Teaching Django to Comrades<\/p>\n
Presented by Issac Kelly<\/p>\n
Inevitably you're going to run into somebody who wants to learn Django,\nor maybe both Python and Django. This talk will help you make it less\npainful for them.<\/p>\n","pubDate":"Mon, 05 Sep 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-09-05:\/djangocon-us-2011\/djangocon-2011--teaching-django-to-comrades.html","category":["DjangoCon US 2011","basic","djangocon","djangocon2011","learning"]},{"title":"Fun with Python's newer tools","link":"https:\/\/pyvideo.org\/europython-2011\/fun-with-pythons-newer-tools.html","description":"
[EuroPython 2011] Raymond Hettinger - 23 June 2011 in "Track Spaghetti"<\/p>\n
Spend ten minutes each learning to work with Counters, named tuples, new\nstring formatting, and the LRU cache. Learn the basic API, see how it\nworks under the hood, enjoy a simple example, and then have fun pushing\nit to the limit in interesting ways.<\/p>\n","pubDate":"Sun, 24 Jul 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-07-24:\/europython-2011\/fun-with-pythons-newer-tools.html","category":["EuroPython 2011","learning"]},{"title":"How to make intelligent web-apps","link":"https:\/\/pyvideo.org\/europython-2011\/how-to-make-intelligent-web-apps.html","description":"
[EuroPython 2011] Deepak Thukral - 22 June 2011 in "Track Ravioli "<\/p>\n
The primary goal of this talk is twofold: to evaluate the need of data\nmining and introduce some very cool, simple yet powerful machine\nlearning techniques to audience such as classification, clustering,\ncollaborative filtering, recommendation etc in your Python web\napplications. This talk will conclude with some explanation and\nlimitations of machine learning algorithms.<\/p>\n
Basic knowledge of Python is sufficient. However some experience with\nDjango, meshups, machine learning or data hunger is encouraged. All talk\nmaterial and django apps will be available after talk under MIT license.<\/p>\n","pubDate":"Thu, 21 Jul 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-07-21:\/europython-2011\/how-to-make-intelligent-web-apps.html","category":["EuroPython 2011","django","learning","web"]},{"title":"Building a website with PyHP and Liwe","link":"https:\/\/pyvideo.org\/europython-2011\/building-a-website-with-pyhp-and-liwe.html","description":"
[EuroPython 2011] Fabio Rotondo - 22 June 2011 in "Training Pizza Napoli"<\/p>\n
In this session, you will start learning how to create a simple PyHP + LiWE website.<\/p>\n
Then, we'll show you how to create new custom modules for your website and we'll show up some great features of the LiWE ecosystem.<\/p>\n","pubDate":"Mon, 18 Jul 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-07-18:\/europython-2011\/building-a-website-with-pyhp-and-liwe.html","category":["EuroPython 2011","learning"]},{"title":"Python's other collection types and algorithms","link":"https:\/\/pyvideo.org\/europython-2011\/pythons-other-collection-types-and-algorithms.html","description":"
[EuroPython 2011] Andrew Dalke - 21 June 2011 in "Track Spaghetti"<\/p>\n
It's impossible to use Python without learning about lists, dictionaries\nand tuples, and most people have at least heard about sets. These four\ncollection types are so important and useful that Python has special\nsyntax for creating them.<\/p>\n
Fewer people know about Python's other built-in collection data types\nand algorithms. A deque supports fast appends and pops from both ends\nand is great for breath-first searches, the heapq module helps you\nconstruct a priority queue on top of lists, and the bisect module is\nhandy for quick binary searches of an already sorted list.<\/p>\n
The defaultdict uses the dict missing<\/strong> hook as a better solution to\nsetdefault, OrderedDict is a dictionary that preserves insertion order,\nand Counter is a dictionary specialized for counting hashable objects. A\nnamedtuple is handy if you want to support both index and attribute\nlookups for the same item, and a frozenset is a hashable form of a set\nwhich can be used as keys in a dictionary or set.<\/p>\n My talk will go over these 8 different classes and modules. I'll give\nconcrete examples of how to use them and why they are useful. The target\naudience is intermediate programmers who are familiar with the Python's\nstandard data types and with data types in general, but who don't know\nall of the functionality available in modern Python.