termie/icalendar output.ics

## icalendar output.ics
BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Pycon 2018//
BEGIN:VEVENT
SUMMARY:Complexity Science
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:Complexity Science is an approach to modeling systems using to
 ols from discrete mathematics and computer science\, including networks\,
 cellular automata\, and agent-based models.  It has applications in many
 areas of natural and social science.\n\nPython is a particularly good lang
 uage for exploring and implementing models of complex systems.  In this t
 utorial\, we present material from the draft second edition of *Think Comp
 lexity*\, and from a class we teach at Olin College.  We will work with r
 andom networks using NetworkX\, with cellular automata using NumPy\, and w
 e will implement simple agent-based models.\n\nAllen Downey
LOCATION:Room 15
END:VEVENT
BEGIN:VEVENT
SUMMARY:Standardized Data Science: The Team Data Science Data Process - wi
 th a practical\, example in Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T103000
DESCRIPTION:In dealing with hundreds of Data Science teams around the worl
 d\, our team has had a consistent request: A proven\, standardized methodo
 logy for Data Science that folds into standard IT practices (such as SDLC
 ’s and DevOps) and governance. The Team Data Science Process (TDSP) is a
  robust\, yet flexible\, fully-documented and defined process you can use
 for consistent\, successful Data Science projects. The TDSP takes into acc
 ount all phases\, roles\, and skills in a solution. Buck Woody\, an Applie
 d Data Scientist from Microsoft Research and AI\, will explain how the TDS
 P works\, provide you resources to use in your organization\, and walk you
  through a practical example using Python.\n\nBuck Woody
LOCATION:Room 25C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Managing Dependencies & Runtime Security
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T103000
DESCRIPTION:In this workshop we’ll explore working with complex dependen
 cy trees in Python\, learn about how to quickly configure virtual environm
 ents\, as well as how you can implement zero-discipline runtime security s
 canning for your applications.\n\nActiveState’s ActivePython distributio
 n and the newly launched ActiveState platform provide solutions to these p
 roblems in simple and powerful ways. During this workshop\, we’ll walk t
 hrough:\n\n- How you can use the ActivePython distribution to have a pre-c
 onfigured\, pre-built environment for many common development tasks and av
 oid dealing with complex build steps for dependencies.\n- How to configure
  a virtual environment for your project\n- How to integrate ActiveState’
 s runtime interpreter plugin to either ActivePython or your existing Pytho
 n distribution to achieve zero-discipline security reporting\n- A demonstr
 ation of how the new ActiveState platform sets you up to solve these probl
 ems and more in a simple\, powerful manner"\n\nPete Garcin
LOCATION:Room 13
END:VEVENT
BEGIN:VEVENT
SUMMARY:Making Art with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:In this workshop\, we’ll learn how to make visual art using
 Processing.py\, the Python mode for a powerful visual language library cal
 led Processing.\n\nThis tutorial walks through Processing.py from the grou
 nd up––from initial setup & foundational concepts\, to the library's c
 ore functions\, as well as its more advanced features. Topics covered incl
 ude the coordinate system\, shape primitives\, lines\, stroke\, fill\, col
 or\, mapping\, events\, and transforms. At the end\, we'll break out of th
 e tutorial format and give free reign for attendees to create\, tinker\, a
 nd experiment freely with the framework.\n\nYou’ll walk away with an art
  piece of your own original design\, as well as a newfound appreciation fo
 r Python as a medium for creative expression. \n\nEmily Xie
LOCATION:Room 16
END:VEVENT
BEGIN:VEVENT
SUMMARY:A Python-flavored Introduction to Containers And Kubernetes
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:Containers have more or less taken over the world of applicati
 on\, web APIs\, mobile endpoints and other forms of deployment. They have
 become the currency\, the "table stakes" and de-facto application deployme
 nt unit. Their raise to the fore has brought about a whole host of use cas
 es which weren't practical or accessible in the world of "classic" paradig
 ms of infrastructure and virtualization. Containers have also brought appl
 ication deployment closer and more accessible to developers. \n\nBut as mo
 re use cases\, deployment styles and exponential adoption of containers wa
 s ongoing\, a new set of problems began to surface: how do you manage the
 ever growing number of containers in a deployment? How do you make sure co
 ntainers have the right resources\, deployed to the right machine\, runnin
 g with the correct parameters\, how do you scale in and out without disrup
 tion? How do you make sure in a fleet of X containers that they’re all r
 unning and in healthy state? Enter Kubernetes.\n\nInitially developed inte
 rnally by Google to replace their own complex container orchestration and
 management framework. It had to meet all the stringent standards and mind-
 boggling scale that Google operates on\, but from the get-go an effort was
  made to make the learning curve and developer experience as approachable
 as possible. At certain point the creators made the case to Google to rele
 ase kubernetes to the open source community -- a crucial decision that has
  helped “k8s” (as it’s commonly referred to as) reach rock star leve
 ls of fame and mind share not just in the FOSS community but also across i
 ndustries and businesses from small operations to gigantic multinational c
 orporations with thousands of deployments.\n\n\nRuben Orduz\,  Nolan Bruba
 ker
LOCATION:Room 20
END:VEVENT
BEGIN:VEVENT
SUMMARY:Network Analysis Made Simple: Part I
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:Have you ever wondered about how those data scientists at Face
 book and LinkedIn make friend recommendations? Or how epidemiologists trac
 k down patient zero in an outbreak? If so\, then this tutorial is for you.
  In this tutorial\, which is Part I of a two-part series\, we will use a v
 ariety of datasets to help you understand the fundamentals of network thin
 king\, with a particular focus on constructing\, summarizing\, and visuali
 zing complex networks. With this tutorial\, you will be well equipped to e
 xplore advanced topics (dynamics on graphs\, evolving graphs\, and network
  propagation methods) in Part II.\n\nEric Ma\, Mridul Seth
LOCATION:Room 19
END:VEVENT
BEGIN:VEVENT
SUMMARY:Build-a-GitHub-Bot Workshop
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:GitHub provides a great platform for collaborating. You can ta
 ke it to the next level by creating custom GitHub bots. By delegating some
  of the chores to a bot\, you get to spend more time developing your proje
 ct and collaborating with others.\n\nLearn how to automate your workflow b
 y building a personal GitHub assistant for your own project. We'll use lib
 raries called `gidgethub` and `aiohttp` to write a GitHub bot that does th
 e following:\n\n- Greet the person who created an issue in your project. \
 n\n- Say thanks when a pull request has been closed.\n\n- Apply a label to
  issues or pull requests.\n\n- Gives a thumbs up reaction to comments **yo
 u** made. (becoming your own personal cheer squad).\n \nThe best part is\,
  you get to do all of the above using Python 3.6! F-strings included!\n\n\
 n\nMariatta Wijaya
LOCATION:Room 16
END:VEVENT
BEGIN:VEVENT
SUMMARY:Build a Search Engine with Python + Elasticsearch
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:One of the most common actions that we take when visiting any
 website is search. A common service that powers search for many sites is E
 lasticsearch -  but what makes it so powerful? What can you do with Elasti
 csearch that you can’t with a regular database?\n\nThis tutorial starts
 with an introduction to Elasticsearch architecture\, including what makes
 it great for search and not so great for other use cases. We will then bui
 ld an application together with a search engine powered by Elasticsearch.
 We will also discuss how to optimize search queries and scale as the volum
 e of data increases.\n\nJulie Qiu
LOCATION:Room 21
END:VEVENT
BEGIN:VEVENT
SUMMARY:Down the rabbit hole. A 101 on reproducible workflows with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:There has been a massive interest in reproducible research / d
 ata\nanalysis pipelines over the last few years.\nBut... how can I ensure
 that what I produce as a Python\nuser is reproducible?\nIn this tutorial w
 e'll be taking you on a journey down the rabbit hole\nof reproducibility.\
 nWe'll be taking a step by step approach to reproducible scientific develo
 pment\nin Python.\nThis means you get a crash course on version control\,
 execution environments\, testing\,\nand continuous integration. And a guid
 e on how to integrate all of these in your\nsoftware projects.\nBy the end
  of the course we hope you will have the necessary tools to make your\nPyt
 hon workflows reproducible no matter if you're starting a brand new projec
 t\nor if this is ready to be shared with the world.\n\nTania  Sanchez Monr
 oy
LOCATION:Room 09
END:VEVENT
BEGIN:VEVENT
SUMMARY:Code Your Heart Out: Beginning Python for Human People with Feelin
 gs
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:This tutorial is for people who are __brand new to Python__. I
 t's for people with curioisty to feed\, anxiety to overcome\, and worlds t
 o change. It's for people named Edna. (And others not named Edna.)\n\nDuri
 ng this tutorial you'll be encouraged to __bring your whole self to learni
 ng__. We'll start with the very basics of Python\, keeping your fingers on
  the keyboard to gain as much practice as possible. Between strings\, func
 tions\, and other fun Python-y things\, we'll discuss learning deeply\, __
 nourishing our brains__\, and boosting happiness with science.\n\nNo prior
  experience required\; come just as you are. __This is about being a whole
  person__. It's about learning Python\, because Python is really cool. It'
 s also about staying afloat. Being productive. Focusing. It's about findin
 g joy in the error codes.\n\nCome play. It'll be awesome.\n\nMelanie Crutc
 hfield
LOCATION:Room 10
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lights Camera Action! Scrape\, explore\, and model to predict Osca
 r winners & box office hits
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:Using Jupyter notebooks\, HTTP requests\, BeautifulSoup\, NumP
 y\, Pandas\, scikit learn\, and matplotlib\, you’ll predict whether a mo
 vie is likely to [win an Oscar](http://oscarpredictor.github.io/) or be a
 box office hit. We’ll step through the creation of an effective dataset:
  asking a question your data can answer\, writing a web scraper\, and answ
 ering those questions using nothing but Python libraries and data from the
  Internet. \n\nDeborah Hanus\, Patricia Hanus\, Sebastian Hanus\, Veronica
  Hanus
LOCATION:Room 15
END:VEVENT
BEGIN:VEVENT
SUMMARY:Foundations of Numerical Computing in Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:Python is one of the world's most popular programming language
 s for numerical\ncomputing. In areas of application like physical simulati
 on\, signal processing\,\npredictive analytics\, and more\, engineers and
 data scientists increasingly use\nPython as their primary tool for working
  with numerical large-scale data.\n\nDespite this diversity of application
  domains\, almost all numerical programming\nin Python builds upon a small
  foundation of libraries. In particular\, the\n`numpy.ndarray` is the core
  data structure for the entire PyData ecosystem\, and\nthe `numpy` library
  provides many of the foundational algorithms used to power\nmore domain-s
 pecific libraries.\n\nThe goal of this tutorial is to provide an introduct
 ion to numpy -- how it\nworks\, how it's used\, and what problems it aims
 to solve. In particular\, we\nwill focus on building up students' mental m
 odel of how numpy works and how\n**idiomatic** usage of numpy allows us to
  implement algorithms much more\nefficiently than is possible in pure Pyth
 on.\n\n\nScott Sanderson
LOCATION:Room 20
END:VEVENT
BEGIN:VEVENT
SUMMARY:Intro to Spatial Analysis and Maps with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:In this tutorial\, we will introduce Python as a spatial probl
 em solving and data visualization tool. To demonstrate the power of Python
  for spatial analysis\, we will solve a spatial problem and make a beautif
 ul map of our results. Along the way\, we will discuss considerations when
  dealing with spatial data and the wide range of Python tools available fo
 r spatial analysis.\n\nChristy Heaton
LOCATION:Room 21
END:VEVENT
BEGIN:VEVENT
SUMMARY:API-Driven Django
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:As the web continues to evolve\, the demand for data-driven ba
 ckends matched with rich frontend experiences grows every day. Django come
 s with a robust templating system and rendering engine\, but more and more
  web applications using Django are just focusing on it’s API abilities.\
 n\nWhat if we could have the best of both worlds? What if we could use Dja
 ngo and django-rest-framework to write views that let us prototype quickly
  using the Django templating system\, and have those same views return API
  responses to rich clients?\n\nIn this tutorial\, we’ll build a sample d
 ata collection and display web application\, taking advantage of the ways
 Django and django-rest-framework work together. The end result will be a w
 eb application you could adapt for many kinds of data collection needs\, a
 nd you’ll come away knowing how to get a rich API and a frontend prototy
 pe out of just one Django view. \n\n\nPhilip James
LOCATION:Room 10
END:VEVENT
BEGIN:VEVENT
SUMMARY:Introduction to TDD with Django
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:Over the past few years\, automated software testing has moved
  from being a niche interest to being the default assumption.  This tutori
 al is an introduction to Test-Driven Development (TDD) for the world of we
 b development in Python using the Django framework.\n\nThe tutorial is sui
 table for people who are new to either testing\, or Django\, or both\, alt
 hough some basic working knowledge of Python syntax (or programming in ano
 ther language) is assumed.\n\nLearn about:\n- Unit testing and Functional
 testing\n- the Selenium browser automation tool\n- Python's unittest stand
 ard library module\n- Django models\, views and templates\n- testing front
 -end and back-end code\n- refactoring\, using tests \n- the unit-test/code
  cycle\, or Red-Green-Refactor\, TDD workflow\n- and the Testing Goat\, Py
 thon's unofficial mascot for testing!\n\nCome prepared!  You'll need a Pyt
 hon 3.6 virtualenv with Django and Selenium installed.  Detailed instructi
 ons are provided [here](https://www.obeythetestinggoat.com/book/pre-requis
 ite-installations.html).\n\n\nHarry  Percival
LOCATION:Room 09
END:VEVENT
BEGIN:VEVENT
SUMMARY:Introduction to Python for Data Science
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T122000
DESCRIPTION:This tutorial introduces users to Python for data science. Fro
 m data cleaning to model building\, we will work through a series of short
  examples together using some real-world health inspection data. Attendees
  will have their hands on the keyboard\, using the Python standard library
  and pandas to clean data and scikit-learn to build some models.\n\nSkippe
 r Seabold
LOCATION:Room 22
END:VEVENT
BEGIN:VEVENT
SUMMARY:Going Serverless with OpenFaaS\, Kubernetes\, and Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:OpenFaaS (Functions as a Service) is a framework for building
 serverless\, event-driven functions with Docker and Kubernetes. \n\nIn thi
 s tutorial\, you'll learn how to build and deploy a full-stack application
  that uses Flask (client-facing app) along with OpenFaaS to handle backgro
 und processes.\n\nMichael Herman
LOCATION:Room 22
END:VEVENT
BEGIN:VEVENT
SUMMARY:Web Applications\, A to Z
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T122000
DESCRIPTION:Modern web applications have gotten complicated -- backend log
 ic\, front-end logic\, storage and deployment options abound.\n\nThis tuto
 rial will take a tour of all the pieces that go into making a web applicat
 ion\, and show how they all fit together -- using specific choices\, speci
 fic examples and a lot of hands-on programming\, to give participants a ch
 ance to  actually write a web applications: all the parts. \n\nWe will use
  some external third-party services\, but care is taken to fit into the fr
 ee tier.\n\n\nMoshe Zadka
LOCATION:Room 19
END:VEVENT
BEGIN:VEVENT
SUMMARY:Data science and Engineering at Shopify
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T090000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T103000
DESCRIPTION:Shopify’s workshop will be a series of two 45 minute tech ta
 lks on our use of Python and its ability to make commerce better for every
 one. Shopify is the leading cloud-based\, multi-channel commerce platform
 where entrepreneurs can design\, set up\, and manage their stores across m
 ultiple sales channels. With a decade worth of data\, we are able to build
  products using Python that help our entrepreneurs run their businesses ev
 eryday. We'll hear about how Python is particularly useful when it comes t
 o understanding our user base by sifting through tons of data from Franço
 ise Provencher and about how PySpark can be used to reliably process data
 to feed reports\, dashboards\, and products from Chris Fournier.\n\nExcite
 d about what you learned and want to continue the conversation\, visit our
  booth in the expo hall!\n\nFrançoise Provencher\, Chris Fournier
LOCATION:Room 23
END:VEVENT
BEGIN:VEVENT
SUMMARY:STEAM Workshops using Jupyter Notebooks\, JupyterHub\, and Binder
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T102000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T105000
DESCRIPTION:Middle School and High School students can learn by doing. Jup
 yter Notebooks and the rich Python ecosystem with libraries on a variety o
 f topics can engage many learners of the sciences and humanities. Interact
 ive content\, using Jupyter Widgets and visualization libraries\, put the
 student in charge of manipulating content and extending their learning. Gi
 ving students engaging content in familiar subjects encourages them to dev
 elop and use computational skills to build upon their interests.\n\nOne di
 fficulty of teaching workshops is access to computers and the ability to i
 nstall software on those systems. This talk will demonstrate how a worksho
 p organizer or teacher can deploy JupyterHub easily for a class using the
 Zero to JupyterHub Guide\, Kubernetes\, and Cloud Services. Even if studen
 ts only have access to smartphones\, tablets\, or shared computers\, a wor
 kshop can be held using Binder which allows a notebook environment to be s
 erved at the click of a button to any modern web browser.\n\nCarol Willing
LOCATION:Room 25A\, Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:How we designed an inclusivity-first conference on a shoestring bu
 dget and short timeline
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T112000
DESCRIPTION:New conferences rarely have resources to run the sort of outre
 ach and inclusion programs that big conferences have. It’s hard to guess
  how much money you’ll have to spend\, how many attendees you’ll have\
 , and what your new community will look like. With so many things to worry
  about\, it’s no surprise that most events don’t prioritise outreach u
 ntil they’ve got a few years under their belt\, if at all.\n\nIt doesn
 ’t have to be this way\, and it can even be easier to build a new event
 around outreach and inclusion than it is to build it in later on!\n\nThis
 talk shares the story of North Bay Python’s inaugural conference\, which
  we planned in under 6 months\, ran on a $40\,000 budget\, and built a wel
 coming community to make it real. We made inclusivity a founding principle
  and did so without compromising our speaker lineup while still attracting
  great sponsorship and hosted an event that almost every attendee wants to
  return to.\n\nIn this talk\, we’re going to share with you how we built
  a conference\, from the ground up\, to be as inclusive as we could make i
 t. We’ll touch on early organisation\, marketing\, and on-the ground log
 istics. Throughout the talk\, you’ll learn:\n\n* How we designed a budge
 t that let us prioritise outreach and inclusion activities\n* How we built
  the community that we wanted before the conference even started\n* How we
  ran an event that proved that we meant everything we said\n\nYou too can
 host a new conference with a great lineup on a shoestring budget and short
  timeline\, and you can do it while being inclusive\, welcoming\, and putt
 ing attendee safety first. Find out how you can have your cake\, eat it\,
 and still have lots to share with your new community.\n\n\nChristopher Neu
 gebauer\, Josh Simmons\, Sam Kitajima-Kimbrel
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Visualizing Algorithms with Python and Programmable LEDs
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T112000
DESCRIPTION:Recently\, a new LED strip specification\, APA102\, has been r
 eleased which allows these strips to be driven by a general purpose CPU in
 stead of a dedicated microcontroller. This allows us the luxury of control
 ling them with Python!\n\nI'll teach you about how to get the the hardware
 \, how to think about programming for lights and how to build anything fro
 m a psychedelic art installation to home lighting to an educational tool.
 \n\nProgramming with lights is awesome because you can SEE bugs with your
 eyes. I think the use of these LED's have great potential as a teaching to
 ol because of the immediacy of the feedback.\n\nLIVE hardware demos!  See
 Quicksort in brilliant colors!\n\nChristopher Beacham / Lady Red
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Coding through Adversity
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T112000
DESCRIPTION:In 2017\, I was released from prison after serving 17 years. O
 ne of the most transformational experiences I had while incarcerated was l
 earning to code\, through a pioneering new program called Code.7370 — th
 e first coding curriculum in a United States prison.\nIn this talk\, I’d
  like to share my experiences learning to code in prison and getting a sof
 tware engineering job after my release\, with the goals of:\nInspiring new
  programmers to stick with it and be confident in their abilities\nInspiri
 ng educators to think about how to support new coders in a broad range of
 learning environments (there’s no internet in prison!)\nInspiring everyo
 ne to think about the potential for rehabilitation in prison in a new way\
 nMy story:\nMy first computer was the Commodore 64. I can still remember w
 riting in Basic when you had to number the lines of code yourself and save
  everything onto cassette drives. Once the Atari 2600 came on the scene\,
 I traded in my keyboard for a joystick and lost myself in each successive
 release. From Intellivision to Colecovision and from Nintendo to Playstati
 on\, I immersed myself to test my skill and lose myself in these magical d
 igital universes.\nAs I left my teen years behind\, I discovered new ways
 of exciting and losing myself in a sea of alcoholism and drug addiction. A
 s Y2K came and went\, American Online and Motorola flip phones were about
 the hottest thing going and I began dealing drugs to support my reckless l
 ifestyle. This decision would lead me to taking a friend’s life over a s
 ix pound suitcase of marijuana. It was the worst decision I’ve ever made
  in my life and would cost me the next 17 years in prison.\nWhile I was in
  prison\, I sat on the sidelines and watched as the world of technology wa
 s passing me by. I spent over a decade in prison with no access to compute
 rs\, the internet\, or email. The only glimmers I saw were from commercial
 s on TV and my subscription to Wired magazine.\nThat was until I was accep
 ted to The Last Mile\, an entrepreneurial program at San Quentin State Pri
 son. We read books by authors like Guy Kawasaki\, Reid Hoffman\, and Andy
 Smith. We also learned to develop business plans for our own entrepreneuri
 al ideas that involved 3 components\, 1) something we were passionate abou
 t 2) something that involved technology 3) something with a social cause.
 My idea was for an online life coaching platform called Fitness Monkey tha
 t would empower addiction recovery through physical fitness.\nIn 2014\, th
 e door to technology was finally opened. The Last Mile started the Code.73
 70 program where I was accepted and began learning front-end web developme
 nt. With every line of HTML\, CSS\, and JavaScript I wrote\, I fell in lov
 e with coding for its ability to solve problems and create and design solu
 tions that could enhance people’s lives.\nI carried a coding book with m
 e everywhere I went and my love for coding would propel me to the head of
 the class. I begin doing breakout sessions with my classmates on coding ch
 allenges assigned to us by Hack Reactor\, a dev bootcamp in San Francisco.
  The first class was such a success\, they started a Track 2 that taught a
 dvanced CSS and went into depth on data structures and algorithms. Later\,
  we began delving into full-stack development with Angular\, Node\, and Wo
 rdPress.\nWith so many qualified inside coders\, The Last Mile partnered w
 ith the Prison Industry Authority to open The Last Mile Works\, a dev shop
  inside the walls of San Quentin. We began developing projects for outside
  companies like Airbnb and the Coalition for Public Safety. As well as\, e
 arning a wage that would provide us with a savings account upon our releas
 e to help transition back into society. I was building a resume and gainin
 g real world experience while incarcerated that would enable me to find em
 ployment with The Last Mile upon my release.\nOn May 4\, 2017\, I was rele
 ased from prison after serving a total of 17 years. I did everything in my
  power to make sure that this time wasn’t wasted. I was hired for a paid
  internship with The Last Mile as a Web Developer to build a learning mana
 gement system that would package the coding program at San Quentin into a
 piece of software that could be distributed to other prisons throughout Ca
 lifornia and eventually the entire country. This internship also included
 our application to Y-Combinator where we pitched our project to Michael Se
 ibel\, the CEO  and his team of VC’s. \nWe finished the project and it
 ’s currently being tested in San Quentin and two different Women’s pri
 sons. As my internship drew to an end\, I began to reach out and see what
 other opportunities would be available to someone with my experience.  I w
 ent through a 4 hour interview process with the entire engineering team at
  FANDOM. I’m not going to say it was as difficult as the parole board\,
 but these guys put me through my paces and in the end offered my a 90 day
 internship. Here was my chance!\nWhen my internship began\, I was given qu
 ite a few tasks that those in the industry call pixel pushing. Design twea
 ks and CSS fixes. All of the cards on the project management board were la
 beled 1-5 on complexity. For the first few weeks\, all of my cards were on
 es. However\, with each successive week\, I became more familiar with the
 code base and started to reach for some of the more difficult assignments.
  I believe one of my strengths was being able to admit when I had no clue
 what I was doing and courage to ask for help. \nMy time at FANDOM was an a
 mazing experience in learning how to collaborate on an international team
 that spans eight different countries. I learned how to effectively communi
 cate with different departments within the office in San Francisco. And\,
 one of the most amazing parts was getting to tell my story to the entire c
 ompany at the all hands meeting. Now\, I was not only the ‘old’ intern
 \, but someone who had persevered through adversity and was willing to sha
 re his truth of committing a horrible crime\, but also of earning a colleg
 e degree\, running marathons\, developing business ideas\, and learning to
  code.\nAll of the work paid off this past December\, when I was offered a
  full-time position as a Software Engineer for FANDOM. One of my biggest f
 ears was what would life be like for me once I was released from prison. T
 he answer is nothing short of incredible. The Last Mile’s mantra is ‘B
 elieve in the Process’ and there were times when I wasn’t even quite s
 ure what the process was\, but I believed anyway. For every step I took le
 arning and growing\, more and more opportunities opened up for me. I found
  great freedom in defaulting to yes.\n\n\nChris Schuhmacher
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mu - How to Make a Kids' Code Editor
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T112000
DESCRIPTION:[The Raspberry Pi Foundation][1] receives feedback about the d
 ifficulties beginner programmers encounter when learning Python from thous
 ands of volunteer mentors at after-school CodeClubs and thousands of teach
 ers from around the world.\n\n[Mu][3] is a Python code editor for beginner
  programmers based upon this feedback.\n\nThis talk tells the story of Mu:
  why it was created\, how we built it and includes a demonstration of how
 it works.\n\n  [1]: http://raspberrypi.org/\n  [3]: http://codewith.mu/\n\
 n\nNicholas Tollervey
LOCATION:Room 25A\, Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Code Reviews Using Art Critique Principles
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T112000
DESCRIPTION:Code reviews don't have to be a time consuming\, morale zappin
 g\, arduous tasks. Not only can they catch bugs and errors but they can co
 ntribute in positive ways to the individual developer\, the team\, managem
 ent and company as a whole.  \n\nArt critiques have existed in academia fo
 r hundreds of years. The methodology of the critique has evolved to be tim
 e sensitive and productive\, while keeping the enthusiasm of the student a
 rtist intact.  \n\nThe purpose of the art critique is to get peers and men
 tors to look at the work and raise any problems they may see. It's also ti
 me where people with more experience could contribute their knowledge in a
  helpful way. This process is about producing the best work\, quickly and
 in a productive and constructive way. \n \nThese methods can be applied to
  code review.  \n\n\n\nStacy Morse
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Build Teams as an Engineer
