List of videos

Harry Percival - PythonAnywhere and Education [EuroPython 2015] [23 July 2015] [Bilbao, Euskadi, Spain] [Education Summit] Python may be the ideal language for teaching because of its simplicity and readability, but actually getting a working and consistent development environment installed on a diverse set of student (or school) computers can be less than straightforward. And then there's pip, numpy, scipy, windows vs unix command lines... As a result, we've had lots of teachers coming to PythonAnywhere over the years, to skip all these problems. We've recently started adding some extra features to help teachers and students which we're keen to share with the EuroPython audience.

Fabio Pliger/Endor - Welcome [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] Welcome to EuroPython 2016

Nicholas Tollervey - A Million Children (and MicroPython) [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/keynote-a-million-children-and-micropython) The BBC micro:bit is a small programmable device for children. A million of them have been handed out to the UK's 11 and 12 years olds. The Python Software Foundation was a partner in this moon-shot scheme and, thanks to the efforts of Damien George, MicroPython runs on the device. My talk will tell the story of the project, describe Python's role in it and explain how the wider Python community can become involved. It may involve demonstrations, live coding and audience participation.

Javier Arias Losada - Machine Learning for dummies with Python [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/machine-learning-for-dummies-with-python) Machine Learning is the next big thing. If you are a dummy in terms of Machine Learning, but want to get started with it... there are options. Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning. ----- Have you heard that Machine Learning is the next big thing? Are you a dummy in terms of Machine Learning, and think that is a topic for mathematicians with black-magic skills? If your response to both questions is 'Yes', we are in the same position. Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.

Kyle Knapp - Dynamic Class Generation in Python [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/dynamic-class-generation-in-python) This talk is about dynamic class generation in python: the practice of writing code that generates classes and their functionality at runtime. It will use boto3, the AWS SDK for Python, as a basis to dive into the basics, the benefits, and the drawbacks to dynamically generating classes. ----- This talk is about the concept of dynamic class generation in python. The whole idea is writing code that generates classes and their functionality at runtime. You now may be asking yourself, “That sounds like a neat trick. Why would I ever generate my classes at runtime?” Here are a few reasons why: - It can decrease the physical size of your code. - It can improve the workflow in adding new functionality. - It can improve reliability of your code. One example where the power of this concept has really been leveraged is in boto3, the AWS SDK for Python. Dynamic class generation has allowed boto3 to become heavily data driven such that most of its classes and methods are generated based off JSON models representing aspects of an AWS service’s API. For example, to add support for a new AWS service API in boto3, just plop in a JSON file into the library with no additional Python code required. Using lessons and techniques drawn from developing boto3, this talk will dive into the following topics related to dynamic class generation: - The basics of dynamic class generation such as how to effectively dynamically generate classes. - How to overcome some of the challenges of dynamic class generation. - The tradeoffs in dynamically generating classes and discussion on when it is appropriate. By the end of this talk, the hope is that you will have a better understanding of dynamic class generation and come away with helpful ideas for your next big project.

David Arcos - Efficient Django [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/efficient-django) Does Django scale? How to manage traffic peaks? What happens when the database grows too big? How to find the bottlenecks? We will overview the basics concepts on scalability and performance, and then see some tips and tricks. These statements will be backed up with experiments and numbers, to show the timing improvements. ----- **Does Django scale?** How to manage traffic peaks? What happens when the database grows too big? How to find the bottlenecks? We will overview the basics concepts on scalability and performance, and then see some tips and tricks. These statements will be backed up with experiments and numbers, to show the timing improvements. Main topics: - System architecture - Database performance - Queues and workers - Profiling with django-debug-toolbar - Caching queries and templates - Dealing with a slow admin - Optimizing the models - Faster tests

Nicolas Lara - Python and Async programming [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/python-and-async-programming) How does the experienced python programmer fair when faced with python's "new" way of doing async programming for the first time? This talk details the different ways python provides for attacking the problem of asynchronous programming and focuses on the best practices for the future (as of python 3.4 and 3.5) ----- How does the experienced python programmer fair when faced with python's "new" way of doing async programing in for the first time? Do we all know how and when to use Futures, yield from, asyncio, coroutines, the async and await keywords, eventloops, and others? A lot has changed in recent versions of Python when it comes to async programming, concurrency, and parallelism. We still have very different ways of approaching the problem in each version, but they are finally (as of python 3.4/3.5) converging to a standard. This talk explores, from the perspective of an experienced python programmer with little to no experience in async programming, what the "one obvious way" to do async programming in Python is supposed to be. It does so but analysing examples of different categories of async problems we may want to solve and what the correct way to solve them with the latest versions of Python would be (along with the trade offs of different approaches). The examples include generic CPU-bound problems, IO-bound problems, and "both-bound" problems; along with common tasks as building a simple server, scraping, deferring a web response, and traversing graphs. When useful, I compare the solutions with the approach we would take in languages that have been design for- and are known to be good at async programming like Javascript and Go.

Lorena Mesa - Is that spam in my ham? [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/is-that-spam-in-my-ham) Beginning programmers or Python beginners may find it overwhelming to implement a machine learning algorithm. Increasingly machine learning is becoming more applicable to many areas. This talk introduces key concepts and ideas and uses Python to build a basic classifier - a common type of machine learning problem. Providing some jargon to help those that may be self-educated or currently learning ----- Supervised learning, machine learning, classifiers, big data! What in the world are all of these things? As a beginning programmer the questions described as "machine learning" questions can be mystifying at best. In this talk I will define the scope of a machine learning problem, identifying an email as ham or spam, from the perspective of a beginner (non master of all things "machine learning") and show how Python can help us simply learn how to classify a piece of email. To begin we must ask, what is spam? How do I know it "when I see it"? From previous experience of course! We will provide human labeled examples of spam to our model for it to understand the likelihood of spam or ham. This approach, using examples and data we already know to determine the most likely label for a new example, uses the Naive Bayes classifier. Our model will look at the words in the body of an email, finding the frequency of words in both spam and ham emails and the frequency of spam and ham. Once we know the prior likelihood of spam and what makes something spam, we can try applying a label to a new example. Through this exercise we will see at a basic level what types of questions machine learning asks, learn to model "learning" with Python, and understand how learning can be measured.

Antonio Spadaro - Build and control a Python-powered robot. [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/build-and-control-a-python-powered-robot) During this talk you will see how to make a robot able to recognize people with a Raspberry Pi as main board and Python as language. The talk will cover the hardware and modules, discuss briefly the alternatives, and finally show a live demo. ----- The robot uses two main modules: - **OpenCV** (_Open Source Computer Vision Library_), an open-source library that includes several hundreds of computer vision algorithms. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics. - **gpiozero**, a simple interface to everyday GPIO components used with Raspberry Pi. The first is used to recognize the people and the object; the second to control the robot.