Xpresso: an extensible and composable ASGI framework By: Adrian Garcia Badaracco
Date: April 14, 2022, 6 p.m.

Xpresso is an extensible and composable ASGI web framework, inspired by FastAPI.

It improves on FastAPI by decoupling the dependency injection from the request/response paradigm and decoupling the framework from Pydantic, using PEP 593 annotations. This enables many nice features, like a more flexible dependency injection system, concurrent execution of dependencies and much more customizable processing of request parameters and bodies.

Introduction to GeoSpatial Data with Python By: Craig Barnes
Date: April 14, 2022, 6 p.m.

Introduction to processing and serving Geospatial Data using Python. The talk will introduce relevant background information, common data formats, and how to create, modify and serve geospatial data. 

Everyday Design Patterns: Observer Pattern By: Aly Sivji
Date: March 10, 2022, 6 p.m.

The Observer Pattern enables us to design event-driven systems using loosely coupled components. In this talk, we will learn how, when, and why to use this pattern; we will explore how popular PyPI packages use the pattern; and, we will design a decorator-based Observer to process GitHub events.

Every Way SpotHero Uses Python By: James Lamb
Date: March 10, 2022, 6 p.m.

We love Python at SpotHero. In this talk, attendees will learn all the ways that SpotHero uses Python. Libraries, web services, machine learning, data processing, and more! After an overview of these use cases, the talk concludes with a set of practical suggestions for effective use of Python in different settings.

The primary goal of the talk is to expand attendees' imaginations around the wide range of use cases that Python can be a good fit for in a consumer-facing company.

Nerf Blaster Turret powered by Python By: Josh Martin
Date: Feb. 10, 2022, 6 p.m.

In this talk, Josh will introduce you into how to choose electrical components for your next project. How to program embedded devices in Python like Raspberry PIs. How to do facial recognition with openCV.  Most importantly, why you should watch squid games, the joys of making memes and a full automatic nerf blaster.

SSH Can Do That? All the Things You Didn't Know About SSH By: Mason Egger
Date: Jan. 13, 2022, 6 p.m.

Need to access resources on a closed network? Create a secure tunnel to an insecure site? SSH has you covered. Many of us use SSH every day and only scratch the surface. In this talk, we'll dive into little-known features of SSH such as user and host configurations, security measures, tunnels, and more. Attendees will learn how to configure SSH to meet their needs, how to utilize SSH to access and protect resources, and realize their everyday tools, like SSH, have much more functionality that is perceived.

mTLS with Python By: Joe Jasinski
Date: Jan. 13, 2022, 6 p.m.

While TLS is foundational to our security and privacy on the internet, Mutual TLS (mTLS) takes TLS to a new level by allowing both client and server to authenticate each other. This talk will provide a brief overview of TLS, describe how mTLS builds upon TLS, will show a practical Python example of how mTLS works, and discuss how service meshes and other technologies use mTLS to secure communication between services.

Machine learning: pitfalls and opportunities By: Paul Ebreo
Date: Dec. 9, 2021, 6 p.m.

Part 1a. What is ai / ml?

Part 1b. Limitations of current approach

The current way of doing ML is necessary but limited. If we truly want "intelligence", we need a new way of doing things i.e. an AI that can think beyond its training, an AI that requires much less data, and an AI that is much more adaptable than current AI. Here we give case studies of current AI systems and biology highlighting the limitations and potential of what a real intelligent system looks like.

Part 1c. The upcoming AI / ML winter

Part 2a. What does python have to do  with ML?

Part 2b. The new approach to ML

What use is Python at all? Is it possible to conceptualize ML with or without python? In this section, I explain how Python is used to conceive AI/ML. Then I propose a new way of approaching the problem. I explain the various ways Python is used in ML from deep learning, reinforcement learning etc.

Part 3. A new way of thinking

In this section, I propose a different way of thinking of the problem and propose a solution.

Using Python to Accelerate Data Science at Nielsen By: Jordan Bettis
Date: Nov. 11, 2021, 7:15 p.m.

Nielsen is a global leader in audience insights, data and analytics, shaping the future of media. Nielsen uses Python to bridge the gap between model development, validation and deployment into production data pipelines to accelerate creation and evolution of analytics products.

Python at Narrative Science - Telling stories at scale By: Santiago Santana
Date: Nov. 11, 2021, 6:45 p.m.

Narrative Science is a data storytelling company that has been dynamically writing stories and reports for over a decade

This talk will go over how we are using Python and its rich ecosystem to move towards a microservices architecture that will create a more scalable and fault tolerant product.

Python at Zoro By: Joe Neylon
Date: Nov. 11, 2021, 6:15 p.m.

Zoro is an online distributor of products for B2B customers, focused on helping small businesses easily find what they need to grow and maintain their businesses. Today, we have over eight million products available—and that number is expected to keep growing. We work with third-party suppliers to provide products and fulfill orders for our customers.

Zoro uses Python with Django for its ecomerce site, as well as for data science, ETLs, and microservices.

Python at NuMat Technologies - Hacking for Cleaner Air By: Patrick Fuller
Date: Nov. 11, 2021, 6:30 p.m.

NuMat Technologies is a team of chemists, chemical engineers, and computer scientists developing advanced materials to remove toxic chemicals and greenhouse gases from air, water, and more.

Founded by computer scientists and working with a new material class that lends itself well to computational design, NuMat puts computation at the forefront of the business. Whether this is developing automated material design "recommendation engines", building robotics for high-throughput experimentation, or maintaining our in-house enterprise resource planning applications, NuMat's computational team touches every aspect of the company.

Expect to see async and advanced communication protocols in robotics, Django+SQL with our ERP applications, and HPC management software like Dask and Jupyter in our computational material design.

Python at JFrog By: Daniel Keler
Date: Nov. 11, 2021, 6 p.m.

At JFrog, we are making endless software versions a thing of the past, with liquid software that flows continuously and automatically from build all the way to deployment.  With this in mind, we’ve developed the world’s first universal artifact management platform, ushering in a new era in DevOps – Continuous Updates. Ten years later, with thousands of customers, and millions of users globally, JFrog has become the “Database of DevOps” and de-facto standard in release and update management.

JFrog embraces the Python language for multiple uses cases and technology solutions including provisioning machines, tooling for Pipelines, creating machine learning models, securing Python modules, and even Python-based micro services in the JFrog Platform.

Financial Dashboard on Streamlit By: Shashank Katyayan
Date: Oct. 14, 2021, 6 p.m.

Easy to build Python Dashboards using Financial data APIs

Speeding up builds with Asynchronous Tests By: Meygha Machado
Date: Oct. 14, 2021, 6 p.m.

Automated tests are a great way to iterate fast and ensure features didn't break. This talk discusses how to speed up your builds and dev cycle even more by running tests asynchronously using a pytest plugin called asyncio-cooperative.

Date: Aug. 12, 2021, 6 p.m.

In Today’s world, AI has become an essential tool for achieving and creating the unthinkable. It is helping in creating innovative solutions for almost every industry there is. In the wake of this ever-growing demand for computerized intelligence, what constitutes an active research domain is how AI-based intelligence can be interpreted and utilized by HR (Human Resources) from predictive analysis to automation. As the HR department is solely responsible for recruiting and bringing valuable talent to the industry, it becomes essential that this task is done with maximum efficiency. Through this project, we intend to predict which employee would prefer a job change and which employee would stay in a company and help assess the input resources required to put in an employee. This presentation will take you through the principles of using python, opinion mining, and various widely used classifiers, namely Random Forest (RF), Cat Boost Classifier, Support Vector Machine (SVM), and Naïve Bayes (NB). 

Production-ready Machine Learning By: Zax Rosenberg
Date: Aug. 12, 2021, 6 p.m.

Building machine learning (ML) models is faster and easier now than ever before. The proliferation of open-source libraries means data scientists can leverage cutting-edge pre-trained models in just a few lines of code. Yet it remains true that most ML models never make it to production. Why? Because making it to production (and staying in production) are about more than just model and code quality. In particular, this talk will discuss how MLOps can greatly accelerate and increase the chances of model success.

Specifically, the talk will walk through the full ML lifecycle and answer: What is MLOps? Why is it important? How can MLOps infrastructure be set up quickly, easily, and with open source tools? How can the system be designed in a user-friendly way, but without too much magic? How can user adoption be accelerated?

While its expected that data-science-related professionals will garner the most value from this talk, no prior MLOps/ML background is required to understand the contents of the talk.

Managing the Test Data Nightmare By: Andrew Knight
Date: July 8, 2021, 6 p.m.

Test data for automated tests can be a nightmare to manage. Data must be prepped in advance, loaded before testing, and cleaned up afterwards. Sometimes, teams don't have much control over the data in their systems under test—it's just dropped in, and it can change arbitrarily. Hard-coding values into tests that reference system tests can make the tests brittle, especially when running tests in different environments. In this talk, I'll teach strategies for managing each type of test data: test case variations, test control inputs, config metadata, and product state. We will cover how to "discover" test data instead of hard-coding it, how to pass inputs into automation (including secrets like passwords), and how to manage data in the system. After this talk, you will wake up from the nightmare and handle test data cleanly and efficiently like a pro!