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T112000
DESCRIPTION:We build product and software as teams. And as anyone who as w
 orked on a team knows\, there’s often a lot more that goes into working
 together to build that product than actually just building the product its
 elf. A highly functional team is not as elusive it may seem. Software engi
 neering is a skill we’ve developed\, but even more importantly software
 engineering on teams is another skill we’ve been practicing and improvin
 g on as an industry. Software engineering principles and best practices ma
 y seem to have very little to do with teamwork\, but being able to thought
 fully apply some of what we’ve learned as engineers towards teamwork\, w
 e can help move towards creating such success with our teams.\n\nJoyce Jan
 g
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Solve Your Problem With Sloppy Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T112000
DESCRIPTION:Stop writing crappy shell scripts—write crappy Python script
 s instead!\n\nOther talks will show you how to write clean\, performant\,
 robust Python.  But that's not always necessary.  When writing personal au
 tomation or solving one-shot problems\, it can be safe (and fun!) to quick
 ly hack something together.\n\nThis talk will show examples of problems su
 itable for this approach\, scenarios where it's reasonable to cut corners\
 , novel techniques that can help break a problem down\, and shortcuts that
  can speed development.\n\nLarry Hastings
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Thinking Outside the GIL with AsyncIO and Multiprocessing
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T112000
DESCRIPTION:Have you ever written a small\, elegant application that could
 n't keep up with the growth of your data or user demand? Did your beautifu
 l design end up buried in threads and locks? Did Python's very special Glo
 bal Interpreter Lock make all of this an exercise in futility?\n\nThis tal
 k is for you! With the combined powers of AsyncIO and multiprocessing\, we
 'll redesign an old multithreaded application limited by the GIL into a mo
 dern solution that scales with the demand using only the standard library.
  No prior AsyncIO or multiprocessing experience required.\n\n\nJohn Reese
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python Data Sonification for Science and Discovery
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T112000
DESCRIPTION:What do geiger counters\, black holes\, heart monitors\, and v
 olcanoes have in common?  They all can use sound to convey information! Th
 is talk will explore using python for sonification: the process of transla
 ting data into sound that could otherwise be represented visually. Have yo
 u ever wondered how to use python to represent data other than making char
 ts and graphs?  Are you a musician looking for inspiration in the world ar
 ound you?  This talk will go over how to use python to translate time seri
 es data to MIDI that can be played back in real time. We’ll sonically in
 terpret light-curve data from the Kepler space telescope using pygame\, MI
 DIUtil\, and astropy\, turning points on a graph into a musical masterpiec
 e! Come learn about how data sonification is used to help people\, to expa
 nd the reach of scientific research\, and to create music from data.\n\nEr
 in Braswell
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Democratizing Distributed Computing with Dask and JupyterHub
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T112000
DESCRIPTION:We use JupyterHub\, XArray\, Dask\, and Kubernetes to build a
 cloud-based system to enable scientists to analyze and manage large datase
 ts.  We use this in practice to serve a broad community of atmospheric and
  climate scientists.\n\nAtmospheric and climate scientists analyze large v
 olumes of observational and simulated data to better understand our planet
 .  They have historically used tools like NumPy and SciPy along with Jupyt
 er notebooks to combine efficient computation with accessibility.  However
 \, as datasets increase in size and collaboration extends to new populatio
 ns of scientists these tools begin to feel their age.  In this talk we use
  more recent libraries to build a modern deployment for academic scientist
 s.  In particular we use the following tools:\n\n- **Dask:** to paralleliz
 e and scale NumPy computations\n- **XArray**: as a self-discribing data mo
 del and tool kit for labeled and index arrays\n- **JupyterLab:** to enable
  more APIs for users beyond the classic notebook\n- **JupyterHub:** to man
 age users and maintain environments for a new population of cloud-friendly
  users\n- **Kubernetes:** to manage everything and deploy easily on cloud
 hardware\n\nThis talk will focus less on how these libraries work and will
  instead be a case study of using them together in an operational setting.
   During the talk we will build up and deploy a running system that the au
 dience can then use to access distributed computing resources.\n\n\nMatthe
 w Rocklin
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Data Visualization in Mixed Reality with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T105000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T112000
DESCRIPTION:Data Visualization charts are supposed to be our map to inform
 ation. However\, when making charts\, customarily we are just re-sizing li
 nes and circles based on metrics instead of creating data-driven version o
 f reality.  The contemporary charting techniques have a few shortcomings (
 especially when dealing with high-dimensional dataset): \n\n* **Context Re
 duction**: in order to fit a high-dimensional dataset into a chart one nee
 ds to filter/ aggregate/ flatten data which results in reduction of full c
 ontext of information.  Without context most of the charts show only a par
 t of the story\, that can potentially lead to data misinterpretation/misun
 derstanding. \n* **Numeric Thinking**: naturally humans have hard time per
 ceiving big numbers. While data visualization is suppose to help us to con
 ceptualize large volumes\,  unless the dataset is carefully prepared\, 2D
 charts rarely give us the intuitive grasp of magnitude. \n* **Perceptual d
 e-humanization**: when examining charts it is easy to forget that we are d
 ealing with activity in real world instead of lines/bars. \n\nAugmented/Mi
 xed Reality can potentially solve all of the issues listed above by presen
 ting an intuitive and interactive environment for data exploration. Three
 dimensional space provides conditions to create complex data stories with
 more “realistic assets” (beyond lines and bars). The talk would presen
 t the architecture required to create MR data visualization story with Pyt
 hon (70% of architecture)\, starting with drawing 3D assets in a data-driv
 en way and finishing with deployment on MR devices. \n\nAnna Nicanorova
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Let's build a Slack app!
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T110000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T123000
DESCRIPTION:In this workshop\, we'll guide you through the process and too
 ls needed to build your first Slack bot. We'll go over our Python SDKs\, w
 alk through an example app and talk about what's going on under-the-hood a
 s your app interacts with Slack's APIs. \n\nThis workshop will be beginner
 -friendly\, but also allow those with prior experience with Slack to learn
  about new features\, tools and ask us questions directly.\n\n\nJason Roch
 e
LOCATION:Room 13
END:VEVENT
BEGIN:VEVENT
SUMMARY:Deploy Your Python Apps Quickly with Heroku
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T110000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T123000
DESCRIPTION:A crash course on deploying Python applications to Heroku. We'
 ll take a pre-made Django app\, spin it up locally\, then deploy it to Her
 oku. We'll then go over the tools that Heroku provides for managing your a
 pplications.\n\nIan Stapleton Cordesco
LOCATION:Room 23
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python with Visual Studio and Visual Studio Code – tips and tric
 ks for a more productive developer l
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T110000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T123000
DESCRIPTION:Developers have many choices when it comes to writing Python c
 ode from editors to IDEs\, from free tools to paid\, from tools designed s
 pecifically for their platform to tools running on many\, from open source
  to closed source tools and so on… In this demo-driven session\, we’ll
  show you why we think the cross-platform\, free\, and open source Visual
 Studio Code will become your favorite tool for all your Python needs. From
  editing to linting\, to debugging and more\, you will learn how to get st
 arted\, as well as tips and tricks to save you time in your everyday devel
 opment lifecycle. You will also learn when to consider Visual Studio 2017\
 , which for certain scenarios (such as mixed C and Python development)\, c
 an offer you more than any other Python tool on the planet.\n\nSteve Dower
LOCATION:Room 25C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Coding as Enrichment: How to Empower Students with Creative Coding
  Experiences
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T112000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T115000
DESCRIPTION:My goal is to share resources\, pedagogy\, best practices and
 lessons learned in bringing hands-on coding workshops to underserved K-12
 students.\n\nFor fall 2017 over ten weeks\, [Code Park Houston](https://co
 deparkhouston.org/)\, a  a 501(c)(3) non-profit organization piloted a fre
 e after-school Creative Coding program at [BakerRipley - Leonel Castillo |
  BakerRipley](https://www.bakerripley.org/locations/leonel-castillo-commun
 ity-center). We focused on **Coding as Enrichment** by encouraging our stu
 dents to pursue creative endeavors such as storytelling with Minecraft and
  making procedural art with [p5.js | get started](https://p5js.org/get-sta
 rted/). Instead of traversing a programming language’s syntax peculiarit
 ies\, our students drew colorful lines\, made interactive buttons\, and sh
 ared their creations.\n\nWe also worked to provide a positive and collabor
 ative learning environment. Our students were encouraged to learn by makin
 g mistakes and exploring his or her own interests. In the process\, we jus
 t happened to facilitate knowledge in coding\, problem-solving\, electroni
 cs science and collaboration.\n\nWhen it comes to technology\, the arts ca
 n be an unusual pathway to success. But we know and see that creativity is
  the secret sauce that inspires the next generation of diverse and passion
 ate students.\n\nThis program was a rewarding experience for everyone invo
 lved. We faced and overcame many challenges. For the talk  I would like to
  share:\n\n**Tools of the Trade** — what software and hardware we us
 ed and why.\n**Classroom Set Up** — how to provide a positive and en
 couraging learning environment.\n**Best Practices for Tech Educators**
 — why we are learning facilitators.\n**Open Source Creative Coding Cur
 riculum** — open source curriculum to be used in your own classroom!
 \n\nWanjun Zhang
LOCATION:Room 25A\, Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Oops! I Committed My Password To GitHub!
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T120000
DESCRIPTION:What do AWS\, GitHub\, Travis CI\, DockerHub\, Google\, Stripe
 \, New Relic\, and the rest of the myriad of services that make our develo
 per life easier have in common?\n They all give you secret keys to authent
 icate with. Did you ever commit one of these to source control by mistake?
  That happened to me more times than I'm willing to admit!\n\nIn this talk
  I'm going to go over the best practices to follow when when writing Pytho
 n applications that prevent this type of accident.\n\nMiguel Grinberg
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Inside the Cheeseshop: How Python Packaging Works
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T120000
DESCRIPTION:Questions and confusion about the Python packaging ecosystem a
 bound. What is this `setup.py` file? What's the difference between wheels
 and eggs? Do I use setuptools or distutils? Why should I use twine? Do I p
 ut my projects dependencies in a `requirements.txt` or in `setup.py`? How
 do I just get my module up on PyPI? Wait\, what is Warehouse?\n\nThis talk
  will identify the key tools one might encounter when trying to distribute
  Python software\, what they are used for\, why they exist\, and their his
 tory (including where their weird names come from). In addition\, we'll se
 e how they all work together\, what it takes to make them work\, and what
 the future has in store for Python packaging.\n\nDustin Ingram
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:How Netflix does failovers in 7 minutes flat
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T120000
DESCRIPTION:During peak hours\, Netflix video streams make up more than on
 e third of internet traffic. Netflix must stream uninterrupted in the face
  of widespread network issues\, bad code deploys\, AWS service outages\, a
 nd much more. Failovers make this possible.\n\nFailover is the process of
 transferring all of our traffic from one region in AWS to another. While m
 ost of Netflix runs on Java\, failovers are powered entirely by Python. Py
 thon's versatility and rich ecosystem means we can use it for everything f
 rom predicting our traffic patterns to orchestrating traffic movement\, wh
 ile dealing with the eventual consistency of AWS.\n\nToday\, we can shift
 all of our 100 million+ users in under seven minutes. A lot of engineering
  work went into making this possible. The issues we faced and solutions we
  created have broad application to availability strategies in the cloud or
  the datacenter.\n\nAmjith Ramanujam
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Strategies to Edit Production Data
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T120000
DESCRIPTION:At some point\, we all find ourselves at a SQL prompt making e
 dits to the production database. We know it's a bad practice and we always
  intend to put in place safer infrastructure before we need to do it again
  — what does a better system actually look like?\n\nThis talk progresses
  through 5 strategies for teams using a Python stack to do SQL writes agai
 nst a database\, to achieve increasing safety and auditability:\n\n(1) Dev
 elop a process for raw SQL edits \n(2) Run scripts locally\n(3) Deploy and
  run scripts on an existing server\n(4) Use a task runner\n(5) Build a Scr
 ipt Runner service\n\nWe’ll talk about the pros and cons of each strateg
 y and help you determine which one is right for your specific needs.\n\nBy
  the end of this talk you’ll be ready to start upgrading your infrastruc
 ture for making changes to your production database safely!\n\nJulie Qiu
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Practical Sphinx
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T120000
DESCRIPTION:Each member of your project team uses something different to d
 ocument\ntheir work -- RestructuredText\, Markdown\, and Jupyter Notebooks
 . How do\nyou combine all of these into useful documentation for your proj
 ect's users.\nSphinx and friends to the rescue!\n\nLearn how to integrate
 documentation into your everyday development\nworkflow\, apply best practi
 ces\, and use modern development tools and\nservices\, like Travis CI and
 ReadTheDocs\, to create engaging and up-to-date\ndocumentation which users
  and contributors will love.\n\n\n\nCarol Willing
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:DragonPaint – Bootstrapping Small Data to Color Cartoons
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T120000
DESCRIPTION:The creation of sufficient quantities of labeled training data
  is one of the biggest challenges for machine learning applications\, espe
 cially when the data itself must be created\, not just labeled. DragonPain
 t presents a generalizable strategy for minimizing the manual creation of
 data using rule based algorithms to automate the creation of a restricted
 subset of data and then bootstrapping to the automated creation of unrestr
 icted (rule breaking) training and test data. A gentle introduction to com
 puter vision\, graphics and machine learning\, we use Python and geometry
 to build an image data set for a TensorFlow model. \n\nGretchen Greene
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Augmenting Human Decision Making with Data Science
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T120000
DESCRIPTION:Do we even need humans? Humans and data science are flawed on
 their own. Humans lack the ability to process large volumes of information
 . Machines lack intuition\, empathy\, and nuance. You'll learn how to guid
 e users of expert-use systems by applying data science to their user exper
 ience. This allows us to take advantage of the human-touch while leveragin
 g our large datasets. What is the relationship between human decisions and
  algorithms? Are we thinking about data science all wrong? In this talk\,
 you'll learn the ways we balance human decisions and data science througho
 ut our applications\, the challenges we have faced along the way and the f
 uture of the relationship between humans and data.\n\nKelsey Pedersen
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Code like an accountant: Designing data systems for accuracy\, res
 ilience and auditability
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T120000
DESCRIPTION:As engineers\, we care a lot about the reliability of our appl
 ications. When a website falls over\, pagers go off\, and engineers burst
 into action to bring a site back to life.  Postmortems are written\, and t
 eams develop strategies to prevent similar failures in the future.\n\nBut
 what about the reliability of our data? Would _you_ trust financial report
 s built on your data? \n\nIf not\, what can you do to improve data health?
  If you _would_ trust these reports\, how can you prove to customers\, inv
 estors\, and auditors alike that they should too?\n\nIn this talk\, you’
 ll learn to apply strategies from the world of dev-ops to data. You’ll l
 earn about questions auditors ask that can help you pinpoint data problems
 . You’ll also learn some accounting-specific tools for accurate and time
 ly record keeping that I’ve found fascinating and helpful!\n\n\n\nSophie
  Rapoport
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Performance Python: Seven Strategies for Optimizing Your Numerical
  Code
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T120000
DESCRIPTION:Python provides a powerful platform for working with data\, bu
 t often the most straightforward data analysis can be painfully slow. When
  used effectively\, though\, Python can be as fast as even compiled langua
 ges like C. This talk presents an overview of how to effectively approach
 optimization of numerical code in Python\, touching on tools like numpy\,
 pandas\, scipy\, cython\, numba\, and more.\n\nJake VanderPlas
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Counter\, Gauge\, Upper 90 - Oh my!
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T113000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T120000
DESCRIPTION:Setting up application monitoring is often an afterthought\, a
 nd in the speaker's opinion can be a bit overwhelming to get started with.
  What is a `metric`? What is a `gauge`? What is a `counter`? What's that `
 upper 90` metric you have up on your `dashboard`? And what *all* metrics s
 hould I monitor?\n\nThis talk aims to get you started on the monitoring jo
 urney in Python. In addition to clearing up some of the jargon\, we will l
 ook at `statsd` and `prometheus` monitoring systems and how to integrate o
 ur applications with these.\n\nWithout the numbers\, we are really flying
 blind!\n\nAmit Saha
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beyond Unit Tests: Taking Your Testing to the Next Level
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T125500
DESCRIPTION:You've used pytest and you've used mypy\, but bugs are still s
 lipping through your code. What's next? In this talk\, we cover two simple
  but powerful tools for keeping your code problem-free. Property-based tes
 ting\, provided by the [Hypothesis](https://hypothesis.readthedocs.io/en/l
 atest/) library\, lets you run hundreds of tests from a single template. C
 ontracts\, via [dpcontracts](https://github.com/deadpixi/contracts)\, make
  your program test itself. You'll learn how and why to use these tools and
  how to combine them with the rest of your testing suite.\n\n\nHillel Wayn
 e
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:import time_travel: A Primer on Timezones in Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T125500
DESCRIPTION:Timezones are one of those things every programmer loves to ha
 te. Most of us\, at\nleast in the US\, just try to ignore them and hope no
 body notices. Then twice a\nyear\, we fear with impending doom those 3 sma
 ll words: Daylight Saving Time.\n\nIt doesn't have to be this way. Armed w
 ith some best practices and a little help\nfrom supporting libraries\, tim
 ezone-related bugs can be a thing of the past.\n\nThis talk explores stand
 ard library and 3rd party library timezone support\, as\nwell as persisten
 ce and serialization techniques for timezone-aware datetimes.\nBy the end
 of the talk\, the listener should feel confident in their ability to\ncorr
 ectly store\, send\, receive\, and manipulate datetime objects in any time
 zone.\n\nDavid Gouldin
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Colossal Cave Adventure in Python... in the browser!
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T125500
DESCRIPTION:Colossal Cave\, also known as Adventure or ADVENT\, is the ori
 ginal text adventure. It was written in FORTRAN IV and there is practicall
 y no way to run the original program without translating it. We'll explore
  software archeology to write a Python interpreter to run the FORTRAN code
  as-is\, without translating it. Come learn about pre-ASCII and 36-bit int
 egers and writing interpreters in Python!\n\nAnd\, we'll show how to use B
 eeWare's Batavia Python interpreter (in JavaScript) to execute the program
 . FORTRAN IV in Python in JavaScript in your browser!\n\nChristopher Swens
 on
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Behavior-Driven Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T124000
DESCRIPTION:Behavior-Driven Development (BDD) is gaining popularity as an
 improved way to collaborate over product features and tests. In Python\, *
 *behave** is one of the leading BDD test frameworks. Using **behave**\, te
 ams write Gherkin behavior scenarios (e.g.\, tests) in plain language\, an
 d then programmers write Python code to automate the steps. BDD testing is
  great because tests are self-documenting and steps abide by the DRY princ
 iple. An example test could be:\n\n> Given the DuckDuckGo home page is dis
 played\n> When the user searches the phrase "Python"\n> Then search result
 s for "Python" are shown\n\nThis talk will teach how to use **behave** to
 develop well-designed test scenarios and a robust automation framework. It
  will focus on the layers of the behave framework: feature files\, step de
 finitions\, support classes\, and config files. A full example project wil
 l be hosted on GitHub for audience members to reference after the talk.\n\
 n\n\nAndrew Knight
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:"WHAT IS THIS MESS?" - Writing tests for pre-existing code bases
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T124000
DESCRIPTION:Many of us practice test driven development\, and pride oursel
 ves in our code coverage. This is relatively easy to do when you begin a n
 ew project\, but what happens when you take over an existing code base wit
 h little to no tests? Where and how do you start writing tests? This task
 can be very intimidating and frustrating\, but can be accomplished!\n\nThi
 s talk will run through some common approaches and methodologies for addin
 g test coverage to pre-existing code (that you might not even be familiar
 with at all). The next time you take over an untested monolith\,  you will
  be able to do the right thing and start writing tests instead of hoping f
 or the best!\n\nJustin Crown
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Demystifying the Patch Function
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T125500
DESCRIPTION:One of the most challenging and important thing fors for Pytho
 n developers learn is the unittest mock library. The patch function is in
 particular confusing- there are many different ways to use it. Should I us
 e a context manager? Decorator? When would I use it manually? Improperly u
 sed patch functions can make unit tests useless\, all the while making the
 m look as if they are correctly testing code.Let’s learn how to wield pa
 tch with confidence!\n\n\nLisa Roach
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:One weird trick to becoming a better software developer
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T124000
DESCRIPTION:Those of us who have worked in software development for longer
  than a few years probably feel we have an intuitive sense of what a great
  developer is. Some traits come more easily to mind than others when it co
 mes to identifying a great developer. In this talk we will take a slightly
  different approach to evaluating software development best practices\, an
 d identify one underrated skill common to great software developers: empat
 hy. I hope to demonstrate that cognitive and emotional empathy skills are
 critical to good software development. We will explore ways to cultivate t
 his trait in order to become better developers\, both for our own sakes an
 d for the sake of the teams in which we work.\n\nEsther Nam
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Programming microbes using Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T124000
DESCRIPTION:The genome of a typical microbe contains roughly 5 million bas
 e pairs of DNA including > 4000 genes\, which provide the instructions for
  cellular replication\, energy metabolism\, and other biological processes
 . At Zymergen\, we edit DNA to design microbes with improved ability to pr
 oduce valuable materials and molecules. Microbes with these edits are buil
 t and tested in high throughput by our fleet of robots. Genomes are far to
 o large for exhaustive search\, so identifying which edits to make require
 s machine learning on non-standard features. Our task to extract informati
 on from trees\, networks\, and graphs of independently representable knowl
 edge bases (metabolism\, genomics\, regulation)\, in ways that respect the
  strongly causal relationships between systems. In this talk\, I will desc
 ribe how we use Python’s biological packages (e.g. BioPython\, CobraPy\,
  Escher\, goatools) and other packages (NetworkX\, TensorFlow\, PyStan\, A
 irFlow) to extract machine learning features and predict which genetic edi
 ts will produce high-performance microbes.\n\nJanet Matsen
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Reinventing the Parser Generator
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T125500
DESCRIPTION:Writing lexers and parsers is a complex problem that often inv
 olves the use of special tools and domain specific languages (e.g.\, the l
 ex/yacc tools on Unix).  In 2001\, I wrote Python versions of these tools
 which can be found in the PLY project.  PLY predates a huge number of mode
 rn Python features including the iteration protocol\, generators\, decorat
 ors\, metaclasses\, and more.  As such\, it relied on a variety of clever
 hacks to layer a domain specific parser specification language on top of P
 ython itself. \n\nIn this talk\, I discuss a modernization of the PLY proj
 ect that abandons its past and freely abuses modern Python features includ
 ing advanced metaclasses\, guaranteed dictionary ordering\, class decorato
 rs\, type hints\, and more.   The result of this work can be found in the
 SLY project.  However\, this talk isn't so much about SLY as it is focused
  on how far you can push Python metaprogramming features to create domain-
 specific languages.   Prepare to be horrified--and to write code that will
  break your IDE. \n \n\nDavid Beazley
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:HOWTO Write a Function
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T121000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T125500
DESCRIPTION:A function is a small chunk of code that does useful work. You
 r job when writing a function is to do it in a way that it easy to read. B
 ased on over 15 years of code reviews here are some tips and guidelines I
 give again and again. \n\nJack Diederich
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Type-checked Python in the real world
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T131000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T134000
DESCRIPTION:You've heard about Python type annotations\, but wondered if t
 hey're useful in the real world? Worried you've got too much code and can'
 t afford to annotate it?  Type-checked Python is here\, it's for real\, an
 d it can help you catch bugs and make your code easier to understand. Come
  learn from our experience gradually typing a million-LOC production Pytho
 n application!\n\nType checking solves real world problems in production P
 ython systems. We'll cover the benefits\, how type checking in Python work
 s\, how to introduce it gradually and sustainably in a production Python a
 pplication\, and how to measure success and avoid common pitfalls. We'll e
 ven demonstrate how modern Python typechecking goes hand-in-hand with duck
 -typing! Join us for a deep dive into type-checked Python in the real worl
 d.\n\nCarl Meyer
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Systemd: why you should care as a Python developer
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T131000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T134000
DESCRIPTION:Done! Your shiny new application is functionally complete and
 ready to be deployed to production! But how exactly do you deploy properly
  on Linux? Wonder no more! In 30 minutes\, this talk explains how you can
 harness the power of the init system and systemd to solve common deploymen
 t problems\, including some that you didn't even know you had. Examples of
  things we will cover:\n\n* How to secure your system by having: private /
 tmp for your process\, read-only paths so that your process can not write
 to them\, inaccessible paths\, protect users home\, network access\, bin d
 irectories\, etc.\n* How to limit the resources you app can consume.\n* Ho
 w to interact directly with systemd\, so it can start transient units\, st