<\/p>\n","pubDate":"Mon, 18 Jul 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-07-18:\/europython-2011\/pythons-other-collection-types-and-algorithms.html","category":["EuroPython 2011","bisect","dictionaries","frozenset","heapq","learning","namedtuple","ordereddict"]},{"title":"Making use of OpenStreetMap data with Python","link":"https:\/\/pyvideo.org\/europython-2011\/making-use-of-openstreetmap-data-with-python.html","description":" [EuroPython 2011] Andrii Mishkovskyi - 22 June 2011 in "Track Lasagne"<\/p>\n Ever wondered how web maps are created? Ever wondered if you could build\nsomething like Google Maps over a weekend? You probably can't, but this\ntalk will show you the basics of what you need to know, such as\nimporting data, rendering maps and even building simple routes. And all\nof this in Python!<\/p>\n Abstract: * Learning how OSM data looks * Parsing and importing the\ndata * Rendering maps with Mapnik * Bits of code required to build\ngeocoder * Building simple router with Python and PostGIS * And anything\nelse I forgot to mention in this abstract but will talk about<\/p>\n Definitions: OpenStreetMap - the so-called "Wikipedia of maps" project,\nwith thousands of contributors who edit the map data of the whole world.\nUnlike similar projects, the map data is completely free (both as in\nbeer and as in speech) and thus anyone can make use of it. Mapnik -\nrendering framework, created specifically for OpenStreetMap, written in\nC++ and Python. PostGIS - an extension of PostgreSQL database, with\nsupport for many useful GIS features.<\/p>\n","pubDate":"Wed, 13 Jul 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-07-13:\/europython-2011\/making-use-of-openstreetmap-data-with-python.html","category":["EuroPython 2011","gis","google","importing","learning","maps","parsing","postgresql","web"]},{"title":"Python 3: the Next Generation (is here already)","link":"https:\/\/pyvideo.org\/europython-2011\/python-3-the-next-generation-is-here-already-0.html","description":" [EuroPython 2011] wesley chun - 21 June 2011 in "Track Lasagne"<\/p>\n Python is currently at a crossroads: Python 2 has taken it from a quiet\nword- of-mouth language to primetime, with many companies around the\nworld using it and an ever-increasing global marketshare of the\nprogramming world. But now comes Python 3, the first version of the\nlanguage that is not backwards compatible with previous releases.<\/p>\n What does this mean? Are all my Python programs going to break? Will I\nhave to rewrite everything? How much time do I have? When is Python 2\ngoing to be EOL'd? Is the language undergoing a complete rewrite and\nwill I even recognize it? What are the changes between Python 2 and 3\nanyway? Also, the next generation is already here, as Python 3 is over\ntwo years old now. What has been ported so far, and what is its current\nstatus? Are migration plans or transition tools available? If I want to\nstart learning Python, should I do Python 2 or Python 3? Are all Python\n2 books obsolete?<\/p>\n We will attempt to answer all of these questions and more. Join us!<\/p>\n OUTLINE\/TOPICS<\/p>\n [EuroPython 2011] Bruno Renie - 21 June 2011 in "Track Tagliatelle"<\/p>\n GeoDjango is the "world-class geographic web framework" everyone has\nprobably heard of. The purpose of this talk, targeted at people familiar\nwith Django itself, is to introduce in more details the capabilities of\nthis framework.<\/p>\n After learning the basics of Geographic Information Systems, we will\nsee:<\/p>\n During this talk we will be building a simple GeoDjango application to\nillustrate the different concepts introduced.<\/p>\n","pubDate":"Wed, 13 Jul 2011 00:00:00 +0000","guid":"tag:pyvideo.org,2011-07-13:\/europython-2011\/spatial-data-and-geodjango.html","category":["EuroPython 2011","django","forms","geodjango","learning","mapping","spatial","web"]},{"title":"Don't do this at work","link":"https:\/\/pyvideo.org\/europython-2019\/dont-do-this-at-work.html","description":" In this talk I reframe a computer programming workshop for kids I\ndelivered earlier this year, exploring and sharing my experience\nthroughout that journey, from preparation to delivery, by recreating a\nsimple yet engaging enough game.<\/p>\n With that I'll both demonstrate several Python related techniques and\ntools many may not be aware of, on one hand, and, on the other, extract\nprovocative questions about general learning processes, especially when\ntargeted at professional developers.