List of videos

Django with PostgreSQL superpowers [EuroPython 2021 - Talk - 2021-07-28 - Ni] [Online] By Paolo Melchiorre Every time we’re going to create a new project with Django we make assessments on its requirements to choose the best architecture, of which, the database is usually the core. Django is a database-agnostic web framework but natively supports only 4 Open Source databases: PostgreSQL, SQLite, MariaDB and MySQL. PostgreSQL has the richest feature set of any supported database and some of these features are natively supported directly in Django via its contrib module. In this talk we’ll see how to use to our advantage the features of PostgreSQL as a database in Django, its exclusive features present in its contrib module and also other superpowers that can be exploited through the use of third-party packages. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

From telemetry data to CSVs with Python, Spark and Azure Databricks [EuroPython 2021 - Talk - 2021-07-28 - Parrot [Data Science]] [Online] By Nicolò Giso Tenova is an engineering company working alongside client-partners to design and develop innovative technologies and services that improve their business, creating solutions that help metals and mining companies to reduce costs, save energy, limit environmental impact and improve working conditions for their employees. In the context of Industry 4.0, Tenova provides each equipment with a field gateway, named Tenova Edge, to collect telemetry data, perform edge analytics with AI models and send data to the Tenova Platform (hosted on Microsoft Azure) for further elaborations. To develop analytics solutions, data scientists and process engineers need the data in a manageable format. Furthermore, continuous retraining of AI models is necessary to guarantee high performances and reliable results. For all of these reasons, we needed to implement an ETL solution to transform the raw data in formats ready for analysis and retraining. In particular, the key requirement was to convert the JSON Lines files coming from the field in CSV files ready to be used. The CSV files have to satisfy the following conditions: - each file contains the data for a device - only one file for device per day - each file has a midnight row containing for each cell the value recorded at midnight or the last value of the previous day (SPOILER: here it’s where the fun happens!) For this purpose, we have implemented a series of Databricks Notebooks, run daily by Azure DataFactory, that leveraging Pyspark and Pandas manipulates the raw JsonLines files in nicely formatted CSVs. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Functional Programming inside OOP? It’s possible with Python [EuroPython 2021 - Talk - 2021-07-28 - Optiver] [Online] By Carlos Villavicencio It is well known that Python 3 is an object-oriented language. But if we look back at the roots of the language, functions as first-class objects were part of the initial design. Recently, functional programming has gained considerable popularity because it is robust, concise, and easier to test. That set of benefits are not only reserved for functional pure programming languages such as Haskell. In this talk, I will run into a series of concepts, such as shared state, pure functions, side effects, and some other functional programming lingo that will help us understand how to use some not well-known Python features influenced by functional principles. We'll see how to implement our functional patterns in the code we write every day. In the end, you will see why this paradigm is relevant right now and why most of the existing languages and libraries actually evolved and keep going in that direction. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Documentation-driven development for Python web APIs [EuroPython 2021 - Talk - 2021-07-28 - Brian] [Online] By Jose Haro Peralta This presentation shows how we can use documentation-driven development to minimise the risk of API integration failures. APIs are the gateways that connect our web applications to the world. Microservices rely on them to work together, and frontend applications use them to connect to our backend. Despite their importance, API integration failures are a daily occurrence. There’re many reasons for this, but a common factor is the lack of API documentation, or the use of bad API documentation. Often, API documentation comes in unstructured formats or in JSON examples, instead of standard formats such as OpenAPI or Schema Definition Language. When we don’t use standards to document our APIs, we give up the whole ecosystem of frameworks and tools built around those standards. Such tools and frameworks are designed to make our lives easier when managing the lifecycle of our APIs. In documentation-driven development, we write the API specification first, and then we implement the API server and the client against the specification. I’ll show how we can use mock servers to test the client while the backend is being developed, and tools like Dredd and schemathesis to validate the backend implementation. I’ll show how we can use these tools in a CI server to validate our code before it’s released. This approach doesn’t eliminate completely the risk of API integration failures, but it helps to minimise it. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