 art/stop services\, mount disks\, resolve addresses.\n* How to isolate you
 r service without containers.\n* How to isolate your service using contain
 ers (using systemd to spawn a namespace).\n\nAll this will be covered from
  a Python developer's perspective.\n\n\nAlvaro Leiva Geisse
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:There and Back Again: Disable and re-enable garbage collector at I
 nstagram
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T131000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T134000
DESCRIPTION:Python's cyclic garbage collector wonderfully hides the comple
 xity of memory management from the programmer. But we pay the price in per
 formance. Ever wondered how that works? In this talk\, you'll learn how ga
 rbage collection is designed in Python\, what the tradeoffs are and how In
 stagram battled copy-on-write memory issues by disabling the garbage colle
 ctor entirely.\n\nYou'll also learn why that isn't such a great idea after
  all and how we ended up extending the garbage collector API which allowed
  us to (mostly) re-enable garbage collection. We'll discuss our upstream c
 ontributions to the garbage collector that landed in Python 3.6 and 3.7.\n
 \nThis is an in-depth talk about memory management but no prior experience
  with CPython internals is necessary to follow it.\n\nZekun Li
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Effortless Logging: A deep dive into the logging module
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T131000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T134000
DESCRIPTION:Logs are our best friend\, especially on those late nights whe
 n we try to troubleshoot a problem in production that was written by a co-
 worker who is on vacation. Logs are the main way to know what is happening
  with an application at runtime\, but we don’t realize how important the
 y are until we actually need them. Unfortunately\, they are usually an und
 er-estimated part of the development process.\n\nThis talk aims to transmi
 t the need for the logging module\, briefly explains how to use it and how
  it is built\, and dives into all the complexity that is hidden to us. Thi
 s will help attendees not just understand all the magic that allows us to
 inspect our applications at runtime\, but also to avoid mistakes and adapt
  the module to our needs for more esoteric scenarios.\n\nThe talk is struc
 tured to simplify the understanding of the logging module. Many people hav
 e read the documentation\, but still struggle to fully understand what is
 happening under the hood. This talk aims to eliminate that barrier by pres
 enting it in an easier-to-digest manner.\n\n\nMario Corchero
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Efficient shared memory data structures
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T131000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T134000
DESCRIPTION:Multithreading makes shared memory easy\, but true parallelism
  next to impossible. Multiprocessing gives us true parallelism\, but it ma
 kes sharing memory very difficult\, and high overhead. In this talk\, we'l
 l explore techniques to share memory between processes efficiently\, with
 a focus on sharing read-only massive data structures.\n\nClaudio Freire
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Using List Comprehensions and Generator Expressions For Data Proce
 ssing
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:Creating one list out of another list is a very common thing t
 o do in Python\, so common that Python includes a special construct just f
 or this purpose: list comprehensions.  We'll get hands-on experience using
  list comprehensions\, set comprehensions\, and dictionary comprehensions
 during this tutorial.  We'll also learn how and when we can slightly tweak
  our comprehensions to turn them into more performant generator expression
 s.\n\nWe will learn some tricks for figuring out which of our "for" loops
 can be rewritten as comprehensions and which cannot.  We will focus heavil
 y on **code readability and code clarity** and we'll discuss when comprehe
 nsions help readability and when they hurt.\n\nAll new skills will be acqu
 ired through practice.  We'll work through many exercises both individuall
 y and as a group.  All students will also receive a cheat sheet which can
 be used for guidance during future comprehension-writing journeys.\n\nA la
 ptop with Python installed is required for this workshop.\n\nTrey Hunner
LOCATION:Room 09
END:VEVENT
BEGIN:VEVENT
SUMMARY:Workflow Engines Up and Running
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:Join us for an introduction hands on tutorial of Python based
 workflow engines.\n\nYou will get to create\, run and monitor a real time
 workflow job with two Python based popular workflow engines: Airflow and L
 uigi.\nDevelopers may have some long running batch jobs and may be using o
 ne of the tools we will cover or might be using a different engine\, and w
 ould like a more in depth hands-on experience learning these tools.\n\nIan
  Zelikman\, Austin Hacker
LOCATION:Room 15
END:VEVENT
BEGIN:VEVENT
SUMMARY:Practical API Security
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:With the dominance of Mobile Apps\, Single Page Apps for the W
 eb\, and Micro-Services\, we are all building more APIs than ever before.
 Like many other developers\, I had struggled with finding the right mix of
  security and simplicity for securing APIs. Some standards from the IETF h
 ave made it possible to accomplish both. Let me show you how to utilize ex
 isting libraries to lock down you API without writing a ton of code.\n\nIn
  this tutorial\, you will learn how to write a secure API with future proo
 f security utilizing JOSE. JOSE is a collection of complimentary standards
 : JWT\, JWE\, JWS\, JWA\, and JWK. JOSE is used by OAuth\, OpenID\, and ot
 hers to secure communications between APIs and consumers. Now you can use
 it to secure your API.\n\nAdam Englander
LOCATION:Room 16
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python Epiphanies
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:This tutorial is for those who've been using Python for a whil
 e and would consider themselves at an intermediate level but are looking t
 o get to the next level.\n\nWe'll explore core language features\, look a
 bit under the hood\, and to understand and learn how not to be too afraid
 of bytecode\, monkey patching\, decorators\, and metaclasses.\n\nIn many w
 ays Python is very similar to other programming languages.  However\, in a
  few subtle ways it is quite different\, and many software developers new
 to Python\, after their initial successes\, hit a plateau and have difficu
 lty getting past it.  Others don't hit or perceive a plateau\, but still f
 ind some of Python's features a little mysterious or confusing.  This tuto
 rial will help deconstruct some common incorrect assumptions about Python.
 \n\nIf in your use of Python you sometimes feel like an outsider\, like yo
 u're missing the inside jokes\, like you have most of the puzzle pieces bu
 t they don't quite fit together yet\, or like there are parts of Python yo
 u just don't get\, this may be a good tutorial for you.\n\nStuart Williams
LOCATION:Room 19
END:VEVENT
BEGIN:VEVENT
SUMMARY:Parallel Data Analysis with Dask
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:The libraries that power data analysis in Python are essential
 ly limited to a single CPU core and to datasets that fit in RAM. Attendees
  will see how dask can parallelize their workflows\, while still writing w
 hat looks like normal python\, NumPy\, or pandas code.\n\nDask is a parall
 el computing framework\, with a focus on analytical computing. We'll start
  with `dask.delayed`\, which helps parallelize your existing Python code.
 We’ll demonstrate `dask.delayed` on a small example\, introducing the co
 ncepts at the heart of dask like the *task graph* and the *schedulers* tha
 t execute tasks. We’ll compare this approach to the simpler\, but less f
 lexible\, parallelization methods available in the standard library like `
 concurrent.futures`.\n\nAttendees will see the high-level collections dask
  provides for writing regular Python\, NumPy\, or Pandas code that is then
  executed in parallel on datasets that may be larger than memory. These hi
 gh level collections provide a familiar API\, but the execution model is v
 ery different. We'll discuss concepts like the GIL\, serialization\, and o
 ther headaches that come up with parallel programming. We’ll use dask’
 s various schedulers to illustrate the differences between multi-threaded\
 , multi-processes\, and distributed computing.\n\nDask includes a distribu
 ted scheduler for executing task graphs on a cluster of machines. We’ll
 provide each person access to their own cluster.\n\nTom Augspurger\, James
  Crist\, Martin Durant
LOCATION:Room 20
END:VEVENT
BEGIN:VEVENT
SUMMARY:Getting Started with Blockchains and Cryptocurrencies in Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:Blockchains and cryptocurrencies are getting more popular ever
 yday. The rise and wide adoption of cryptocurrencies such as Bitcoin has a
 ttracted a lot of attention\, ranging from developers to bankers. However\
 , many people are still not very comfortable with the ideas and concepts b
 ehind the blockchain\, and workings of cryptocurrencies such as Bitcoin. T
 herefore\, it stops them from entering and exploring the blockchain and cr
 yptocurrency world.\n\n\nIn this tutorial\, we first explore the cryptogra
 phic ideas behind the cryptocurrencies\, including hashing\, public/privat
 e cryptography. This will be followed by the basics of a simplified blockc
 hain. We cover mining\, incentives\, payment records\, ownership\, etc. Th
 en we delve into working and playing with a private Bitcoin network\, by i
 mplementing simple programs in Python to create public/private keys\, acco
 unts\, and transactions. We further look into services that provide exchan
 ge rate data on cryptocurrencies and analyze the data.\n\nAmirali Sanatini
 a
LOCATION:Room 21
END:VEVENT
BEGIN:VEVENT
SUMMARY:Faster Python Programs - Measure\, don't Guess
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:Optimization can often help to make Python programs faster or
 use less memory. Developing a strategy\, establishing solid measuring and
 visualization techniques as well as knowing about algorithmic basics and d
 atastructures are the foundation for a successful optimization. The tutori
 al will cover these topics.\nExamples will give you a hands-on experience
 on how to approach efficiently.\n\nPython is a great language. But it can
 be slow compared to other languages\nfor certain types of tasks. If applie
 d appropriately\, optimization may reduce\nprogram runtime or memory consu
 mption considerably. But this often comes at a price. Optimization can be
 time consuming and the optimized program may be more complicated. This\, i
 n turn\, means more maintenance effort. How do you find out if it is worth
 while to optimize your program? Where should you start?\n\nThis tutorial w
 ill help you to answer these questions. You will learn how\nto find  an op
 timization strategy based on quantitative and objective criteria.\nYou wil
 l experience that one's gut feeling what to optimize is often wrong.\n\nTh
 e solution to this problem is: „Measure\, Measure\, and Measure!“. You
  will\nlearn how to measure program run times as well as profile CPU and m
 emory.\nThere are great tools available. You will learn how to use some of
  them.\nMeasuring is not easy because\, by definition\, as soon as you sta
 rt to measure\,\nyou influence your system. Keeping this impact as small a
 s possible is\nimportant. Therefore\, we will cover different measuring te
 chniques.\n\nFurthermore\, we will look at algorithmic improvements. You w
 ill see that the\nright data structure for the job can make a big differen
 ce. Finally\, you will\nlearn about different caching techniques.\n\nMike
 Müller
LOCATION:Room 22
END:VEVENT
BEGIN:VEVENT
SUMMARY:Statistics and probability: your first steps on the road to data s
 cience
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:An introduction to statistics and probability geared toward en
 abling attendees to understand the capabilities and limitations of statist
 ics and probability and to help them implement calculations in their proje
 cts. Where possible/feasible\, attendees will build their own tools to hel
 p them grasp the underlying concepts. In addition\, attendees will be intr
 oduced to the pre-built tools in world-class Python and data science libra
 ries to help them capitalize on the efficiencies and utility that those li
 braries offer.\n\nChalmer Lowe
LOCATION:Room 09
END:VEVENT
BEGIN:VEVENT
SUMMARY:Intermediate testing with Django:  Outside-in TDD and Mocking effe
 ctively
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:Once developers have got the hang of the basics of testing\, p
 roblems of applying it in the real world soon start to manifest themselves
 \, and common questions come up.  \n\n- What order should I write my tests
  and code in to avoid wasting time on blind alleys?  \n- If I'm using Mock
 s in my tests to avoid external dependencies\, how do I avoid getting stuc
 k with unwieldy\, unreadable tests that don't actually tell me when things
  have gone wrong?\n- Unit tests vs integration tests vs functional tests\,
  which should I use when\, and what are the trade-offs?\n\nIn this tutoria
 l we'll work through an example of using an existing Django codebase\, add
 ing a new feature\, and experimenting with different testing techniques al
 ong the way to illustrate the pros and cons of each\n\n- bottom-up vs outs
 ide-in development\n- double-loop TDD\n- using Mocks to isolate applicatio
 n layers from each other\n- "listen to your tests"\, and learning to use u
 gly or convoluted tests as a signal for improving design.\n\nSome familiar
 ity with Django is desirable\, although skills learned in other web framew
 orks are transferrable.  By the end\, you'll be able to go back to your ow
 n projects with practical experience\, and a new way of thinking about how
  to optimise your tests for your own circumstances.\n\n\nHarry  Percival
LOCATION:Room 10
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beyond Django Basics
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:Finished with the official Django getting started guide\, and
 not sure what to do next?  This tutorial has you covered.  \n\nWe'll exten
 d the blog built in the official guide\, using a variety of slightly more
 advanced Django features.  Topics to be covered include: extending the in-
 built user model\, using the in-built login system\, enhancing forms\, usi
 ng view mixins and overriding view methods\, and changing up your database
  backend.  \n\nWith each extension\, we'll talk about not just how to use
 these features but also *why* you'd want to use them.  We'll conclude by t
 alking about other Django features you may want to learn about as you grow
  more proficient with this versatile framework.\n\nShauna Gordon-McKeon
LOCATION:Room 15
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python by Immersion
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:A fast introduction to Python for software developers with exp
 erience in other programming languages. Instead of a traditional top-down
 presentation of Python's features\, syntax\, and semantics\, students are
 immersed in the language bottom-up with hundreds of small examples using t
 he interactive interpreter to quickly gain familiarity with most of the co
 re language features. Special attention is given to concepts in Python tha
 t often trip up those new to the language.\n\nNote that this tutorial is n
 ot designed to pair with the Python by Epiphanies tutorial\, i.e. we recom
 mend you sign up for one or the other\, but no both.\n\nStuart Williams
LOCATION:Room 16
END:VEVENT
BEGIN:VEVENT
SUMMARY:Network Analysis Made Simple: Part II
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:Daenerys or Jon Snow? Diffusion of news through Twitter? JFK\,
  ORD or ATL\, do these codes look familiar? In this tutorial we build up o
 n the fundamentals of Part 1 tutorial and look at various applications of
 network analysis to real world datasets like the US Airport Dataset\, Game
  of Thrones character co-occurrence network\, and foray into diffusion pro
 cesses on networks. \n\n\nMridul Seth\, Eric Ma
LOCATION:Room 19
END:VEVENT
BEGIN:VEVENT
SUMMARY:Docker for Data Science
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:Jupyter notebooks simplify the process of developing and shari
 ng Data Science projects across groups and organizations. However\, when w
 e want to deploy our work into production\, we need to extract the model f
 rom the notebook and package it up with the required artifacts (data\, dep
 endencies\, configurations\, etc) to ensure it works in other environments
 . Containerization technologies such as Docker can be used to streamline t
 his workflow.\n\nThis hands-on tutorial presents Docker in the context of
 Reproducible Data Science - from idea to application deployment. You will
 get a thorough introduction to the world of containers\; learn how to inco
 rporate Docker into various Data Science projects\; and walk through the p
 rocess of building a Machine Learning model in Jupyter and deploying it as
  a containerized Flask REST API.\n\nAly Sivji\, Joe Jasinski\, tathagata d
 asgupta (t)
LOCATION:Room 20
END:VEVENT
BEGIN:VEVENT
SUMMARY:Exploratory Data Visualization with Vega\, Vega-Lite\, and Altair
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:Exploring a new dataset visually can provide quick intuition i
 nto the relationships within the data. There are a few well-developed visu
 alization packages in Python\, but they often have very imperative APIs th
 at force the user to focus on the mechanics of the visualization – tick
 locations\, axis limits\, legends\, etc. – rather than the salient relat
 ionships within the data.\n\nThis tutorial will introduce data visualizati
 on with [Altair](http://altair-viz.github.io)\, a package designed for exp
 loratory visualization in Python that features a declarative API\, allowin
 g data scientists to focus more on the data than the incidental details. A
 ltair is based on the [Vega](https://vega.github.io/) and [Vega-Lite](http
 s://vega.github.io/vega-lite/) visualization grammars\, and thus automatic
 ally incorporates best practices drawn from recent research in effective d
 ata visualization.\n\nThe tutorial will provide an introduction to the Alt
 air package and its API\, but more importantly will dive into the core con
 cepts of effective data visualization that can be applied using any visual
 ization package or tool.\n\nJake VanderPlas
LOCATION:Room 21
END:VEVENT
BEGIN:VEVENT
SUMMARY:Using pandas for Better (and Worse) Data Science
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T164000
DESCRIPTION:The pandas library is a powerful tool for multiple phases of t
 he data science workflow\, including data cleaning\, visualization\, and e
 xploratory data analysis. However\, proper data science requires careful c
 oding\, and pandas will not stop you from creating misleading plots\, draw
 ing incorrect conclusions\, ignoring relevant data\, including misleading
 data\, or executing incorrect calculations.\n\nIn this tutorial\, you'll p
 erform a variety of data science tasks on a handful of real-world datasets
  using pandas. With each task\, you'll learn how to avoid either a pandas
 pitfall or a data science pitfall. By the end of the tutorial\, you'll be
 more confident that you're using pandas for good rather than evil!\n\nPart
 icipants should have a working knowledge of pandas and an interest in data
  science\, but are not required to have any experience with the data scien
 ce workflow. Datasets will be provided by the instructor.\n\nKevin Markham
LOCATION:Room 22
END:VEVENT
BEGIN:VEVENT
SUMMARY:Introduction to Digital Signal Processing
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180509T132000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180509T164000
DESCRIPTION:Spectral analysis is an important and useful technique in many
  areas of science and engineering\, and the Fast Fourier Transform is one
 of the most important algorithms\, but the fundamental ideas of signal pro
 cessing are not as widely known as they should be.\n\nFortunately\, Python
  provides an accessible and enjoyable way to get started.  In this tutori
 al\, I present material from my book\, *Think DSP*\, and from a class I te
 ach at Olin College.  We will work with audio signals\, including music a
 nd other recorded sounds\, and visualize their spectrums and spectrograms.
   We will synthesize simple sounds and learn about harmonic structure\, c
 hirps\, filtering\, and convolution.\n\nAllen Downey
LOCATION:Room 10
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hard Shouldn't be Hardship: Supporting Absolute Novices to Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T133000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T140000
DESCRIPTION:When we tell novices that programming is hard\, what are we wa
 rning them about? The intent may be to impress upon learners the importanc
 e of taking the workload seriously and starting early on the homework. How
 ever\, “hard” is a loaded word and leaving novices alone with that wor
 d and their imaginations can create an unbounded variable\, normalizing em
 otional extremes and all nighters.  The instructor often has no idea that
 "hard" has become "hardship".\n\nAn instructor's expertise can become a bl
 indspot. We've learned what is normal through experience and can easily fo
 rget that we didn't know from the start.  We presume that those experienci
 ng trouble will stop and reach out for help\, but this will not always be
 the case if they don't know those situations look like.  Saying "when you'
 re stuck" is not an objective\,  actionable statement and leaves the unbou
 nded suffering monster in play\, particularly for students who are afraid
 of being a bother or being seen as asking a stupid question.  We need to b
 e clear with our learners about when difficult material has moved outside
 our expectations and create a classroom environment where questions and cl
 arifications are celebrated.\n\nThis talk will cover real and practical me
 thods to help learners succeed in intensive programming courses\, such as
 making your expectations clear\, helping your students recognize when and
 how they should reach out for help\, creating a positive emotional atmosph
 ere in the classroom\, and providing help efficiently.  Topics will includ
 e a ban on demotivational words\, strategies for soliciting questions\, th
 e "2 hour" rule\, the educational benefits of live coding\, and recontextu
 alizing error messages.\n\nElizabeth Wickes
LOCATION:Room 25A
END:VEVENT
BEGIN:VEVENT
SUMMARY:The Five Kinds of Python Functions
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T133000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T150000
DESCRIPTION:Audience level: Intermediate\n\nWe'll look at the wide variety
  of ways that we can leverage Python functions. This will show provide hel
 pful background in ordinary functions\, as well as callable objects and la
 mbdas. We'll look closely at how to use generator functions\, also. The fi
 fth type of function is a function wraps a special method\, like len().\n\
 n\n\nSteven Lott
LOCATION:Room 23
END:VEVENT
BEGIN:VEVENT
SUMMARY:Learning Python like a Pro
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T133000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T140000
DESCRIPTION:There are many awesome tools to learn and teach Python to begi
 nners. You can teach it with games\, interactive tutorials and microcontro
 llers. However\, if one is to stay in the world of programming\, sooner or
  later one needs to start using more professional tools such as IDEs\, deb
 uggers\, profilers\, version control systems and databases. Needless to sa
 y\, the transition from IDLE to PyCharm can be very frustrating.\n\nCreati
 ng your own course is a time-consuming process\, but after the course has
 been created you also need to check students' solutions which consumes eve
 n more time. Wouldn't it be great to automate checking process and to conc
 entrate on the creative part of content creation and explaining concepts t
 o students?\n\nAs an attempt to solve these problems [EduTools plugin](htt
 ps://plugins.jetbrains.com/plugin/10081-edutools) was created. It's a free
  open-source plugin that can be installed on PyCharm/IntelliJ IDEA IDEs to
  create and take programming courses right inside the IDE. This way a novi
 ce programmer dives into professional environment straight away and an edu
 cator gets a tool to automate solution checking. Not only the plugin allow
 s to create courses\, but it also comes with integration with [Stepik MOOC
  platform](https://stepik.org).\n\nIn this talk I'd like to demonstrate ho
 w one can create a course with automated checks using EduTools plugin\, sh
 are it with students and connect to Stepik platform to gather solutions an
 d statistics. I'll show how to create a simple course containing theory\,
 code task with feedback prompts and test with choice problems. I'll also s
 how how to use our existing free courses in class with the ability to view
  your students' solutions.\n\nLiana Bakradze
LOCATION:Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bridging the gaps between Science and Engineering with Jupyter Not
 ebooks
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T133000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T150000
DESCRIPTION:When it comes to data science in the enterprise\, organization
 s often face a culture of self-interest that leads to unnecessary friction
  between the Data Science and Software engineering teams\, causing ineffic
 iencies\, and ultimately a failure to realize the full potential of data s
 cience. In this talk\, we’ll show how Jupyter Notebooks combined with th
 e PixieDust open source library can address the expectations and challenge
 s around data science\, including the shortage of technical expertise\, ov
 erly complex and inaccessible analytics tools and more. \n\nDavid Taieb
LOCATION:Room 13
END:VEVENT
BEGIN:VEVENT
SUMMARY:A Whirlwind Tour of Google Cloud Services
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T133000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T150000
DESCRIPTION:Did you know Google offers object recognition as an API? Join
 us for a rapid tour of services you can add to your applications to save t
 ime and add features to delight your users! All of these services can be u
 sed wherever your core application is running -- as long as it has interne
 t access. :)\n\n- Identify objects and locate faces in photos & videos wit
 h Cloud Vision. Also speech to text and back!\n- Easy\, scalable logging w
 ith Google Stackdriver\, then extract and graph metrics from those logs.\n
 - Interactively query all of your logs (or any other data) with BigQuery.\
 n- Connect your distributed processes with fast and reliable messaging usi
 ng Cloud PubSub.\n- And maybe more? Or less! It will be interesting and us
 eful.\n\nThis is explicitly a sponsored product tour - we promise to focus
  on solving real problems with these tools and to include lots of interact
 ive hands-on demos and example code. Some of the demos will be easy to fol
 low along on your own laptop. Please bring yours if that sounds interestin
 g!\n\nJon Wayne Parrott
LOCATION:Room 25C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Graph Databases: Talking about your Data Relationships with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T134000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T142500
DESCRIPTION:Have you ever considered how many relationships you have in yo
 ur virtual life? Every friend or page liked on Facebook\, each connection
 in LinkedIn or Twitter account followed is a new relationship not only bet
 ween two people\, but also between their data. In Brazil only\, we have 16
 0 millions Facebook users. How can we represent and manipulate all these r
 elationships? Graph Databases are storage systems that use graph structure
  (nodes and edges) to represent and store data in a semantic way.\n\nThis
 talk will begin approaching the challenge in representing relationships in
  Relational Databases and introducing a more friendly solution using graph
 . The definition of Graph Database\, its pros and cons and some available
 tools (Neo4J\, OrientDB and TitanDB) will be shown during the presentation
 \, as well as how these tools can be integrated with Python.\n\n\nNicolle
 Cysneiros
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Win back lovely API: GraphQL in Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T134000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T142500
DESCRIPTION:RESTful has been the go-to choice of API world. Why another AP
 I approach? To support more data-driven applications\, to provide more fle
 xibility and ease unnecessary code and calls\, to address a wide variety o
 f large-scale development problems\, **GraphQL** comes with HTTP\, JSON\,
 Versioning\, Nullability\, Pagination\, and Server-side Batching & Caching
  in mind to make API "Simple yet Powerful".\n\nBy applying [Graphene-Pytho
 n](http://graphene-python.org/)\, a library for building GraphQL APIs in P
 ython easily\, this talk will go through the background and challenges of
 applying GraphQL as the new API service in a restaurant POS (point of sale
 ) system within complex cloud infrastructure in Python. Introduction\, tes
 ting\, and live demo is included for sure.\n\nKeith Yang
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Software Library APIs: Lessons Learned from scikit-learn
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T134000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T142500
DESCRIPTION:When you think of an API\, you’re probably thinking about a
 web service. But it’s important to think about your developer interface
 when designing a software library as well! I’ll talk about the scikit-le
 arn package\, and how its API makes it easy to construct complex models fr
 om simple building blocks\, using three basic pieces: transformers\, estim
 ators\, and meta-estimators. Then I’ll show how this interface enabled u
 s to construct our own meta-estimator for model stacking. This will demons
 trate how to implement new modeling techniques in a scikit-learn style\, a
 nd more generally\, the value of writing libraries with the developer inte
 rface in mind.\n\nLiz Sander
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY: Bayesian Non-parametric Models for Data Science using PyMC3
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T134000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T142500
DESCRIPTION:Nowadays\, there are many ways of building data science models
  using Python\, including statistical and machine learning methods. I will
  introduce probabilistic models\, which use Bayesian statistical methods t
 o quantify all aspects of uncertainty relevant to your problem\, and provi
 de inferences in simple\, interpretable terms using probabilities.  A part
 icularly flexible form of probabilistic models uses Bayesian *non-parametr
 ic* methods\, which allow models to vary in complexity depending on how mu
 ch data are available. In doing so\, they avoid the over-fitting that is c
 ommon in machine learning and statistical modeling. I will demonstrate the
  basics of Bayesian non-parametric modeling in Python\, using the PyMC3 pa
 ckage. Specifically\, I will introduce two common types\, Gaussian process
 es and Dirichlet processes\, and show how they can be applied easily to re
 al-world problems using two examples.\n\nChristopher Fonnesbeck
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:The Hare Wins the Race: Getting the most out of RabbitMQ in distri
 buted applications
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T135000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T142000
DESCRIPTION:As web apps grow increasingly complex\, distributing asynchron
 ous work across multiple background workers is often a basic requirement o
 f a performant app.  While there are a variety of tools that exist to solv
 e this issue\, one common feature among them is the need for a robust mess
 aging platform.\n\n[RabbitMQ][1] is a stable\, full-featured\, and mature
 solution that is usually found in the Python ecosystem backing [Celery][2]
  implementations.  While Celery's utilization of RabbitMQ works just fine
 out of the gate\, users with complex workflows\, unique constraints\, or t
 ight budgets can take advantage of the flexibility of RabbitMQ to streamli
 ne their data pipelines and get the most out of their infrastructure.\n\nT
 his talk will provide an overview of RabbitMQ\, review its varied message-
 routing capabilities\, and demonstrate some of the ways in which these fea
 tures can be utilized in Python applications to solve common yet difficult
  use-cases.\n\n  [1]: https://www.rabbitmq.com/\n  [2]: http://www.celeryp
 roject.org/\n\n\nSteven Sklar
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pumping up Python modules using Rust
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T135000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T142000
DESCRIPTION:If you’ve spent much time writing (or debugging) Python perf
 ormance problems\, you’ve probably had a hard time managing memory with
 its limited language support. \n\nIn this talk\, we venture deep into the
 belly of the Rust Language to uncover the secret incantations for building
  high performance and memory safe Python extensions using Rust. \n\nRust h
 as a lot to offer in terms of safety and performance for high-level progra
 mming languages such Python\, Ruby\, Js and more with its easy Foreign Fun
 ction Interface capabilities which enables developers to easily develop bi
 ndings for foreign code. \n\n\nvigneshwer dhinakaran
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Learning From Failure: Post Mortems
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T135000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T142000
DESCRIPTION:Unless you work on pacemakers or at NASA\, you've probably acc
 epted the fact that you will make mistakes in your code\, and those mistak
 es will creep into production. This talk will introduce you to post-mortem
 s\, and how to use them as a vehicle for improving your code and your proc
 ess.\n\nAlex Gaynor
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:A practical guide to Singular Value Decomposition in Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T135000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T142000
DESCRIPTION:Recommender systems have become increasingly popular in recent
  years\, and are used by some of the largest websites in the world to pred
 ict the likelihood of a user taking an action on an item. In the world of
 Netflix\, this means recommending similar movies to the ones you have seen
 . In the world of dating\, this means suggesting matches similar to people
  you already showed interest in!\n\nMy path to recommenders has been an un
 usual one: from a Software Engineer to working on matching algorithms at a
  dating company\, with a little background on machine learning. With my kn
 owledge of Python and the use of basic SVD (Singular Value Decomposition)
 frameworks\, I was able to understand SVDs from a practical standpoint of
 what you can do with them\, instead of focusing on the science.\n\nIn my t
 alk\, you will learn 2 practical ways of generating recommendations using
 SVDs: matrix factorization and item similarity. We will be learning the hi
 gh-level components of SVD the "doer way": we will be implementing a simpl
 e movie recommendation engine with the help of Jupiter notebooks\, the Mov
 ieLens database\, and the Surprise recommendation package.\n\n\nDaniel Pyr
 athon
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Clearer Code at Scale: Static Types at Zulip and Dropbox
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T135000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T142000
DESCRIPTION:Python now offers static types! Companies like Dropbox and Fac
 ebook\, and open-source projects like Zulip\, use static types (with [PEP
 484](https://www.python.org/dev/peps/pep-0484/) and [mypy](https://github.
 com/python/mypy)) to make Python more productive and fun to work with —
 in existing codebases from 40k lines to 4 million\, in Python 2 and 3\, an
 d while preserving the conciseness and flexibility that make Python a grea
 t language in the first place. I’ll describe how.\n\nReading and underst
 anding code is a huge part of what we do as software developers. If we mak
 e it easier to understand our codebases\, we make everyone more productive
 \, help each other write fewer bugs\, and lower barriers for new contribut
 ors. That's why Python now features optional static types\, and why Dropbo
 x\, [Facebook](https://engineering.instagram.com/let-your-code-type-hint-i
 tself-introducing-open-source-monkeytype-a855c7284881)\, and [Zulip](https
 ://blog.zulip.org/2016/10/13/static-types-in-python-oh-mypy/) use them on
 part or all of their Python code.\n\nIn this talk\, I’ll share lessons f
 rom Zulip’s and Dropbox’s experience — having led the mypy team at D
 ropbox and working now on the Zulip core team — for how you can start us
 ing static types in your own codebases\, large or small. We’ll discuss h
 ow to make it a seamless part of your project’s tooling\; what order to
 approach things in\; and powerful new tools that make it even easier today
  to add static types to your Python codebase than ever before.\n\n\nGreg P
 rice
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:By the Numbers: Python Community Trends in 2017/2018
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T135500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T142500
DESCRIPTION:Want to know about the latest trends in the Python community a
 nd see the the big picture of how things have changed over the last few ye
 ars? Interested in the results of the latest official Python Developers Su
 rvey 2017 which was supported by the Python Software Foundation and gather
 ed responses from more than 10.000 Python developers? Come learn about the
  most popular types of Python development\, trending frameworks\, librarie
 s and tools\, additional languages being used by Python developers\, Pytho
 n versions usage statistics and many other insights from the world of Pyth
 on. All derived from the actual data and professional research such as the
  Python Developers Survey 2017 which collected responses from over 10.000
 Python developers\, organized in partnership between the Python Software F
 oundation and JetBrains\, the Python Developers Survey 2016\, 3rd party su
 rveys and supplementary analytical research.\n\nDmitry Filippov\, Ewa Jodl
 owska
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Flourishing FLOSS: Making Your Project Successful
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T135500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T142500
DESCRIPTION:You maintain an Open Source project with great code? Yet your
 project isn’t succeeding in the ways you want? Maybe you’re struggling
  with funding or documentation? Or you just can’t find new contributors
 and you’re drowning in issues and pull requests?\nOpen Source is made up
  of many components and we are often better-trained in methods for writing
  good code\, than in methods for succeeding in the other dimensions we wan
 t our project to grow. \nIn this talk we’ll explore the different compon
 ents of an Open Source project and how they work together. After this talk
  you’ll be well-equipped with a ideas and strategies for growing\, culti
 vating\, and nourishing your Open Source project. \n\nFor your project to
 succeed\, all of its non-code components must be well-maintained. What are
  these different components and what methods can we learn to maintain them
 ?\n\n* Build real relationships with your sponsors and determine ways how
 both sides can benefit from this relationship\, don’t just ask people fo
 r money. \n* Establish a good communication system with your contributors:
  Keep them informed\, listen to their feedback and input\, make them feel
 heard. \n* Thank the people who worked on ticket triage or marketing\, not
  just those who wrote code\, in your release notes. \n* Make it easy for n
 ew contributors to get started: Write and maintain good documentation\, an
 swer questions in a friendly and timely manner. \n* Market and evangelize
 in the right places and at the right time: Give conference talks\, organiz
 e sprints\, keep your project’s Twitter account active\, always curate n
 ew and interesting content on your blog or website.\n* Implement a Code of
  Conduct and enforce it if needed: Make your project a safe space to contr
 ibute for everyone. \n\nWith these methods and a half-dozen others\, you
 ’ll handle beautifully all the components your project needs to succeed.
 \n\nAnna Ossowski
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fighting Gerrymandering with PyMC3
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T135500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T142500
DESCRIPTION:At the end of 2017\, there were seven states with ongoing redi
 stricting litigation.  We will discuss a statistical model that the United
  States Supreme Court declared to be appropriate in cases of racial gerrym
 andering\, and show how it can be implemented and used with the library `P
 yMC3`.  We will also discuss what the model tells us about racial gerryman
 dering in North Carolina.\n\n\nColin Carroll\, Karin C. Knudson
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Don't Look Back in Anger: Wildman Whitehouse and the Great Failure
  of 1858
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T135500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T142500
DESCRIPTION:In the 1850s\, Edward Orange Wildman Whitehouse was appointed
 the lead engineer of the first attempt to build a trans-Atlantic telegraph
  cable. With the entire population of two continents waiting for his go-li
 ve\, their handlebar moustaches aquiver\, he demonstrated in fine form jus
 t how spectacularly a big project can be a bigger disaster.\n\nThis is a t
 ale of long-winded rants\, spectacular sideburns\, and gentlemen scientist
 s behaving badly. It is also a lesson about the importance of honest refle
 ction in technical teamwork. Lilly outlines some of the mistakes made duri
 ng one of the biggest tech delivery projects in history\, and how a constr
 uctive view of failure helped to turn it all around. Through the public me
 ltdowns of Wildman Whitehouse you will learn the importance of feedback\,
 how to handle complex tasks gracefully\, and the best way to recover from
 having your pipeline eaten by a whale.\n\nLilly Ryan
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Using Python to build an AI to play and win SNES StreetFighter II
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T135500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T142500
DESCRIPTION:![ryu-python](http://www.thesimplelogic.com/wordpress/wp-conte
 nt/uploads/2017/12/ryu-python.png)\n\nHear the story of how we used Python
  to build an AI that plays Super StreetFighter II on the Super NES. We'll
 cover how Python provided the key glue between the SNES emulator and AI\,
 and how the AI was built with `gym`\, `keras-rl` and `tensorflow`. We'll s
 how examples of game play and training\, and talk about which bot beat whi
 ch bot in the bot-v-bot tournament we ran. \n\nAfter this talk you'll know
  how easy it is to use Python and Python's machine learning libraries to t
 each a computer to play games.  You'll see a practical example of the same
  type of machine learning used by AlphaGo\, and also get to find out which
  character in StreetFighter II is best to pick when playing your friends.\
 n\n  [1]: https://lh3.googleusercontent.com/Mh9uzCm4JeevMN5w-SWJgzWabrqOCl
 AVMsa4jJtMRm-il1dP6oVTsRstJSQlbgKf4qh3A08yMZ36pwezsITA=w3230-h1786\n  [2]:
  http://www.thesimplelogic.com/wordpress/wp-content/uploads/2017/12/ryu-py
 thon.png\n\n\nAdam Fletcher\, Jonathan Mortensen
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:The importance of exploratory data analysis and data visualization
  in machine learning
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T135500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T142500
DESCRIPTION:All the data in the world is useless if you cannot understand
 it. EDA and data visualization are the most crucial yet overlooked stage i
 n analytics process. This is because they give insights on the most releva
 nt features in a particular data set required to build an accurate model.
 It is often said that the more the data\, the better the model but sometim
 es\, this can be counter-productive as more data can be a disadvantage. ED
 A helps avoid that.\n\nEDA is useful for professionals while data visualiz
 ation is useful for end-users. \n\nFor end-users: \nA good sketch is bette
 r than a long speech. The value of a machine learning model is not known u
 nless it is used to make data driven decisions. It is therefore necessary
 for data scientists to master the act of telling a story for their work to
  stay relevant. This is where data visualization is extremely useful.  \nW
 e must remember that the end-users of the results are not professionals li
 ke us but people who know little or nothing about data analysis. For effec
 tive communication of our analysis\, there is need for a detailed yet simp
 le data visualization because the work of a data scientist is not done if
 data-driven insights and decisions are not made.\n\nFor professionals:\nHo
 w do you ensure you are ready to use machine learning algorithms in a proj
 ect? How do you choose the most suitable algorithms for your data set? How
  do you define the feature variables that can potentially be used for mach
 ine learning? Most data scientists ask these questions.  EDA answers these
  questions explicitly.\nAlso\, EDA helps in understanding the data. Unders
 tanding the data brings familiarity with the data\, giving insights on the
  best models that fit the data set\, the features in the dataset that will
  be useful for building an accurate machine learning model\, making featur
 e engineering an easy process.\n\nIn this talk\, I will give a detailed ex
 planation on what EDA and data visualization are and why they are very hel
 pful in building accurate machine learning models for analytics as well as
  enhancing productivity and better understanding for clients. I will also
 discuss the risks of not mastering EDA and data visualization as a data sc
 ientist.\n\nOpetunde Adepoju
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Teaching Python 101
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T140000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T143000
DESCRIPTION:My name is Devishi Jha\, and I am a freshman at Valparaiso Hig
 h School\, Indiana. I have been taught programming by the Python community
 \, and currently\, I have been teaching python to children in my local com
 munity. Throughout elementary and middle school\, I have been taught progr
 amming in many different ways. Then in late middle school\, I taught progr
 amming to children at my local library. In this talk\, I have compiled man
 y teaching methods that I have found useful when learning python/teaching
 python. I also cover the ages to start coding\, access to computers\, and
 teaching at school vs. an after-school program. With the help of several c
 oding teachers\, school principals\, and after-school coding program direc
 tors\, I hope that this talk will serve as a guide to anyone who wants to
 teach python or find a good way to learn it.\n\n\nDevishi Jha
LOCATION:Room 25A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lessons Learned from Python-related Tips and Tricks to keep studen
 ts engaged
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T140000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T143000
DESCRIPTION:(30 minutes)\n\n**Background**: How does one keep a set of pro
 fessional students interested in Python and data science for 2 whole weeks
 ? Esp. when their employers have paid for the class? Twice a year\, for 2
 full weeks\, I have to teach students data science (using Python) to busin
 ess and industry professionals\, who are not programmers.\n\n**Outline**:
 In this 30-minute talk\, I hope to share several of the Python-related tea
 ching 'tricks' that I tried\, along with a few thoughts on why some worked
  while many failed.\n\n**Students**: My students are professionals working
  in developing countries (Asia\, Africa\, the Middle East) whose companies
  have sent them for training. They have some quantitative background but a
 lmost no computer programming experience (not counting Excel).  Hence\, th
 e choice of Python for instruction. The students are in the age group from
  25-30\, and at the slightest chance\, they will revert to checking their
 smartphones.\n\n**Curriculum**: I teach a hands-on Python course. In nine
 full days\, I get them started on Jupyter\, Python basics\, and Pandas. I
 then introduce data analysis using Pandas\, plotting\, and introductory ma
 chine learning. At the end of two weeks\, there is a final project (with c
 oding and data analysis) that is due.\n\n**Challenges**: Out of a class of
  around 30\, there are always 4-5 students who have prior programming expe
 rience and want a lot more out of the class. This “mixed-ability” clas
 s adds to the difficulty of keeping them all engaged. One additional diffi
 culty is that this being a “professional development course\,” there i
 s no grading as such.\n\n**The talk**: Through repeated failures\, I’ve
 had to adapt and develop several tricks and tips that impart Python and da
 ta analytics skills to the students while simultaneously keeping them enga
 ged. I will share the tools and techniques that I have discovered to work
 (Ex. RISE by Damian Avila on top of Jupyter)\, Jupyter NBextensions\, the
 frequent use of random numbers in various guises.\n\n**What worked & What
 didn’t work**: Tapping into their competitiveness by awarding ‘points
 ’ worked.  Just handing out handouts didn’t work well\, but Jupyter no
 tebooks with descriptive text in Markdown worked better. It took me a long
  time to realize that I was jumping into techniques and algorithms with to
 o little of preface. Spending a good deal of time on motivating the proble
 m and ‘building the narrative’ helped get them interested. When trying
  to teach “*Classification Algorithms*\,” using the canonical *Iris* d
 ataset (to identify species) flopped\, whereas classifying Tweets as 'from
  bots' or 'from humans' kept them interested. Web-scraping for Ebola incid
 ences flopped but getting live soccer scores made the students come alive.
 \n\nI’ve made an embarrassing number of mistakes\, some of which I will
 share\, along with my thoughts on why some of these techniques work and ot
 hers don't. Though I teach regularly\, I am not an educator by training. I
  am proposing this talk in the hope that I can share my insights and lesso
 ns\, and learn a few things from seasoned educators a lot more experienced
  than myself\, for the next time.\n\n\n\nRam Narasimhan
LOCATION:Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Types\, Deeper Static Analysis\, and you
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T150000
DESCRIPTION:Many projects already take advantage of static analysis tools
 like flake8\, PyLint\, and MyPy. Can we do better? In this talk\, I'll dis
 cuss how to take a type checker\, bolt on an interprocedural static analyz
 er\, and delight your security team with high quality results.\n\nAbstract
  \n\nIt is incredibly challenging to build a halfway decent static analysi
 s tool for a dynamic language like Python. Fortunately\, it gets quite a b
 it easier with Python type annotations. To explain why\, I'll present a to
 ol that finds security vulnerabilities by tracking dangerous flows of info
 rmation interprocedurally across an entire codebase. **Then\,** I'll demon
 strate how that tool is really just a slightly slower\, more sophisticated
 \, type checker.\n\n\n\nPieter Hooimeijer
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Automating Code Quality
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T150000
DESCRIPTION:Writing quality Python code can be both tough and tedious. On
 top of the general design\, there are many code quality aspects that you n
 eed to watch out for when writing and reviewing code such as adherence to
 PEP8\, docstring quality\, test quality\, etc. Furthermore\, everyone is h
 uman. If you are catching these code quality issues by hand\, there is a g
 ood chance that at some point you will miss an easy opportunity to improve
  code quality. If the quality check can be done by a machine\, then why wo
 uld you even try to catch the code quality issue by hand? In the end\, the
  machine will be able to perform the quality check with much more speed\,
 accuracy\, and consistency than a person.\n\nThis talk will dive into how
 existing open source projects offload and automate many of these code qual
 ity checks resulting in:\n\n- A higher quality and a more consistent codeb
 ase\n- Maintainers being able to focus more on the higher level design and
  interfaces\n  of a project.\n- An improved contribution process and highe
 r quality pull requests from\n  external contributors\n\nBy diving into ho
 w these open source projects automate code quality checks\, you will learn
  about:\n\n- The available tooling related to checking code quality such a
 s `flake8`\,\n  `pylint`\, `coverage`\, etc.\n- How to automate code quali
 ty checks for both a development and team \n  setting.\n- First-hand accou
 nts of the benefits and lessons learned from automating\n  code quality ch
 ecks in real-life open source projects.\n\n\n\nKyle Knapp
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:How We Do Identity Wrong
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T150000
DESCRIPTION:Most software has a user. Depending on the software\, the user
  may need to provide various details about themselves for proper operation
  -- their name\, their date of birth\, where they live. However\, it is qu
 ite common for software systems such as these to ask the wrong questions\,
  collect too much data\, and when it comes down to it\, serialise the part
 s of the user's identity wrongly. This talk will discuss common ways that
 real-world systems store identity wrong\, what questions you shouldn't ask
 \, and how you can fix it in your own projects.\n\nAmber Brown ("HawkOwl")
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Big-O: How Code Slows as Data Grows
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T150000
DESCRIPTION:Big-O is a computer science technique for analyzing how code p
 erforms as data gets larger.  It's a very handy tool for the working progr
 ammer\, but it's often shrouded in off-putting mathematics.\n\nIn this tal
 k\, I'll teach you what you need to know about Big-O\, and how to use it t
 o keep your programs running well.  Big-O helps you choose the data struct
 ures and algorithms that will let your code work efficiently even on large
  data sets.\n\nYou can understand Big-O even if you aren't a theoretical c
 omputer science math nerd. Big-O isn't as mystical as it appears. It's wra
 pped in mathematical trappings\, but doesn't have to be more than a common
 -sense assessment of how your code will behave.\n\n\nNed Batchelder
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:User focused API Design
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180513T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180513T150000
DESCRIPTION:When we talk about Web API Design\, we're usually driven to th
 ink in architecture\, verbs\, and nouns. But we often forget our user: the
  developer.\n\nUX designers rely on many techniques to create great experi
 ences. User research\, User Testing\, Personas\, Usage Data Analysis and o
 thers. However when creating `invisible products` we’re not used to thin
 k in usability. So why don’t we take advantage of this background to imp
 rove our APIs experiences?\n\n\n\nRenato Oliveira
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:GenZeal: A new generation of thinkers who design\, develop\, and d
 istribute for tomorrow
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T150000
DESCRIPTION:The GenZeal project is an after-school program where students
 learn design thinking\, applied computational thinking\, and programming s
 kills to solve a problem in their community. Students research and work wi
 th local organizations and businesses to design and program microcontrolle
 rs that run entirely on renewable energy sources to solve a specific need.
  Through the process\, students earn physical and digital badges for each
 skill they learn and apply with their project. Students learn applied desi
 gn thinking\, computational thinking\, and open-source programming languag
 es. They use what they learn to design\, prototype\, and program their pro
 ject. The student projects are installed in the community and they monitor
  their use in conjunction with the community coach. \n\nI will provide det
 ails and examples of this project that can be replicated in small through
 large school districts\, rural and urban. I will also explain how our scho
 ol district redefined our curriculum from little computer science concepts
  to purposeful\, applied PreK-12 computer science principles. \n\nLaura Ja
 cob
LOCATION:Room 25A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Using GitHub\, Travis CI\, and Python to Introduce Collaborative S
 oftware Development
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T143000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T150000
DESCRIPTION:Real-world software engineering is collaborative\, commonly in
 volving the use of Git\, GitHub\, and continuous integration with Travis C
 I. This presentation will explain how to use these technologies and platfo
 rms to teach interdisciplinary and introductory courses in computer progra
 mming and software engineering. This presentation will first show how to c
 reate a GitHub organization connected to a GitHub Classroom with unlimited
  private repositories that contain instructor solutions and starter kits a
 nd assignment submissions for both individual and team-based programming a
 ssignments. The talk will next explain how to connect GitHub repositories
 to continuous integration servers hosted by Travis CI\, thus supporting th
 e cloud-based execution of tests and checks.\n\nThe presentation will subs
 equently introduce a Python program\, called [GatorGrader](https://github.
 com/gkapfham/gatorgrader)\, that supports the local and cloud-based checki
 ng of a student's source code and technical writing for a programming proj
 ect. GatorGrader can check\, for example\, that a submission contains the
 required number of comments and produces the correct number of lines of co
 nsole output. Suitable for use on either a local workstation or a cloud-ba
 sed server provided by Travis CI\, GatorGrader can\, for instance\, ensure
  that a student makes the requisite number of commits to a GitHub reposito
 ry and structures a program in a specified fashion. GatorGrader can also i
 nvoke external programs that ensure the quality of a student's technical w
 riting. Finally\, since most of the aforementioned assignments are designe
 d to be completed in teams\, this presentation introduces [GatorGrouper](h
 ttps://github.com/GatorGrouper/gatorgrouper)\, another Python program that
  uses student responses on a Google Form to create suitable groups of stud
 ents who collaboratively complete programming projects with GitHub.\n\nGre
 gory M. Kapfhammer
LOCATION:Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY: Randomness in Python: Creating Chaos in an Ordered Machine/Contro
 lled Environment
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T150500
DESCRIPTION:There are many computational needs for randomness--from creati
 ng a game to building a simulation involving naturally occurring randomnes
 s similar to the physical world. For most purposes using the python math m
 odule to create random numbers within a specific range can be done with no
  further questions\, but sometimes we require a more nuanced implementatio
 n. \n\nWe will look at both pseudo-random number generators\, which use st
 atistically repeatable processes to generate seemingly random series and t
 rue random number generators\, which inject physical processes like atmosp
 heric noise to generate sequences of numbers. We will discuss the benefits
  and drawbacks of both approaches and common methods of implementing these
  two types of generators in python. \n\nFinally\, we will look at several
 real applications for randomness and discuss the best method for generatin
 g “randomness” in each scenario. \n\nAmanda Sopkin
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python across the USA: This is the Bus.
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T150500
DESCRIPTION:For 2 years\, a family of three has traveled on a converted sc
 hool bus from conference to conference\, building tooling for the road in
 Python and visiting Python families in every corner of the country.\n\nJus
 tin Myles Holmes
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Birding with Python and Machine Learning
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T150500
