Past Meetings • Recent Topics


Thu, May 12 2016 at 06:00 PM at Braintree

The 'collections' module
(0:10:00 Minutes)
By: Phil Robare

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.
pyStan: Bayesian Inference for Fun and Profit
(0:30:00 Minutes)
By: Stephen Hoover
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
(0:20:00 Minutes)
By: Matt Erickson

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.
160 Python enthusiasts attended this meeting.


Thu, Apr 14 2016 at 06:00 PM at Akuna Capital

module of the month - usaddress
(0:10:00 Minutes)
By: Cathy Deng

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.
Hacking Bokeh
(0:20:00 Minutes)
By: Brian Ray

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.
Multiple System Failure: A case study in debugging
(0:30:00 Minutes)
By: Adam Forsyth

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.
155 Python enthusiasts attended this meeting.


Thu, Mar 10 2016 at 07:00 PM at Bank of America Plaza

ChiPy Python Mentorship
(0:07:00 Minutes)
By: Tathagata
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/
Job Market
(0:15:00 Minutes)
By: Jerry Dumblauskas

Let's see what's happening in the Python Job market in Chicago!
The wonder and the horror of the mock module
(0:05:00 Minutes)
By: Stephen Hoover

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-based data science to understand knowledge discovery and expertise: A science perspective
(0:45:00 Minutes)
By: Daniel E. Acuna

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.
158 Python enthusiasts attended this meeting.


Thu, Feb 11 2016 at 07:00 PM at "1871"

Python-Powered Data Science at Civis
(0:07:00 Minutes)
By: Stephen Hoover

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 Datascope Analytics
(0:07:00 Minutes)
By: Brian Lange

How Datascope Analytics uses Python to improve business and society through science and design.
Python at Deloitte
(0:07:00 Minutes)
By: Brian Ray

How Deloitte uses Python within the Enterprise Science Team.
Python at Vokal
(0:07:00 Minutes)
By: Chris Foresman, Adam Bain

How Vokal uses Python with Adam Bain and Chris Foresman
Python at Modest
(0:07:00 Minutes)
By: Emily Anderson, Mark Ashton

We will describe how Modest, recently acquired by Braintree (PayPal) uses Python.
Python at ShiftGig
(0:07:00 Minutes)
By: Brian Eagan, Tyler Hendrickson

How ShiftGig, which connects the right people with the right job, uses Python.
Python at Cofactor
(0:07:00 Minutes)
By: Hector Rios

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 OptionsAway
(0:07:00 Minutes)
By: Tim Saylor

How OptionsAway, an airfare lock startup, uses Python.
Python at Credit Suisse
(0:07:00 Minutes)
By: David Matsumura

How Credit Suisse uses Python.
How SpotHero uses Python
(0:07:00 Minutes)
By: Cezar Jenkins

SpotHero is one of the leading online parking reservation companies in the country. Come see how were using Python to make that happen.
193 Python enthusiasts attended this meeting.


Thu, Jan 14 2016 at 07:00 PM at gogoair

Constructing a risk metric from google query data
(0:07:00 Minutes)
By: Michael Tamillow

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.
Using Python for Kaggle competitions
(0:05:00 Minutes)
By: Hana Lee

(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
(0:07:00 Minutes)
By: Ellie Anderson

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.
Dustin Shapiro's Python 101 Menteeship!
(0:05:00 Minutes)
By: Dustin Shapiro

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!
Web App for Caregivers
(0:05:00 Minutes)
By: Shannon Cochran

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.
Shuang Qiu
(0:05:00 Minutes)
By: Shuang Qiu

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
*half time special* imposter syndrome.
(0:10:00 Minutes)
By: David Beazley

Seeing as this winter marks 20 years of my using Python, I might be inclined to say a few short words about imposter syndrome.
108 Python enthusiasts attended this meeting.