<\/p>\n I promise zero slides and a somewhat fast-paced live (re)coding session,\nintertwined with comments on good\/bad techniques, along with a\nsurprising exploration of the turtle module in the Standard Library --\nit is more capable than you think.<\/p>\n I wrap up with a self-code review and with thoughts on how such a game\ncould be improved, what implications that could have for both beginners\nand seasoned professionals: should you do this at work?<\/p>\n Targeting 10 minute Q&A \/ discussion by the end!<\/p>\n","pubDate":"Fri, 12 Jul 2019 00:00:00 +0000","guid":"tag:pyvideo.org,2019-07-12:\/europython-2019\/dont-do-this-at-work.html","category":["EuroPython 2019","Best Practice","Education","Learning","Life","Python Skills"]},{"title":"Game Development with CircuitPython","link":"https:\/\/pyvideo.org\/europython-2019\/game-development-with-circuitpython.html","description":" Making computer games is difficult: it requires creativity,\nmultidisciplinary knowledge of art, psychology, math, computer science,\nphysics and others, patience, open mind and dedication. Making computer\ngames with Python is a nightmare. You hit practically every sharp corner\nthat Python has, starting with installation, through binary libraries,\npoor hardware support, up to distribution.<\/p>\n PewPew devices are an attempt at solving the worst problems by giving\nyou a dedicated, cheap, simple and portable gaming device, that you can\neasily program with Python with just a simple text editor. They also\nmake pretty neat conference badges. I will talk about how they were\nconceived, how they are used, and how you can extend and improve them\nyourself.<\/p>\n At the end of the talk you should have a good idea about what is\nCircuitPython and MicroPython and how they can be used to build and\nprogram such simple devices. You should also know where to find the\nresources necessary to try designing and building your own.<\/p>\n","pubDate":"Fri, 12 Jul 2019 00:00:00 +0000","guid":"tag:pyvideo.org,2019-07-12:\/europython-2019\/game-development-with-circuitpython.html","category":["EuroPython 2019","Education","Gadgets","Hardware\/IoT","Learning","MicroPython"]},{"title":"How to read (code)","link":"https:\/\/pyvideo.org\/europython-2019\/how-to-read-code.html","description":" When you learn a new language, like French or German or even English,\nyou first learn how to read. Then you learn how to write. When you learn\na new programming<\/em> language, you first learn how to write. And that\u2019s\nit.<\/p>\n Imagine that you were never formally taught how to read. And that you\nwere told that you should just figure it out \u2026 by writing \u2026 a whole\nbunch. How would that even work? I don\u2019t think it would.<\/p>\n If you can\u2019t read. You can\u2019t write. It\u2019s that simple.<\/p>\n Do you think that Shakespeare would be Shakespeare if he never read a\nsingle book in his entire life? No. Nothing is created in a vacuum. Good\nwriters are good writers because they\u2019re good readers.<\/p>\n Just as reading is an invaluable skill so to is reading code. It\u2019s a\nskill that\u2019s never formally taught. But it\u2019s a skill that is essential\nnonetheless. In this talk I\u2019ll show you how to effectively read code so\nthat might write better code.<\/p>\n","pubDate":"Fri, 12 Jul 2019 00:00:00 +0000","guid":"tag:pyvideo.org,2019-07-12:\/europython-2019\/how-to-read-code.html","category":["EuroPython 2019","Beginners","Clean Code","Documentation","Learning","Teaching"]},{"title":"Accessible Python education for schoolgirls using Avocados, Zombies, and Korean!","link":"https:\/\/pyvideo.org\/europython-2020\/accessible-python-education-for-schoolgirls-using-avocados-zombies-and-korean.html","description":" Imagine this school scenario: an entire year group of students aged 11-12, the majority completely new to coding, undergoing 6 hours of compulsory lessons on Python for Scientific Computing.<\/p>\n Now imagine these outcomes:\n\u2022 Students wanting to continue coding from the lessons outside of class in their own time\n\u2022 Students asking to replicate the lesson computing environment at home\n\u2022 Students disappointed for the lessons to come to an end and asking for more\n\u2022 Students struggling in Science discovering intrinsic ability in computing, bringing new enjoyment and confidence<\/p>\n And lastly, imagine that all the students are girls!