DESCRIPTION:In the past few years\, the power of computer vision has explo
 ded. In this talk\, we'll apply a deep learning model to a bird feeder. We
 'll use that model to detect\, identify\, and record birds that come to a
 smart bird feeder.\n\nAlong the way\, we'll learn about different platform
 s to deploy deep learning cameras on\, from the lowly Raspberry PI all the
  way up to the powerful NVIDIA Jetson embedded computer with a built in GP
 U.\n\nKirk Kaiser
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adapting from Spark to Dask: what to expect
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T150500
DESCRIPTION:Until very recently\, Apache Spark has been a de facto standar
 d choice of a framework for batch data processing. For Python developers\,
  diving into Spark is challenging\, because it requires learning the Java
 infrastructure\, memory management\, configuration management. The multipl
 e layers of indirection also make it harder to debug things\, especially w
 hen throwing the Pyspark wrapper into the equation.\n\nWith Dask emerging
 as a pure Python framework for parallel computing\, Python developers migh
 t be looking at it with new hope\, wondering if it might work for them in
 place of Spark. In this talk\, I’m using a data aggregation example to h
 ighlight the important differences between the two frameworks\, and make i
 t clear how involved the switch may be.\n\nNote: Just in case it's unclear
 \, there's no Java of any kind in this talk. All the code / examples use P
 ython (PySpark).\n\n\nIrina Truong
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Building a cross-platform native app with BeeWare
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T150500
DESCRIPTION:Have you ever wanted to write a GUI application you can run on
  your laptop? What about an app that you can run on your phone? Historical
 ly\, these have been difficult to achieve with Python\, and impossible to
 achieve without learning a different API for each platform. But no more.\n
 \nBeeWare is a collection of tools and libraries that allows you to build
 cross-platform native GUI applications in pure Python\, targeting desktop\
 , mobile and web platforms. In this talk\, you'll be introduced to the Bee
 Ware suite of tools and libraries\, and see how you can use them to develo
 p\, from scratch\, a GUI ChatBot application that can be deployed as a sta
 ndalone desktop application\, a mobile phone application\, and a single pa
 ge webapp - without making any changes to the application's codebase.\n\nR
 ussell Keith-Magee
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pipenv: The Future of Python Dependency Management
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T150500
DESCRIPTION:This talk is about the history of Python packaging\, the tools
  that have been historically available for application deployment\, the pr
 oblems/constraints presented by them\, and presents a holistic solution to
  many of these problems: Pipenv.\n\nA live demo of the tool will be presen
 ted\, as well as a Q&A session.\n\nKenneth Reitz
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:You're an expert. Here's how to teach like one.
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T150500
DESCRIPTION:Knowing how to code and being able to teach it are two separat
 e skills. When we have expertise in a subject\, it's common to take for gr
 anted that we'll be able to effectively communicate our expertise to someo
 ne else. Come learn (or re-learn!) how to teach and discover practical exa
 mples you can put to work right away.\n\nBy sharpening your teaching skill
 s\, you'll be a more effective mentor\, trainer\, and team member.\n\nShan
 non Turner
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Beyond scraping: how to use machine learning when you're not sure
 where to start
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T150500
DESCRIPTION:Scraping one web site for information is easy\, scraping 10000
  different sites is hard. Beyond page-specific scraping\, how do you build
  a program than can extract the publication date of (almost) any news arti
 cle online\, no matter the web site?\n\nWe’ll cover when to use machine
 learning vs. humans or heuristics for data extraction\, the different step
 s of how to phrase the problem in terms of machine learning\, including fe
 ature selection on HTML documents\, and issues that arise when turning res
 earch into production code.\n\nJulie Lavoie
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:CuPy: A NumPy-compatible Library for GPU
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T150500
DESCRIPTION:![Logo][https://raw.githubusercontent.com/cupy/cupy/master/doc
 s/image/cupy_logo_1000px.png]\n\n# CuPy : NumPy-like API accelerated with
 CUDA\n\n[**Website**](https://cupy.chainer.org/) | [**Docs**](https://docs
 -cupy.chainer.org/en/stable/) | [**Install Guide**](https://docs-cupy.chai
 ner.org/en/stable/install.html) | [**Tutorial**](https://docs-cupy.chainer
 .org/en/stable/tutorial/) | **Examples** ([Official](https://github.com/cu
 py/cupy/blob/master/examples)) | [**Forum**](https://groups.google.com/for
 um/#!forum/cupy)\n\nCuPy is an open-source library with NumPy syntax that
 increases speed by doing matrix operations on NVIDIA GPUs. It is accelerat
 ed with the CUDA platform from NVIDIA and also uses CUDA-related libraries
 \, including cuBLAS\, cuDNN\, cuRAND\, cuSOLVER\, cuSPARSE\, and NCCL\, to
  make full use of the GPU architecture. CuPy's interface is highly compati
 ble with NumPy\; in most cases it can be used as a drop-in replacement. Cu
 Py supports various methods\, data types\, indexing\, broadcasting\, and m
 ore.\n\nShohei Hido
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Easy 2D Game Creation With Arcade
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T143500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T150500
DESCRIPTION:Want to have fun with Python? Do something visual? Get started
  today? Learn how to draw\, animate\, and use sprites for games with the [
 Python Arcade](http://arcade.academy/) library.\n\n"Arcade" is an easy-to-
 use Python library for creating 2D arcade games. We'll show you how to get
  started creating your own game\, and find plenty of example code to get a
 n idea of what you can do with this library. If you are familiar with PyGa
 me\, Arcade is easier\, more powerful\, and uses recent Python features li
 ke type hinting and decorators.\n\nThis talk is great for beginners\, educ
 ators\, and people who want to create their own arcade games.\n\nPaul Vinc
 ent Craven
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:When data meets education! The secret life of data in education
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T151000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T154000
DESCRIPTION:In the last decades\, the power of data and analytics has tran
 sformed instruction in education. Increasingly\, large-scale data is avail
 able on student learning and interaction online. Much of this data represe
 nts student behavior. This has allowed researchers to model and track many
  elements of student learning that were not previously feasible at scale:
 engagement\, affect\, collaborative skill\, and robust learning. In turn\,
  these models can be used in prediction of long-term student outcomes\, an
 d to analyze the factors driving long-term success of students\, \n\nThis
 talk will focus on secret life of data in education\, the key points of my
  talk will be:\n\n- Data meets education\n- Course tracking\n- Student Beh
 aviour\n- Data-driven education\n- Finishing strong\n\n\n\n\nRizky Ariesti
 yansyah
LOCATION:Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python for N00bs: A cognitive and educational approach
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T151000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T154000
DESCRIPTION:This talk will put a new spin on helping complete beginners to
  use Python. We all tend to teach Python the way that we learned it. Howev
 er\, by applying lessons learned from cognitive science and educational ps
 ychology\, we can better help a wide range of beginners and open up Python
  to new audiences. \n\nThe speaker will share examples of successes and fa
 ilures in teaching Python in several contexts including high school classr
 ooms\, the Young Coders program\, as well as online and printed curricula.
  Topics covered will include teaching the command line\, helping new progr
 amming concepts to “stick\,” increasing engagement with abstract conce
 pts\, and supporting the independence of new programmers.  \n\nThe Python
 community is an ecosystem built on learning from others and giving back to
  others. The strategies covered in this talk are useful for creating docum
 entation for new users\, writing a book or tutorial\, teaching a child or
 adult\, running a workshop\, or teaching a class. \n\n\nMeg Ray
LOCATION:Room 25A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Debugging PySpark -- Or trying to make sense of a JVM stack trace
 when you were minding your own bus
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T154500
DESCRIPTION:Apache Spark is one of the most popular big data projects\, of
 fering greatly improved performance over traditional MapReduce models. Muc
 h of Apache Spark’s power comes from lazy evaluation along with intellig
 ent pipelining\, which can make debugging more challenging. This talk will
  examine how to debug Apache Spark applications\, the different options fo
 r logging in PySpark\, as well as some common errors and how to detect the
 m.\n\nSpark’s own internal logging can often be quite verbose\, and this
  talk will examine how to effectively search logs from Apache Spark to spo
 t common problems. In addition to the internal logging\, this talk will lo
 ok at options for logging from within our program itself.\n\nSpark’s acc
 umulators have gotten a bad rap because of how they interact in the event
 of cache misses or partial recomputes\, but this talk will look at how to
 effectively use Spark’s current accumulators for debugging as well as a
 look to future for data property type accumulators which may be coming to
 Spark in future version.\n\nIn addition to reading logs\, and instrumentin
 g our program with accumulators\, Spark’s UI can be of great help for qu
 ickly detecting certain types of problems.\n\nDebuggers are a wonderful to
 ol\, however when you have 100 computers the “wonder” can be a bit mor
 e like “pain”. This talk will look at how to connect remote debuggers\
 , but also remind you that it’s probably not the easiest path forward.\n
 \n\nHolden Karau
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python 3: ten years later
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T160000
DESCRIPTION:Looking back at Python evolutions over the last 10 years.\n\nP
 ython 3.0 was released ten years ago (December 2008). It's time to look ba
 ck: analyze the migration from Python 2 to Python 3\, see the progress we
 made on the language\, list bugs by cannot be fixed in Python 2 because of
  the backward compatibility\, and discuss if it's time or not to bury Pyth
 on 2.\n\nPython became the defacto language in the scientific world and th
 e favorite programming language as the first language to learn programming
 .\n\n\n\nVictor Stinner
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python for the quantum computing age
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T160000
DESCRIPTION:Quantum computers are slowly turning in to reality more than 3
 0 years after they were first theorized. The need for quantum computers ha
 ve become clear as we reach the limits of Moore’s law and yet we need mo
 re computational power. We are at a very early stage of quantum computing.
  Yet Python is slowly becoming a defacto language for programming quantum
 computers. \n\nIn this talk\, we will discuss the difference a traditional
  computer and a quantum computer. We will learn about the two architecture
 s namely Quantum annealing and Quantum gate. Finally\, we will learn to pr
 ogram quantum computers using Python.\n\n\nRavi Chityala
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:The AST and Me
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T160000
DESCRIPTION:Get under the hood and learn about Python's beloved Abstract S
 yntax Tree. Ever wonder how Python code is run? Overheard people arguing a
 bout whether Python is interpreted or compiled? In this talk\, we will del
 ve into the lifecycle of a piece of Python code in order to understand the
  role that Python's Abstract Syntax Tree plays in shaping the runtime of y
 our code. Utilizing your newfound knowledge of Python's AST\, you'll get a
  taste of how you probably already rely on ASTs and how they can be used t
 o build awesome tools.\n\n\nEmily Morehouse-Valcarcel
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Taking Django Async
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T160000
DESCRIPTION:The Django Channels project has taken a major turn with versio
 n 2.0\, embracing Python's async functionality and building applications a
 round an async event loop rather than worker processes.\n\nDoing this\, ho
 wever\, wasn't easy. We'll look through some of the techniques used to mak
 e Django coexist in this async world\, including handing off between async
  and sync code\, writing fully asynchronous HTTP and WebSocket handling\,
 and what this means for the future of Django\, and maybe Python web framew
 orks in general.\n\nAndrew Godwin
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Analyzing Data: What pandas and SQL Taught Me About Taking an Aver
 age
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T154500
DESCRIPTION:“So tell me\,” my manager said\, “what is an average?”
 \n\nThere’s probably nothing worse than that sinking feeling when you fi
 nish an analysis\, email it to your manager or client to review\, and they
  point out a mistake so basic you can’t even fathom how you missed it. \
 n\nThis talk is about mine: how to take an average.\n\nAverages are someth
 ing we use everywhere - it’s a simple np.mean() in pandas or AVG() in SQ
 L. But recently I’ve come to appreciate just how easy it is to calculate
  this statistic incorrectly. We learn once - in middle school no less - ho
 w to take an average\, and never revisit it. Then\, when we are faced with
  multidimensional datasets (ie. pretty much every dataset out there)\, we
 never reconsider whether we should be taking an average the same way.\n\nI
 n this talk\, we follow my arduous and humbling journey of learning how to
  properly take an average with multidimensional data. We will cover how im
 properly calculating it can produce grossly incorrect figures\, which can
 slip into publications\, research analyses and management reports.\n\nAlex
  Petralia
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:The human nature of failure & resiliency
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T160000
DESCRIPTION:Projects fail in droves. Systems hiccup and hours of downtime
 follows. Screws fall out all the time\; the world is an imperfect place.\n
 \nWe talk a lot about building resilient systems\, but all systems are (at
  least for now) built by humans. Humans who have been making the same type
 s of mistakes for thousands of years. \n\nJust because failure happens doe
 sn’t mean we can’t do our best to prevent it or—at the very least—
 to minimize the damage when it does. As a matter of fact\, embracing failu
 re can be one of the best things you do for your system. Failure is a vita
 l part of evolution. By learning to love failure we learn how to take the
 next step forward. Ignoring or punishing failure leads to stagnation and w
 asted potential.\n\nThis talk distills 3000 pages of failure research into
  40 minutes of knowledge about the human factors of failure\, how it can b
 e recognised\, and how you can work around it to create more resilient sys
 tems.\n\nBy the end of this talk the audience will have an awareness of th
 e most common psychological reasons for mistakes and failures and how to d
 evelop systems and processes to protect against them.\n\nVM (Vicky) Brasse
 ur
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dataclasses:  The code generator to end all code generators
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T160000
DESCRIPTION:The PEP 557 dataclasses module is available in starting in Pyt
 hon 3.7.   It will become an essential part of every Python programmer's t
 oolkit.  This talk shows what problem the module solves\, explains its key
  design decisions\, and provides practical examples of how to put it to wo
 rk.\n\nDataclasses are shown to be the next step in a progression of data
 aggregation tools: tuple\, dict\, simple class\, bunch recipe\, named tupl
 es\, records\, attrs\, and then dataclasses. Each builds upon the one that
  came before\, adding expressiveness at the expense of complexity.\n\nData
 classes are unique in that they let you selectively turn-on or turn-off it
 s various capabilities and it lets the user choose the underlying data sto
 re (either instance dictionary\, instance slots\, or an inherited base cla
 ss).\n\nDataclasses and typing.NamedTuple both use variable annotations wh
 ich were new in Python 3.6.\n\n\n\n\n\n\n\n\n\nRaymond Hettinger
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Controlling apples with snakes: Automating mobile apps with Appium
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T154500
DESCRIPTION:Testing mobile applications is hard. Testing manually is nearl
 y impossible.\nThat’s where automated testing shines. Just sit back and
 watch the machine go!\nPython is a very powerful language for writing auto
 mated tests\, but since Python is not installed on mobile platforms\, we n
 eed to find a way to remotely control and monitor the device.\nBut how do
 we automate a device remotely? The answer is Appium.\n\nIn this talk I wil
 l go over the process of deploying and testing iOS (or Android) applicatio
 ns\, and how to work with Appium to easily generate Python 3 code for test
 ing your application.\n\nNir Arad
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Surviving (and thriving!) when you are overloaded
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T151500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T154500
DESCRIPTION:Taking on leadership roles always includes new demands on your
  attention and time. Inevitably\, your finite work week will conflict with
  the sheer amount of tasks you have to do. How can we as leaders keep step
 ping up to new responsibilities while balancing our pre-existing ones?\n\n
 This talk will focus on strategies for managing a too-large workload witho
 ut abandoning important tasks or doing a shoddy job. We’ll look at techn
 iques to prioritize what work matters most\, identify tasks we should be d
 oing ourselves\, and finally delegate the rest to build our team’s skill
 s while reducing our own workload.\n\nScott Triglia
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:How I Learned to Stop Worrying and Love Python at Google | Tech Ta
 lk Series
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T153000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T170000
DESCRIPTION:Continuing with tradition\, our sponsor workshop session featu
 res three awesome half-hour Python tech talks as they relate to Google. He
 ar about Python tools we’ve built in-house\, using Python with Google de
 veloper tools to build your web & mobile applications with or best practic
 es on how we use Python internally at Google. You’re invited to stop by
 and hear from a cadre of world-class Google engineers from around the worl
 d! :-)\n\nWesley Chun
LOCATION:Room 23
END:VEVENT
BEGIN:VEVENT
SUMMARY:Datadog: Distributed Tracing for Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T153000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T170000
DESCRIPTION:Tracing is a specialized form of logging that is designed to w
 ork effectively in large\, distributed environments. When done right\, tra
 cing follows the path of a request across process and service boundaries.
 This provides a big step-up in application observability and can help info
 rm a developer why certain requests are slow\, or why they might have beha
 ved unexpectedly. This tutorial will familiarize users with the benefits o
 f tracing\, and describe a general toolkit for emitting traces from Python
  applications in a minimally intrusive way. We will walk through a simple
 example app\, which receives an HTTP request and gradually instrument it t
 o be observable via traces. We will discuss language constructs that can g
 enerate traces - namely decorators\, monkey-patching and context managers
 - and give users hints on how they might add tracing to their own applicat
 ions and libraries. In the process\, users will become familiar with the e
 xisting standards for modeling traces\, and some of the challenges involve
 d in adhering to this model in a distributed\, asynchronous environment.\n
 \nMichael Whetten
LOCATION:Room 13
END:VEVENT
BEGIN:VEVENT
SUMMARY:Build serverless applications easily with Chalice – an open sour
 ce Python framework
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T153000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T170000
DESCRIPTION:Serverless applications make it easy for you to run your code
 without managing servers. AWS Chalice is a serverless Python framework for
  getting your serverless application up and running in minutes. In this tu
 torial\, you'll learn how to build a serverless application using AWS Chal
 ice. We will cover authoring a RESTful service that leverages Amazon API G
 ateway and AWS Lambda as well as various deployment options.\n\nJames Sary
 erwinnie
LOCATION:Room 25C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jupyter Tools for Teaching and Learning
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T154000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T161000
DESCRIPTION:Project Jupyter is the center of a set of technologies that gr
 ew out of simple tools to make Python easier to use. Today\, Jupyter is co
 mposed of powerful client-server applications and protocols for computing
 in many programming languages. This talk focuses on using these technologi
 es for pedagogical purposes. \n\nEvery course I have taught since the Fall
  semester of 2014 has been over the web via our JupyterHub server. These c
 ourses have included firstyear writing seminars\, as well as courses in Pr
 ogramming Languages\, Assembly Language\, Introduction to Biology (in Pyth
 on)\, and in Processing (Java-based). In this talk I hope to help identify
  best-practices for using Jupyter in the classroom. I will discuss and dem
 onstrate tools and techniques\, and explore the challenges of using Jupyte
 r for teaching and learning.\n\nDouglas Blank
LOCATION:Room 25A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pycamp 2K17: A Disclaimer
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180510T154000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180510T161000
DESCRIPTION:Nagpur is a tier-two city in India. There were no user groups\
 , programming meetups and that too in Python? Impossible! It really seemed
  impossible to organise people and do something that will make them intere
 sted in learning and contributing. \nAs a student of Computer Engineering
 Bachelor's Degree Program\, I always felt that drive missing in my fellow
 classmates.\nMy talk covers the journey of organising first Django Girls M
 eetup and first ever Python BootCamp of my city called "Pycamp 2k17". \nIt
  covers the journey of survival\, how being a women it made tougher and ho
 w we ended up putting a disclaimer on our website because we never knew th
 at name 'PyCamp' has a copyright! \nHow me and my team managed to sell tic
 kets of worth INR 50\,000 to the people who hardly ever heard of the name
 'Python'. \nAnd cherry on the cake to get 'PSF' sponsoring the event.\n\nA
 nubha Maneshwar
LOCATION:Room 25B
END:VEVENT
BEGIN:VEVENT
SUMMARY:How to Write Deployment-friendly Applications
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T161500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T170000
DESCRIPTION:The DevOps movement gave us many ways to put Python applicatio
 ns into production.  But should your *application* care?  Should it need t
 o know whether it’s running on your notebook\, on a server\, in a Docker
  container\, or in some cloud platform as a service?\n\nIt should not\, be
 cause environment-agnostic applications are easier to **test**\, easier to
  **deploy**\, easier to **handle**\, and easier to **scale**.\n\nBut how c
 an you *practically* structure and configure your applications to make the
 m indifferent to the environment they run in?  How do secrets fit into the
  picture?  And where do you put that log file?\n\nBy the end of this talk
 you’ll know the tools and techniques that enable you to write such Pytho
 n applications and you’ll be ready for the next big change.\n\nHynek Sch
 lawack
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:What is a Python Core Developer?
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T161500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T170000
DESCRIPTION:How do you become a Python core developer? How can I become on
 e? What is it like to be a Python core developer?\n\nThese are the questio
 ns I often receive ever since I became a Python core developer a year ago.
  Contributing to Python is a long journey that does not end when one earns
  the commit privilege. There are responsibilities to bear and expectations
  to live up to.\n\nIn the past year\, I've been learning more about what i
 t really means to be a Python core developer. Let me share all of that wit
 h you.\n\n\nMariatta Wijaya
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Exploring Deep Learning Framework PyTorch
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T161500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T170000
DESCRIPTION:Anyone who is interested in deep learning has gotten their han
 ds dirty playing around with Tensorflow\, Google's open source deep learni
 ng framework. Tensorflow has its benefits like wide scale adoption\, deplo
 yment on mobile\, and support for distributed computing\, but it also has
 a somewhat challenging learning curve\, is difficult to debug\, and hard t
 o deploy in production. PyTorch is a new deep learning framework that solv
 es a lot of those problems.\n\nPyTorch is only in beta\, but users are rap
 idly adopting this modular deep learning framework. PyTorch supports tenso
 r computation and dynamic computation graphs that allow you to change how
 the network behaves on the fly unlike static graphs that are used in frame
 works such as Tensorflow. PyTorch offers modularity which enhances the abi
 lity to debug or see within the network and for many\, is more intuitive t
 o learn than Tensorflow.\n\nThis talk will objectively look at PyTorch and
  why it might be the best fit for your deep learning use case and we'll lo
 ok at use cases that will showcase why you might want consider using Tenso
 rflow instead.\n\n\n\n\nStephanie Kim
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Reactive Programming with RxPy
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T161500
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T170000
DESCRIPTION:Web applications contains lots of database operations\, networ
 k calls\, nested callbacks and other computationally expensive tasks that
 might take a long time to complete or even block other threads until it's
 done\, here is where ReactiveX enters\, it doesn't only gives us the facil
 ity to convert almost anything to a stream\; variables\, properties\, user
  inputs\, caches\, etc to manage it asynchronously. But it also gives us a
 n easy way to handle errors which is a hard task within asynchronous progr
 amming. ReactiveX makes our code more flexible\, readable\, maintainable a
 nd easy to write.\n\nWe will be exploring how ReactiveX help us to make th
 ings easier with its operators toolbox that can be used to filter\, create
 \, transform or unify any of those streams. We will learn that in just a f
 ew lines of maintainable code\, we can have multiple web sockets which rec
 ieves multiple requests all handled by an asynchronous process that serves
  a filtered output.\n\nTo do that I decided to explain an example of the u
 se with an example by implementing observables\, observers/subscribers and
  subjects. We will start by requesting our data stream from the Github API
  with a Tornado web socket and then filtering and processing it asynchroun
 osly.\n\nValery Calderon
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Building a Data Pipeline with Testing in Mind
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T163000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T170000
DESCRIPTION:It’s one thing to build a robust data pipeline process in py
 thon but a whole other challenge to find tooling and build out the framewo
 rk that allows for testing a data process. In order to truly iterate and d
 evelop a codebase\, one has to be able to confidently test during the deve
 lopment process and monitor the production system. \n\nIn this talk\, I ho
 pe to address the key components for building out end to end testing for d
 ata pipelines by borrowing concepts from how we test python web services.
 Just like how we want to check for healthy status codes from our API respo
 nses\, we want to be able to check that a pipeline is working as expected
 given the correct inputs. We’ll talk about key features that allows a da
 ta pipeline to be easily testable and how to identify timeseries metrics t
 hat can be used to monitor the health of a data pipeline.\n\nJiaqi Liu
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Listen\, Attend\, and Walk : Interpreting natural language navigat
 ional instructions
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T163000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T170000
DESCRIPTION:Imagine you have an appointment in a large building you do not
  know. Your host sent instructions describing how to reach their office. T
 hough the instructions were fairly clear\, in a few places\, such as at th
 e end\, you had to infer what to do. How does a _robot (agent)_ interpret
 an instruction in the environment to infer the correct course of action? E
 nabling harmonious _Human - Robot Interaction_ is of primary importance if
  they are to work seamlessly alongside people.\n\nDealing with natural lan
 guage instructions in hard because of two main reasons\, first being\, Hum
 ans - through their prior experience know how to interpret natural languag
 e but agents can’t\, and second is overcoming the ambiguity that is inhe
 rently associated with natural language instructions. This talk is about h
 ow deep learning models were used to solve such complex and ambiguous prob
