List of videos

iztok kucan/Joris Peeters - Algorithmic Trading with Python [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/algorithmic-trading-with-python) This is a look behind the scenes at Winton Capital Management- one of Europe’s most successful systematic investment managers. The talk will mainly focus on how Python gives researchers fine-grained control over the data and trading systems, without requiring them to interact directly with the underlying, highly-optimised technology. ----- Have you ever wondered what technologies are used in a systematic trading system that utilises computer models and accounts for the majority of trading on the stock market? This is a look behind the scenes at Winton Capital Management- one of Europe’s most successful systematic investment managers. In this talk, we’ll run through an overview of Winton’s trading infrastructure, including data management, signal generation and execution of orders on global exchanges. The talk will mainly focus on how Python gives researchers fine-grained control over the data and trading systems, without requiring them to interact directly with the underlying, highly- optimised technology.

Alexys Jacob/Guillaume Gelin - Planning for the worst [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/planning-for-the-worst) Sharing our worst production experiences and the tricks, good practices and code we developed to address them. ----- This talk is about sharing our experience about how we handled production problems on all levels of our applications. We'll begin with common problems, errors and failures and dig on to more obscure ones while sharing concrete tips, good practices and code to address them ! This talk will make you feel the warmth of not being alone facing a problem :)

Andreas Dewes - Analyzing Data with Python & Docker [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/analyzing-data-with-python-docker) Docker is a powerful tool for packaging software and services in containers and running them on a virtual infrastructure. Python is a very powerful language for data analysis. What happens if we combine the two? We get a very versatile and robust system for analyzing data at small and large scale! I will show how we can make use of Python and Docker to build repeatable, robust data analysis workflows which can be used in many different contexts (possibly with a live demo). ----- Docker is a powerful tool for packaging software and services in containers and running them on a virtual infrastructure. Python is a very powerful language for data analysis. What happens if we combine the two? We get a very versatile and robust system for analyzing data at small and large scale! I will show how we can make use of Python and Docker to build repeatable, robust data analysis workflows that can be used in many different contexts. I will explain the core ideas behind Docker and show how they can be useful in data analysis. I will then discuss an open-source Python library (Rouster) which uses the Python Docker-API to analyze data in containers and show several interesting use cases (possibly even a live-demo). Outline: 1. Why data analysis can be frustrating: Managing software, dependencies, data versions, workflows 2. How Docker can help us to make data analysis easier & more reproducible 3. Introducing Rouster: Building data analysis workflows with Python and Docker 4. Examples of data analysis workflows: Business Intelligence, Scientific Data Analysis, Interactive Exploration of Data 5. Future Directions & Outlook

Jose Manuel Ortega - Ethical hacking with Python tools [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/ethical-hacking-with-python-tools) Python, as well as offering an ecosystem of tools for testing security and application pentesting.Python offers a tool ecosystem for developing our own tools security for testing applications and the servers security,identifying information about servers and potential vulnerabilities. The ultimate objective is show a pentesting tool integrating some of the modules commented and try a demo showing info about our domain target and find vulnerabilities in it, ----- Nowdays, Python is the language more used for developing tools within the field of security. Many of the tools can be found today as port scanner, vulnerability analysis, brute force attacks and hacking of passwords are written in python. The goal of the talk would show the tools available within the Python API and third-party modules for developing our own pentesting and security tools and finally show a pentesting tool integrating some of the modules. The main topics of the talk could include: **1.Enter Python language as platform for developing security tools** Introduction about the main libraries we can use for introducing in development of security tools such as socket and requests. **2.Libraries for obtain servers information such as Shodan, pygeocoder,pythonwhois** Shodan is a search engine that lets you find specific computers (routers, servers, etc.) and get information about ports and services that are opened. **3.Analysis and metadata extraction in Python for images and documents** Show tools for scraping web data and obtain metadata information in documents and images **4.Port scanning with tools like python-nmap** With python-nmap module we can check ports open for a target ip or domain. **5.Check vulnerabilities in FTP and SSH servers** With libraries like ftplib and paramiko we can check if the server is vulnerable to ftp and ssh anonymous connections.

Anjana Vakil - Using and abusing Python’s double-underscore methods and attributes [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/using-and-abusing-pythons-double-underscore-methods-and-attributes) Python’s double-underscore ('`__`') methods and attributes go by many names, including “special”, “dunder”, and “magic”. You already use some, like `__init__`, but there are many more! In this talk, we’ll see how dunders can be useful, silly, dangerous, and fun! We’ll trick Python’s arithmetic and comparison operators. We’ll make objects behave like dictionaries and containers. We’ll reduce an object’s memory usage, and speed up membership tests. We’ll even try some naughty function hacks! ----- The curious Python methods and attributes surrounded by double underscores ('`__`') go by many names, including “special”, “dunder”, and “magic”. You probably use some of them, like `__init__`, every day. But that’s just the tip of the iceberg! In this talk, we’ll explore the weird and wonderful world of the double-underscore, and find out how dunders can be useful, silly, dangerous, and just fun! We’ll play pranks on Python’s builtin operators for arithmetic and comparison. We’ll make arbitrary objects behave like dictionaries and containers. We’ll reduce an object’s memory usage, and speed up tests for membership. We’ll even try some naughty function hacks that we should never use in real life! You'll get the most out of this talk if you're already comfortable writing object-oriented Python code. If you already use special dunder magic in your own code, that's excellent! You’ll have a chance to share your tips & tricks with the rest of the audience at the end of the talk. _Talk repo_: [https://github.com/vakila/dunders][1] [1]: https://github.com/vakila/dunders