Bootstrapping your Local Python Environment By: Calvin Hendryx-Parker
Date: July 8, 2021, 6 p.m.
You cracked open your brand new Mac or Linux dream machine and low and behold, it has Python out-of-the-box and ready to roll… Or so you think? Maybe you want to get started doing Python development on Windows and see that you can grab Python easily from the Microsoft Store. Should you? Let’s talk about getting started with the end in mind and making sure your development computer doesn’t become the next superfund site We’ll quickly go through a tour of the various options such as pyenv, venv, virtualenv, conda and Docker as great ways to make sure you can develop in a sane environment.
Anvil: Full Stack Web with Nothing but Python By: Meredydd Luff
Date: June 10, 2021, 6 p.m.

Building a modern web app requires so much: HTML, CSS, JS, Python, SQL, React, Bootstrap, Webpack, Django... What if we could build a better abstraction?

In this talk, I'll introduce Anvil, a full-stack Python environment where everything is a Python object, from your UI components to your database rows. I'll walk you through how and why we constructed this new approach to the web.

We'll start with a question: Why is web programming hard? It's because your data takes so many forms: database rows, Python objects, JSON on REST, JS objects, HTML DOM, and finally pixels. Most of a web developer's job is translating between these awkwardly different representations. Frameworks like Django help, but now you have a stack of leaky abstractions: web frameworks, ORMs, JS frameworks, CSS frameworks, build tools... These frameworks help you go faster, but they double the amount you need to know!

So I'll show our stab at an answer: A framework where everything is a Python object, requests to the server are function calls, and Python is a browser-side language. I'll talk about running Python in the browser. I'll talk about full-stack autocompletion. There will even be live coding. 

Dangers of the Python Standard Library By: Andrew Scott
Date: April 8, 2021, 6 p.m.

As a developer, you are the first line when it comes to security for any products you may be building. There is often a misconception that all software security vulnerabilities are due to misconfigurations, using unmaintained open source libraries, using "insecure" languages, or by making dumb mistakes like hard-coding passwords. In actuality, it can be very easy to make potentially extreme security mistakes even only using built-in functions and libraries bundled with the latest version of Python. This talk will cover a number of these potential security mistakes that can be all too easy to make.

Getting up to speed with Dask By: Aaron Richter
Date: April 8, 2021, 6 p.m.

Dask is a parallel computing library for Python people. This talk will be a gentle introduction to Dask, showing how you can improve the speed of data science code on your laptop with a simple "pip install". Then we will use the same code to process big data on a cluster of machines. We will be going through an end-to-end data science pipeline, from ETL and exploratory analysis to machine learning model training and scoring.

We will cover:
- Example using publicly available data and single-node Python
- Pandas for data cleaning/transformation
- Scikit-learn for machine learning
- How to parallelize this workflow on a laptop and then a cluster using Dask
- Distributed model training
- Distributed inference/scoring

Adding structure to a sea of chaos: a principled approach to authorization using Python + SQLAlchemy By: Sam Scott
Date: March 11, 2021, 6 p.m.

Authorization is an unstructured problem. Writing code to decide who can do what in your app can cover a broad set of cases. The most structure that typically gets applied to this problem area is a set of if statements and roles, but in reality, there are a lot more patterns and structure that we can apply. oso is an open source system for building authorization into applications. It's a bit like SQLAlchemy in that it provides a structured approached to authorization, much like SQLAlchemy does for data modeling and access. In this talk, oso cofounder/CTO, Sam Scott, will provide a mental model for authorization and show how to apply it using oso, Python and SQLAlchemy.


Some useful links:

DevOps for developers (or maybe against them?!) By: Baruch Sadogursky
Date: March 11, 2021, 6 p.m.

"DevOps" is the operations people’s crafty plan to make developers do other people's work, but we are smart enough to see right through this naive rebranding trick! Baruch suggests you think about it: we, the developers, have written all the code. It passes all the tests; it obviously works, and works well (Are we a little proud? We are!); so we are DONE. Now, out of the blue, a bunch of "thought leaders" (all with an operations background, mind you!) are trying to tell us that we have to learn YAML, Docker, Kubernetes, and Terraform to deploy our software because suddenly it is our concern?! In this talk, we'll discuss why developers do or don’t need DevOps. We'll consider arguments made by DevOps visionaries and see whether they hold water. Hopefully, by the end of the talk, we'll understand whether DevOps really helps developers to deploy better code to production more often, or if it is just another scam made up by marketing and evangelists.

ChiPy Website Redesign By: Raymond Berg
Date: Feb. 11, 2021, 6 p.m.

The ChiPy Web Guild recently completed a one-week sprint to rehaul the ChiPy Homepage. In this talk we'll talk about what, why, and how the redesign took place.

The Enters and Exits of Context Managers By: Mason Egger
Date: Feb. 11, 2021, 6 p.m.

Have you ever opened a file using the with keyword in Python? That little keyword is one of the many fascinating parts of the Python programming language, the Context Manager. The Python Context Manager is a tool that allows the programmer to reliably create and tear down temporary contexts within a program. This allows programmers to reduce duplicate code, improving the maintainability and reliability of the code. This talk will cover all things Context Manager, from what they are, how to build them, when to use them, and more.


Making Scaled Data Science Work For People, And Not The Other Way Around By: Hugo Bowne-Anderson
Date: Jan. 14, 2021, 6 p.m.

Data science is too often discussed as a technical discipline, rather than a social and cultural one. But the role of data science is to both inform and automate decision-making processes, which require, in turn, humans to collaborate and communicate with each other and humans to collaborate with machines, both of which have key cultural and social dimensions. Why do so many executives feel that so little of the data work in their organizations actually delivers returns? How can we reduce friction in factoring the process of turning business questions into business answers through the intermediaries of data questions and data answers? What provisions need be in place to make sure that everybody is speaking enough of the same data languages to excel at their jobs? How do we promote data literacy throughout organizations while getting the job done? This talk is aimed at data professionals (and anybody else) who want to figure out how to establish healthy and productive data cultures in the workplace. I’ll conclude by interrogating the example of establishing the culture of modern distributed data science work in organizations and all the moving parts that need to be in place for it to function.

From Python To Rust By: Kevin Nasto
Date: Jan. 14, 2021, 6 p.m.

Ever been curious about the Rust programming language? This talk will describe the experience of going through the Advent of Code puzzles in Rust from the point of view of a Python user. Discover the alternatives to pip, functions and passing values, exception handling, and more.

First Time Advent of Coding By: Stephen Ianno
Date: Jan. 14, 2021, 6 p.m.

Recalling my experience doing Advent of Code for the first time. How being part of a small community of others completing each challenge really motivated me to complete each challenge myself. It was also really amazing being able to look at the solutions from other, more experienced participants and discuss the solutions through Slack. I learned a lot of really useful tricks and insight to going about challenging coding problems and it really helped prepare me for future technical interviews. It was also really great getting to interact with others during the pandemic.

How ChiPy Works: A Look Inside the Most Active Python Usergroup By: Raymond Berg
Date: Dec. 10, 2020, 6 p.m.

You've participated in our events, but who are we. With elections around and volunteering options in our virtual world, this will be an in depth look at ChiPy, its history, and its future. If you ever wanted to know more, hear about what we've been working on, or get is your chance!

Image processing firmware with Python, Raspberry Pi and Nvidia Jetson Nano By: James Barkley, Talish Barmare, and BinBin He
Date: Dec. 10, 2020, 6 p.m.

This talk will discuss a python implementation of image processing firmware for the rPi and Jetson Nano boards. The software architecture covers a camera frame grab -> image processing -> output loop as well as some machine learning models for feature detection, a Flask-based front end, and an OpenAPI-based Swagger interface and API design using connexion.

Venmo @ Graduation By: Josh Martin
Date: Nov. 12, 2020, 6 p.m.

In search of good memes, emojis, and a quick scheme to make fast cash: I decided to put an LED Matrix into my college graduation cap. While making some missteps along the way, I learned a lot of valuable lessons including how to retrieve data from websites easily and regularly (even if they do not want you to), sourcing and evaluating hardware components, and connecting everything using plain Python. I will describe my experience going from a complete beginner to an expert as I step into the next phase of my life, making my mother proud along the way.

zoneinfo: A stunning module of exceptional quality By: Paul Ganssle
Date: Nov. 12, 2020, 6 p.m.

Python 3.9 introduces the `zoneinfo` module, which brings concrete time zone support to the standard library. In this talk, I'll discuss the history of time zone support in Python, make the case for migrating your code to `zoneinfo`, and hopefully give you an understanding of everything you'll need to know to successfully make use of the new module.

Don't be beholden to your tools By: Dave Trollope
Date: Sept. 10, 2020, 6 p.m.

How KnowledgeHound innovated by breaking usage of existing tools to solve two immediate problems. A discussion of Django, SQLAlchemy, Pure Python, and Pandas.

Fun with Finite State Machines By: Aly Sivji
Date: Sept. 10, 2020, 6 p.m.

Finite State Machines (FSM) are tools we can use to model simple and complex workflows. In this (non-mathematical) talk, we will learn about FSMs and examine how they can be used to improve software design. We’ll finish by diving deep into a couple of Python implementations of FSMs. Full disclosure: one of the implementations is a library I created.