 lem of converting natural language instruction into its corresponding acti
 on sequence.\n\nFollowing verbal route instructions requires knowledge of
 language\, space\, action and perception. In this talk I shall be presenti
 ng\, a neural sequence-to-sequence model for direction following\, a task
 that is essential to realize effective autonomous agents.\n\nAt a high lev
 el\, a sequence-to- sequence model is an end-to-end model made up of two r
 ecurrent neural networks: \n\n - **Encoder** - which takes the model’s i
 nput sequence as input and encodes it into a fixed-size context vector.\n
 - **Decoder** - which uses the context vector from above as a seed from wh
 ich to generate an output sequence. \n\nFor this reason\, sequence-to-sequ
 ence models are often referred to as _encoder-decoder_ models. The alignme
 nt based encoder-decoder model would translate the natural language instru
 ctions into corresponding action sequences. This model does not assume any
  prior linguistic knowledge: syntactic\, semantic or lexical. The model le
 arns the meaning of every word\, including object names\, verbs\, spatial
 relations as well as syntax and the compositional semantics of the languag
 e on its own.\n\nIn this talk\, steps involved in pre-processing of data\,
  training the model\, testing the model and final simulation of the model
 in the virtual environment will be discussed. This talk will also cover so
 me of the challenges and trade-offs made while designing the model.\n\nPad
 maja Bhagwat
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python Performance Investigation by Example
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T163000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T170000
DESCRIPTION:Occasionally we’ll find that some bit of Python we’ve writ
 ten doesn’t run as fast as we’d like\, what can we do? Performance bot
 tlenecks aren’t always intuitive or easy to spot by reading code so we n
 eed to collect data with [profiling](https://docs.python.org/3.6/library/p
 rofile.html). Once we’ve identified the bottleneck we’ll need to chang
 e our approach\, but what options are faster than others?\n\nThis talk ill
 ustrates a Python performance investigation and improvements using an [Adv
 ent of Code](http://www.adventofcode.com/) programming challenge. I’ll w
 alk through starting from a slow (but correct) solution\, look at profilin
 g data to investigate _why_ it’s slow\, and explore multiple paths for i
 mproving performance\, including more efficient algorithms and using third
 -party tools like [Cython](http://cython.org/). You’ll leave this talk w
 ith a recipe for analyzing Python performance and information about some o
 ptions for improved performance.\n\nMatt Davis
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python 2 to 3: How to Upgrade and What Features to Start Using
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T163000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T170000
DESCRIPTION:The end of life for Python 2 is 2020. Python 3 is the future a
 nd you'll need to consider both your upgrade plan and what steps you'll ta
 ke after upgrading to start leveraging Python 3 features.\n\nDuring this t
 alk we'll briefly discuss how to start **the process of upgrading your cod
 e to Python 3**. We'll then dive into some of **the most useful Python 3 f
 eatures** that you'll be able to start embracing once you drop Python 2 su
 pport.\n\nA number of the most powerful Python 3 features are syntactic fe
 atures that are **Python 3 only**. You won't get any experience using thes
 e features until you fully upgrade. These features are an incentive to dro
 p Python 2 support in existing 2 and 3 compatible code. You can consider t
 his talk as a teaser of Python 3 features that you may have never used.\n\
 nAfter this talk I hope you'll be inspired to fully upgrade your code to P
 ython 3.\n\nTrey Hunner
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Trio: Async concurrency for mere mortals
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T163000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T170000
DESCRIPTION:Concurrent programs are super useful: think of web apps juggli
 ng lots of simultaneous downloads and websocket connections\, chat bots tr
 acking multiple concurrent conversations\, or web spiders fetching pages i
 n parallel. But *writing* concurrent programs is complicated\, intimidatin
 g to newcomers\, and often challenging even for experts.\n\nDoes it have t
 o be? Python is famous for being simple and straightforward\; can Python m
 ake concurrent programming simple and straightforward too? I think so. By
 carefully analyzing usability pitfalls in other libraries\, and taking adv
 antage of new Python 3 features\, I've come up with a new set of primitive
 s that make it dramatically easier to write correct concurrent programs\,
 and implemented them in a new library called [Trio](https://trio.readthedo
 cs.io). In this talk\, I'll describe these primitives\, and demonstrate ho
 w to use them to implement a basic algorithm for speeding up TCP connectio
 ns. Compared to the best previous Python implementation\, our version turn
 s out to be easier to understand\, more correct\, and dramatically shorter
 .\n\nThis talk assumes basic familiarity with Python\, but does *not* requ
 ire any prior experience with concurrency\, async/await\, or networking.\n
 \n\nNathaniel J. Smith
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fighting the Good Fight: Python 3 in your organization
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T163000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T170000
DESCRIPTION:Today\, services built on Python 3.6.3 are widely used at Face
 book. But as recently as May of 2014 it was actually impossible at all to
 use Python 3 at Facebook. Come learn how we cut the Gordian Knot of depend
 encies and social aversion to the point where new services are now being w
 ritten in Python 3 while older Python 2 projects are actively migrated to
 Python 3. All accomplished by a small group of individual contributors in
 their spare time. Learn to fight the good fight and upgrade your organizat
 ion to Python 3 like we did at Facebook.\n\nJason Fried
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Secrets of a WSGI master.
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T174000
DESCRIPTION:The WSGI (Web Server Gateway Interface) specification for host
 ing Python web applications was created in 2003. Measured in Internet time
 \, it is ancient. The oldest main stream implementation of the WSGI specif
 ication is mod_wsgi\, for the Apache HTTPD server and it is over 10 years
 old.\n\nWSGI is starting to be regarded as not up to the job\, with techno
 logies such as HTTP/2\, web sockets and async dispatching being the way fo
 rward. Reality is that WSGI will be around for quite some time yet and for
  the majority of use cases is more than adequate.\n\nThe real problem is n
 ot that we need to move to these new technologies\, but that we aren't usi
 ng the current WSGI servers to their best advantage. Moving to a new set o
 f technologies will not necessarily make things better and will only creat
 e a new set of problems you have to solve.\n\nAs one of the oldest WSGI se
 rver implementations\, Apache and mod\\_wsgi may be regarded as boring and
  not cool\, but it is still the most stable option for hosting WSGI applic
 ations available. It also hasn't been sitting still\, with a considerable
 amount of development work being done on mod\\_wsgi in the last few years
 to make it even more robust and easier to use in a development environment
  as well as production\, including in containerised environments.\n\nIn th
 is talk you will learn about many features of mod\\_wsgi which you probabl
 y didn't even know existed\, features which can help towards ensuring your
  Python web application deployment performs to its best\, is secure\, and
 has a low maintenance burden.\n\nTopics which will be covered include:\n\n
 * Easy deployment of Python web applications using mod\\_wsgi-express.\n*
 Integration of mod_wsgi-express with a Django web application.\n* Using mo
 d\\_wsgi-express in a development environment.\n* How to make use of mod\\
 _wsgi-express in a production environment.\n* Using mod_wsgi-express in a
 containerised runtime environment.\n* Ensuring consistency between develop
 ment and production environments using warpdrive.\n* Using mod\\_wsgi-expr
 ess to bootstrap a system Apache installation for hosting WSGI application
 s.\n* Why you should be using daemon mode of mod\\_wsgi and not embedded m
 ode.\n* How to properly associate mod\\_wsgi with a Python virtual environ
 ment.\n* Building a robust deployment that can recover from misbehaving ap
 plication code\, backend services\, or request overloading.\n* Using hooks
  provided by mod\\_wsgi to monitor the performance of your Python web appl
 ication.\n\nIf you are a beginner\, come learn why mod\\_wsgi is still a g
 ood option for deploying your Python web applications. If you are an old t
 ime user of mod\\_wsgi\, find out about all the features you probably didn
 't know existed\, revisit your current Python web application deployment a
 nd make it even better.\n\nGraham Dumpleton
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bowerbirds of Technology: Architecture and Teams at Less-than-Goog
 le Scale
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T174000
DESCRIPTION:Facebook\, Google\, Uber\, LinkedIn\, and friends are the rare
 fied heights of software engineering. They encounter and solve problems at
  scales shared by few others\, and as a result\, their priorities in produ
 ction engineering and architecture are just a bit different from the rest
 of us down here in the other 99% of services. Through deconstructing a few
  blog posts from these giants\, we’ll evaluate just what is it that they
 ’re thinking about when they build systems and whether any of their choi
 ces are relevant to those of us operating at high scale yet still somethin
 g less than millions of requests per second.\n\nThis talk will go into dep
 th on how to make technological decisions to meet your customers’ requir
 ements without requiring a small army of engineers to answer 2 AM pages\,
 and how to set realistic goals for your team around operations\, uptime\,
 communications\, and disaster recovery.\n\nWith these guidelines in mind\,
  you should be better equipped to say no (or yes!) the next time your team
 ’s software hipster proposes moving everything to the Next Big Thing.\n\
 nSam Kitajima-Kimbrel
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Visual Testing with PyCharm and pytest
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T174000
DESCRIPTION:Know you should be doing testing but haven’t gotten over the
  hurdle to learn it? pytest is Python’s modern\, friendly\, and powerful
  testing framework. When paired with an IDE\, testing gets a visual interf
 ace\, making it much easier to get started.\n\nIn this talk we cover “vi
 sual testing”: starting\, learning\, using\, and mastering test-driven d
 evelopment (TDD) with the help of a nice UI. We’ll show PyCharm Communit
 y Edition\, a free and open-source Python IDE\, as a productive TDD enviro
 nment for pytest. Specifically\, we’ll show a workflow using pytest and
 PyCharm that helps make tests speed up development\, or at the very least
 help to make testing seem less "in the way" of other development activitie
 s\n\nBrian Okken\, Paul Everitt
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:All in the timing: How side channel attacks work
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T174000
DESCRIPTION:In this talk\, you’ll learn about a category of security iss
 ue known as side channel attacks. You’ll be amused to see how features l
 ike automatic data compression\, short-circuit execution\, and determinist
 ic hashing can be abused to bypass security systems. No security backgroun
 d knowledge is required. The talk assumes at least intermediate Python exp
 erience.\n\nWe’ll take a tour of real side channel vulnerabilities in op
 en source Python codebases\, including the patches that fixed them. It als
 o offers practical advice for avoiding these issues. My goal is to demysti
 fy this topic\, even if you aren’t writing security-critical software.\n
 \n\nPhilip James\, Asheesh Laroia
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Elegant Solutions For Everyday Python Problems
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T174000
DESCRIPTION:Are you an intermediate python developer looking to level up?
 Luckily\, python provides us with a unique set of tools to make our code m
 ore elegant and readable by providing language features that make your cod
 e more intuitive and cut down on repetition. In this talk\, I’ll share p
 ractical pythonic solutions for supercharging your code. \n\nSpecifically\
 , I'll cover:\n\n- What magic methods are\, and show you how to use them i
 n your own code.\n- When and how to use partial methods.\n- An explanation
  of ContextManagers and Decorators\, as well as multiple techniques for im
 plementing them.\n- How to effectively use `NamedTuples`\, and even subcla
 ss and extend them!\n\nLastly\, I'll go over some example code that ties m
 any of these techniques together in a cohesive way. You'll leave this talk
  feeling confident about using these tools and techniques in your next pyt
 hon project!\n\nNina Zakharenko
LOCATION:Room 26A/B/C
END:VEVENT
BEGIN:VEVENT
SUMMARY:A Bit about Bytes: Understanding Python Bytecode
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T174000
DESCRIPTION:At some point every Python programmer sees Python bytecode fil
 es -- they're those '.pyc' files Python likes to leave behind after it run
 s. But have you ever wondered what's really going on in those files? Well\
 , wonder no more! In this talk you'll learn what Python bytecode is and ho
 w it's used to execute your code\, as well as how to decipher and read it\
 , and how to reason about bytecode to understand the performance of your P
 ython code.\n\nJames Bennett
LOCATION:Grand Ballroom B
END:VEVENT
BEGIN:VEVENT
SUMMARY:The Journey Over the Intermediate Gap
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T174000
DESCRIPTION:Congratulations on finishing your first tutorials or classes i
 n python! In the parlance of the hero’s journey myth\, you’ve had your
  ‘threshold moment”: you’ve started down a path that could lead to a
  long and fulfilling career. But the road to this glorious future is frust
 ratingly obscured by a lack of guidance in the present. You know enough to
  realize that you don’t have all the skills you need yet\, but it’s ha
 rd to know how to learn those skills\, or even articulate what they are. T
 here are no easy solutions to this problem. There are\, however\, a few fu
 ndamental things to know and advice to keep in mind. Drawing from my own e
 xperience and with input from others\, I’ve compiled some helpful hints
 about the skills\, tools\, and guiding questions that will get you to mast
 ery.\n\nSara Packman
LOCATION:Grand Ballroom A
END:VEVENT
BEGIN:VEVENT
SUMMARY:Get your resources faster\, with importlib.resources
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T174000
DESCRIPTION:Resources are files that live within Python packages.  Think t
 est data files\, certificates\, templates\, translation catalogs\, and oth
 er static files you want to access from Python code.  Sometimes you put th
 ese static files in a package directory within your source tree\, and then
  locate them by importing the package and using its `__file__` attribute.
  But this doesn't work for zip files!\n\nYou could use `pkg_resources`\, a
 n API that comes with `setuptools` and hides the differences between files
  on the file system and files in a zip file.  This is great because you do
 n't have to use `__file__`\, but it's not so great because `pkg_resources`
  is a big library and can have potentially severe performance problems\, e
 ven at import time.\n\nWelcome to `importlib.resources`\, a new module and
  API in Python 3.7 that is also available as a standalone library for olde
 r versions of Python.  `importlib.resources` is build on top of Python's e
 xisting import system\, so it is very efficient.  It also defines an abstr
 act base class which loaders can implement to provide their own resource a
 ccess.  Python's built-in zipimporter uses this to provide efficient acces
 s to resources within a zip file.  Third party import hooks can do the sam
 e\, so resources can come from anything that is importable by Python.\n\nT
 his talk will step through the motivations behind `importlib.resources`\,
 the library's usage\, its interfaces\, and the hooks made available to thi
 rd party packages.  It will also talk about the minor differences between
 the standalone version and the version in Python 3.7's standard library.
 Hopefully audience members will come away with compelling reasons to port
 their code to this much more efficient library.\n\nBarry Warsaw
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python\, Locales and Writing Systems
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180512T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180512T174000
DESCRIPTION:Python 3 removes a lot of the confusion around Unicode handlin
 g in Python\, but that by no means fixes everything. Different locales and
  writing systems have unique behaviours that can trip you up. Here’s som
 e of the worst ones and how to handle them correctly.\n\nRae Knowler
LOCATION:Global Center Ballroom AB
END:VEVENT
BEGIN:VEVENT
SUMMARY:Love your bugs
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20180511T171000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20180511T174000
DESCRIPTION:Wrestling bugs can be one of the most frustrating parts of pro
 gramming - but with the right framing\, bugs can also be our best allies.
 I'll tell the tales of two of my favorite bugs\, including the time I trig
 gered a DDOS of a logging cluster\, and explain why I love them. I'll also
  give you concrete strategies for approaching tricky bugs and making them
 easier and more fun.\n\nAllison Kaptur
LOCATION:Grand Ballroom C
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python Developers Survey 2017: Findings
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Want to know about the latest trends in the Python community a
 nd see the big picture of how things have changed over the last few years?
  Interested in the results of the latest official Python Developers Survey
  2017 which was supported by the Python Software Foundation and gathered r
 esponses from more than 10.000 Python developers? Come learn about the mos
 t popular types of Python development\, trending frameworks\, libraries an
 d tools\, additional languages being used by Python developers\, Python ve
 rsions usage statistics and many other insights from the world of Python.
 All derived from the actual research: Python Developers Survey 2017 which
 collected responses from over 10.000 Python developers\, organized in part
 nership between the Python Software Foundation and JetBrains.\n\nDmitry Fi
 lippov
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Building Reproducible Machine Learning Models with Python and Dock
 er
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:A 2016 study by the journal _Nature_ found that 90% of working
  scientists surveyed think there is a "slight" or "significant" crisis in
 experimental reproducibility. For the working data scientist\, the process
 es and models used in analysis must be able to be used to reproduce result
 s or to be applied to new data. We ran into these same problems while buil
 ding an API for developers to easily incorporate Machine Learning algorith
 ms in their software. We were able to leverage Docker\, Python\, Flask\, a
 nd Amazon S3 to enable the reuse of the exact model generated in the initi
 al analysis. This poster will show how those pieces are put together and h
 ow you could create a similar system for your analysis. \n\nJeff Espenschi
 ed
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Spfy: analyzing E.coli genomes as graph data
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Whole genome sequencing isn't only for humans\, but also plays
  a key role in our understanding of bacteria. Spfy uses WGS data to predic
 t traditional lab results for E.coli genomes. The results are then stored
 in a linked graph for identifying and comparing different subtypes.\nThe e
 ntire platform is packaged as a web-app\, and the analysis modules are wri
 tten in Python (+ a bit of R). Part of a larger open-source initiative by
 the National Microbiology Lab of Canada.\n\nKevin K Le
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Curriculum for project-based data science classes and their buildi
 ng blocks
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Machine learning\, artificial intelligence and data science ar
 e \ninterdisciplinary subjects and therefore they are difficult to teach.
 We suggest educational building blocks that help students to understand  m
 achine learning software (such as scikit-learn) and create their own artif
 icial intelligence algorithms. These building blocks are theoretical compo
 nents of global optimization algorithms (derived in [1]- more information
 of these building blocks will be given later ) as well as some other numer
 ical procedures(such as automatic differentiation\, see\, for example\, Py
 thon implementations Autograd and AlgoPy). \nBased on these building block
 s the programming projects \nare created that demonstrate certain steps in
  machine learning algorithms and show how these steps appear in popular  m
 odern machine learning methods.\nThese projects become a foundation of pro
 ject based data \nscience courses\, such as Data Analysis and Decision Mak
 ing using Python\, Python for Financial Applications\, Operations Research
  models using Python\, etc. \n\nBuilding Blocks\n\nIn [1] the theoretical
 approach for the design of global optimization \nmethods based on potentia
 l theory was introduced. This approach extends \nthe theory of gradient ba
 sed optimization for algorithmically defined \nfunctions (or black box fun
 ctions)\, where analytical representation of the function is not available
  or too complicated to work with (say\, too hard to compute derivatives).
 Such situations are very common in real world applications. Based on this
 theory\, the parsimonious set of building blocks \nof the optimization alg
 orithms was obtained. The hypothetical algorithm \nwhere all these buildin
 g blocks are present in their full form is given \nin [1]-[3]. It is shown
  that by varying the parameters of the building blocks we obtain the whole
  universe of optimization methods\, some of them we recognize as well know
 n heuristic techniques such as covariance matrix adaptation evolution stra
 tegy\, Shor r algorithm\, Nelder and Mead algorithm etc. Main building blo
 cks are defined as linear algebra operations: space dilation operator (bas
 ed on this operator space transformation is defined)\, Householder transfo
 rmation as a certain case of space dilation operator and memory accumulati
 on module (accumulates information from the previous iterations of the alg
 orithms).  \nOther numerical procedures that algorithms are built upon are
  automatic differentiation and natural gradient.\n\nExamples of the projec
 ts\n\nHouseholder transformation in Nelder and Mead algorithm (fmin functi
 on in SciPy)\n\nSpace dilation operator in Shor r-algorithm [5] and in Cov
 ariance matrix \nadaptation evolution strategy(CMA-ES) [6]\n\nCombining au
 tomatic differentiation with gradient-based algorithms for \noptimization
 of algorithmically defined functions.\n\nNatural gradient evolution strate
 gy [4]\n\nCoordinate transformation\, cootdinate descent algorithm and sep
 arable \nfunctions. Invariance of coordinate descent method with respect t
 o \nscaling and rotation of the search space.\n\nMemory accumulation modul
 e. Comparizon of memory accumulation in Shor \nalgorithm and genetic algor
 ithm\n\nMulti objective optimization vs constrained optimization\n\n[1]Kap
 linskii A.I.\, Propoi A.I.\, First-order nonlocal optimization methods tha
 t use potential  theory\,  Automation  and Remote Control \, 1994\, №7\,
  pp.94-103.\n[2]Kaplinskii A.I.\, Propoi A.I.\, Nonlocal optimization meth
 ods that   use potential theory\, Automation  and Remote  Control \, 1993\
 , №7\,  pp.55-66.\n[3]Kaplinskii A.I.\, Propoi A.I.\, Second-order refin
 ement conditions in nonlocal optimization	methods that use potential theor
 y\,   Automation  and Remote Control \, 1994\, №8\, pp.104-113.\n[4]N. H
 ansen\, Covariance Matrix Adaptation Evolution Strategy:A Tutorial \, N.Ha
 nsen \, Cornell University Library\, 2016\nhttps://arxiv.org/abs/1604.0077
 2\n[5]Tom Schaul\, Studies in Continuous Black Box Optimization\, PhD diss
 ertation\, 2011 \n[6]N.Shor \,Minimization Methods for Non-Differentiable
 functions\, Springer 1985\n\n\n\nNadia Udler
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python and Windows C++ desktop app: how we made them the best frie
 nds
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Have you ever dreamed of live peeking and hacking inside big C
 ++ desktop application? Controlling it with simple and elegant Python code
 ? That's what we do in our test automation team at Avast software. \n\n\nW
 hen we were thinking about test automation for software which protects hun
 dreds of millions users worldwide\, we wanted our tests to be as stable as
  possible. That's why we targeted internals of the application.\n\n\nBoost
  Python allows us to easily export internal C++ interfaces of our applicat
 ion to be controlled with Python. This approach allows us to automatically
  create binary that is importable by Python like a standard Python module
 and natively call the C++ code of the application. It provides us with the
  best of both worlds:\n\n * Python allows easy and fast development of tes
 ts\n\n * Invoking code of our C++ application directly reduces the test st
 ability problems\n\n * We are directly at the core\, no extra layers/testi
 ng frameworks needed\n\n * No need to beg for some extra code in the appli
 cation: we just use it as it is \n\n * We can script our Python tests\, as
  well as interactively control the application\n\nIn this poster\, we show
  the basic principles of this architecture and present guiding steps for t
 hose who would like to start leveraging convenient Python in the cruel C++
  world.\n\n\n\nTomas Danek\, Lukas Kucera
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:PyBites Code Challenges
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Code challenges work. We hosted [47 Python challenges last yea
 r](https://pybit.es/pages/challenges.html) and we have received amazing fe
 edback. Not only do we get [amazing PR submissions](https://pybit.es/guest
 -telegram-python-chatbot.html)\, we hear people stretching beyond what the
 y thought capable of. As Sean Connery said: “there is nothing like a cha
 llenge to bring out the best in man.” and we’re in the business of bri
 nging this to the Python world.\n\nWe just launched our [Code Challenges P
 latform](https://codechalleng.es) and > 500 Github users jumped on it the
 first 2 weeks. We did a live code workshop at [Alicante University](https:
 //pybit.es/alicante-pychallengeday.html) where we learned about typical hu
 rdles people need to overcome to start leveraging the power of Python. And
  we’re delighted to host a live code challenge at Pycon 2018! ([proposal
  227](https://us.pycon.org/2018/proposals/227/)). We think it would be nic
 e to further support this initiative with an attractive and data-driven po
 ster about our project.\n\nBob Belderbos\, Julian Sequeira
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lessons Learned from Civic Hacking
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Budgets\, Housing\, and Hurricanes. What do all of these thing
 s have in common? Civic Hacking. These were local problems that were tackl
 ed by a village of coding warriors.\n\nThere’s a lot Civic Hacking happe
 ning in the state of Texas. From giving citizens exposure to city budgetin
 g with Award-winning Budget Party\, tackling Section 8\, to the Hurricane
 app creating effort covered by Forbes.\n\n\nCarrie Maxwell
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Django-Herald: A Django Messaging Library
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:A Django messaging library that features:\n\n - Class-based de
 claration and registry approach\, like Django Admin\n - Supports multiple
 transmission methods (Email\, SMS\, Slack\, etc) per message\n - Browser-b
 ased previewing of messages\n - Maintains a history of messaging sending a
 ttempts and can view these messages\n - Disabling notifications per user\n
 \n\n\nRobert Roskam
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:15 years of PyCon: insight into Python's language and community vi
 a seminar abstracts
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Since its inaugural conference in 2003\, the Python community
 has hosted over 1200 talks\, in over 30 countries\, with more than 20\,000
  attendees in the U.S. alone. \n\nThat’s a lot of Python -- and a lot of
  extractable data. \n\nBy examining the categories and topics of seminar a
 bstracts over the years\, observations about the development of the langua
 ge and the Python community's interests can be found. These insights are i
 nvaluable to understanding how both the community and language have grown\
 , where they are headed\, and how people may support the continued growth
 of Python for years to come.\n\nThis year for the 15th anniversary of PyCo
 n\, we will highlight some of the many diverse presentations of previous c
 onferences\, piquing not only your nostalgia\, but also your appreciation
 for the impact this event has on what is shaping to truly be a hundred-yea
 r language.\n\nTanya Schlusser\, Hailey Hoyat
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:AREPL: real-time evaluation of python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:[AREPL](https://github.com/almenon/AREPL) is a scratchpad in w
 hich debugging\, running\, and writing your code can all be done at the sa
 me time.  Whenever you stop typing your code is evaluated and the output i
 s shown.  But not just the output - your local variables are saved for ins
 pection as well.  Update the code\, and the values seamlessly change.  Eve
 n if you make a mistake\, you will still get the state of your variables r
 ight before\, along with the error and stack trace.\n\n**Other Features:**
 \n* human-readable display of certain types\, like dates.\n* Automatic res
 tart for GUI development\n* Ability to 'save' a section so it only runs on
 ce\n\n----------\n\n**Links:**\nhttps://github.com/almenon/AREPL\nhttps://
