongsathorn By: Catherine Vongsathorn
Date: March 12, 2015, 7 p.m.
Xan Vongsathorn and Catherine Vongsathorn will be giving a talk about talks. It turns out that many of python's core principles apply very well to presentations -- or for that matter, communication more generally -- which may be why we like python so much. Xan and Catherine want to get people excited not only about giving talks but also about using them to *actually communicate*. You don’t have to be an expert, nor do you need natural talent, to give a good talk; this metatalk will discuss guiding principles that set effective presentations apart and can be applied to any technical talk.
Django+Elasticsearch+Haystack to Search PDFs and Such By: Joe Jasinski
Date: Feb. 12, 2015, 7 p.m.
Have you ever wanted to search the contents of uploaded PDFs, Docs, and other document formats on your website? Django Haystack (with the Elasticsearch search backend) is a great way to add search to your site, but it does not support full document indexing out of the box. I'd like to share a solution that I cobbled together to allow this combination of tools the ability to search document contents
REST on Django By: Adam Bain
Date: Feb. 12, 2015, 8 p.m.
A quick overview through the components that make up Django REST Framework with a dive into a sample project. Video Link: <<https://www.youtube.com/watch?v=UVC62eGQTOQ>>
ChiPy Mentorship 7-Minute Retrospective By: Paul Ebreo
Date: Jan. 21, 2015, 7 p.m.
Tom Yarrish and Paul Ebreo will talk about their experience of the 12 week mentorship program. They will talk about what went well and what went not-so well. They will share what they learned and give tips and tricks for a successful mentor/mentee relationship. Paul is very passionate about programming, software testing, open hardware and teaching and Tom is a Digital Forensic Analyst and teaches at Loyola University.
Python Mentors Lightning Talk – Chris & Rahul By: Chris Foresman
Date: Jan. 21, 2015, 7 p.m.
Chris and Rahul would be talking about making RESTful API with Python. Chris was an Associate Writer at Ars Technica and is currently a Senior Systems Engineer at Vokal. Rahul is pursuing his MS in Computer Science at Illinois Institute of Technology. Chris is @foresmac on Twitter and Rahul can be reached at https://www.linkedin.com/in/rahul013k
Example app using Flask and pg8000 (Postgres) on Heroku By: Tanya Schlusser
Date: Jan. 21, 2015, 7 p.m.
We walk through the architecture, development process, and a few gotchas of deploying a web application on Heroku using their free Postgresql instance, and the Python libraries 'flask' and 'pg8000'
Being A Mentee In The ChiPy Mentorship By: Zachary Kerr
Date: Jan. 21, 2015, 7 p.m.
Mentors can be incredibly valuable in helping understand software. I want to share some of the insights I have learned from my mentorship. I believe there are important lessons to be learned from mentors that can make programming a much better experience.
Python Data Science 101 - how mentoring helped me get from raw data to SKLearn by Ben Reid By: Ben Reid
Date: Jan. 21, 2015, 7 p.m.
Ben will be talking about his experience getting started with Python Data Science using pandas and sci-kit learn, with Don's assistance, via the Chipy mentoring pilot program. Don is an Independent Technology Consultant, iPhone Developer and Software Architect and currently consulting with clients using Hadoop. Ben is a Senior Business Development Manager at Orbitz Worldwide and is a self taught programmer. Don is @dondrake on Twitter and Ben can be reached at https://www.linkedin.com/in/reidbenj
MM - Japhy/Sebastian - Mining and charting By: Japhy Bartlett
Date: Jan. 21, 2015, 7 p.m.
We'll go over how to set up a daemon for mining public data using tornado, then loading that data into some web based charts.
A lightning look at O'Reilly's Python books By: Tanya Schlusser
Date: Dec. 11, 2014, 8 p.m.
Slides Link
Wouldn't it be awesome if ChiPy wrote its own book? We'd be able to get BEvERages for weeks, maybe months on the royalty! If so, we'd need to see what's already out there. This lightning talk takes a look at O'Reilly's Python books using requests and BeautifulSoup, with a little of scipy's hierarchical clustering on the book descriptions. It is presented in an iPython notebook.