Snakes on a Car: Or, Over-engineering a Toy By: Kat Cosgrove
Date: Sept. 10, 2020, 6 p.m.

Like a lot of engineers, I like to tinker. I also like hardware hacking, video games, and over-engineering the hell out of something. When my team at work decided to build a proof of concept demonstrating the possibility of fast over-the-air updates for edge devices, we settled on using a car as the example of an edge device. It’s flashy, you know? This also presented me with an opportunity to do all of the things I love, and call it work: build a self-driving RC car, and then let people race it around a track using a repurposed USB racewheel, a handful of open source tools, and whole lotta Python. DevOps, but make it fun.

Goodbye Print, Hello Debugger! By: Nina Zakharenko
Date: Aug. 13, 2020, 6 p.m.

Still debugging your code with print? Learn how to level up your ability to troubleshoot complex code situations by using the power of a fully-featured debugger in this talk aimed at all levels of programming ability. Debuggers allow you to examine your program state, watch as the values of important variables change, and even modify the content of variables on the fly. Once I gave up using print to debug, my productivity as a programmer increased, and yours can too! I’ll showcase the variety of debugger tools available - from pdb, the simplest command line debugger that’s part of the standard library, to fancy graphical debuggers available in Python IDEs. Join me as we walk through real code together using debugger tools in a hands-on way to help us diagnose problems and bugs. The skills you’ll learn in this talk will allow you to quickly use these tools in your own code bases for fun, school, or work.

Principles Driven Development - How PursuedPyBear decides what's important. By: Piper Thunstrom
Date: Aug. 13, 2020, 6 p.m.

PursuedPyBear (ppb) is a Python game development library.

PPB started like many projects: “How do I make my life easier?” Then teachers started asking if it could be built for teaching CS. That started the project on a path to have an extreme focus on API design and education. This distills the concepts that the ppb community have decided matter for long term health of the project, and the technical principles that came out of it.

What the heck's a Pixel and the California Consumer Privacy Act (CCPA) By: Sree Prasad
Date: Aug. 13, 2020, 6 p.m.

Even though it's technically only applicable to residents of California, the California Consumer Privacy Act (CCPA) is a major step in comprehensive data privacy legislation in the US that affects every single person in the US's most populated state. I'll go over what's in the CCPA and why it matters as well as share how my team managed to meet all the requirements for compliance just in time for the new year (when the CCPA went into effect).

ChiPy Mentorship Returns By: Ben Xia-Reinert
Date: July 9, 2020, 6 p.m.

The new ChiPy Mentorship site is going live on July 4th. While the structured, 13-week ChiPy Mentorship program is not returning, this app will enable members of the Chicago Python community who wish to be mentees and mentors to find and connect with each other. In this talk, we will explain how the app was made, what we are looking to accomplish, and how you can be a part of it.

Ten Ways to Fizz Buzz By: Joel Grus
Date: July 9, 2020, 6 p.m.

Fizz Buzz is the following (simple) problem:

Print the numbers from 1 to 100, except that if the number is divisible by 3, instead print "fizz"; if the number is divisible by 5, instead print "buzz"; and if the number is divisible by 15, instead print "fizzbuzz".

My association with this problem began in 2016, when I wrote a blog post called Fizz Buzz in Tensorflow, the (possibly fictional) story of one such insulted programmer who decided to show up his interviewer by approaching Fizz Buzz as a deep learning problem. This post went modestly viral, and ever since then I have been seen as a thought leader in the Fizz Buzz space.

Accordingly, over the years I have come up with and/or collected various other stupid and/or clever ways of solving Fizz Buzz. I have not blogged about them, as I am not the sort of person who beats a joke to death, but occasionally I will tweet about them, and recently in response someone suggested that I write a book on "100 Ways of Writing Fizz Buzz in Python."

Now, I could probably come up with 100 ways of solving Fizz Buzz, but most of them would not be very interesting. Luckily for you, I was able to come up with 10 that are interesting in various ways, which I will barrel through in 15 minutes or less.

Introduction to AutoML By: Paco Nathan
Date: July 9, 2020, 6 p.m.

AutoML is a very active area of AI research in academia as well as R&D work in industry. The public cloud vendors each promote some form of AutoML service. Tech unicorns have been developing AutoML services for their data platforms. Many different open source projects are available, which provide interesting new approaches.

But what does AutoML mean? Ostensibly automated machine learning will help put ML capabilities into the hands of non-experts, help improve the efficiency of ML workflows, and accelerate AI research overall. While in the long-term AutoML services promise to automate the end-to-end process of applying ML in real-world business use cases, what are the capabilities and limitations in the near-term?

This talk surveys the landscape and history for projects and research efforts related to AutoML, looking beyond just hyperparameter optimization and considering the impact on end-to-end workflows and data science practices. We'll show sample code using different open source projects and provide pointers to online resources to learn more.

CoderHeroes + Code Your Dreams: Teaching every child to code, one app at a time By: Brianne Caplan
Date: June 11, 2020, 6 p.m.

Brianne Caplan is the CEO and Founder at CoderHeroes, a kid-centered “learn to code” program where kids ages 7- 18 team-up with other brave and aspiring coders to build world-changing apps. Its buy-one-give-one model means that families who pay for classes are helping to fund Code Your Dreams programs for students in underserved neighborhoods. Brianne will talk about her team's work in bringing culturally relevant coding programs to underserved youth in Chicago. She will speak about the equity challenges that exist, as well as the opportunities that exist for us all to make a difference.

How to run your favorite Python package in R By: Jessica Garson
Date: June 11, 2020, 6 p.m.

Reticulate is a package for R that allows you to run Python code inside of R. Since both Python and R are very popular for common data science tasks, it makes sense that you would want to use them together. In this talk, I'll demo how to run a Python package inside of R.

Play Sounds AND Blink Lights: Cooperative Multitasking with CircuitPython By: Adam Forsyth
Date: June 11, 2020, 6 p.m.

The Circuit Playground Express (CPX) is a small circuit board you can program with Python. But what if you want to make the lights blink and play sounds at the same time, while keeping the code for those two things separate? I'll briefly show you how to program the CPX with Python, then talk about how to run multiple pieces of code at the same time using generators. This talk was inspired by the closing keynote at PyCon 2019.

From Spreadsheets to DataFrames: Escaping Excel Hell with Python By: Ryan McCoy
Date: May 14, 2020, 6 p.m.

A spreadsheet is a wonderful invention and an excellent tool for certain jobs. All too often, however, spreadsheets are called upon to perform tasks that are beyond their capabilities. It is like the old saying, “If the only tool you have is a hammer, every problem looks like a nail.” But some problems are better addressed with a screwdriver, with glue, or with a swiss army knife.

Python is often called the Swiss army knife of the programming world, due to its versatility and flexibility in use. That is why it has become increasingly popular over time. Companies can adopt Python to perform some uniquely complex processes over the long-term. 

During this talk, Ryan will discuss his firsthand account of Excel Hell and how he managed to escape it using Python. He will also discuss of the relevant libraries he uses for web scraping, data processing, analysis, and visualization, including Requests, Pandas, Flask, and Airflow, as well as few strategies he uses when approaching problems with data. 

Ryan S. McCoy is a Data Engineer at gotem, LLC, where he is responsible for helping modernize the systems, data infrastructures, and analytics of companies primarily in the Financial Services industry, including Investment Managers, Hedge Funds, Venture Capital funds, and data vendors. Previously he spent a decade at several institutional investment funds located in St. Louis.

Github Repo ->

Intro to Python kubernetes (k8s) client By: Nate Rock
Date: May 14, 2020, 6 p.m.

Python has a great library for interacting with kubernetes (k8s) clusters. This talk will discuss two quick tools to get your feet wet when it comes to interacting with k8s using python and show you some of the things to look out for, as well as the basics of local vs intra-cluster security.

"the phone calls are coming from inside the house!"

The first service is a simple flask based application that will be running as a pod inside the cluster exposing the endpoint using a Service and Ingress resources. When you call the "/pod/versions" endpoint, it will return the versions of any applications running in the cluster as JSON. There are some security constraints built into k8s that you should be aware of when trying to access the k8s API internally. We will walk you through how to allow this service to access this API even with Role Based Access Control (RBAC) enabled using a ServiceAccount. This method will only grant this specific service inside a particular namespace read-only access to pod information for the cluster.

The second application will make use of this flask endpoint and be run from your local command. k8s config file to get access. We will then use it to compare a secondary application running in a different namespace. This is a smaller version of some real world tooling we use at Rally Health as we migrate from mesos to k8s and need to compare state between these two environments as well as between clusters in different environments. These techniques are just the tip of the iceberg, but ideally they should give you some idea as to what the kubernetes python client is capable of handling.

pudb By: Erik Johnson
Date: May 14, 2020, 6 p.m.

pudb is a feature-rich terminal-based debugger that is a great alternative to Python's built-in debugger (pdb). This demo will demonstrate how to launch into the debugger, as well as how to use its remote functionality to connect to and troubleshot multi-process apps which do not run in the foreground.