<\/p>\n This talk will share this actual case study of a pioneering Python education initiative implemented at a secondary school for girls in London, UK for a cohort of 120 students.<\/p>\n The audience will gain actionable insights of the factors that enabled these children to develop basic but working proficiency of a mainstream scientific data stack using typical school IT resources.<\/p>\n Ultimately, this talk aims to increase awareness of Scientific Computing & Data Science as potentially effective and empowering Python education for young people.<\/p>\n","pubDate":"Thu, 23 Jul 2020 00:00:00 +0000","guid":"tag:pyvideo.org,2020-07-23:\/europython-2020\/accessible-python-education-for-schoolgirls-using-avocados-zombies-and-korean.html","category":["EuroPython 2020","europython","europython-2020","europython-online","Case Study","Data Science","Education","Learning","Scientific Libraries (Numpy\/Pandas\/SciKit\/...)"]},{"title":"Podcast: Experience Learning Flask With David Carmichael","link":"https:\/\/pyvideo.org\/flaskcon-2021\/podcast-experience-learning-flask-with-david-carmichael.html","description":" David talks about his experience learning Flask and about what he currently does with Flask. He's come a long way!<\/p>\n","pubDate":"Tue, 07 Dec 2021 00:00:00 +0000","guid":"tag:pyvideo.org,2021-12-07:\/flaskcon-2021\/podcast-experience-learning-flask-with-david-carmichael.html","category":["FlaskCon 2021","flask","podcast","learning"]},{"title":"ZimboPy: Empowering Zimbabwean Girls As Change Makers","link":"https:\/\/pyvideo.org\/pycon-italia-2017\/zimbopy-empowering-zimbabwean-girls-as-change-makers.html","description":" ZimboPy is an organic, on-the-ground effort by a local non-profit\norganization and Python developers in the Harare software development\ncommunity to advance the cause of women in technology in Zimbabwe. The\nprogram operates in community centers, universities, high schools and\ntech hubs to make programming accessible to girls regardless of their\nsocio-economic status. Upon initially joining a ZimboPy club, many of\nthe girls would have never used a computer before, let alone code.<\/p>\n In Zimbabwe, only 17% of computer science undergraduate majors are\nwomen, and in the developing world, women make up less than 20% of the\ninformation and technology workforce. ZimboPy exists to empower\nZimbabwean girls with the skills and confidence necessary to not only\nenter the local tech industry, but to lead it.<\/p>\n In addition to learning to code, ZimboPy club members also join a global\nnetwork of women in technology that are working to tackle social\nchallenges through human-centered design and computer science. ZimboPy\u2019s\nmentorship program invites experienced women developers, mainly from the\nUnited States and Europe, to help Zimbabwean girls address local\nproblems which can be solved with technology, such as clean water and\ne-commerce solutions for small shops in towns and villages. Mentors will\ntravel to Zimbabwe and work with girls as they develop a plan for their\napplications and pair-program with them, answering questions and\nproviding feedback along the way. To ensure that the girls are\nsuccessful, mentors will continue to work with their groups even after\nleaving the country through weekly video conferences and email feedback.\nOverall, ZimboPy looks forward to changing Zimbabwe\u2019s future through\ncreativity, collaboration and the power of Python programming.<\/p>\n","pubDate":"Sun, 09 Apr 2017 00:00:00 +0000","guid":"tag:pyvideo.org,2017-04-09:\/pycon-italia-2017\/zimbopy-empowering-zimbabwean-girls-as-change-makers.html","category":["PyCon Italia 2017","internationalization","School","Learning","problem solving"]},{"title":"Learning Python (or Anything) Effectively","link":"https:\/\/pyvideo.org\/pycon-se-2019\/learning-python-or-anything-effectively.html","description":" When we're learning something new - even something as friendly as Python - it can be difficult to make details stick long-term. This talk will give you some tips to help you learn Python - or anything else - more effectively. Although Python beginners will benefit the most from applying these concepts to Python, this talk is for anyone who wants to hack into their potential to learn more efficiently.