Python For Humans By: Kenneth Reitz
Date: Dec. 11, 2014, 7 p.m.
Hidden Markov Models to improve activity recognition in patients with spinal cord injury By: Asma Mehjabeen
Date: Nov. 13, 2014, 7 p.m.
Fitness tracking is great for calories and steps, but similar sensors are capable of reporting much more about how we move throughout the day. This is especially important in assessing the quality of movement for those with limited mobility. Doctors often want to know more detail about patient behavior after therapy to select and adjust the appropriate intervention. Using machine learning on wearable accelerometer signals, we estimate the activities patients with incomplete spinal cord injury are performing. By combining windowed classifier estimates over time using a hidden markov model, we show how error rates can be significantly decreased, which brings more detailed assessments of patient activity closer to a clinical reality.
Innate learning: training the brain before the eyes open By: Isaac Adorno
Date: Nov. 13, 2014, 7 p.m.
Amorphous, blob-like patterns of neural activity form and move over the eye during visual development in animals. Why do such patterns exist? We show that these patterns are this way to better prepare the visual system for natural vision. Essentially, these are movies played in the eyes to refine the visual system before the eyes even open. We use python to model the developing visual system, produce an efficient code based on those patterns, and show how that code matches what is seen biologically. In this way, we show that during your early development you are learning from innately generated patterns - a unique twist in the debates of nature and nurture.
Write Pretty Code By: Brian Ray
Date: Oct. 9, 2014, 8 p.m.
Journey into the world of poorly formatted code to beautiful written pep8 styled goodness.
Data Science Pipeline in Python By: Kevin Goetsch
Date: Oct. 9, 2014, 7 p.m.
In my view, the core of Data Science is the development of predictive models (recommendation engines, fraud detection, churn prediction, etc.). While predictive models can be built in a number of languages I choose to do my work in Python because the Data Science Pipeline is more than just building models. I'll talk about the larger model development process and how I use Python to automate and document my work.
Automated testing with the robot framework By: Bryan Oakley
Date: Sept. 11, 2014, 7 p.m.
Robot framework (robotframework.org) is an automated acceptance testing framework written in python. It can be used for a wide range of testing activities, from web, mobile and desktop UI testing, to database testing, RESTful and SOAP services, and much more. Bryan will give a brief overview, do some demonstrations, and answer questions.
Conservation Institute By: *Varun Chandola, Nadya Calderon, Scott Cambo, Christopher Lazarus, Raphael Stern
Date: Aug. 14, 2014, 7 p.m.
Conservation International (CI) is a non-profit organization that works to protect nature through scientific research and partnerships with communities, industry, and governments. A key aspect for evaluating the impact of conservation projects is to account for natural capital – ecosystem goods and services, such as fresh water, flood control, agriculture, and forest products.
Nurse-Family Partnership By: *Young-Jin Kim, Sarah Abraham, Jeff Lockhart, Sarah Tan, Rafael Turner
Date: Aug. 14, 2014, 9 p.m.
Young, low-income, first-time mothers and their babies often face dramatically increased risks to their health, education, and economic self-sufficiency. Nurse-Family Partnership (NFP), a national nonprofit organization, intervenes by pairing these mothers with specially-trained, registered nurses. Expectant mothers receive regular home visits from pregnancy until the baby is two years old. The result: healthier pregnancies, more stable families, and better developmental outcomes for children.
Mexico By: *Ben Yuhas, Julius Adebayo, Nick Eng, Eric Potash, Layla Pournajaf
Date: Aug. 14, 2014, 8:30 p.m.
The maternal deaths in Mexico from pregnancy, childbirth or postpartum complications have decreased from 89 deaths per 100,000 live births in 1990 to 43 in 2011. Despite this improvement, the rate of decline has significantly slowed and Mexico is not on track to achieve its Millennium Development Goal of reducing maternal mortality 75% by 2015.