SLIs, SLAs, SLD’OHs! Learning About Service Uptime from Homer Simpson By: Mason Egger
Date: April 9, 2020, 6 p.m.

Building services is important, but what happens after they are built and running in production? How do we establish trust with our customers that our service will actually be available? Who creates these definitions and how do we measure them? Service Level Indicators (SLI), Agreements (SLA), and Objectives (SLO) are central to an operations mindset and foundational tools for effective Site Reliability Engineering. This talk will take you on a journey through Springfield as we discuss exactly what SLIs, SLAs, and SLOs are, how to measure them, what targets should be measured, how to define uptime, availability, and acceptable error rates, and what happens when they are breached. Attendees will leave with a clear understanding of how to monitor and report for their services, how SLIs, SLAs and SLOs can aid in this process, and how to implement them within their own teams.

Ray: A System for High-performance, Distributed Python Applications By: Dean Wampler
Date: April 9, 2020, 6 p.m.

Ray is a framework for distribution and scaling of clustered, high-performance, Python applications. It is used in several ML/AI systems and production deployments. This talk explains the problems that Ray solves, including rapid execution of “tasks” and management of distributed state, such as model parameters during training. I’ll use several example applications to illustrate. You'll learn when and how to use Ray in your projects.

What even is a process anyway? By: David Sutton
Date: March 12, 2020, 6 p.m.

Have you ever wondered how your computers knows what programs are running? What about what happens behind the scenes when you start a program? This talk will cover the basics of how processes work, and how your operating system keeps track of what's running. By the end of it you will know enough to write your own basic versions of 'ps' or 'top'.

Python's Numeric Tower By: Phil Robare
Date: March 12, 2020, 6 p.m.

Most computer languages offer "int"s and "reals" and maybe some support for "complex" or fixed point decimal. Python goes further. This talk will discuss built-in numeric types (such as Rational and Decimal), numeric types from Numpy, and the Abstract Base Classes that make it possible to add your own specialized numeric type and have it appear as part of the language.

Experiences in the Guild By: Sam Mahisekar
Date: Feb. 13, 2020, 6 p.m.

If you have attended a few ChiPy events, chances are you have used the website. The ChiPy Web Guild is a group of volunteers that help maintain the site. In this talk, I will give a brief description of how the Web Guild works and touch on some aspects of the site. We will then go through an example of how team members were able to address a flaw in the code enhancing user experience. Finally, I will share some thoughts on what I learned and what the group might work on next.

Migrating from VMs to K8s - We did it, and so can you! By: Nick Petrovits
Date: Feb. 13, 2020, 6 p.m.

Join us as we describe our migration from a limiting cloud deployment on long-running VMs with shared infrastructure to a streamlined immutable infrastructure built on top of Docker and K8s. We'll also discuss techniques to support local development during this transition. Many teams wish they could reap the widely known benefits of Kubernetes (K8s), but most struggle to migrate to a new infrastructure while simultaneously supporting two deployment models and avoiding impacts to the velocity of software development. In this talk, we describe the particular challenges we faced during our incremental migration from multiple long-running singleton EC2 instances to a containerized solution. We'll highlight: - What challenges motivated us to transition to K8s? - Approaching an infrastructure migration incrementally to minimize impacts to local development and production deployments - Developing a solution to provide the same abstraction for local development that exists in production - Concurrently supporting multiple deployment models to reduce risk and simplify migration - Strategy variations for synchronous and asynchronous services - Networking challenges with Vagrant and Docker - Integrating K8s with a CI/CD pipeline - Tuning the environment

Cloud Data Warehouses with Python By: Michael McCarthy
Date: Feb. 13, 2020, 6 p.m.

The rapid growth of Python is, in part due, to it's exceptional toolkit for Data Analysts, Scientists, and Engineers. Packages like Pandas, Scikit-Learn, PySpark, and Dask have become staples for teams looking to process data. However, when processing large amounts of data there are times when Python might not be the right solution for your task. In this conversation, we'll learn about Cloud based Data Warehouses, such as Google's BigQuery, Amazon's Redshift, and Snowflake. You'll learn about the advantages of these platforms compared to in-memory processing in Python. We'll also show examples of how you can use Apache Airflow to automate recurring tasks, turning your Data Warehouse into the cornerstone of your Data Science infrastructure.

Fall Mentorship Presentations
Date: Dec. 12, 2019, 6 p.m.

Ten mentees will present the projects that they have been working on with their mentoors for the past 3 months. 

pyplot-themes By: Ray Buhr
Date: Nov. 14, 2019, 6 p.m.

I made a package, pyplot-themes, that helps make it easier to: 1. have decent looking matplotlib/pandas plots 2. have some decent color palettes 3. create your plot themes

Python at Nielsen By: Jordan Bettis By: Meygha Bhat By: Vamsi Guntamukkala
Date: Nov. 14, 2019, 6 p.m.

We will talk to you about Nielsen's Connect Platform, our global, unified, open data ecosystem powered by Microsoft Azure and how we're building platform components using Python. Specifically, we'll deep dive into object-oriented data flows. As more and more data scientists write software beyond statistical models, object thinking from the field of programming can help them write test-able, maintainable and reusable components.

Demystifying Machine Learning By: Nikola Novakovic
Date: Nov. 14, 2019, 6 p.m.

Machine Learning is something you'll see referenced very frequently now in everything from marketing materials to sales pitches, and job postings. With so much hype it can be hard to distinguish what people mean when they say Machine Learning. In this talk we will demystify Machine Learning by understanding its core concepts and applying that knowledge to real world examples. We'll explain basic concepts like linear algebra and loss functions, figure out when to use machine learning and build an ML model that we'll be able to use in real world apps. Here’s an in-depth list of what we'll cover: * What Machine Learning is and where it’s being used * How to recognize when machine learning is necessary * Math 101 * Linear Regression * Live Coding Session Salary Estimator * Q & A

What's new in Python 3.8? Assignment Expressions & More By: Adam Forsyth
Date: Oct. 10, 2019, 6 p.m.

Come learn about the new features in Python 3.8!

Application Security for Python Programmers By: James Jeffryes
Date: Oct. 10, 2019, 6 p.m.

Python is a growing choice for business applications processing sensitive user data and performing mission-critical tasks. That makes it vital for programmers to be aware of common security vulnerabilities that can undermine the Confidentiality, Integrity, and Accessibility of these Python applications. Fortunately, many of these risks can be managed with patterns for safe handling of user input as well as tools for dependency monitoring and static code analysis.

Celebration of the life of Tanya Schlusser By: ChiPy Community
Date: Oct. 10, 2019, 6 p.m.

A chance for our community to remember and celebrate the life of Tanya Schlusser. Tanya has a long history at ChiPy and beyond. She was a mentor, a speaker, a writer, an education advocate, a loving daughter, and much much more. Members of the community will be invited to share their memories of Tanya.

Follow your Python Path: Learning the Right Way (for you) By: Ray Berg
Date: Sept. 12, 2019, 6 p.m.

We are all on a Python learning path somewhere. For some, finding the right path to set out on is hard; others have been on the trail so long we forget to look around at our surroundings. In all cases, we should be mindful about how and what we learn In this talk we'll cover tips and tools for each stage in your development as a Python programmer including some live examples. How to set goals, how to find mentorship experiences, how to grow technically and how to grow interpersonally. Something for everyone!

How to help out with python. By: Joshua Herman
Date: Sept. 12, 2019, 6 p.m.

Here we will go through my own personal saga of adding documentation to the Python Man pages.

XOR many ways: an whirwind tour of python deep learning libraries By: Rick Galbo
Date: Sept. 12, 2019, 6 p.m.

We will be preparing the famous XOR example or one of the staples of non-linearly separable feature spaces. We will use the classic techniques like tensorflow and keras. We will also check out some of the newer examples like caffe, mxnet, pytorch, deeplearning4j, and many others. Aprons will be provided. No prior experience cooking necessary.

Scaling out Airflow By: Katie Simmons
Date: Aug. 8, 2019, 6 p.m.

Katie Simmons, a data engineer at ActiveCampaign, will speak about the challenges and benefits of using Airflow for ETL at a rapidly growing company. ActiveCampaign has many thousands of databases - some including tables with up to a trillion rows - several APIs and new source requests coming in every week. This lightning talk will be an overview of using Airflow to extract, load and transform that data into our data lake so that it can be used for Business Intelligence and Data Science.

The Philosopher's Groan: How I Finally Fell In Love With SQLAlchemy By: Ainsley McGrath
Date: Aug. 8, 2019, 6 p.m.

I first encountered SQLAlchemy several years ago. I didn't get it. It seemed every line I attempted to write would drop me into 50 tabs of labarynthine documentation. Why do we have the ORM *and* Core? Should I build my tables as `Table` instances or should I be extending `Base`? How is `Base` more declarative than a function that returns `Table`s?? Can I please just write SQL??? :sob: I'm still hesitant to peek too far behind the curtain, but I do think I've finally wrapped my head around the philosophical underpinnings of the library and the different problems SQLAlchemy allows us to solve. After all, who among us works with databases that aren't problems in and of themselves?

Throw away your shell scripts By: Nick Timkovich
Date: Aug. 8, 2019, 6 p.m.

One of the most common languages used by Python developers is some shell script (sh, bash, cmd.exe, or PowerShell), but why torture yourself with poor design decisions from the 70s when you know Python?

Web Scraping for Fun and Profit (Profit not Included) By: Matt Inwood
Date: July 11, 2019, 6 p.m.
Not all data is easily accessible. Taking info from a website that requires authentication, interaction, or even just to load a fancy script. This talk will discuss using Selenium to level up your web scraping skills, with examples and suggested practices.
Limit the Guesswork in Fantasy Baseball by Using Python for Data Analysis By: Nicholas Marey
Date: July 11, 2019, 6 p.m.
Daily fantasy sports has become a booming industry. One leader in the space is Fanduel who after raising $275 million in their series E funding, brought their valuation to over $1 billion. As the popularity of daily fantasy sports has taken off, critics have argued that success is the result of luck; yet the consistent success of a select group of players would suggest that success can be driven by skill. This talk, will go over how one could use Python to level the playing field.
Lowering the Stakes of Failure with Pre-mortems and Post-mortems By: Elizabeth Sander
Date: July 11, 2019, 6 p.m.
Failure can be scary. There are real costs to a company and its users when software crashes, models are inaccurate, or when systems go down. The emotional stakes feel high-- no one wants to be responsible for a failure. We can lower the stakes by creating spaces to learn from failures, and minimize their impact. This talk introduces two ways to address failure: blameless post-mortems, to learn from an incident; and pre-mortems, to identify modes of failure upfront.
Python @ Strata By: Steven Lefar
Date: July 11, 2019, 6 p.m.
An overview of Strata Data Science and python use cases inside the company.
Come for the language, stay for the community By: Naomi Ceder
Date: June 13, 2019, 6 p.m.
In 2014 Brett Cannon summed it up when he said of Python, "I came for the language, but I stayed for the community." These are some reflections on what makes Python communities so special, the challenges they face, and how to take part in them and help them (and ourselves) grow and thrive.
Past, Present, and Future of the Python Software Foundation's Community Infrastructure By: Ernest W. Durbin III
Date: June 13, 2019, 6 p.m.
In fulfilling the mission of the Python Software Foundation, infrastructure to support the development, use, and promotion of the language has seen steady investment. We'll take a stroll through the services that the PSF currently supports, the history and current state, as well as a brief introduction to future initiatives. No experience or prior knowledge is needed!
Shaping Chicago PyLadies: An organizer’s perspective By: Sand Ip
Date: June 13, 2019, 6 p.m.
From its founding by Lorena Mesa in 2015 until now, the Chicago PyLadies chapter has evolved into a strong presence by the help of many passionate organizers and fellow tech community partners. In this talk, we will take a look at Chicago PyLadies history, partnerships, and how our future is actively being shaped by our global PyLadies.
Python Software Foundation Annual Impact Report By: Ewa Jodlowska
Date: June 13, 2019, 6 p.m.
In 2019, the PSF released its first Annual Impact Report. This report details the financial standing of the PSF. Additionally, the report describes how funds we receive support various PSF programs. During this talk we will go through the data and discuss upcoming programs the PSF will be funding.
Introduction to Full Stack Development: Creating a Sports Web App By: Pedro Lopez IV
Date: May 9, 2019, 6 p.m.
Lobules and beyond: a Flask app for community science data processing By: Victoria Reese
Date: May 9, 2019, 6 p.m.
Applying Natural Language Processing to the Employee Hiring Process By: Kamil Mysiak
Date: May 9, 2019, 6 p.m.
Melody Maker: simple music making for everyone By: Ramon Cardenas
Date: May 9, 2019, 6 p.m.
Reddit Word Usage and Using Python in the Real World By: Fazal Abbas
Date: May 9, 2019, 6 p.m.
Empowering Early-Career Developers By: Mercedes Bernard
Date: April 11, 2019, 6 p.m.
How can teams invest in and grow their less experienced developers into team-leading senior devs? I believe the first step is empowering them. On my team, we’ve created a process for each team member to lead and own one of our core features. Our early-career developers are learning client management and team leadership skills that they wouldn’t usually get to practice until they stepped into a senior role. In this talk, I’ll share what we’ve learned and a framework for you to create a process tailored to your team so you can provide your early-career developers the opportunities they need to become successful, senior team members.
Starting Your Data Science Journey By: Aaron Yang
Date: April 11, 2019, 6 p.m.
There have been great talks in chipy showcasing the amazing new technologies people are using with python, but we often forget where we first started. Some of you all are coming to Chipy for the first time and hoping to take your first steps into the world of tech. This talk will be targeted towards people looking to transition their career or the rest of us looking to find community in the continual gauntlet of developing our skills in the tech industry. I'll be showcasing my process and my thoughts on developing your skills.
Plug-n-Stream Player Piano: Signal Processing With Python By: JP Bader
Date: April 11, 2019, 6 p.m.
Digital Signal Processing and Player Piano don't normally come together in the same sentance. Player Pianos that are 100+ years old are awesome artisan artifacts, but they don't play digital formats very well. This talk will show how we take a 100+ year old technology and marry it to the digital age via Python libraries and precision lasers! In this discussion we will cover how we are creating our own "Plug-n-Stream Player Piano". We will take a look at the different digital signal processing Python libraries, their functionality, and requirements for converting audio streams to piano playable audio files.
Python at Blick Art Materials By: Naomi Ceder
Date: March 14, 2019, 6 p.m.
Blick uses a wide range of technologies across over 70 stores nationwide and on 2 e-commerce sties, and Python (and Flask & Django) powers the systems we use to price our almost 90,000 items competitively.
Omega Grid's Python Blockchain By: Killian Tobin
Date: March 14, 2019, 6 p.m.
Omega Grid created a python blockchain to help utilities manage all the new solar panels, electric vehicles, and batteries on the grid. We are also running a python backend for our public facing rewards platform. We launched the rewards platform on Jan 1 in Burlington, VT as part of a peak shaving program.
Python at ActiveCampaign By: Ben Levin
Date: March 14, 2019, 6 p.m.
ActiveCampaign is using Python to transition from a PHP monolith to scalable microservices that can keep pace with the demands of our rapidly-growing customer base, as well as to train and deploy customer-facing ML models.
Python at Imaginary Landscape By: Noel Taylor
Date: March 14, 2019, 6 p.m.
Noel from Imaginary Landscape will discuss, and demonstrate, 'Iscape Search', a homegrown web scraper and search engine designed to replace the discontinued Google Site Search
Python at Impossible Objects By: Len Wanger
Date: March 14, 2019, 6 p.m.
Impossible Objects is an award winning manufacturer of 3D printers for the industrial market. We use Python extensively throughout the organization from MIS systems, data analysis to preparing models and running our printers. This talk is an overview of how Python is used throughout the organization.
Python @ PricewaterhouseCoopers By: PwC AI Team
Date: March 14, 2019, 6 p.m.
Kay and Kevin from PwC will discuss how they use AI and Python in their every day work to help PwC internally and our external clients to solve their toughest problems with unique solutions.
Python Powered Business - Quicket Solutions By: Bryan Chance
Date: March 14, 2019, 6 p.m.
Python powered platform at Quicket Solutions.
Python@Panopta By: Jason Abate
Date: March 14, 2019, 6 p.m.
Python has been one of the fundamental technologies for Panopta's global monitoring infrastructure. I'll cover some of the more interesting ways we've used Python over the years, and our plans going forward.
Doctors, Devices, Drugs, and Django - Python at a Healthcare Startup By: Scott Sexton
Date: March 14, 2019, 6 p.m.
At Lumere, we make software to help hospitals get the clinical data they need to make decisions leading to better outcomes and lower costs. We use python to power our web application, data science tools, and research platform. Why did we decide on python, and how has it enabled us to grow?
Python at Nielsen By: Jordan Bettis
Date: March 14, 2019, 6 p.m.
Jordan will discuss how Nielsen uses Python to connect and process massive amounts of data in a next generation environment.
Python @ Ascent By: Spencer Allee
Date: March 14, 2019, 6 p.m.
Ascent uses Python for most of its data science, data engineering, and machine learning / NLP - I'll give an overview of some of our key apps and use cases.
such "DSON" is "Awesome" wow By: Erik Johnson
Date: Feb. 14, 2019, 6 p.m.
This will be a brief and humorous demonstration of DSON, a JSON-like serialization format in which the punctuation used to represent dictionaries and lists is replaced with words from one of 2014's most pervasive memes.
Rust By: Eliah Burns
Date: Feb. 14, 2019, 6 p.m.
LaTex By: Kevin Nasto
Date: Feb. 14, 2019, 6 p.m.
Yes! Another Markup Language By: Aly Sivji
Date: Feb. 14, 2019, 6 p.m.
YAML is a human-readable data serialization language that has taken the world by storm. We will explore features of the YAML syntax that will save keystrokes and clean up messy configuration files.
The Cell: a Turing complete language By: Phil Robare
Date: Feb. 14, 2019, 6 p.m.
"DNA is the programming language of the cell" - A computer science look at what this means, how the cell can "compute" and produce "output" that responds to the environment.
Go (lang) for Python devs at a 10,000 foot view By: Ray Buhr
Date: Feb. 14, 2019, 6 p.m.
What is the Go programming language and why should pythonistas concern themselves with it? In only 5 minutes, I'll try and show how cool Go and the tools that come with it are, and maybe convince you why it might be a better choice for some projects.
J By: Hillel Wayne
Date: Feb. 14, 2019, 6 p.m.
Scala By: Nathan Linebarger
Date: Feb. 14, 2019, 6 p.m.
Crystal By: Jack Throne
Date: Feb. 14, 2019, 6 p.m.
LaTeX vs. Python By: Kevin Nasto
Date: Feb. 14, 2019, 6 p.m.
This talk will go over the language LaTeX for the shootout. LaTeX is used to generate PDF documents such as books, research papers, math formulas, and more.
Date: Jan. 17, 2019, 6 p.m.
Challenges are fun, hands-on coding exercises covering a variety of topics -- such as pure problem solving, web development, and data science (see past challenges: Participants will be assigned to teams of four, which will then have an hour to solve the problem at hand together. Teams are designed to have diverse experience levels, giving team members equal opportunity to learn and share ideas.
Date: Jan. 17, 2019, 6 p.m.
This is an open space to collaborate with others, ask questions, or help someone else if there's a question you can answer! No problem is too big or too small. If you're working on a personal project and looking for some Python help, or you want to join forces with someone who's working on an interesting idea, this option is for you.
Advent of Code By: Sree P
Date: Jan. 10, 2019, 6 p.m.
Breif overview of “Advent Of Code” and walkthrough of one of the challenges
Staying alive with systemd By: Siva Manivannan
Date: Jan. 10, 2019, 6 p.m.
Keep your Python applications alive and kicking with systemd.
Three Python Nuances I Wish I'd Known Earlier By: Jess Unrein
Date: Jan. 10, 2019, 6 p.m.
I'll briefly go over three Python gotchas that have given me headaches in the past that I wish I had known about earlier. We'll talk about how lists store references to objects, why default mutable arguments are unexpectedly tricky, and the difference "is" and "==" comparators.
What's the deal with Florida's news? By: Jordan Nelson
Date: Jan. 10, 2019, 6 p.m.
When thinking about Florida News many have heard of the ubiquitous Florida man. This talk will look at news from around the country and attempt to quantify if Florida man truly exists. I used Python to build functions that scrape satirical, national, and local news sites and built a basic model to compare news across various states. Python libraries highlighted in this talk include: requests, Beautiful Soup, and sklearn.
Busy-Beaver: Increasing Community Engagement with Python By: Aly Sivji By: Chris Luedtke
Date: Jan. 10, 2019, 6 p.m.
With over four thousand members, the Chicago Python Users Group is one of the largest Python communities in the world. Slack has become the primary method of communication amongst members in-between events. We developed an open-source Slack bot, codename: Busy Beaver, to increase member engagement. This talk will introduce Busy Beaver, provide a high-level walkthrough of its architecture and code, and discuss the future roadmap of the project.
From Python to Rust By: Kevin Nasto
Date: Nov. 8, 2018, 6 p.m.
Ever been curious about the Rust programming language? Although Rust is a low level language, some similarities exist with Python. This talk describes it from the point of view of a Python user. Discover the alternatives to pip, functions and passing values, lists, classes, import statements, exception handling, and more.
Python in a Pod in a Kube in a Pi By: Joe Jasinski
Date: Nov. 8, 2018, 6 p.m.
Have some extra Raspberry Pi's laying around? Ever want to learn what this Kubernetes thing is about? Do you love running Python inside of Docker? Then this talk is for you! This talk will dive into some core Kubernetes concepts, using a Raspberry Pi cluster as a learning tool.
Beating Mastermind: Winning Games, Translating Math to Code, and Learning from Donald Knuth By: Adam Forsyth
Date: Nov. 8, 2018, 6 p.m.
Mastermind is a logic-based guessing game. Many years ago, Donald Knuth described a way to win the game in 5 moves or less. We’ll implement the game and the algorithm from the article. Come learn how to beat Mastermind and turn a paper by a famous scientist into code!
Why Learn PySpark? By: David Liao
Date: Oct. 11, 2018, 6 p.m.
Grubhub has chosen to adopt the Spark Big Data computing framework to underpin it’s internal Grubhub Data Platform Spark was adopted very early by Silicon Valley FANG companies.. What features make Spark a great computing platform for both Analytical reporting and Machine Learning? Tips on how to install PySpark on a Mac OSX system so one can play wit PySpark without paying for a cloud cluster
Tour of job scheduling in Python By: Raymond Buhr
Date: Oct. 11, 2018, 6 p.m.
Once you've started to learn python, you're going to want to use it to automate tasks. Their are lots of ways to do this, each with it's own set of pros and cons. This talk will go over a few options for scheduling the execution of python scripts and the tradeoffs that come with each. Tools that will be covered include crontab, schedule, celery, airflow, and cloud options AWS Lambda and GCP functions.
Defining services with grpc and protocol buffers By: Patrick Boland
Date: Oct. 11, 2018, 6 p.m.
gRPC and protocol buffers offer a high performance, open source way to define services and messages for the future. Think of it like REST, but for the http2 protocol.
Why python is the best first programming language and here is how to make it even better By: Jhankar Mahbub
Date: Sept. 13, 2018, 6 p.m.
Meet Simon! He doesn't have a technology background, but he wants to be a programmer. Should he go to a boot camp with 17k or read 29,900,000 options provided by Google when he searched "Learn Python". Or he can join ChiPy mentorship program. While all of these will work, I would like to make his journey bit more enjoyable by presenting a more natural, friendlier, and a more interactive way to learn programming concepts. In this talk, we will look at functions, for loops, list comprehensions, and generators in a way that is easy for people like Simon to understand and use.
Machine Learning and Deduplication By: Forest Gregg
Date: Sept. 13, 2018, 6 p.m.
Machine learning and record linkage: Finding duplicates or matching data when you don't have primary keys is one of the biggest challenges in preparing data for data science. At DataMade we have built a python, open source machine learning library to help developers, and a product to help everyone else. We describe the problem and how we use machine learning to scale to tens of millions of records.
How to install Anaconda By: Kevin Nasto
Date: Sept. 13, 2018, 6 p.m.
Ever want to avoid installing Python packages with complex dependencies such as sklearn? Ever have permissions issues installing a package? Anaconda is the answer. This talk describes why use it and how to get it set up.
Interactive Introspection with `ls` By: Aly Sivji
Date: Aug. 9, 2018, 6 p.m.
Walkthrough of `python-ls`, a new utility that allows users to interactively introspect Python objects.
Pandas MultiIndex Tutorial and Best Practices By: Zax
Date: Aug. 9, 2018, 6 p.m.
While Pandas is one of the most well known Python libraries for working with array-like data, many users limit themselves to just two dimensions of data. This talk will walk through Pandas' MultiIndex DataFrames, which extend traditional DataFrames by enabling effective storage and manipulation of arbitrarily high dimension data in a 2-dimensional tabular structure. ((If that sentence doesn't make sense yet, don't worry - it should by the end of the tutorial.)) While the displayed version of a multiindexed DataFrame doesn't appear to be much more than a prettily-organized regular DataFrame, it's actually a pretty powerful structure if the data warrants its use. This talk is beginner friendly, and will start from the assumption of having never used Pandas, though some Pandas experience will aid understanding.
Python Magic Methods By: Nick Timkovich
Date: Aug. 9, 2018, 6 p.m.
Everything in Python is an object and nothing is special. Python's built-in objects can be added, called, indexed, or with'd, and with a little magic, so can yours! Use of magic methods, those prefixed/suffixed with double underscores, can increase the flexibility of your code while also making it shorter and simpler.
Mocking with MITM By: Quentin Bayart
Date: Aug. 9, 2018, 6 p.m.
Every developer (eventually) writes tests. Unit tests, Integration tests, End-to-end tests, Regression tests.. All of those tests are necessary but can become a nightmare when you need to refactor some code. I personally don't like the amount of time I spend to manually mock my dependencies / functions / objects. This talk is about a simple docker-compose / pytest / mitm setup which aims at speeding up the mocking process and the maintenance of those mocks when refactoring or when updating the interface of your services. Q&A: Many of you are dealing with this mocking process regularly so you can expect many comments / questions if you come to this talk :) Contact: Quentin Bayart, Software Engineer @ Nielsen A couple of days before the presentation, I will push my demo to my github so you should be able to find it there after the presentation.
Python 3.7 Below the Fold: `mock.seal` By: Aly Sivji
Date: July 12, 2018, 6 p.m.
`unittest.mock` provides a flexible implementation of mock objects we can use to write isolated unit tests. In this lightning talk, we will explore the new `mock.seal()` function that was added in Python 3.7.
Data Classes in Python 3.7: Why and How do They Compare to Existing Solutions? By: Brian Stempin By: Yiu Ming Huynh
Date: July 12, 2018, 6 p.m.
Python prides itself on being a language where “There should be one – and preferably only one – obvious way to do it” (PEP 20). One place where this isn’t really true is when it comes to the question of how to store data. There are several options: dictionaries, tuples, named tuples, vanilla Python classes, and Python classes decorated with the attrs library. PEP 557 adds a new way: Data classes. In this talk we will compare and contrast each approach, give listeners a way to figure out which one is best for their particular project, and share some performance metrics for those who are concerned with speed and memory footprints.
Intro to SaltStack By: Erik Johnson
Date: July 12, 2018, 6 p.m.
SaltStack is open-source software for modern IT automation. The project was created in 2012 and today is used by tens of thousands of DevOps and enterprise IT organizations to automate the management of data center infrastructure and application environments. With its core remote-execution functionality, it is flexible enough to run shell commands, perform configuration management tasks, orchestration, and more. Erik Johnson, a SaltStack core developer and Chicago-area native, will demo the basics of how to get started using Salt, as well as how to use its powerful event bus for automation tasks.
Going with the flow: Intro to Airflow By: Matt Inwood
Date: April 12, 2018, 6 p.m.
Airflow is a great open source resource for managing ETL, or any other scheduled jobs. We'll go over the DAG-Task-Script Hierarchy; job triggers, logging, and the web interface. I'll also talk about some best practices, and different caveats and gotchas that you can come across from my personal experience implementing it.
A Robust Dev-to-Production Workflow for Home Use, Using Jupyter Notebooks and PyTest By: Leon Shernoff
Date: April 12, 2018, 6 p.m.
Working on a substantial Python project at home can be confusing and frustrating. A work environment can suddenly impact the direction of a project in unexpected ways, because of the many stakeholders; but they usually have a robust process in place for actually doing the coding (otherwise nothing gets done). Implementing a solid and productive workflow routine at home can be a challenge, but it is of great benefit for complex projects. This talk uses a sample text-processing project to demonstrate a home workflow design featuring sandboxing in Jupyter notebooks, migration of working routines to project-specific modules and straight-ahead Python files, and writing unit tests for these in PyTest.
Calculating pi using Django and Solidity on the Ethereum Blockchain By: Joshua Herman
Date: April 12, 2018, 6 p.m.
After giving a whirlwind tour of what Ethereum and Solidity are I will show how to use Django and Web3py to deploy a smart contract that performs division.
Introduction to Keras By: Chris Gruber
Date: March 8, 2018, 6 p.m.
Keras is a popular framework for building neural networks in Python. Using Keras, a developer can define and train a neural network in just a few lines of code. Keras also includes a number of pre-built networks to build state-of-the-art models for language translation, image recognition, etc. This talk will consist of an overview of Keras and its features, and a demo in which we build and train a classifier for the MNIST hand-written digit dataset.
mitmproxy: Lift the veil on server-side HTTP(s) interaction By: Ross Heflin
Date: March 8, 2018, 6 p.m.
