The Python Package Index (PyPI) is the principal repository of software packages for the Python programming language. In this talk, Nicole will dig into PyPI's history, explore recent improvements, and pose questions about the future of the service.

Test code, no different from the production code, needs to follow some principles, patterns, and practices to make it usable, standing the test of time and change, without rotting and becoming a maintenance burden.
Lets talk about making good tests.

A collection of 3 short stories, each one crazier than the last: from tracing and metaprogramming to patching frame objects and rewriting bytecode.

Most people would not consider the language written within legal documents to be "natural" to any human being. We would demonstrate how their structure can be proved handy with several NLP techniques.

This presentation will review the strengths and weaknesses of using pre-trained word embeddings, and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, AMR and SDP into your applications.

Most projects have a point where they become a product, python projects are no different.
Productionization of python projects starts with the first file in the repository, this talk will try to go through the process.

How debugging actually works in Python? what are the differences between CPython and PyPy interpreters and what's their underlying debugging mechanism? learn how to utilize this knowledge at work and up your watercooler talk game.

A summary of commercial and open source tooling for python code security automatic scan.

Writing tests is a great way to improve the quality of your application,
but in a complex application that depends on various 3rd party APIs it
can be quite hard.

Pytest makes it easier.

The centralized database that holds clinical trial data is in need of standardization - python tools are used to help this effort

The new eBPF technology in the Linux kernel allows us to perform production-safe analytics in real time with minimal impact on the running system. We show how a developer can monitor and detect performance issues on a live production system, explain

Amenity Analytics does NLP and machine learning using serverless infrastructure almost exclusively. In this talk we'll cover what we do, how we optimize it, and how we scale analysis to meet demand

How to create QGIS Python processing tools to improve sharing and integration. Using the power of QGIS to work on location data and use Python power to share the data in various ways and to integrate the data into other systems.

Descriptors are a less know but powerful Python tool. We'll see what they are and how to use them.

Numpy is used as a data container and its APIs are well-defined. What you might not know about things that have changed lately and what you need to know about changes that are coming

In this talk, I want to share experiences of migrating from a standard Django architecture to a GraphQL-based app using Graphene framework, including keeping consistency in the implementation and shape of API, common API design issues and pitfalls.

Data classes are very useful, but not often used in Python. This talk will discuss how using data classes in Python improves code readability, usability and robustness, and show how to create them easily with the attrs and dataclasses libraries.

Model explainability, why should we care. How to explain ML models?
SHAP (SHapely Additive exPlanations),

Managing Python environment dependencies across an organization and between environments is always a pain. Pipenv makes it a little less painful by managing both the environment itself and its configuration.

Data scientists spend over 60% of their time doing feature engineering. I will discuss automation of the feature engineering process, using Featuretools, in order to significantly reduce time investment, make it repeatable, more robust and creative.

logger.info - practical considerations when using loggers

A practical guide for defining effective interfaces between Python applications and Python-based Deep Learning algorithms

Listing the different concurrency options in python

Social network analysis is the study of social structures through the use of graph theory. In this talk I will present network theory and application of building and analyzing social networks for practical use-cases in Python with NetworkX.

Ansible is a popular tool for configuration management and automation. Let's learn how to use Python to extend Ansible with our own custom plugins and modules.

In this talk I will demonstrate how AI outperforms traditional triage measures for predicting early and late mortality after emergency department (ED) visit, using EMR data of ~56K ED visits of adults patients over a period of 5 years.

Extendable and easy-to-use lightweight enumeration alternative.

lets take a tour into Rust's Python ecosystem, reviewing what rust has to offer to python and visit the crates that will build your next Rust Python native extensions.

Fact checking using python with Jupyter notebooks

This real-world model operationalization case study will highlight common mistakes, propose solutions, workarounds and practical tips to successfully deploy a ML model

As a full-stack developer in an Algo-trading / HFT world, I’m being asked very often: “Yeah but, python is too slow for that, nope?”
During my talk, I will demonstrate how I deal with performance / high throughput problems using PyPy, including exam

This talk will go over what's new in Python 3.8, with emphasis on assignment expressions as well as the controversial talks that led Guido to quit his position as BDFL

In this talk I will present the architecture of our simulation infrastructure written in Python which allows to simulate hours of real-life in only minutes of simulation. I will describe challenges and issues we encountered and how we handled them.

Programs which aim eradicate disease must rely on interpretable models. These models quickly become hard to solve, not to mention train on missing parameters. Scipy and PyMC come to our rescue for the heavy lifting.

A new debugging solution for Python that became a huge hit overnight

This session might give you what you need to assess whether the latest async Python APIs are right for your environment. We’ll look at challenges, solutions, considerations and most importantly examples.

Pylint is a Widely used and scalable Python static code analysis tool. In this lecture we will learn how to configure, extend and run it asw part of your CI.

Hierarchical Temporal Memory is a novel framework for biological and machine intelligence. It learns patterns from relatively little data and is well suited for prediction, anomaly detection, classification and ultimately sensorimotor applications.

Did you ever wonder how do ORM systems do their magic? In this talk we'll cover the Python features that enabled us to implement an ORM for a new DB system in less than a day.

Challenges and architectural solutions for mass usage of async FaaS workers.

Kubernetes/OpenShift is a portable, extensible open-source platform for managing containerized workloads and services. In this session, you will learn how to extend Kubernetes/OpenShift with Python to make you a cup of coffee.

Pandas is easy and fun to use, so much so that even a Python newbie can use it. I tell the story of how, as a new Python developer, I quickly learned enough Pandas to be able test some hypotheses about financial markets.

Using DASK in an ETL pipeline has some gotcha's.
Although there are many similarities to pandas there are some issues and best practices that can optimize the usage of DASK in general

How did we port our code from Python two to three is less than two months with one dedicated developer while ongoing development? Find out and learn why SAS and microservices are your friends.

In this talk, we will walk through a step by step example of building a prediction algorithm, focusing on areas where bias could be inadvertently introduced. Then, we'll look at some real examples and solutions.

DataCity is a project aimed at creating a single repository of all municipal data in Israel.
I'll talk about the project and the Python toolset we've built to create and manage this large ETL operation.

In this talk I will describe an end-to-end solution to a text classification problem using publicly available frameworks. I will focus on the practicalities of getting a Deep Learning-based text classification model up and running.

How easy is it to create tools with some Python and Bluetooth devices? If you never want your office mates to mess with our PC in the name of security, this talk is for you.

Implementing your own filesystem used to be difficult, requiring in-depth kernel knowledge. For this reason FUSE (Filesystem in Userspace) was introduced. We will discuss how we can implement our filesystem in python using the fuse python module.

In this talk I'll present the python scope of variables, global, nonlocal, closures, LEGB rule and some known and less-known gotcha's of python scopes! attending this talk will might result a small headache ;)

Data is Twiggle's bread and butter, so choosing the right data pipelining framework was critical for us. After comparing Luigi and Airflow pipelines we ended up selecting both! We’ll explain why, present our unique challenges and chosen solutions.

Our web is a world of silos, we need to decentralize that! Let's try to make it simple, and without the drawbacks normally associated with blockchain dApps. In python, of course.