Sunlight Foundation By: *Varun Chandola, Nadya Calderon, Scott Cambo, Christopher Lazarus, Raphael Stern
Date: Aug. 14, 2014, 8 p.m.
Government legislation is not designed for readability, and their volumes of text are not easily analyzed. Advocacy and research groups would like a way to digest bills quickly, filtering out the bureaucratic jargon and leaving the important details. The Sunlight Foundation is a nonpartisan nonprofit that uses technology to make governments more accountable. Their API for federal bills are valuable streams of legislative text that can be used for analysis given the right tools.
World Bank By: *Eric Rozier, Jeff Alstott, Dylan Fitzpatrick, Carlos Petricioli, Misha Teplitskiy
Date: Aug. 14, 2014, 7:30 p.m.
The World Bank Group lends billions of dollars every year to fund large infrastructure projects around the globe. Project-related contracts are awarded to companies and entities via open and competitive bidding processes. Such processes can sometimes be subject to collusion and corruption risks.
Webhooks @ Braintree By: Brian Lesperance
Date: July 10, 2014, 7 p.m.
At Braintree, we use Tornado to send thousands of simultaneous webhook requests and Pika to pull incoming webhooks from RabbitMQ. Learn about how we've set it up and problems we've had to overcome with this approach.
Engineering at Groupon - Beyond the Daily Deal By: Tyler Jennings
Date: June 12, 2014, 7 p.m.
Tyler Jennings, Director of Engineering at Groupon, will be providing a high level overview of the unique problems our domain presents and the systems we've built to overcome them.
You Down With EPP? Yeah, You Know Me! By: Jason Wirth
Date: June 12, 2014, 7 p.m.
You Down With EPP? Yeah, You Know Me! In this talk I'll discuss the Embarrassing Parallel Problems and introduce the basics of GPU computing with Python.
Computations comparisons between pure Python vs using numpy, or PyPy, or a C extension... By: Brad Martsberger
Date: June 12, 2014, 7 p.m.
Brad will talk on computation comparisons of collection of tools "Logistic Map Bifurcation Diagram" (https://github.com/martsberger/LogisticMapBifurcationDiagram) for creating pretty images of the bifurcation diagram of the logistic map.
An IRC Connection: Implementation and Bot By: Aaron Brady
Date: May 8, 2014, 7:50 a.m.
Slides Link
IRC is a protocol for text exchanges with multiple recipients with publish/subscribe capabilities. A basic program that interacts with an IRC server is easy to make, but becomes more difficult with additional functionality. The task involves a few domains: sockets, parsing, and a multi-way mapping object for the state. We take a look at 4 custom modules to get it done: Multi-connection dispatch, Raw to dict, Connection model, and Relation; plus one for "main" for the bot itself.
PyCon Lightning Talks By: Jason Wirth
Date: May 8, 2014, 7:25 p.m.
Let's go over what people saw at PyCon
DJ'ing our site - How & why we replatformed to Django By: Jake Kreider
Date: May 8, 2014, 7 a.m.
We'd like to discuss Zoro’s adoption of Django for our main website — What the key motivators were, the result, and lessons learned from the experience.
How to teach programming to novices By: John Blischak
Date: March 13, 2014, 8 p.m.
Starting Over From Scratch By: Malcolm Newsome
Date: March 13, 2014, 7 p.m.
Often developers get too attached to the code that they write.  So much so that we dread losing it.  But, what happens when you intentionally delete code and rewrite it?  You might be surprised at the result.
Simple Websockets in Flask By: Daniel Hodges
Date: March 13, 2014, 7:45 p.m.
Using flask, websockets, and redis to make a simple multi-user drawing surface in D3.
R for Python Programmers By: John Blischak
Date: March 13, 2014, 9 p.m.
Curiosity.com By: Christopher Coté
Date: Feb. 6, 2014, 7 p.m.