When writing web frontends there's powerful tools for understanding backend calls made by a website (Network tab in Chrome, Firefox, Webkit'sm Dev Tools and HAR analyzers). These are (reasonably) great for figuring out what requests a browser is making to backend servers & what came back. When dealing with server-side code its somewhat harder to see all requests made to other systems in context of what requests came into the server-side api without instrumenting your code with lots of (often incomplete) logging. During the last 5 years, I've worked through many issues in various languages/frameworks and libraries, where the only common thread was (sometimes complex) communication with other systems over HTTP(S) by using mitmproxy. This talk will cover a variety of use cases, demonstrating some useful capabilities of this versatile tool with minimal (if any) changes to existing code regardless of source language, server-side framework, and HTTP client used.
Formatted strings in Python 3.6 By: Phil Robare
Date: March 8, 2018, 6 p.m.
3.6 has introduced a fourth way to format output from a Python program. PEP 498 introduced a new kind of string literals: f-strings, or formatted string literals. Formatted string literals are prefixed with 'f' and are similar to the format strings accepted by str.format(). They contain replacement fields surrounded by curly braces. The replacement fields are expressions, which are evaluated at run time, and then formatted using the format() protocol This talk will give a quick overview of syntax, usage, and possibly abuse of this new feature.
Pyo: DSP and synthesis software for Python By: Aaron Krister Johnson
Date: Jan. 11, 2018, 6 p.m.
This presentation will introduce "pyo", a C-level library with Python bindings that is designed for musical/audio synthesis, and my own software "microcsound", which is a score-generation front-end to Csound, a well-know audio synthesis programming language. In particular, microcsound was designed to allow a richer vocabulary of musical pitch (alt-tuned scales, microtones, N-pitches per octave, etc.) than the one available via standard 12-notes per octave tuning of typical Western music. So, there will be some explanation of the historical background that drove the creation of this software, but it should be a fascinating topic for all. I will give brief demonstrations of the kind of out-of-the-box fun one can have with sound using Pyo, and snippets of the kind of work I've done with both it, and with microcsound. Anyone interested in the possibilities presented by Python software for electronic music, and/or electronic music in general, should find this talk interesting, informative, and entertaining.
I Am Open Source (And So Can You!) By: Aly Sivji
Date: Jan. 11, 2018, 6 p.m.
Open Source Software (OSS) has changed the world in countless ways and has provided us with wonderful innovations such as the Python programming language. As Pythonistas, we use OSS every single day but only a fraction of us give back to the community. This talk will discuss the benefits of contributing to open source in the context of my experience as a newbie pandas contributor. I will also provide a Getting Started guide so you, too, can become an Open Source Contributor!
Mentorship Finals Fall '17 By: Sydney Huppert
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Joe Kresach
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Shea Munion
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Anna Felicity Friedman
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Yvonne Matos
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Daniel Nunez
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Adam Patni
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Zax Rosenberg
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Ryan Regan
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
Mentorship Finals Fall '17 By: Jaimie Catoe
Date: Dec. 14, 2017, 6 p.m.
Mentorship Finals Fall '17
(Title to come) By: Rob Roy Campbell
Date: Nov. 9, 2017, 6 p.m.
In order to explain the problem I had that prompted my interest in Python (and led me to become a Chipy mentee), I'm going to briefly summarize the history of the electronics industry, beginning with the invention of the light bulb in 1880, to the triode vacuum tube in 1906, to early tube-based digital computers, to the invention of the transistor, to the development of the integrated circuit, to the microprocessor, and finally to the present day. Then I will explain that I use vacuum tubes in modern audio products, and will explain how I overcame a common problem with tubes using Python and accomplished a level of precision that my competitors said was not possible. Unlike most Chipy presentations, mine will focus less on my project and more on the history, with extensive images, video clips, and diagrams of a century of development in the field of electronics and computers. I think I can make it fun and interesting for all.
SQL indexes: what, why and how By: Chris Foresman
Date: Nov. 9, 2017, 6 p.m.
A brief overview of what SQL indexes are, why you need them, and how to add them (using SQL or Django code).
PyWeek24 - team development By: Michael Tamillow
Date: Nov. 9, 2017, 6 p.m.
Unicorn Markets has come out with another game. The quality and purpose of this game is a level beyond that of PyWeek 23. I will talk about the development process of working on a team in a short 1 week span, the actual game, the learning experience about the design and architecture of code. And if there is nothing left to discuss, we will just play and investigate video games from the contest. If you want to play the game before just run `>git clone' cd into the directory and run: '>python' you will need pygames and python, 2 or 3 should work. And because I forgot to remove the dependency, Python Image Library (PIL) - though it is not necessary for the game.
Full Stack Python for AI: How open source Python enables each phase of the AI workflow By: Tripp Smith
Date: Oct. 12, 2017, 6 p.m.
Investments in AI are heating up, with the total market estimated as high as $126B by 2025. This talk will present case studies and code samples of how our clients are using Python today and how we expect this to evolve over the next few years as AI becomes increasingly ubiquitous. Python enables each phase of the AI pipeline: DevOps, Data Engineering, Model Development, Deep Learning, Cognitive User Interfaces, and Microservices. This talk will highlight how Python is a common glue across multiple disciplines that will allow cross functional teams work together to get real results from AI.
Data Science Workflows using Docker Containers By: Aly Sivji
Date: Oct. 12, 2017, 6 p.m.
Containerization technologies such as Docker enable software to run across various computing environments. Data Science requires auditable workflows where we can easily share and reproduce results. Docker is a useful tool that we can use to package libraries, code, and data into a single image. This talk will cover the basics of Docker; discuss how containers fit into Data Science workflows; and provide a quick-start guide that can be used as a template to create a shareable Docker image! Learn how to leverage the power of Docker without having to worry about the underlying details of the technology. Although this session is geared towards data scientists, the underlying concepts have many use cases (come find me after to discuss).
PyWeek 24 and pygames By: Michael Tamillow
Date: Oct. 12, 2017, 6 p.m.
Pyweek 24 is coming up October 15th through October 22nd! For pyweek 23, I created a game called the evolution of evil, which is available on my github repository. I will (very quickly) walk through the steps of how I created the game, and what it is.
Anaconda: The Best of Everything in PyData By: Patrick Boland
Date: Sept. 14, 2017, 6 p.m.
A continued narrative of the tale of two snakes. In this talk, we will discuss some of the most impressive features of Anaconda, including built in binaries, command line interface, the history of the distribution, and why it is the right choice for just about every Python stack. This talk does not assume audience familiarity with the distribution. We will take advantage of the *better* batteries included nature of this distribution to step through beginner and intermediate concepts. I intend for the audience to feel comfortable and excited to give this a try on their own.
Getting Off the Struggle Bus: Learning From Transit Data By: Spencer Chan
Date: Sept. 14, 2017, 6 p.m.
In an extended version of the lightning talk I gave for the spring ChiPy mentorship final presentations, I will go into more depth about how I collected and processed bus location data from the CTA's bus tracker API. I will also discuss interesting discoveries I made once I plotted the data, work I have done on the project since completing the mentorship (collecting data from 30 additional bus routes, converting visualizations from Bokeh/Python to D3.js, analyzing and visualizing bus bunching, etc), as well as future plans for the project.
Storm surge: hurricane flooding simulation using Python, Fortran, and GeoClaw By: Marc Kjerland
Date: Sept. 14, 2017, 6 p.m.
The 2017 hurricane season is proving to be one of the strongest in history, and predictive modeling plays an important role in evacuation and mitigation planning. Coastal communities in the path of hurricanes face several major hazards - strong winds, heavy rainfall, relentless waves, and storm surge. Storm surge is a type of transient sea level rise where water is forced towards the shore by winds, and the right conditions can produce very high levels - Hurricane Harvey raised Galveston Bay by upwards of ten feet, and in 2012 Hurricane Sandy produced 12-foot surge in Lower Manhattan. I'll discuss the current state of storm surge modeling with focus on an open-source package called GeoClaw, developed by academic researchers across the U.S. GeoClaw uses Python and Fortran to run a dynamic simulation of coastal flooding using storm and topography datasets, and thanks to some novel dimensionality reduction it can be run on a laptop.
Selenium: Doing Magic with Websites Using Python By: Seth Weidman
Date: Aug. 10, 2017, 6 p.m.
Selenium is an excellent package that lets you dynamically interact with websites right from your Jupyter Notebook. At Metis, we teach Selenium early in our bootcamp. We have a great Selenium tutorial that involves lets you make a reservation on OpenTable using Python. In addition, several students have already completed excellent projects using Selenium. During this talk, Seth Weidman, a Senior Data Science Instructor at Metis, as well as the following two current Metis students [TO BE DETERMINED] will be presenting the projects that they just completed.
Tracking FCC bots with Python By: Chris Sinchok
Date: Aug. 10, 2017, 6 p.m.
I've been doing a bunch of analysis on the recent FCC public comments in Python ( Due to this work, I was quoted in Gizmodo, Ars Technica, and the BBC. I'd like to talk about how I approached this problem, how Python helped make sense of my findings, and what my conclusions are.
Fortune-Telling with Python: An Intro to Time Series Modeling By: Jonathan Balaban
Date: Aug. 10, 2017, 6 p.m.
Description: a pythonic tour of time series methodologies and packages, including ARIMA, seasonal models, and Markov approaches. Intermediate level with basic statistics and time data familiarity required. Bio: Jonathan Balaban is a senior data scientist, strategy consultant, and entrepreneur with ten years of private, public, and philanthropic experience. He currently teaches business professionals and leaders the art of impact-focused, practical data science at Metis.