<\/p>\n","pubDate":"Thu, 31 Oct 2019 00:00:00 +0000","guid":"tag:pyvideo.org,2019-10-31:\/pycon-se-2019\/learning-python-or-anything-effectively.html","category":["PyCon SE 2019","Learning","Beginners"]},{"title":"Genotype Phenotype Modelling with Python and Machine Learning","link":"https:\/\/pyvideo.org\/pydata-chicago-2016\/genotype-phenotype-modelling-with-python-and-machine-learning.html","description":" PyData Chicago 2016<\/p>\n Genotype-phenotype studies are done for predicting traits such as whether someone will go bald or have a particular disease given their only genome. We look at how Python libraries such as scikit-learn and keras have made it easier to develop these statistical models. We describe a pipeline to predict antimicrobial resistance in bacteria and elaborate on challenges when working with genomic data.<\/p>\n","pubDate":"Sun, 28 Aug 2016 00:00:00 +0000","guid":"tag:pyvideo.org,2016-08-28:\/pydata-chicago-2016\/genotype-phenotype-modelling-with-python-and-machine-learning.html","category":["PyData Chicago 2016","learning","machine learning"]},{"title":"Implementing distributed grid search for deep learning using scikit learn and joblib","link":"https:\/\/pyvideo.org\/pydata-chicago-2016\/implementing-distributed-grid-search-for-deep-learning-using-scikit-learn-and-joblib.html","description":" PyData Chicago 2016<\/p>\n Slides: https:\/\/mheilman.github.io\/pydata_chicago_2016\/#\/<\/a><\/p>\n Grid search over hyperparameters is an important but computationally expensive process in machine learning, particularly for deep learning and tree ensembles. In this talk, I will describe how one can use joblib's recently added custom backend functionality to do distributed grid search on Amazon EC2 for a TensorFlow deep text classifier that follows the scikit-learn estimator API.<\/p>\n","pubDate":"Sun, 28 Aug 2016 00:00:00 +0000","guid":"tag:pyvideo.org,2016-08-28:\/pydata-chicago-2016\/implementing-distributed-grid-search-for-deep-learning-using-scikit-learn-and-joblib.html","category":["PyData Chicago 2016","deep learning","distributed","learning","scikit","search"]},{"title":"Machine learning techniques for data cleaning","link":"https:\/\/pyvideo.org\/pydata-chicago-2016\/machine-learning-techniques-for-data-cleaning.html","description":" PyData Chicago 2016<\/p>\n Slides: https:\/\/docs.google.com\/presentation\/d\/1k42esoWoc_WezfPfQ5vxbHTsuFOvAshEusD-GFCElTQ\/edit#slide=id.g166bf446d8_1_12<\/a><\/p>\n Often, the most interesting datasets - data about people and organizations - are the messiest and most difficult to analyze. When data comes from multiple sources, or when data is entered manually, variation & ambiguity are inevitable. Learn about ways to infer structure and relationships in messy data, using open source Python libraries.<\/p>\n","pubDate":"Sun, 28 Aug 2016 00:00:00 +0000","guid":"tag:pyvideo.org,2016-08-28:\/pydata-chicago-2016\/machine-learning-techniques-for-data-cleaning.html","category":["PyData Chicago 2016","Data","learning","machine learning"]},{"title":"Deploying Machine Learning using sklearn pipelines","link":"https:\/\/pyvideo.org\/pydata-chicago-2016\/deploying-machine-learning-using-sklearn-pipelines.html","description":" PyData Chicago 2016<\/p>\n Sklearn pipeline objects provide an framework that simplifies the lifecycle of data science models. This talk will cover the how and why of encoding feature engineering, estimators, and model ensembles in a single deployable object.<\/p>\n","pubDate":"Sat, 27 Aug 2016 00:00:00 +0000","guid":"tag:pyvideo.org,2016-08-27:\/pydata-chicago-2016\/deploying-machine-learning-using-sklearn-pipelines.html","category":["PyData Chicago 2016","deploying","learning","machine learning","sklearn"]},{"title":"Learning scikit learn - An Introduction to Machine Learning in Python","link":"https:\/\/pyvideo.org\/pydata-chicago-2016\/learning-scikit-learn-an-introduction-to-machine-learning-in-python.html","description":" PyData Chicago 2016<\/p>\n This tutorial provides you with a comprehensive introduction to machine learning in Python using the popular scikit-learn library. We will learn how to tackle common problems in predictive modeling and clustering analysis that can be used in real-world problems, in business and in research applications. And we will implement certain algorithms as scratch as well, to internalize the inner workings<\/p>\nSummary<\/h3>
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