I am Director of Engineering for Discovery Communications Emerging Business and Strategy team. We just relaunched Curiosity.com. We use Python all over the place along with MongoDB/Redis/ElasticSearch The site lives within AWS utilizing several of their services. Including EC2, ELB, Route53, Cloudwatch, S3 I would like to discuss our overall architecture and our use/love of Python. And answer any questions on architecture/scalability/process/code.
Lexical Graphs with Natural Language Processing using NLTK By: Brian Ray
Date: Jan. 9, 2014, 7:02 p.m.
Brian will talk about his experiences using Python and NLTK http://nltk.org/ to run language comparisons to generate lexical difference graphs like the one mentioned in the "Lexical Distance Among the Languages of Europe" article. http://bit.ly/1cS46Ba The focus will be on the NLTK and how its internals work to process a language. This talk will be his best one ever.
Garbage Collection w/ Ref. Cycles By: Aaron Brady
Date: Jan. 9, 2014, 8 p.m.
Slides Link
Reference counting is very useful but it has an odd problem. We employ a technique from graphs to approach it. The solution works but it's a bit slow.
There were 986 roadway fatalities in Illinois in 2013. Where's the data? By: Nick Bennett
Date: Jan. 9, 2014, 9 p.m.
Seen on garish LED roadway signs all around Chicago on New Year's Eve, 2013: 986 TRAFFIC DEATHS IN 2013. It leads to many questions: On what roads? When did the accidents happen? What do we do now? I'm scared to drive. I will talk about purging my fears by finding the data to answer some of those questions. http://tothebeat.github.io/fatal-car-crashes/ This talk will involve PythonAnywhere, IPython, a module that's not even on PyPi (dbfpy), searching for and finding open government data, CartoDB, Google Fusion Tables, csv, and maybe Pandas. Rest assured, there will be no graphic photos.
A Visual Guide To Pandas By: Jason Wirth
Date: Dec. 12, 2013, 7:40 p.m.
Pandas is the data-munging Swiss Army knife of the Python world. Often you know how your data should look but it's not so obvious how to get there, so I'll present a visual approach to learning the library and data manipulation.
The Chicago Process: How Braintree Develops Software By: Adam Forsyth
Date: Dec. 12, 2013, 7:01 p.m.
Braintree needs to be highly available and secure, while still maintaining a rapid development pace and strict backwards compatibility. In order to achieve that, we use what has become known as the "Chicago Process". This involves pairing, strict TDD, a team structure, and weekly iterations, all to empower the devs to make decisions and get work of a high quality done while avoiding siloing.
Storm (with python (and a side of clojure)) By: Philip Doctor
Date: Dec. 12, 2013, 8 p.m.
A walking tour of Storm, what it is, what you can do, and how you can use it with python.
Monoids in Python By: Philip Doctor
Date: Nov. 14, 2013, 7:44 p.m.
Monoids are largely badly explained, but actually quite beautiful. I would like to take a brief tour of what a monoid is and how they can help out with mundane every day tasks in python.
PyData Recap Lightning Talk By: Jason Wirth
Date: Nov. 14, 2013, 11 p.m.
Recap of last weeks PyData conference in NYC.
What happened at #aaronswhack? By: Sheila Miguez
Date: Nov. 14, 2013, 9 p.m.
Slides Link
Many python programmers showed up to participate in the Chicago #aaronswhack. Here's a list of what they worked on, and here are pointers to local projects as well as worldwide projects.
Python heart Open Source Hardware By: Paul Ebreo
Date: Nov. 14, 2013, 10 p.m.
Open Source Hardware is going to change the world. But the hardware is still going to need software to control it. Can Python take the lead and become the de facto language of open source hardware control? Paul Ebreo talks about the three keys to Python's success in open hardware.
Measure It By: Peter Fein
Date: Nov. 14, 2013, 7 p.m.
measure_it provides timing and counting for iterators (and other code segments). https://measure_it.readthedocs.org/en/latest/
CivicLab and Between the Bars By: Benjamin Sugar
Date: Nov. 14, 2013, 7:33 p.m.