A Gentle Introduction to Context Managers By: Aly Sivji
Date: June 8, 2017, 6 p.m.
We've all used context managers provided by the Python Standard Library to read from/write to a file. Have you ever wondered what was happening underneath the hood when you used a with statement? This talk will explore context managers, discuss various use cases, and show you how to implement a context manager to manage MongoDB connections.
Python for Home-Ec By: Adam Forsyth
Date: June 8, 2017, 6 p.m.
Have you ever tried to make something with scrap wood, and wondered how to use it optimally? Do have a bunch of pickles and jams you made, and you want to eat them in an order that maximizes variety? These are real problems a co-worker of mine had, and we used Python to solve them. I'll show the data we started with, the solutions we came up with, and a bit of the computer science behind them. See some examples of how to think through problems and design your own algorithms to solve them.
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.
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, 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.
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.
Trolling databases with Python! By: Loren Velasquez
Date: April 13, 2017, 7:35 p.m.
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!
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.
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.
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.
Quick prototyping with redis-helper By: Kenneth Wade
Date: March 9, 2017, 6 p.m.
In this talk, I will demonstrate some uses of and how you can easily store, index, and modify Python dicts in Redis. Some asciinema demos are available at
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.
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.
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.
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 ( 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:
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.
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.
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.
Introducing Python in an after school setting By: Kenneth Wade
Date: Nov. 10, 2016, 6 p.m.
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.
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.
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
Popular ORM Libraries By: Tanya Schlusser
Date: Sept. 8, 2016, 6 p.m.
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.
Using Tasks in Asyncio Web Apps By: Feihong Hsu
Date: Sept. 8, 2016, 6 p.m.
In this talk, I will be talking about starting, stopping, and displaying incremental data from long-running tasks in an asyncio-based web application.
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.
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.
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.
Getting meaningful results from unit tests By: Manu Phatak
Date: July 14, 2016, 6 p.m.
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.
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.
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!
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.
PyCon 2016 recap By: Jerry Dumblauskas
Date: June 9, 2016, 6 p.m.
Let's give an overview of Pycon
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?
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.
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.
pyStan: Bayesian Inference for Fun and Profit By: Stephen Hoover
Date: May 12, 2016, 6 p.m.
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.
Hacking Bokeh By: Brian Ray
Date: April 14, 2016, 6 p.m.
I brief introduction into Bokeh 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.
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.
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.
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!
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 (, scikit-learn (http://, Django (, Celery (, and or-tools ( First, we will present the infrastructure behind Scholarfy, a recommender system for massive scientific conferences ( We will also present a machine learning approach to automatically match expert scientific reviewers to research proposals ( Finally, we will present the work behind our award-winning visualization, World’s Science Map (, 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.
ChiPy Python Mentorship By: Tathagata
Date: March 10, 2016, 12:10 p.m.
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:
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 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 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 OptionsAway By: Tim Saylor
Date: Feb. 11, 2016, 7 p.m.
How OptionsAway, an airfare lock startup, 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 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 Credit Suisse By: David Matsumura
Date: Feb. 11, 2016, 7 p.m.
How Credit Suisse uses Python.
Python at Deloitte By: Brian Ray
Date: Feb. 11, 2016, 7 p.m.
How Deloitte uses Python within the Enterprise Science Team.
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
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!
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
*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.
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.
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.
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
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) ( 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.
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 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.
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.
Python-fu in the GIMP By: Tanya Schlusser
Date: Nov. 12, 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, with a little (slightly ugly) hacking to add external libraries.
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: 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.
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
ChiPy Mentorship Oct-Dec 2015 By: Tathagata
Date: Sept. 10, 2015, 7 p.m.
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.
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.
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.
Data Games in Python By: C. S. Schroeder
Date: Aug. 13, 2015, 7 p.m.
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.
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 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.
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 will be covered briefly.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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
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.
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: <>
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
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'
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
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.
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.
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 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
A lightning look at O'Reilly's Python books By: Tanya Schlusser
Date: Dec. 11, 2014, 8 p.m.
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.
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.
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.
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.
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.
Automated testing with the robot framework By: Bryan Oakley
Date: Sept. 11, 2014, 7 p.m.
Robot framework ( 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.
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.
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.
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.
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.
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.
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.
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.
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" ( for creating pretty images of the bifurcation diagram of the logistic map.
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.
PyCon Lightning Talks By: Jason Wirth
Date: May 8, 2014, 7:25 p.m.
Let's go over what people saw at PyCon
An IRC Connection: Implementation and Bot By: Aaron Brady
Date: May 8, 2014, 7:50 a.m.
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.
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.
R for Python Programmers By: John Blischak
Date: March 13, 2014, 9 p.m.
How to teach programming to novices By: John Blischak
Date: March 13, 2014, 8 p.m.
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. 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 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.
Garbage Collection w/ Ref. Cycles By: Aaron Brady
Date: Jan. 9, 2014, 8 p.m.
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. 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.
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 to run language comparisons to generate lexical difference graphs like the one mentioned in the "Lexical Distance Among the Languages of Europe" article. The focus will be on the NLTK and how its internals work to process a language. This talk will be his best one ever.
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.
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.
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.
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.
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.
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.
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.
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.
Measure It By: Peter Fein
Date: Nov. 14, 2013, 7 p.m.
measure_it provides timing and counting for iterators (and other code segments).
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.
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.
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.
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
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 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
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.
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.
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]( from the reproducible science track at SciPy2013.
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.
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: Ruby By: Ross Heflin
Date: June 13, 2013, 7:04 p.m.
Ruby, what you need to know
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.
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: 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: 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.
Hy: A Lisp that transforms itself into the ython AST. By: Christopher Webber
Date: May 9, 2013, 7:45 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.
In-project virtualenvs By: Christopher Webber
Date: May 9, 2013, 8 p.m.
apprenticeship model By: JP Bader
Date: May 9, 2013, 8:20 p.m.
Pythonic protégés.
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.
SXSW Interactive 2013 By: Adam Forsyth
Date: April 11, 2013, 7 p.m.
- Themes - Keynotes - Chicago Tech @ SXSW - Other Highlights - Q&A
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.
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.
329 talks