 www.youtube.com/watch?v=GxryBUukTyM\n\n\n\nCaleb Collins-Parks
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:MFP: Making music with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:MFP (Music For Programmers) is an application that lets users
 make music by creating a dataflow diagram called a "patch" (really a progr
 am) that generates or processes sound.  MFP is strongly inspired by graphi
 cal patching languages such as Max/MSP and Pure Data\, but is a completely
  new implementation written in Python with C extensions.\n\nUsing MFP\,  y
 ou can quickly start making sound\, processing the inputs and outputs of o
 ther audio programs\, and interfacing with MIDI devices and control surfac
 es by drawing diagrams.   But you also have access to Python data and libr
 aries from within patches\, which makes it possible to write programs that
  have nothing to do with audio\, or to bring in Python's power to handle f
 iles\,  strings or whatever\, or to write extensions to MFP itself. \n\nBi
 ll Gribble
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Reproducible environments for reproducible results
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Trustworthy results require reproducibility\, which begins wit
 h an environment that won’t change under your feet. A truly stable envir
 onment requires a guarantee of dependency immutability and the ability to
 roll back if problems arise. [Pulp](https://pulpproject.org/) is open sour
 ce\, written in Python\, and can be used to fetch\, upload\, organize\, an
 d distribute software packages. With Pulp you can host and manage multiple
  PyPI-like instances\, called repositories\, which are versioned and conta
 in any packages you choose. These repositories can range from a mirror to
 a carefully curated [known good set](https://packaging.python.org/glossary
 /#term-known-good-set-kgs) and they can be promoted through your software
 development life cycle.\n\nYou can fetch packages from external repositori
 es\, upload private packages\, and create a pull-through cache of the Pyth
 on Package Index\, all while keeping fine control from development to prod
 uction. Each Pulp repository can act as a package index that plays nice wi
 th pip. Pulp's plugin architecture enables users to manage RPM Packages\,
 Debian Packages\, Docker Containers\, and ISOs all in one place. View our
 source code on [github](https://github.com/pulp/pulp)\, or read more about
  us in our [documentation](https://docs.pulpproject.org/en/3.0/nightly/).
 \nCome by and talk Python workflows\, learn how Pulp can help you\, and te
 ll us your use cases.\n\nAustin Macdonald\, Bihan Zhang
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Exploring generative models with Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Do you know that Neural Networks learn an inner representation
 ? This could be useful for data understanding and disentangling relationsh
 ips between features. Over this manifold\, their last layers can perform t
 he classification. But what if we change the target of these networks? Ins
 tead of trying to classify correctly\, we could want to reconstruct the in
 put. Based on this idea\, there are different approaches using Neural Netw
 orks that are known as generative models\, models that can provide new ins
 tances learning a manifold of the original data. Generative Adversarial Ne
 tworks (GAN) and Variational Autoencoders (VAE) are two well-known techniq
 ues to do this.\n\nRegarding this learnt manifold\, if the network can rec
 onstruct the input after projecting it into this subspace\, What is in bet
 ween two projected samples? Is it useful or just noise? We have explored t
 his manifold empirically and we have found that some paths between project
 ed samples provide unseen instances that are a combination of the inputs\,
  i.e: digits that are rotated or shrunk\, style combination\, etc. This co
 uld be useful to understand the underlying distribution or to provide new
 instances when data is scarce.\n\nIn order to carry on these experiments\,
  we have relied on Python\, in particular\, we have extensively used Tenso
 rflow. In this poster\, the Python tools that one could use to explore and
  reproduce these experiments will be described as well as the datasets tha
 t we have used.\n\nJavier Jorge
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:How a Python Application Can Support Everything Else
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:This poster will walk through the full replacement of a legacy
  suite of applications. The final system had a series of projects that all
  plugged into a Python project that versioned every document for storage.
 Python was the beginning and end state of every piece of data whether it w
 as going through Javascript manipulation\, PHP\, or Knime. Python was the
 backbone\, and it had to perform over very large data sets that were expec
 ted to grow quickly.\n\nThis poster will present a visual representation o
 f all the systems and how this project used Python to solve the problems i
 n question.\n\nLaurie Barth
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Converting unstructured web data into sequenced STEM educational g
 ames
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Vocabulary\, repetition\, and examples are fundamental to huma
 n learning.  These fundamental tools help humans to teach each other\, com
 municate\, and innovate. Yet\, vocabulary building and reading comprehensi
 on games specifically geared for science\, technology\, engineering and ma
 th (STEM) disciplines are lacking. They are expensive to make and the cont
 ent has a short life span.\n\nFirst\, a topic and grade level must be chos
 en by the game designer. Second\, an age appropriate curriculum must be de
 veloped. Third\, the content must be researched and edited. And finally\,
 the content needs to be transformed into a game by game developer(s). This
  process needs to be repeated for each topic.\n\nHowever\, this process ca
 n be automated using natural language processing (NLP)\, the digitization
 of primary sourced information\, and vibrant open source ecosystems. Autom
 ating this process enables educators to create STEM educational games with
  just four user inputs: (1) Term\, (2) Topic\, (3) Grade Level\, (4) Game
 type. The corresponding output is a set of sequenced games that can be adj
 usted for reading comprehension levels for particular students.\n\nThe pro
 cess to build content for the games is built on open source packages such
 as beautiful soup\, pandas\, textacy\, gensim\, scikit-learn\, and network
 x.  Client side work is done in javascript and is served by Flask.\n\nItay
  Livni\, Michael Wehar
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Cart Pole AI Controller
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Cart Pole Balancing Problem is one of the standard classical c
 ontrol problem. Building an AI Reinforcement learning agent to balance the
  pole connected with one joint on top of the moving cart is a challenging
 problem. This live demo showcases the working of the adaptive actor critic
  game controller. It has applications in designing future AI game controll
 ers\n\nSRIVIGNESSH PACHAM SRI SRINIVASAN
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:A Python-Friendly Computer Keyboard
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:If we could create a computer keyboard specifically for Python
  programmers\, what would the layout of the letters and symbols be?\n\nThe
  QWERTY keyboard we use today was developed in 1878 to avoid jamming typew
 riters' metal arms when typing quickly. But some of the most commonly used
  letters and symbols aren't easy for our fingers to get to on our modern\,
  two-dimensional computer keyboards!\n\nComputer programmers know this pro
 blem well: We have to press the `SHIFT` key every time we need a curly bra
 cket\, a parenthesis or a colon. And we use lots of these punctuation mark
 s\, creating a lot of extra keystrokes.\n\nBy running character analysis o
 n the source code of Python's 30 most widely used libraries—presumably g
 ood examples of high-quality\, Pythonic code—we can discover character f
 requencies that will help us assemble a new keyboard layout that helps Pyt
 hon programmers make more efficient keystrokes.\n\nA Python-friendly keybo
 ard would have the most common letters and symbols on the home row\, the m
 oderately-used letters and symbols on the top row\, and infrequently used
 letters and symbols on the bottom row.\n\nAnd with keyboard re-mapping\, w
 e can actually implement such a keyboard!\n\n\n\nErin Allard
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Fighting Documentation Drift
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:One of the main arguments against documentation is that it fre
 quently falls out of sync with the code it is supposed to describe.  Howev
 er\, the solution isn't to document less\; the solution is to automate the
  process of checking our documentation.  This is the idea behind [darglint
 ](https://github.com/terrencepreilly/darglint)\, a docstring argument lint
 er.\n\n*Darglint* can identify certain types of documentation drift with d
 ocstring\, such as\n\n- missing/extraneous parameters\,\n- missing/extrane
 ous return or yield statements\,\n- missing/extraneous exception descripti
 ons\,\n\nAs well as a variety of stylistic errors which can make parsing t
 he docstring difficult.  This poster will describe and demonstrate *dargli
 nt*\, measure its effectiveness against open-source projects\, and explore
  other possibilities for fighting documentation drift.\n\nTerrence Reilly
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Building your own Messenger Chatbot using Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:While designing and developing your own functional chatbot mig
 ht seem like a herculean task\, python makes it extremely easy to add inte
 lligent conversational functionality\, and with pretty good accuracy! In a
 ddition to this\, once you are familiar with Facebook messenger API (which
  also has a wide range of wonderful conversational interface elements) it
 ’s actually not that hard a task to get your chatbot into production on
 Messenger.\n\nMy poster\, with the help of a messenger bot I have programm
 ed myself as an example\, will be taking you through the whole workflow of
  setting up your own chatbot. We will start by looking at the important fe
 atures of the Messenger API\, such as automatically sending texts\, quick
 replies and images. Python libraries such as _spaCy_ and _NLTK_ make it ve
 ry intuitive to add functionality to your bot. I will then be explaining h
 ow you can use NLTK for text classification\, and spaCy language models fo
 r entity recognition and part-of-speech tagging. These python libraries wi
 ll enable us to add natural language conversational ability to the chatbot
 .\n\nTo get your bot up and running on messenger\, you need to deploy it o
 n a cloud server. I will be going through the steps involved in getting yo
 ur app up and running on one such cloud service\, _Heroku_. Following this
 \, we will be integrating Messenger with the application deployed on Herok
 u\, for which we need to set up WebHooks (after I tell you what webhooks a
 re!) and authorise the app.\n\nFinally\, I will be talking about how to ge
 t your bot into production for which you will need to do complete some saf
 ety formalities (such as setting a privacy policy) as per Facebook’s rul
 es & regulations. We will then discuss how different lines of business can
  leverage chatbots\, and what the potential advantages and disadvantages o
 f chatbots are. By the end\, you will be equipped with all the tools neces
 sary to design your own chatbot for your product\, and get it up and runni
 ng on messenger for your Facebook product page! \n\nAkilesh Lakshminarayan
 an
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Practical Sphinx
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Each member of your project team uses something different to d
 ocument their\nwork --- RestructuredText\, Markdown\, and Jupyter Notebook
 s. How do you\ncombine these into useful documentation for your project's
 users? Sphinx and\nfriends to the rescue!\n\nLearn how to integrate docume
 ntation into your everyday development\nworkflow\, apply best practices\,
 and use modern development tools and services\,\nlike Travis CI and ReadTh
 eDocs\, to create engaging and up-to-date\ndocumentation which users and c
 ontributors will love.\n\n\nCarol Willing
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Open Source Metrics at Twitter: A USF Capstone Project
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Through a Capstone Program partnership between the Open Source
  Program at Twitter\, the University of San Francisco Computer Science Dep
 artment\, and members of the Linux Foundation's CHAOSS Community\, undergr
 aduate students researched and developed numerous proof of concept tools t
 o gather and and visualize metrics that assess Open Source Community Healt
 h.\n\nRemy DeCausemaker
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Build secure and reliable continuous delivery deployment for pytho
 n microservices
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:In the agile methodology\, the speed at which software gets sh
 ipped these days got very fast. In many cases the initiative of building s
 oftware in a secure way get’s deprioritized. There is no time to do “t
 raditional secure process” where every stage of the software development
  cycle has a security checklist. This talk presents proposal about process
 es that support or could support secure software development. \nContinuous
  Delivery is a software development discipline where you build your python
  microservices in such a way that it can be released to production at any
 time. Microservice security relies on automating the Continuous Delivery d
 eployment process. \nMaking deployments secure and reliable before they la
 nd in production should be a goal for every software developer. With Conti
 nuous Delivery and security automation software developer doesn’t have t
 o be a security expert in everything to work within a microservices archit
 ecture.  \n\nA poster will contain (suggestions and code snippets):\n\n1)
 Challenges when using Microservices and Continuous Deployment\n2) Secure C
 ontinuous Delivery Microservices Production pipeline\n - access control se
 ttings\n -  secure deploy an API gateway \n - centralized security or conf
 iguration policies\n - secure source code management using GitHub \n - use
  Security Policies tailored for microservices workflow\n3) Conclusion\n\nN
 atalie Serebryakova
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Managing Machine Learning Experiments
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Managing experimental results in machine learning can be a dau
 nting task. Researchers and practitioners  often try a variety of algorith
 ms\, hyper-parameters\, and pre-processing techniques\, each resulting in
 different outcomes. Tracking and analyzing each of these outcomes is a bur
 den further amplified when dealing with multiple collaborators and several
  computer nodes.\n\nIn this presentation I will share my experience of man
 aging over a 1\,200 experimental results\, ran in parallel on 8 computer n
 odes with 5 collaborators over a time span of 6 months. I will focus on th
 e usage of the [randopt](https://seba-1511.github.io/randopt/) package for
  experimental management and visualization. Specifically\, I will introduc
 e the typical randopt workflow which consists of experiment creation\, hyp
 er-parameter selection\, and results visualization.\n\nRandopt is an [open
 -source](https://github.com/seba-1511/randopt) library for experiment mana
 gement. It is written in pure Python\, is dependency-free\, and available
 on [PyPI](https://pypi.python.org/pypi/randopt). Interactive\, web-based e
 xperimental reports are generated via the built-in command line utility an
 d a programmatic API is also available. It is compatible with all Python p
 ackages\, including PyTorch\, TensorFlow\, scikit-learn\, and numpy/scipy.
  While randopt was developed with machine learning in mind\, its agnostici
 ty with respect to the nature of the experiments makes it suitable for gen
 eral-purpose scientific experiment management.\n\nSeb Arnold
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Building your own weather app using NOAA open data and Jupyter not
 ebooks
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:The National Weather Service (NWS) estimates that its open dat
 a support a [1.5 billion dollar industry](http://informationdiet.com/blog/
 read/how-did-weather-data-get-opened). However\, if you are a Python enthu
 siast  and love open data you don't need that industry to get your very ow
 n customized weather app \;-)\n\nWe will walk through all the steps to cre
 ate a fully-featured GIS interactive map (mobile friendly too!).\n\nThanks
  to the Open Geospatial Consortium (OGC) standards\, and NOAA's open data
 policies\, it is quite easy to set up a data discovery system based on loc
 ation\, time\, and variable of interested.\n\nIn [this example](https://oc
 efpaf.github.io/python_hurricane_gis_map/irma.html) we'll explore the Nati
 onal Hurricane Center (NHC) predictions for hurricane Irma and fetch all o
 pen data we can find along its path.\n\nFilipe Fernandes
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Instrumenting Python
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:This poster presents [Affirm](https://www.affirm.com/)'s appro
 ach to instrumenting Python. We place great importance on our metrics infr
 astructure because metrics help engineers find and fix bugs quickly\, conf
 idently iterate on features\, and make data-driven decisions. \n\nWe start
  by discussing how we emit metrics in our Python code by extending the Pyt
 hon logger. We consider how we ensure this process is reliable and how we
 make sure it will not impact application performance. \n\nWe also present
 our metrics pipeline--how we use open source software for metrics collecti
 on ([Riemann](http://riemann.io/))\, storage ([Elasticsearch](https://www.
 elastic.co/products/elasticsearch))\, visualization ([Grafana](https://gra
 fana.com/))\, and alerting ([Cabot](https://github.com/Affirm/cabot)). We
 explain the reasons we believe the tools we chose are reliable and scalabl
 e and the checks we have built to ensure our pipeline is working. \n\nAs a
 n example\, we present the metrics we collect for [celery](http://www.cele
 ryproject.org/) tasks and examples of the insights these metrics have give
 n us. \n\nElaine Arbaugh\, James Lim
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Improving command line experience for managing databases with mssq
 l-cli and mssql-scripter
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Since the announcement that SQL Server 2017 supports Linux and
  Docker\, there was a need for creating modern\, cross-platform CLI tools
 to provide DBA's and developers the choice to use SQL Server anywhere. \n\
 nWhile rethinking our tools strategy\, our team chose to collaborate with
 the open source community to create two great Python-based tools: mssql-cl
 i and mssql-scripter.\n\n- **mssql-cli** is an interactive T-SQL query too
 l which includes features such as auto-completion\, syntax highlighting\,
 and pretty formatting. To create this tool\, we collaborated with the [dbc
 li community](https://github.com/dbcli) which includes other CLI tools suc
 h as pgcli and mycli.\n\n- **mssql-scripter** is a scripting tool for SQL
 Server databases and can easily generate CREATE and INSERT t-sql scripts f
 or database objects\, similar to pg_dump and mysqldump.\n\nTo learn more\,
  please visit our GitHub repos for [mssql-cli](https://github.com/dbcli/ms
 sql-cli) and [mssql-scripter](https://github.com/microsoft/mssql-scripter)
 \n\n\nAlan Yu
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:BAMnostic: an OS-agnostic port of genomic sequence analysis
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:As genome sequencing and testing gets [cheaper](https://www.ge
 nome.gov/27565109/the-cost-of-sequencing-a-human-genome/) and becomes more
  [mainstream](http://bgr.com/2017/11/27/23andme-dna-test-price-drop-amazon
 -cyber-monday/)\, the amount of data being generated is staggering. Much l
 ike other scientific fields\, **Python** has become one of the predominant
  programming languages used to process such data. What most people do not
 know is that a majority of genome analytics can be boiled down to clever s
 tring comparison and matching algorithms. The caveat here being a single f
 ile can be as ≥**300 Gb** in its [compressed binary encoded format](http
 s://samtools.github.io/hts-specs/SAMv1.pdf). A high-throughput sequencing
 library ([htslib](https://github.com/samtools/htslib)) was developed to es
 tablish a standard encoding and compression schema that enabled researcher
 s to have random access to these large files. As it stands\, htslib is the
  industry standard in the realm of genomics. \n\nOne of the most popular P
 ython libraries for handling genomic data ([PySAM](http://pysam.readthedoc
 s.io/en/latest/)) is essentially a wrapper for htslib. As widely used as b
 oth htslib and PySAM are for developers\, a large contingent of users (bot
 h end and developer) are excluded simply because htslib and PySAM do not s
 upport [***Windows***](https://github.com/pysam-developers/pysam/issues/57
 5) environments outside of contrived builds and dependencies that many end
 -users would not be willing to implement.\n\nTo overcome this issue\, pure
  Python ports of the random access\, unpacking\, and decoding components o
 f htslib were developed as a lightweight toolkit called **BAMnostic**. BAM
 nostic was developed to be a drop-in alternative for a majority of PySAM's
  workload when working in a Windows environment or projects that require a
 n OS-agnostic approach. As a drop-in\, it retains the same interface as Py
 SAM for each of its supported functions. This interface also provides a me
 ans of simple extensibility for machine learning and statistical analysis
 through libraries such as [TensorFlow](https://www.tensorflow.org/api_docs
 /python/)\, [scikit-learn](http://scikit-learn.org/stable/)\, and [statsmo
 dels](http://www.statsmodels.org/stable/index.html). Additionally\, it mak
 es piping desired data into data visualization libraries\, such as [Plotly
 ](https://plot.ly/)\, a simple task. Lastly\, as pure Python\, it can now
 be easily embedded into a socketed [Flask](http://flask.pocoo.org/) web se
 rver or used as standalone application.\n\nMarcus Sherman
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Supervised and Unsupervised Machine Learning of Electroluminescent
  Images of Photovoltaic Modules
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Electroluminescence (EL) is a process in which materials emit
 light when an electric current is passed through it. In this method\, elec
 tricity is passed through photovoltaic (PV) modules and EL light is emitte
 d from the solar cells which are captured by an infrared sensitive camera.
  EL images are useful for characterization of electrical properties of pho
 tovoltaic (PV) modules based on the intensity of light in the images. The
 goal of the project is to build an automated pipeline for EL image supervi
 sed classification and unsupervised clustering. The motivation behind EL i
 mage processing is to study the effect of degradation in electrical proper
 ties based on physical appearances captured by the images.\n\nTo study PV
 module degradation\, EL images of crystalline silicon PV solar panels were
  captured under multiple test conditions at various periodic intervals. Da
 mp-heat and thermal cycling cause corrosion and cracks\, respectively\, wh
 ich can be seen in an EL image with regions of dark areas. Cracks orientat
 ion and thickness of corrosion is correlated to resistive losses which cau
 se EL images to have lower light intensity at affected areas.\n\nThis work
  is part of our US Dept. of Energy\, SunShot project “MLEET”. To enabl
 e in-place analytics we store all datasets and results from different sour
 ces in Hadoop with an HBase NoSQL database and we integrate it with python
  using the happybase module. For feature extraction and machine learning f
 rom these EL images\, we use scipy\, sklearn\, and opencv.\n\n\n\nAhmad Ma
 roof Karimi\, Justin Fada\, Roger French
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bringing ML to the Front Lines of Healthcare
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:# Introducing healthcareai\n\nA python and R library focused o
 n increasing the national adoption of machine learning in healthcare.\n\n#
 # Healthcare is behind\n\nHealthcare is far behind other industries techno
 logically\, especially when it comes to analytics and machine learning. Th
 is means that the industry is full of easy to capture value\, that can man
 ifest as cost savings\, clinical outcome improvements and lives saved.\n\n
 ## Challenges to ML in Healthcare\n\n- There are many skilled analysts tha
 t lack the skills to use ML.\n- Deploying models with pipelines is non-tri
 vial even with experience.\n- Workflows are expensive and slow to change.\
 n- Model interpretability is a big deal for front line clinicians.\n\n## S
 uccesses\n\n- LACE vs ML\n- 50% reduction in CLABSI cases at a large acade
 mic medical center.\n- Improved self-pay collection workflows.\n- Reducing
  Heart Failure Readmissions by \n\n## Lessons Learned\n\n- Pair analysts w
 ith domain experts.\n- Be agile in model development.\n- Surface predictio
 ns in the right workflow.\n- Continually monitor models.\n- Interpretation
  is hard.\n\n## The Library\n\n### Benefits\n\n- Learn ML with the dozen b
 uilt-in healthcare datasets.\n- Many built in visual model evaluation tool
 s.\n- Data preparation pipelines are baked in from training to production
 deployment.\n- Advanced users can use the full power of scikit-learn while
  leveraging some nice conveniences.\n\n### Sample Usage of healthcareai\n\
 n```\nimport healthcareai\n... more to come here\n```\n\n### Ways You Can
 Help\n\nThough we have some great successes (and failures) under our belts
 \, we are early on our journey with ML + healthcare. Our goals are to make
  healthcareai a low-friction way to get into ML. We are eager to provide i
 ssues across a wide range of abilities and encourage PRs!\n\nTaylor Miller
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Running the #1 Brazilian Telegram Bot on a Raspberry Pi using Pyth
 on
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Telegram Messenger is an instant messaging app. It has a well
 documented chatbots API and a great amount of users on multiple countries.
  \n\nFrom the need to save some time\, I learned to develop chatbots\, aut
 omating tasks. A server is needed to run them 24/7. Then\, questions arris
 e:\n- How to develop them?\n- Where to run it?\n- How much will it cost?\n
 \nOn this poster I'll show people how I'm running a few Telegram Chatbots
 as cheap as possible\, making possible to achieve it's goals without spend
 ing lots of money.\n\nGabriel Ferreira
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Python for Passwords: Diceware
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION: A Beginner's Guide to Crypto Magic ⚡️ Welcome to Professo
 r Speir's crypto class for first years. Passwords are an integral part of
 our lives both in the Wizarding World of Technology™️ and consumers of
  the internet. In this lesson\, we will learn a fun and safe methodology o
 f creating passwords for humans that also contain a great deal of entropy.
  Why are passwords so difficult?\n\nPassword best practices. \nHow can we
 create good passwords with Python?\n>Diceware: A History\n>Diceware: The P
 yPi Package\n\nIt all comes down to being random. Explain how `random.Syst
 emRandom` works.\n\nI would have a demo application built where people cou
 ld test generating such a password to take the experience from the poster
 to the attendee. \n\n\nMegan Speir
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hack your Kinect!
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:Has your Kinect\, too\, been collecting dust at the back of yo
 ur consoles console? Time to get it out and put it to good use again!  \n\
 nWith a little Python and OpenCV magic\, you can easily breathe new life i
 nto this super innovative gaming device from a few years ago.  \n  \nMy po
 ster will show you what your trusty Kinect is capable of and what you can
 do with the built-in hardware – which does not limit you to playing game
 s\, but opens up other\, even more interesting possibilities of interactio
 n\, especially when combined with a projector. Not a Kinect owner yet? No
 problem\, my poster has you covered! I will let you know what equipment yo
 u need and how to get it (good news: you do not need to also get the conso
 le it came bundled with!).  \n \nAs space is limited on a poster\, I will
 likely not be able to provide a full working example script\, but I will m
 ake sure to include code snippets and links to resources to help you with
 your own future Python + Kinect projects.\n\nKay Kollmann
LOCATION:Poster Room
END:VEVENT
BEGIN:VEVENT
SUMMARY:I contributed! But what now ? Data analytics for understanding you
 r open source community
DTSTART;TZID=US/Eastern;VALUE=DATE-TIME:20140317T100000
DTEND;TZID=US/Eastern;VALUE=DATE-TIME:20140317T131000
DESCRIPTION:For any community especially those in open source\, newcomer o
 nboarding is important but retaining them is critical. Since past two year
 s\, we at the Fedora Community Operations team have been working on unders
 tanding community health and on improving contributor retention rates in o
 ur community. Using this poster session\, I would like to share some of th
 e findings from our work\, similar research done by other open source proj
 ects and how we can use and apply these findings.\n\nThe poster will discu
 ss some community-oriented metrics we use at Fedora to understand communit
 y health like contributor engagement metrics\, contributor onboarding and
 retention rates etc. I will then share some insights derived from finding
 common patterns in contribution activity of long-term active volunteers i.
 e. Are they involved in many different areas of their projects? What are s
 ome good measures to predict how long a volunteer will stay ? What is the
 magic element X that makes people stay ? The poster will also discuss abou
 t similar research done by other open source communities in this area and
 how these insights  can be applied to improve contributor retention rates
 and overall community health on an individual as well as community level.\
 n\nBee
LOCATION:Poster Room
END:VEVENT
END:VCALENDAR