In this talk, I will present on a slice of the maker movement called "civic making" and a new space that has opened up in Chicago to encourage this type creation, CivicLab. As an example of "civic making" I will discuss Between the Bars, a paper based blogging platform for those who are incarcerated, built in Django. I will also discuss our choice in framework and the pros/cons of our approach.
Finite State Machine: fysom By: Brian Ray
Date: Oct. 10, 2013, 8:30 p.m.
Rendering Data with D3 By: Japhy Bartlett
Date: Oct. 10, 2013, 7:30 p.m.
How to begin rendering data with D3, by way of a simple python web server.
5-Minutes Of Pandas [Lightning Talk] By: Jason Wirth
Date: Oct. 10, 2013, 8 p.m.
A lighting talk introducing Pandas, a library for data manipulating and overall munging goodness. If you do stuff with data and you don't use Pandas, you're doing it all wrong.
Post djangocon: An overview of edX By: yarko
Date: Sept. 12, 2013, 7 p.m.
edx is a major django application serving huge numbers of students for MIT, Harvard, Stanford, Berkely, and more. - A brief history of Computer-Based Instruction (python has a role); - incomplete survey of current open-source CBI; - edX: how's it different / what's it's rough structure, what (besides django/python) is involved; - edX: hacking the platform (django development); - edX: hacking courses; a deployment-level VM, and how to get started there; - finally: future topics: deployment; what this can't do (maybe) and why; - wrapup: call for interest & edx project night(s); I'll try to have some USBs for anyone who want to try one of the edX VMs during the talk
What's Love Got to do with It? / Love: for techies By: yarko
Date: Sept. 12, 2013, 7:40 p.m.
What you think Love is - is (probably) wrong. The correct metaphor / definition for live will make much more sense to the software person. In fact, it will help with team building and design too. Yup. Grab a beer. I'll tell you a story about how this evolved (turing machine example), how and where evolution selected it, and why it works - and how it works for approaching problems (design) too. Then I'll lay out the "api" (functional description). Don't take it too seriously. You couldn't have known. Now you will. Cheers!
Set it, and forget it! Auto Scale on Rackspace By: Brian Curtin
Date: Sept. 12, 2013, 7:30 p.m.
Rackspace is rolling out a new service to allow your cloud to scale on its own, called Auto Scale. Built on Monitoring, Auto Scale allows you to grow or shrink your fleet of resources as demand changes. pyrax, a Python package for working with OpenStack-based clouds like Rackspace's, just released Auto Scale and Monitoring support with version 1.5.0. I'll show how you can use pyrax to deploy servers and automatically add or remove them based on their usage.
Cluster Fun By: Joseph Curtin
Date: Aug. 1, 2013, 8 p.m.
An overview of deploying to a cloud solution while retaining the ability to deploy to a raspberry pi. Automate the instantiation of your cluster no matter the hardware. Utilizing libcloud, we'll talk to AWS and Rackspace. Utilizing Paramiko we'll talk to a Raspberry-Pi, AWS, and Rackspace. - Source code and slides will be available at the start of the presentation. https://github.com/jbcurtin/cedar
Lightening talks on Summer Fellows for "Data Science for Social Good"
Date: Aug. 1, 2013, 7:04 p.m.
4-6 presentations 5-7 minutes each from the summer fellowship program lead by The University of Chicago on "data science for social good" (ref http://dssg.io) Come hear from the 40 fellows (mostly grad students and some undergrads in CS and stats) from around the country. Most of the work is done in Python and partnering with non profits and government organizations
Asynchronous I/O in Python 3 By: Feihong Hsu
Date: July 11, 2013, 7:30 p.m.
I'm going to talk about PEP 3156 and go over basic usage of the reference implementation, codenamed Tulip.
A SciPy recap: Tracking history and provenance with Sumatra By: Sheila Miguez
Date: July 11, 2013, 8:01 p.m.
