List of videos

Networks are all around us. While Facebook and Twitter are the obvious examples, every time we shake hands, drive from point A to B, push code to github, check out a meetup or rate a show on IMDB, we’re participating in network activity. People, places, things and even ideas are inter-connected in innumerable networks, and these can have a great (yet sometimes inconspicuous) impact on our lives. The purpose of this talk is to introduce members of the audience to network analysis and its importance, and give them the basic building blocks for applied network analysis with Python. Slides: https://github.com/alonnir/PyCon-Us-2021-Talk/

“Let me just go run this on my quantum computer.” Quantum computers aren’t what-ifs anymore — they are available now, and publicly accessible over the internet. And Python is rapidly becoming the language of choice for accessing and programming quantum computers, with Python SDKs available from Google (Cirq), IBM (Qiskit), and others. However, early quantum computers are small, noisy, and error prone. Simultaneously, it has never been easier to perform differentiable programming in Python; simply swap out NumPy for TensorFlow, PyTorch, or JAX, and you have the ability to differentiate and train the program itself. So what would happen if we attempted to combine the two? Using a mixture of real Python examples and illustrated diagrams, we show how to not only evaluate, but also differentiate small quantum programs directly on quantum hardware. By extracting the gradients, we can integrate these quantum programs directly into larger differentiable programs in Python, and train/optimize the full (hybrid quantum-classical!) program. Over the course of this talk, quantum-curious Python developers will see first-hand how quantum programming looks in Python, and get an idea of how (and when) it makes sense to take advantage of these novel hardware devices. Slides: https://iza.ac/pdf/pycon2021.pdf

With the skyrocketing popularity of Python as a language for web development, a wide array of tools now exist for the creation and documentation of REST APIs. Enter FastAPI, a quick, modern and extensible solution for rapidly creating RESTful services. This talk will explain the features, advantages, and utility of the FastAPI framework for developing comprehensive and useful APIs.

Did you know that Python is being taught to more secondary students than ever before? Understanding of the landscape of Python in education, Learn practical, evidence-based strategies for teaching Python programming, and Get involved in the Python education community. Slides: http://bit.ly/mray-pycon21

Data science and machine learning rely on high quality datasets for visualization, statistical inference, and modeling. However, the barriers to testing data processing, analysis, or model-training code are high, even with the extensive tooling that the python ecosystem offers, such as pandas, pytest, and hypothesis. To address this problem, in this talk I define statistical typing as a general concept describing a runtime typing system, which extends primitive data types like bool, str, and float into the class of statistical data types. By providing additional semantics about the properties held by a collection of data points, statistical typing enables us to naturally express types as multivariate schemas. It also enables us to implement schemas as generative data contracts, which serve to both validate data at runtime and generate valid samples for testing purposes. I'll use pandera, a pandas data testing library, to illustrate how statistical typing makes data testing easier by enabling you to validate real-world data with reusable schemas and isolate units of processing, analysis, and model-training code. Slides: https://pandera-dev.github.io/pandera-presentations/slides/20210515_pycon_statistical_typing.slides.html

Every Python user knows that you can execute code using eval or exec, but what about yaml or str.format? This talk will take you on a walk through all the weird and wild ways that you can achieve code execution on a Python server (and trust me, I didn’t spoil the surprise by putting the weirdest ones in the description). The talk should be equal parts practical and entertaining as we work through both real examples of code execution vulnerabilities found in running code as well as absurd remote code execution exploits. The talk will end on a practical note by explaining how Facebook detects and prevents the exploit vectors we discussed, using an open source Python Static Analyzer called Pysa. All demos are available at: https://github.com/gbleaney/python_security Attendees are encouraged to download the demos and follow along at home. To get started using static analysis to detect the vulnerabilities discussed in this talk, check out: https://pyre-check.org/docs/pysa-quickstart/

Whether for a CEO in a boardroom or a family creating next month’s budget, people need continual access to data. Problems occur when web applications used to visualize large datasets reach browser limits for the number of open connections that can be created, due to multiple queries. To overcome this limitation, presenters will discuss asynchronous methods of retrieving data, focusing on Python Celery. Celery task queues distribute data queries while the web application polls for results, creating a better user experience. Slides: https://noti.st/hustjl22/n3KqaM/optimizing-data-retrieval-with-python-celery

MindData - Data Pre-processing in MindSpore