## icalendar version.py
import json
import icalendar as ical
import datetime
import pytz

def desc(event):
    desc = event["description"]
    authors = ", ".join(event["authors"])

    return "%s\n\n%s" % (desc, authors)


def utf8(s):
    return s.encode("utf-8")


def ptime(s):
    t = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%S")
    tz = pytz.timezone("US/Eastern")
    return tz.localize(t)

if __name__ == "__main__":
    with open("conference.json") as f:
        data = json.load(f)

    c = ical.Calendar()
    c.add("prodid", "-//Pycon 2018//")
    c.add("version", "2.0")


    for item in data:
        #print str(item["name"].encode('utf-8'))
        #print str(item["description"].encode('utf-8'))
        e = ical.Event()

        e.add("summary", utf8(item["name"]))
        e.add("dtstart", ptime(item["start"]))
        e.add("dtend", ptime(item["end"]))
        e.add("description", utf8(desc(item)))
        e.add("location", item["room"])
        c.add_component(e)

    with open("my.ics", "w") as f:
        f.write(c.to_ical())

## ics output.txt
python t.py
Complexity Science
Complexity Science is an approach to modeling systems using tools from discrete mathematics and computer science, including networks, cellular automata, and agent-based models.  It has applications in many areas of natural and social science.

Python is a particularly good language for exploring and implementing models of complex systems.  In this tutorial, we present material from the draft second edition of *Think Complexity*, and from a class we teach at Olin College.  We will work with random networks using NetworkX, with cellular automata using NumPy, and we will implement simple agent-based models.
Traceback (most recent call last):
  File "t.py", line 32, in <module>
    print e
  File "/usr/local/lib/python2.7/dist-packages/ics/component.py", line 93, in __str__
    output(self, container)
  File "/usr/local/lib/python2.7/dist-packages/ics/event.py", line 392, in o_description
    container.append(ContentLine('DESCRIPTION', value=escape_string(event.description)))
  File "/usr/local/lib/python2.7/dist-packages/ics/utils.py", line 170, in escape_string
    string = string.replace("\\", "\\\\")
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 177: ordinal not in range(128)

## ics version.py
import ics

def desc(event):
    desc = event["description"]
    authors = ", ".join(event["authors"])

    return "%s\n\n%s" % (desc, authors)


def utf8(s):
    return s.encode("utf-8")


if __name__ == "__main__":
    with open("conference.json") as f:
        data = json.load(f)

    c = ics.Calendar()


    for item in data:
        print str(item["name"].encode('utf-8'))
        print str(item["description"].encode('utf-8'))
        e = ics.Event(name=utf8(item["name"]),
                      begin=item["start"],
                      end=item["end"],
                      description=utf8(desc(item)),
                      location=item["room"],
                      )
        print e
        c.events.append(e)

    with open("my.ics", "w") as f:
        f.writelines(c)
	import json
	import icalendar as ical
	import datetime
	import pytz

	def desc(event):
	desc = event["description"]
	authors = ", ".join(event["authors"])

	return "%s\n\n%s" % (desc, authors)


	def utf8(s):
	return s.encode("utf-8")


	def ptime(s):
	t = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%S")
	tz = pytz.timezone("US/Eastern")
	return tz.localize(t)

	if __name__ == "__main__":
	with open("conference.json") as f:
	data = json.load(f)

	c = ical.Calendar()
	c.add("prodid", "-//Pycon 2018//")
	c.add("version", "2.0")


	for item in data:
	#print str(item["name"].encode('utf-8'))
	#print str(item["description"].encode('utf-8'))
	e = ical.Event()

	e.add("summary", utf8(item["name"]))
	e.add("dtstart", ptime(item["start"]))
	e.add("dtend", ptime(item["end"]))
	e.add("description", utf8(desc(item)))
	e.add("location", item["room"])
	c.add_component(e)

	with open("my.ics", "w") as f:
	f.write(c.to_ical())
	python t.py
	Complexity Science
	Complexity Science is an approach to modeling systems using tools from discrete mathematics and computer science, including networks, cellular automata, and agent-based models. It has applications in many areas of natural and social science.

	Python is a particularly good language for exploring and implementing models of complex systems. In this tutorial, we present material from the draft second edition of Think Complexity, and from a class we teach at Olin College. We will work with random networks using NetworkX, with cellular automata using NumPy, and we will implement simple agent-based models.
	Traceback (most recent call last):
	File "t.py", line 32, in <module>
	print e
	File "/usr/local/lib/python2.7/dist-packages/ics/component.py", line 93, in __str__
	output(self, container)
	File "/usr/local/lib/python2.7/dist-packages/ics/event.py", line 392, in o_description
	container.append(ContentLine('DESCRIPTION', value=escape_string(event.description)))
	File "/usr/local/lib/python2.7/dist-packages/ics/utils.py", line 170, in escape_string
	string = string.replace("\\", "\\\\")
	UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 177: ordinal not in range(128)
	import ics

	def desc(event):
	desc = event["description"]
	authors = ", ".join(event["authors"])

	return "%s\n\n%s" % (desc, authors)


	def utf8(s):
	return s.encode("utf-8")


	if __name__ == "__main__":
	with open("conference.json") as f:
	data = json.load(f)

	c = ics.Calendar()



	for item in data:
	print str(item["name"].encode('utf-8'))
	print str(item["description"].encode('utf-8'))
	e = ics.Event(name=utf8(item["name"]),
	begin=item["start"],
	end=item["end"],
	description=utf8(desc(item)),
	location=item["room"],
	)
	print e
	c.events.append(e)

	with open("my.ics", "w") as f:
	f.writelines(c)