Adam Dangoor - Another pair of eyes: Reviewing code well [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/another-pair-of-eyes-reviewing-code-well) Many of us have been taught to code, but we know that software engineering jobs are so much more than that. Programmers can spend 5-6 hours per week on code review, but doing that is almost ignored as a skill. How many of us have seen poor reviews, which don't catch bugs, make people feel bad or block important features being merged? An introduction to what code review is alongside guidelines, tips, tricks and anecdotes to help make your code reviews be as productive as possible. ----- Many of us have been taught to code, but we know that software engineering is so much more than that. Programmers can spend 5-6 hours per week on code review, but doing that is almost ignored as a skill, and instead it is often treated as a rote chore. How many of us have seen poor reviews - those which upset people, don't catch bugs or block important features being merged? This talk explores the social and technical impacts of various code review practices as well as helpful tooling. The goal is to provide a structure to help improve how teams review code, and to introduce the costs and benefits of code review to anyone unfamiliar with the practice. There are always trade-offs to be made - e.g. think how costly a security flaw in this code could be to your organisation - perhaps intense scrutiny is not necessary for prototypes soon to be thrown away. It is useful to consider the trade-offs in order to optimise for a particular problem domain. Perhaps right now it is more important to look for issues with maintainability, functionality or performance. I talk about how some fantastic code reviews from mentors, colleagues and strangers have helped me become a better programmer and team member, as well as occasions where code review has been detrimental by slowing things down and causing arguments. This is aimed at everyone from beginner to advanced programmers.

James Saryerwinnie - Writing Redis in Python with asyncio [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/writing-redis-in-python-with-asyncio) In this talk, I'll show you how to write redis using asyncio. You'll see how you can create a real world application using asyncio by creating a python port of redis. ----- Python has been adding more and more async features to the language. Starting with asyncio in python 3.4 and including the new async/await keywords in python 3.5, it's difficult to understand how all these pieces fit together. More importantly, it's hard to envision how to use these new language features in a real world application. In this talk we're going to move beyond the basic examples of TCP echo servers and example servers that can add number together. Instead I'll show you a realistic asyncio application. This application is a port of redis, a popular data structure server, written in python using asyncio. In addition to basic topics such as handling simple redis commands (GET, SET, APPEND, etc), we'll look at notifications using pub/sub, how to implement the MONITOR command, and persistence. Come learn how to apply the asyncio library to real world applications.

Scott Triglia - Protect your users with Circuit Breakers [EuroPython 2016] [19 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/protect-your-users-with-circuit-breakers) Failures are the bane of scaling a modern web service and can cause serious pain for end users! Lucky for us, there are techniques that can help protect your product and handle failures in subsystems gracefully. This talk will dive into one of these in depth, the Circuit Breaker pattern, and explore the options it gives us for keeping all our users safe. We will be focusing on several real-world problems and options for how to implement your circuit breaker setup in nice, readable python code. ----- The inevitability of failures is the bane of scaling any modern web service and can cause serious pain for end users! Lucky for us, there are techniques that can help protect your product handle failures in subsystems gracefully. This talk will dive into one of these in depth, the Circuit Breaker pattern, and explore the options it gives us for keeping our users safe. We will be focusing on several real-world problems and how they can be addressed by circuit breakers. You should expect to leave the talk with details on simple circuit breakers as well as understanding how they can be adapted for more complex situations. We’ll also discuss some options for how to implement your circuit breaker in readable python. **Contrived FAQ time!** **I don’t know what Circuit Breakers are, should I come?** Definitely! We’re going to start from scratch and work our way up. Only requirement is basic familiarity with backend services receiving and making HTTP requests. **I totally know what Circuit Breakers are, should I come?** Definitely! After the intro, the main meat of the talk will be working through a series of more advanced situations and talking about how we can alter the basic circuit breaker setup to address them. **I want real-world advice, not made up hypotheticals!** Well that’s not really a question, but you’ll be happy to know that the examples we’ll discuss come straight from my experience at Yelp. They should be very realistic and broadly applicable.

Ilia Kurenkov - re-Discovering Python's Regular Expressions [EuroPython 2016] [19 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/rediscovering-pythons-regular-expressions) As Armin Ronacher pointed out in a recent blog post, there is more to Python's regular expression module than meets the eye. His post made me wonder what other “hidden gems” are stashed away in Python’s `re`. In the talk I share what I’ve learned about the inner workings of this extremely popular and heavily used module. ----- Anyone who has used Python to search text for substring patterns has at least heard of the regular expression module. Many of us use it extensively for parsers and lexers, extracting information . And yet we know surprisingly little about its inner workings, as Armin Ronacher demonstrated in his recent blog post, “Python's Hidden Regular Expression Gems”. Inspired by this, I want to dive deeper into Python’s `re` module and share what I find with folks at EuroPython. My goal is that at the end of the day most of us walk away from this talk with a better understanding of this extremely useful module. Here are a few examples of the kinds of things I would like to cover: - A clear presentation of `re`’s overall structure. - What actually happens behind the scenes when you “compile” a regular expression with `re.compile`? - What are the speed implications of using a callable as the replacement argument to `re.sub`? - re.MatchObject interface: `group` vs. `groups` vs `groupdict` To keep the talk entertaining as well as educational I plan to pepper it with whatever interesting and/or funny trivia I find about the module’s history and structure. Prerequisites: If you've ever used the `re` module, you should be fine :)