This lightning talk recaps a [talk on Sumatra](http://pyvideo.org/video/2039/using-sumatra-to-manage-numerical-simulations-sc) from the reproducible science track at SciPy2013.
ipython / notebook demo By: Jason Wirth
Date: July 11, 2013, 8 p.m.
ipython was a big focus of Scipy, Fernando gave a keynote, Brian gave a talk, and there was a tutorial. ipython appeals to a broad audience from beginners to advanced users. IDLE is awful and I basically learned Python using iPython. Presenter will touch on the powerful features and extensibility for advanced users.
Ultimate Language Shootout IV: CoffeeScript By: Feihong Hsu
Date: June 13, 2013, 7:02 p.m.
A brief introduction to CoffeeScript.
Ultimate Language Shootout IV: Ruby By: Ross Heflin
Date: June 13, 2013, 7:04 p.m.
Ruby, what you need to know
Ultimate Language Shootout IV: Haskell or: How a List Comprehension Is Like a Burrito By: Greg Kettler
Date: June 13, 2013, 7:01 p.m.
It's a compiled, statically typed, lazy, purely functional programming language. About as far as possible from Python? Not quite. The languages have a lot in common and Python has already borrowed a few tricks from Haskell.
Ultimate Language Shootout IV: Go: come drink the delicious kool-aid By: David Sutton
Date: June 13, 2013, 7:05 p.m.
From the makers of the wildly successful Plan 9 operating system and B programming language. Go is google's stab at a systems programming.
Ultimate Language Shootout IV: C# is slightly better than you might imagine By: Philip Doctor
Date: June 13, 2013, 7:10 p.m.
If you find yourself accidentally writing c#, you can still have some fun.
Ultimate Language Shootout IV: QUASI By: Randy Baxley
Date: June 13, 2013, 7:01 p.m.
1977 - A language, the description of which was handed to me on about one hundred and fifty mimeographed eight and one half by eleven sheets. Robert Sibley handed it to the class to use as our compiler project.
In-project virtualenvs By: Christopher Webber
Date: May 9, 2013, 8 p.m.
Who saved The Onion, from being hacked by "Syrian Electronic Army" By: Sean Bloomfield
Date: May 9, 2013, 8:10 p.m.
Well, this isn't at all Python related (or even all that technical), but at The Onion, we recently had a little run-in with the "hackers" from the "Syrian Electronic Army", and could talk about some lessons learned from that, if there's any interest.
Hy: A Lisp that transforms itself into the ython AST. By: Christopher Webber
Date: May 9, 2013, 7:45 p.m.
apprenticeship model By: JP Bader
Date: May 9, 2013, 8:20 p.m.
Pythonic protégés.
Threadless Loves Python By: Mike Steder
Date: April 11, 2013, 7 p.m.
In the last year the Threadless engineering department has almost completely changed from PHP and MySQL to Python and MongoDB. I would like to do a brief overview of how we use Python today which will cover our replatformed website, our API, and our internal message queuing system.
SXSW Interactive 2013 By: Adam Forsyth
Date: April 11, 2013, 7 p.m.
- Themes - Keynotes - Chicago Tech @ SXSW - Other Highlights - Q&A
Concurrency in Python and other Languages By: Daniel Griffin
Date: April 11, 2013, 7 p.m.
- 1 minute pitch about OpDemand and what we do. - Processing HTTP requests with Twisted. - Dealing with blocking code in Twisted (couchdb-python and pika). - Doing long running work with Celery from Twisted. - Communicating between web workers with ZMQ. - Writing code that can be concurrent.
Using PyJnius to talk to Android devices By: Matt Dorn
Date: March 21, 2013, 8 p.m.
Overview of a library that facilitates communication with Android devices via Python -- depending on interest, could include an overview of other Python/Java libraries and/or other ways to use Python with Android.
Python Deployments at The Onion (and elsewhere) By: Chris Sinchok
Date: March 21, 2013, 7:20 p.m.
Chris will cover various Python deployment strategies and technologies, ranging from the naïve (git pull) to the more robust (fabric, capistrano) to the "Enterprise" (Python native package deployments, etc). In order to illustrate these different strategies and technologies, he will take examples from my past projects, and the constantly-evolving Onion deploy process.
164 talks