"Practical Optimisations for Pandas EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Eyal Trabelsi Writing performant pandas code is not an easy task, in this talk I will explain how to find the bottlenecks and how to write proper code with computational efficiency, and memory optimisation in mind. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Diffprivlib: Privacy-preserving machine learning with Scikit-learn EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Naoise Holohan Data privacy is having an ever-increasing impact on the way data is stored, processed, accessed and utilised, as the legal and ethical effects of data protection regulations take effect around the globe. Differential privacy, considered by many to be the strongest privacy guarantee currently available, gives robust, provable guarantees on protecting privacy, and allows tasks to be completed on data with guarantees on the privacy of individuals in that data. This naturally extends to machine learning, where training datasets can contain sensitive personal information, that are vulnerable to privacy attacks on trained models. By using differential privacy in the training process, a machine learning model can be trained to accurately represent the dataset at large, but without inadvertently revealing sensitive information about an individual. Diffprivlib is the first library of its kind to leverage the power of differential privacy with scikit-learn and numpy to give data scientists and researchers access to the tools to train accurate, portable models with robust, provable privacy guarantees built-in. In this talk, we will introduce attendees to the idea of differential privacy, why it is necessary in today's world, and how diffprivlib can be seamlessly integrated within existing scripts to protect your trained models from privacy vulnerabilities. Attendees will be expected to have a basic understanding of sklearn (i.e., how to initialise, fit and predict a model). No knowledge of data privacy or differential privacy will be assumed or required. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Clean Architectures in Python EuroPython 2020 - Talk - 2020-07-23 - Ni Online By Leonardo Giordani Architectural considerations are often overlooked by developers or completely delegated to a framework. We should start once again discussing how applications are structured, how components are connected and how to lower coupling between different parts of a system, to avoid creating software that cannot easily be maintained or changed. The “clean architecture” model predates Robert Martin, who recently brought it back to the attention of the community, and is a way of structuring applications that leverages layers separation and internal APIs to achieve a very tidy, fully-tested, and loosely coupled system. The talk introduces the main ideas of the architecture, showing how the layers can be implemented in Python, following the content of the book “Clean Architectures in Python”. The book recently reached 11,000 downloads and many readers found it useful to start learning how to test software and how to structure an application without relying entirely on the framework. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Bringing your Python script to more users! EuroPython 2020 - Talk - 2020-07-23 - Ni Online By Takuya Futatsugi It is wonderful to automate boring stuffs with Python. In Japan, new introductory books are being published every month and more and more Pythonistas are working on automation. A Python script that is useful to you may be useful to others. So I talk about how to enable others to use your script. In this talk, I use a simple script which can reduce the width and the height of a specified image in your computer. I assume someone who's read the introductory book can understand the script. First, I introduce Command Line Interface (CLI) to solve hardcoding in the sample script. After implementing CLI, you don't need to edit the script. Second, I introduce Grafical User Interface (GUI) to make the script more user-friendly. Finally, I introduce web application so that users can the script without installation. All users have to do is connecting the Internet! The timeline is supposed to be the following: h1Introduction (3min)/h1 self-introduction motivation of this talk sample script (pathlib, Pillow) h1CLI (5min)/h1 problem: need to edit the script introduce argparse module (pass target file path from command line) about positional arguments and optional arguments check whether a path points an existing file (type parameter of add_argument()) h1GUI (9min)/h1 problem: CLI is not easy to handle than GUI introduce eel eel's elements: HTML, CSS, JavaScript hello world in eel file access in eel app convert sample script to eel app (user can see which image is specified as target😃) how to distribute h1Web app (9min)/h1 problem: Users need to install introduce Web app server / client actually eel's elements are used in web app (we can recycle codes) introduce Flask hello world in flask static files in web app how to deploy (heroku) h1wrap up (1min)/h1 h1Q&A (3min)/h1 License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"15 Things You Should Know About Spacy EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Alexander Hendorf spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. It was specifically designed for production use and it's a great tool to build applications that process text and help to “understand” large corpuses. A robust understanding of Python inner mechanisms is very useful for getting your head around what Space can do for you, what is fast or time consuming. Ans who do you store Space models anyway and what are they. Plugins? - sure! In this talk I'll present 15 things you should know about Spacy for the better and worse. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "